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Son yıllarda ekonominin de büyümesiyle beraber, hemen hemen her sektörde rekabet artmıştır. Rekabetin artmasıyla birlikte firmalar tanınırlıklarını artırmak amacıyla geleneksel reklamların yanında çeşitli organizasyonlar da düzenlemektedir. Tanıtım organizasyonları günümüzde her geçen gün büyüyen bir sektör haline gelmiştir. Bu sektör üzerinde çalışmalar yapan özel bir araştırma şirketinin verilerine göre, Türkiye’de her yıl 36 binden fazla profesyonel organizasyon gerçekleşmektedir ve sektör bütçesi 4 milyar dolara ulaşmıştır. Organizasyon planlamalarında, bütçenin büyük bir bölümünün personel giderlerine ayrıldığı gözlenmektedir. Ayrıca hizmet esnasında herhangi bir aksama, işi yüklenen firma için diğer sektörlere nazaran geri dönülemez negatif sonuçlar doğurabilmektedir. Bu nedenle nitelikli eleman seçimi ve elemanların doğru işe atanması oldukça önemlidir. Dolayısıyla, bilimsel yöntemlerin kullanılacağı bir personel çalışma planı ile maliyetler enküçüklenirken, hizmet kalitesi de artırılabilmektedir. Bu çalışmada, 10 farklı istasyona hizmet verecek 40 personelin atama ve çizelgeleme problemi hedef programlama modeli ile çözülmüştür. Problem çözümünde Ibm Ilog Cplex Optimization Studio programı kullanılmıştır., With the growth of the economy in recent years, competition has increased in almost every sector. With the increase of competition, companies get services to organize various organizations in addition to traditional advertisements in order to increase their recognition. Promotion organizations have become a growing sector today. According to the data of a private research company working on this sector, in Turkey, every year more than 36,000 professional and industry organizations are held and budget has reached $4 billion. In organizational planning, it is observed that a large part of the budget is allocated to personnel expenses. In addition, any disruption during the service can cause irreversible negative consequences for the company compared to other sectors. For this reason, it is very important to select qualified personnel and assign them to the right job. Therefore, with a staff work plan using scientific methods, costs can be minimized and service quality can be increased. In this study, the assignment and scheduling problem of 40 personnel who will serve 10 different stations has been solved with the goal programming model. IBM Ilog Cplex Optimization Studio program was used to solve the problem.

PT KHW is a company engaged in the manufacture of cotton and gauze for health and cosmetic purposes. The gauze products produced by the company sometimes exceed the number of consumer demands and sometimes there is a shortage of products. Therefore, good production planning in order to meet consumer demand with the right amount and the right time is needed so the increased profits are obtained. This study aims to determine the optimal amount of production, determine the profits obtained by the company and determine the production costs that must be incurred by the company. This study uses the goal programming method to optimize the amount of production by determining the decision variables, constraint functions and target functions. The goal functions are arranged based on the priority level desired by the company. The goal priorities considered include the goal of maximizing the amount of production, maximizing product sales profits, minimizing production costs, minimizing the use of raw materials, maximizing production time, and minimizing overtime. &nbsp

Peer-to-peer electricity markets are dedicated markets that enable the direct participation of small electricity end-users in energy trading activities. They are seen as a promising alternative that can empower end-users and accelerate the energy transition, by researchers, business developers, and legislators. Moreover, they can include environmental, social, or altruistic preferences that are relevant to end-users, in addition to the economic perspective. Such preferences are sometimes included in the modeling of P2P markets in the existing literature, but the assumptions behind them are rarely validated in practice. To investigate the desired attributes and preferences of end-users to participate in P2P markets, an online survey including a discrete choice experiment was conducted in The Netherlands The results of the survey are used to design a P2P electricity market with product differentiation. The participants in the market are residential end-users that are equipped with a home energy management system that can control some of the household appliances and automate the decision-making process for participation in the market. To facilitate this, a multi-objective stochastic optimization model is presented that incorporates results from the discrete choice experiment and real smart-meter measurements. The case study results demonstrate user preferences’ influence on market outcomes.

This paper introduces a modelling framework which combines Data Envelopment Analysis and Markov Chains into an integrated decision aid. Markov Chains are typically used in contexts where a system (e.g. staff profile in a large organisation) is at the start of the planning horizon in a given state, and the aim is to transform the system to a new state by the end of the horizon. The planning horizon can involve several steps and the system transits to a new state after each step. The transition probabilities from one step to the next are influenced by both organisational and external (non-organisational) factors. We develop our generic methodology using as a vehicle the homogeneous Markov manpower planning system. The paper recognizes a gap in existing Markovian manpower planning methods to handle stochasticity and optimization in a more tractable manner and puts forward an approach to harness the power of DEA to fill this gap. In this context, the Decision Maker (DM) can specify potential anticipated future outcomes (e.g. personnel flows) and then use DEA to identify additional feasible courses of action through convexity. These feasible strategies can be evaluated according to the DM's judgement over potential future states of nature and then employed to guide the organisation in making interventions that would affect transition probabilities to improve the probability of attaining the ultimate state desired for the system. The paper includes a numerical illustration of the suggested approach, including data from a manpower planning model previously addressed using classical Markov modelling.

This paper presents a multi-criteria methodology for allocating a small number of novel robotic devices to a set of potential treatment centres in the South of the UK. The robotic devices provide a novel means of diagnosis and treatment of prostate cancer and are currently in the design phase of an EU-sponsored research project. The aim of the project is to provide enhanced access to the new prostate cancer diagnosis and treatment technology. Hence, location and allocation decisions need to be optimised to facilitate this goal. A fuzzy logic based methodology based on measures of prevalence and current access to treatment centres is developed in order to identify a set of priority locales (postcode districts) for improved access. An extended goal programming facility location model is then formulated which incorporates both priority and general postcode access measures. The p-median, p-centre and coverage variants of facility location are modelled via a set of six goals. A comprehensive weight sensitivity analysis is used to generate 25 distinct solutions across the six-dimensional objective space. Analysis of the results with respect to methodology, location and allocation decisions, number of robotic devices and trade-offs between goals is undertaken and conclusions are drawn.

In the consensus reaching process (CRP), decision-makers (DMs) often present behaviors of tolerance and limited compromise when adjusting their opinions, which may lead to the actual decision deviating from the solution recommendation obtained by applying the current minimum cost consensus theory. In this paper, we propose a consensus-reaching approach based on maximum satisfaction under budget constraints for DMs with these two behaviors. To obtain a feasible budget constraint of the CRP, a reference budget consensus model (RBCM) incorporating both of these behaviors is first developed. Under the constraint of the limited budget, we propose a maximum satisfaction consensus model (MSCM) to maximize the satisfaction level of all DMs based on the goal programming theory. To further achieve different levels of consensus, we propose a soft MSCM under a certain threshold using soft consensus measures. Moreover, the proposed consensus models are illustrated by the data of China’s Taihu Lake pollution control. The results suggest that: First, the individual tolerance behavior has a higher impact on the consensus outcome than the compromise limit, whereas the latter is more likely to affect the satisfaction of each DM. Second, as the budget increases, the consensus opinion and group satisfaction level will increase toward a higher average cost until they remain constant. Last, a higher consensus threshold will reduce DMs’ satisfaction levels, and an appropriate threshold should be selected as the control baseline of the CRP in the real application.

The widespread uncertainty that characterizes the current world economic situation has also influenced the real estate market, leading investors towards generally lower risk profiles and more stable returns. However, in the absence of adequate skills, it is difficult to carefully manage the main risks factors which occur during the decision-making process. The aim of the research is to define and implement an asset allocation evaluation model able to support public and private investors for the identification of the most suitable allocation of limited financial resources for core/core plus real estate investments. The computational logic of goal programming is applied for detecting the optimal composition of property portfolios in terms of risk/return. The proposed model has been implemented to an Italian case study, proving to be a valid support tool in the definition of efficient real estate investment strategies. L’instabile congiuntura economica ha determinato un atteggiamento prudente degli investitori immobiliari pubblici e privati, indirizzandoli verso strategie di allocazione delle risorse caratterizzate da rischi contenuti e da rendimenti costanti. Nonostante ciò, i fattori di rischio che possono influenzare la performance di un portafoglio immobiliare sono molteplici e spesso difficilmente gestibili senza le competenze adeguate. L’obiettivo della presente ricerca riguarda la definizione e la sperimentazione di un asset allocation model che, attraverso la logica e gli algoritmi del Goal Programming, possa supportare il generico investitore – dotato di budget finanziario prestabilito e interessato ad investimenti di tipo core e core-plus - nel complesso processo decisionale finalizzato alla costruzione di portafogli immobiliari “ottimali” in termini di rischio/rendimento. I risultati ottenuti dall’applicazione a un ipotetico caso studio inerente a immobili ubicati nel contesto territoriale italiano, evidenziano le potenzialità del modello proposto, quale valido strumento di supporto alle decisioni nella definizione di strategie d’investimento immobiliare efficienti.

Background/Aim. Among the other challenges of the 21st century, medical waste has become an arising problem for both the environment and people because of its increasing amount, variety, and complexity. With this, medical waste management started to present a highly important environmental issue. Serbia with no potential for appropriate disposal of all medical waste is forced to export medical waste to countries with medical waste incineration facilities. The reason lies in the fact that the incinerator facility represents an adequate way and a priority in handling medical waste in its end-of-life phase. It must be noted that incineration lowers the possible risks of inappropriate disposal and the emission of environmental pollutants, which leads to the need for a “clever” choice of the incinerator facility location which needs to meet diverse environmental, economic and technical criteria to manage successful processing of medical waste. Methods. In the paper, we applied the criteria for the choice of optimal locations for a medical waste incinerator facility as follows: the amount of waste that needs to be transported, the transport time from other locations, the current pollution of the location, the unemployment rate and the location safety in terms of natural disasters and accidents. By using the obtained results for seven efficient locations gained by Data Envelopment Analysis, we used a goal programming for the analysis of the most suitable location for a medical waste incineration facility. Results. After applying the proposed methodology on the chosen scenario and analysing the criteria relevant for selecting the most suitable location, optimal location is determined for a medical waste incineration facility. Conclusion. Based on the obtained results, we demonstrated that it is possible to develop a methodology that can used goal programming in optimal location selection for a medical waste incineration facility in risk and medical waste management.

One-fourth population of the world lives in South Asia. The electricity generation for this population consumes the substantial fossil fuels, resulting in the significant emissions of harmful gases in the global environment. To protect the natural environment, South Asian Association for Regional Cooperation (SAARC) has planned a super smart grid for the power sharing between South Asian countries through the integration of the renewable energy resources (RERs). However, the capability of these countries to construct such a mega power grid is unclear due to the unavailability of proper strategic analysis, lack of information about RERs potential, and the lack of decision making models. This paper analyzes the existing power grids in SAARC countries, identifies their electricity generation potential, and develops an integer linear programming model based on the power generation cost, CO2 emissions, and disruption risk. This study solves the proposed model using goal programming methods. The results exhibited that the SAARC countries have the capability to share the sustainable super smart grids through the substantial integration of RERs. For instance, the model solution based on 100% renewable power (i.e. Case 5) exhibited 36% cost savings, 86% CO2 emission reduction, and 39% risk reduction compared with corresponding non-renewable power based model solution (i.e. Case 1). In addition, this study performs the cost-benefit analysis for the existing and proposed power systems of SAARC countries. The results provide the various insights to policy makers and practitioners for the strategic decision making related to the SAARC super smart grid. In addition, the methodology of this paper can be adopted for other super smart grids around the world.

In order to support the decision-making process related to the reduction of land consumption into the urban regeneration interventions, the present research has the aim to define and propose a goal programming-based model that can be adopted for the negotiation phases of public and private subjects involved. In particular, the proposed model can provide for a range of feasible scenarios that, according to the specific purposes of the Public Administration, can be implemented in order to achieve the financial, environmental and social level of sustainability targets set by the Agenda 2030. In this way even the private entrepreneur can verify his personal convenience to participate in the investment. Furthermore, the possibility provided by the model to choose a different combination of urban parameters that define the convenience of interventions before their implementation, could reduce the increasingly significant problem of badly concluded interventions, interrupted because they lack an effective ex ante evaluation.

Lot sizing and cutting stock problems generally arise in manufacturing as a two stage connected process. Although these two problems have been addressed separately by many authors, recently, inspired by practical applications, some studies have emerged analyzing their integration. The mono-objective version of the integrated lot-sizing and cutting stock problem can be considered as an enhancement that minimizes the global production cost. However, it does not include the multiple criteria that can arise from the inclusion of multiple stakeholders in a modern, distributed manufacturing process. This paper aims to introduce a new extended goal programming model for the integrated lot-sizing and cutting stock problem which models the arising multiple criteria by a set of goals representing the interests of different stakeholders in the manufacturing process. This formulation allows for the consideration of the balance between the conflicting goals of multiple stakeholders and the cost efficiency of the overall process. In order to efficiently solve the proposed model, a column generation based heuristic procedure is applied. The trade-offs among the various criteria related to the problem are assessed, and a series of computational experiments are performed. The computational results compare individual criteria weighting schemes and evaluate the goals’ sensitivity using performance profiles and time-to-target plots methodologies.

Son yıllardaki hızlı nüfus artışı ile birlikte, toplu taşımada kullanılan şehir içi otobüslerin sayısının artması nedeniyle; çevre kirliliği, gürültü, yolcu memnuniyetsizliği, maliyet vb. gibi sorunlar da artmaktadır. Bu kapsamda; otobüslerin güzergahı, otobüs sefer sayısı ve sıklığı, yolcu taşıma kapasitesi, bekleme süreleri gibi faktörlerin planlanması büyük önem arz etmektedir. Toplu taşıma ve ulaşım sistemlerinde yapılacak yatırımların ekonomik maliyetlerinin çok yüksek olması nedeniyle toplu taşıma ve ulaşım sistemlerinin verimli ve rasyonel planlanması ve sürdürülebilirliği, mevcut kapasitenin optimum kullanılması, karar vericiler için kritik bir konudur. Bu nedenle, çevresel, ekonomik ve toplumsal refah boyutları açısından şehir içi toplu taşıma sistemlerinin optimum planlanması, verimli ve sürdürülebilir olması mutlak bir zorunluluk olarak görülmektedir. Bu çalışmada; doğrusal hedef programlama yaklaşımı uygulanarak, yolcu taleplerini karşılamayı temel alan yeni bir sistem tasarımı geliştirilerek, şehir içi otobüs sefer çizelgeleme optimizasyonu yapılmıştır. Otobüs ulaşım sistemi için; sefer sayısı, sefer sıklığı, yolcu kapasitesi vb. gibi parametreler açısından mevcut durum analiz edilmiş olup, günlük yolcu talebini karşılayan sefer sayısı optimizasyon modeli oluşturulmuştur. Sonuçta; optimum sefer sayısı ve sefer sıklığı model sonuçları değerlendirilmiş ve elde edilen maddi kazanımların boyutu irdelenmiştir., With the rapid population growth in recent years, due to the increase in the number of city buses used in public transportation; environmental pollution, noise, passenger dissatisfaction, cost, etc. such problems are also increasing. In this context; it is of great importance to plan factors such as the route of the buses, the number and frequency of bus trips, passenger carrying capacity, and waiting times. Efficient and rational planning and sustainability of public transport and transportation systems, optimum use of the existing capacity are critical issues for decision makers, due to the high economic costs of investments in public transport and transportation systems. Therefore, in terms of environmental, economic and social welfare aspects, it is seen as an absolute necessity for urban public transportation systems to be optimally planned, efficient and sustainable. In this study; by applying a linear goal programming approach, a new system design based on meeting passenger demands was developed and city bus schedule optimization was performed. For the bus transportation system; number of voyages, voyage frequency, passenger capacity, etc. The current situation has been analyzed in terms of such parameters, and an optimization model has been created to meet the daily passenger demand. After all; the optimum number of voyages and voyage frequency model results were evaluated and the size of the financial gains obtained was examined.

Optimal financial planning plays a vital role in maintaining concentration and on the path as the organization extends, when new challenges materialize, and when unpredictable situations pounded. This study aims to develop and implement a goal programming model to evaluate financial planning based on the annual financial report of Saudi Basic Industries Corporation (SABIC), which assisted it in developing the financial planning model. This study is mainly designed to analyze SABIC’s budgeting structure; therefore, in order to maximize the benefits from the whole budget, goal programming is implemented for the entire budget. As a result of this study, we identified the following objectives as specific: reduced expenses, increased revenue, increased net profit, increased fixed assets, reduced debt, and increased equity share participation as a result of this project. Moreover, the analysis involved determining whether all objectives were met at the end of the study. Consequently, this study will benefit industrial institutions in achieving their financial objectives.

IntroductionLinear programming (LP) is often used within diet optimization to find, from a set of available food commodities, the most affordable diet that meets the nutritional requirements of an individual or (sub)population. It is, however, not always possible to create a feasible diet, as certain nutritional requirements are difficult to meet. In that case, goal programming (GP) can be used to minimize deviations from the nutritional requirements in order to obtain a near feasible diet. With GP the cost of the diet is often overlooked or taken into account using the ε-constraint method. This method does not guarantee to find all possible trade-offs between costs and nutritional deficiency without solving many uninformative LPs.MethodsWe present a method to find all trade-offs between any two linear objectives in a dietary LP context that is simple, does not solve uninformative LPs and does not need prior input from the decision maker (DM). This method is a bi-objective algorithm based on the NonInferior Set Estimation (NISE) method that finds all efficient trade-offs between two linear objectives.ResultsIn order to show what type of insights can be gained from this approach, two analyses are presented that investigate the relation between cost and nutritional adequacy. In the first analysis a diet with a restriction on the exact energy intake is considered where all nutrient intakes except energy are allowed to deviate from their prescription. This analysis is especially helpful in case of a restrictive budget or when a nutritionally adequate diet is either unaffordable or unattainable. The second analysis only relaxes the exact energy intake, where the other nutrients are kept within their requirements, to investigate how the energy intake affects the cost of a diet. Here, we describe in what situations the so-called more-for-less paradox takes place, which can be induced by requiring an exact energy intake.ConclusionTo the best of our knowledge, we are the first to address how to obtain all efficient trade-offs of two linear objectives in a dietary LP context and how this can be used for analyses.

El artículo tiene como objetivo equilibrar los desbalances o desequilibrios sectoriales en la producción de bienes y servicios, identificados en la estructura productiva de Cuba, siguiendo una metodología que incluye la modelización matemática. Se diseñan y aplican modelos econométricos y de programación por metas (GP) para evaluar la importancia relativa, estimar y resolver los desequilibrios entre los sectores económicos de destinos intermedio y final de la economía cubana. Lo que posibilita el diseño de políticas públicas enfocadas a la mejora de la complementariedad e integración económica; aspecto relevante en el proceso de actualización del modelo económico y social cubano, refrendado en los documentos gubernamentales que rigen la reforma económica emprendida en 2011. Se aplica la metodología en la provincia de Santiago de Cuba, donde se estimaron desequilibrios sectoriales e identificaron reservas de capacidades productivas de bienes y servicios por 42.500 millones de pesos para equilibrar los desbalances, lo que puede contribuir a la toma de decisiones de inversión en el marco de la estrategia de desarrollo económico y social del país hasta 2030., The objective of the paper is to equilibrate the sectorial imbalances of the production of goods and services, identified in the Cuban productive structure, following a methodology that includes mathematical modeling. Econometric and goal programming (GP) models are designed to assess the relative importance, to estimate and to solve the imbalances between the intermediate and final destination in economic sectors of the Cuban economy. This makes it possible to design public policies focused on improving economic complementarity and integration, a relevant aspect in the process of updating the Cuban economic and social model, endorsed in the government documents governing the economic reform undertaken since 2011. As a result, the methodology is applied in the province of Santiago de Cuba, where sectorial imbalances were estimated and productive capacities reserves of goods and services by 42.500 million pesos were identified to equilibrate the imbalances, which can contribute to investment decision-making within the framework of the country's economic and social development strategy until 2030., Universidad Pablo de Olavide

Nowadays, researchers have focused on manufacturing processes that extensively consume energy. Low-carbon production, purposing to reduce carbon emission, is a hot topic in the literature. This paper presents an integrated Taguchi goal programming (GP) model of quantifying carbon emissions of a conventional turning process. The study also investigates the breakdown of the turning machine tool sub-systems that contribute to the overall carbon emissions of a turning process, such as cutting tools, electricity, workpiece, cutting tool material specifications, and chip disposal. We developed a model for quantifying the carbon emissions from individual sub-systems. Also, we integrate the Taguchi model and GP to determine the values of the factors that achieved a sustainable turning process. In this paper, a different perspective from the literature-based carbon emission optimization studies is developed. Hence, a sub-cutting factor (parameter) level-based optimization study that searches the more sensitive parameter ranges for the turning process is presented. So, Taguchi’s principle-based GP model is an appropriate way to reach this objective.

Since 2015, the United Nations sustainable development goals (SDGs) agenda 2030 has been designed with 17 goals, 169 targets, and 232 unique indicators to help address environmental, development, and sustainability issues globally. India, like other developing nations, desired to achieve its vision 2030 targets. Several authors studied India's SDGs with different approaches. However, none of the studies explores the concept of neutrosophic programming (NP). It is against this drawback; this study presents an optimization model for India's socio-economic and environmental goals based on the NP concept. The NP model is capable of handling indeterminacy in optimization-related problems for which other techniques do not. The formulated models simultaneously optimized the gross domestic product (GDP) growth, electricity consumption, and greenhouse gas (GHG) emissions. The solutions revealed that the degree of satisfaction for the goals related to GDP, electricity consumption, and GHG emissions could be achieved partially. Also, it suggested the required optimal number of employment for each economic sector to achieve India's vision 2030. The SDGs model was further solved with the goal programming (GP) technique and compared with the NP results to validate the proposed concept. It has been found that the proposed model gives a better compromise solution than the GP model. The study can help and guide policymakers in working toward vision 2030 attaintment. Other interested researchers can use the concept in other countries to help decision-makers understand managerial policy implications.

Stochastic chance-constrained optimization has a wide range of real-world applications. In some real-world applications, the decision-maker has to formulate the problem as a fractional model where some or all of the coefficients are random variables with joint probability distribution. Therefore, these types of problems can deal with bi-objective problems and reflect system efficiency. In this paper, we present a novel approach to formulate and solve stochastic chance-constrained linear fractional programming models. This approach is an extension of the deterministic fractional model. The proposed approach, for solving these types of stochastic decision-making problems with the fractional objective function, is constructed using the following two-step procedure. In the first stage, we transform the stochastic linear fractional model into two stochastic linear models using the goal programming approach, where the first goal represents the numerator and the second goal represents the denominator for the stochastic fractional model. The resulting stochastic goal programming problem is formulated. The second stage implies solving stochastic goal programming problem, by replacing the stochastic parameters of the model with their expectations. The resulting deterministic goal programming problem is built and solved using Win QSB solver. Then, using the optimal value for the first and second goals, the optimal solution for the fractional model is obtained. An example is presented to illustrate our approach, where we assume the stochastic parameters have a uniform distribution. Hence, the proposed approach for solving the stochastic linear fractional model is efficient and easy to implement. The advantage of the proposed approach is the ability to use it for formulating and solving any decision-making problems with the stochastic linear fractional model based on transforming the stochastic linear model to a deterministic linear model, by replacing the stochastic parameters with their corresponding expectations and transforming the deterministic linear fractional model to a deterministic linear model using the goal programming approach, Стохастическая оптимизация с вероятностными ограничениями имеет широкий спектр реальных задач. В некоторых реальных задачах лицу, принимающему решение, необходимо сформулировать проблему в виде дробной модели, в которой некоторые или все коэффициенты являются случайными величинами с совместным распределением вероятностей. Таким образом, эти типы задач позволяют решать двойственные задачи и отражают эффективность системы. В данной работе мы представляем новый подход к формулировке и решению стохастических моделей дробно-линейного программирования с вероятностными ограничениями. Этот подход является продолжением детерминированной дробной модели. Предлагаемый подход для решения этих типов стохастических задач принятия решений с помощью дробной целевой функции построен по следующей двухэтапной процедуре. На первом этапе с использованием метода целевого программирования мы преобразуем стохастическую дробно-линейную модель в две стохастические линейные модели, где первая цель представляет числитель, а вторая представляет знаменатель для стохастической дробной модели. Сформулирована результирующая задача стохастического целевого программирования. Второй этап предполагает решение задачи стохастического целевого программирования путем замены стохастических параметров модели их математическими ожиданиями. Результирующая задача детерминированного целевого программирования построена и решена с помощью решателя Win QSB. Затем, используя оптимальное значение для первой и второй цели, получено оптимальное решение для дробной модели. Для иллюстрации нашего подхода представлен пример, в котором мы предполагаем равномерное распределение стохастических параметров. Следовательно, предлагаемый подход к решению стохастической дробно-линейной модели является эффективным и простым в реализации. Преимуществом предлагаемого подхода является возможность его использования для формулировки и решения любых задач принятия решений с помощью стохастической дробно-линейной модели, основанной на преобразовании стохастической линейной модели в детерминированную линейную модель, путем замены стохастических параметров соответствующими математическими ожиданиями и преобразования детерминированной дробно-линейной модели в детерминированную линейную модель с использованием метода целевого программирования, Стохастична оптимізація з імовірнісними обмеженнями має широкий спектр реальних задач. У деяких реальних задачах особі, яка приймає рішення, необхідно сформулювати проблему у вигляді дробової моделі, в якій деякі або всі коефіцієнти є випадковими величинами з спільним розподілом ймовірностей. Таким чином, ці типи задач дозволяють вирішувати двоїсті задачі та відображають ефективність системи. У даній роботі ми представляємо новий підхід до формулювання і вирішення стохастичних моделей дробово-лінійного програмування з імовірнісними обмеженнями. Цей підхід є продовженням детермінованої дробової моделі. Запропонований підхід для вирішення цих типів стохастичних задач прийняття рішень за допомогою дробової цільової функції побудований за наступною двоетапною процедурою. На першому етапі з використанням методу цільового програмування ми перетворюємо стохастичну дробово-лінійну модель в дві стохастичні лінійні моделі, де перша ціль представляє чисельник, а друга представляє знаменник для стохастичної дробової моделі. Сформульована результуюча задача стохастичного цільового програмування. Другий етап передбачає вирішення задачі стохастичного цільового програмування шляхом заміни стохастичних параметрів моделі їхніми математичними очікуваннями. Результуюча задача детермінованого цільового програмування побудована і вирішена за допомогою вирішувача Win QSB. Потім, використовуючи оптимальне значення для першої та другої цілі, отримано оптимальне рішення для дробової моделі. Для ілюстрації нашого підходу представлений приклад, в якому ми припускаємо рівномірний розподіл стохастичних параметрів. Отже, запропонований підхід до вирішення стохастичної дробово-лінійної моделі є ефективним і простим у реалізації. Перевагою запропонованого підходу є можливість його використання для формулювання і вирішення будь-яких задач прийняття рішень за допомогою стохастичної дробово-лінійної моделі, заснованої на перетворенні стохастичної лінійної моделі в детерміновану лінійну модель, шляхом заміни стохастичних параметрів відповідними математичними очікуваннями і перетворення детермінованої дробово-лінійної моделі в детерміновану лінійну модель з використанням методу цільового програмування

Peer-to-peer (P2P) lending has emerged as an alternative method of financing. Keeping pace with this development, many P2P lending studies have provided approaches to select investment portfolios for individual lenders. However, none of these approaches consider how long it takes for an individual loan to be fully funded so as to reduce the opportunity cost incurred due to delayed investment. In this paper, we propose a goal programming framework to develop an optimal P2P lending portfolio that considers not only the expected returns but also this opportunity cost for individual investors. First, for each loan proposal, a logistic regression model is used to predict the loan default probability while a Weibull regression is used to determine the opportunity cost incurred due to the time taken to obtain the loan. Next, goal programming is applied to construct a portfolio that minimizes the slack from the desired return on investment as well as the surplus from the preset opportunity cost due to a prolonged bidding period. The proposed approach is then applied to Prosper platform data and is expected to help investors’ portfolio decisions in the P2P lending market.

We demonstrate a new approach to conducting a military force structure study under uncertainty. We apply the stochastic preemptive goal program approach, described by Ledwith et al., to balance probabilistic goals for military force effectiveness and the force’s cost. We use the Bayesian Enterprise Analytic Model (BEAM), as described in “Probabilistic Analysis of Complex Combat Scenarios,” to evaluate effectiveness, expressed in terms of the probability of achieving campaign objectives, in three hypothetical scenarios. We develop cost estimates along with their uncertainty to evaluate the force’s research and development, production, and annual operating and support costs. Our summary depicts how the trade-off between various prioritized goals influences the recommended robust force. Our approach enables defense leaders to balance risk in both force effectiveness in various scenarios along with risk in different types of cost categories.

One of the serious supply chain management (SCM) challenges is the effective selection of suppliers and the right allocation of orders to achieve an SCM's suitable financial and technical status. This paper introduced a multi-objective linear programming model to prioritize and weigh suppliers using the modified best–worst method (BWM). The proposed model uses evaluation criteria and used fuzzy variables to determine the right numbers of suppliers as well as order quantity of raw materials each supplier should provide. The model has been made up of four objective functions, and the required constraints have been solved using the goal programming method. The proposed model can take a set of opposing goals into account and prioritize the goals to maximize access to each goal. The uncertain concepts such as fuzzy and rough theories in some criteria enabled the proposed model to use the imprecise information in the best possible manner. This study has solved an example using the proposed model through analytic network process and BWM. Then, the obtained results were compared with our findings for adopting interval-valued fuzzy-rough numbers BWM (IVFRN-BWM) and it was shown that the modified BWM approach generates lower costs and better criteria.

Made available in DSpace on 2022-04-29T08:31:12Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-10-01 Organization and planning of reverse logistics networks make sustainable processes more efficient. Thus, an important sector for connecting the collection and waste paper recycling echelons is the intermediate center. In this study, a Mixed Integer Linear Programming model, which is multi-objective, multi-product, multi-level and multi-period, was developed to optimize the waste paper logistics processes of intermediate centers. The formulation includes the following echelons: collection, considering vehicle routing with different capacities; inventory of non-baling materials; baling sorted waste and inventory of bales; selling bales; disposal of non-recyclable waste. The aims of the model include: maximizing the collected waste; minimizing distances; maximizing both the production and sale of bales, and minimizing costs. According to research carried out in the literature, the developed model is a new proposal and to implement it, the Weighted Goal Programming and Revised Multi-Choice Goal Programming approaches were used to deal with multiple objectives and incorporate uncertainty into the quantity of waste available for collection. To analyze the proposed model, computational tests were executed with instances based on real data from a Brazilian company in the sector. For all performed tests, General Algebraic Modeling System 23.6.5 modeling language and CPLEX 12.2.0.2 solver were used for modeling and optimization. The results show that this study presents formulation and technological approaches that represent real situations and provide competitive solutions to the problem. Sáo Paulo State University “Júlio de Mesquita Filho” [Universidade Estadual Paulista Júlio de Mesquita Filho] School of Engineering of Guaratinguetá [Faculdade de Engenharia de Guaratinguetá] Production Department, Av. Ariberto Pereira da Cunha, 333, Portal das Colinas Agulhas Negras Military Academy [Academia Militar das Agulhas Negras] Rodovia Presidente Dutra, km 306, s/n Escola Preparatória de Cadetes do Exército Avenida Papa Pio XII 350 Jardim Chapadão Campinas - SP Brazil, Avenida Papa Pio XII, 350, Jardim Chapadão, Campinas - SP Sáo Paulo State University “Júlio de Mesquita Filho” [Universidade Estadual Paulista Júlio de Mesquita Filho] School of Engineering of Guaratinguetá [Faculdade de Engenharia de Guaratinguetá] Production Department, Av. Ariberto Pereira da Cunha, 333, Portal das Colinas

In vision-driven development plans, such as the Kuwait Mid-Range Development Plan 2015/2016–2019/2020, themes and pillars are derived from the plan's vision, and global indices are assigned by international organizations to accurately measure the performance against the vision's themes. This allows for comparison with other countries, and it also set targets for progression over time. One or more projects are assigned to the indicators of these global indices. A Multi-Criteria Mathematical Programming Technique (e.g., Goal Programming) is used with multiple goals and priorities where an Optimal Portfolio of Projects is found that satisfied the selection criteria.

How to choose the optimal project portfolio based on the enterprise objectives is very important for the survival and development of an enterprise. However, the current portfolio analysis needs to be improved in the aspects of interaction effect analysis, multichoice target planning, and method simplicity and practicability. The method of portfolio selection proposed in this study takes into consideration the complex interaction within the portfolio, which makes up for the deficiency of the existing research on portfolio selection. This paper illustrates the application of the multichoice object programming method in the analysis of the combination of two-project and three-project interaction effects by a series of examples. The results show that the method is applicable in portfolio evaluation. The method of multichoice goal programming of the interaction effect provides the basis for enterprise to measure the complex relationship within the portfolio and also provides a new method and idea for the selection of the enterprise project portfolio.

In this paper, we consider a fuzzy portfolio selection problem with systematic risk and non-systematic risk simultaneously. These two kinds of risks are measured by beta coefficient and random error variance obtained by Sharp Single Index Model. The total risk as the objective of portfolio decision is obtained by weighting the two kinds of risk. Among them, the weight of systematic risk \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda$$\end{document}λ is regarded as the degree of investors' attention to system risk in economic sense. In addition, the fuzzy return and the degree of diversification are measured by triangular fuzzy number and entropy, respectively. And they are also considered the goal of investment decisions. Hence, a tri-objective portfolio is proposed in this paper. For the fuzzy objectives in the model, a goal programming method based on fuzzy dominance is proposed, which can help investors better capture the ideal point of fuzzy returns according to their risk preference. Finally, combined with the systematic impact of COVID-19 on the financial market, we make an empirical analysis based on our proposed model. The results show that the total risk will be on the high side when \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda$$\end{document}λ value is too large or too small. That means paying too much or little attention to the systematic risk will lead investors to bear more risk. In addition, when investors ignore the systematic risk; that is, the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda$$\end{document}λ value is low, and investors will concentrate their funds in the same industry.

Many workplaces encounter complex problems in preparing an optimal work scheduling to meet the 24 hours work demand especially in shift working hours. The schedule needs to consider many constraints and multi objectives at the same time. A mathematical model such as Goal programming is able to cater this kind of problems. Thus, this study was designed to provide a systematic and optimal schedule for police officers at Criminal Unit, IPD Kuala Muda, Kedah. This study is aimed to formulate the best model for the shift rotating schedule of the police officers and to find the best way to optimize the police scheduling related to the limitations, requirements of the police station and the preferences of the police. Lingo software is used to run the model. However, only one out of three goals set for the study was achieved. The new schedule obtained shows that all police officers have the same number of working days, which is 21 days in the 28-day planning period. The new schedule produced is better than the previous manual schedule since it takes less time to prepare it without neglecting the constraints involved. To improve efficiency and flexibility on the generated schedules, it is recommended to use other methods such as hybrid swarm-based optimization and many new limitations and preferences should be also considered in the analysis.

Project scheduling is one of the most important and applicable concepts of project management. Many project-oriented companies and organizations apply variable cost reduction strategies in project implementation. Considering the current business environments, in addition to lowering their costs, many companies seek to prevent project delays. This paper presents a multi-objective fuzzy mathematical model for the problem of project scheduling with the limitation of multi-skilled resources able to change skills levels, optimizing project scheduling policy and skills recruitment. Given the multi objectivity of the model, the goal programming approach was used, and an equivalent single-objective model was obtained. Since the multi-skilled project scheduling is among the NP-Hard problems and the proposed problem is its extended state, so it is also an NP-Hard problem. Therefore, NSGA II and MOCS meta-heuristic algorithms were used to solve the large-sized model proposed using MATLAB software. The results show that the multi-objective genetic algorithm performs better than the multi-objective Cuckoo Search in the criteria of goal solution distance, spacing, and maximum performance enhancement.

In this paper, a strategic decision making model for the sustainable development of marine renewable energy is proposed, and a specific application to the United Kingdom (UK) is demonstrated. As an island nation the UK benefits from significant marine energy potential which is providing an increasing contribution to UK's renewable energy portfolio. The paper investigates the question of how decision makers can be aided to reach a decision on which types of marine renewable energy projects should be chosen for development given that strategic energy planning is subject to a number of uncertain parameters and multiple sustainability objectives. In this context, the contribution of this paper lies in the combination of renewable energy portfolio selection and the application of multi-objective methods. Interval coefficient goal programming models are adopted in order to address the impreciseness and uncertainty associated with the goals and coefficients of the models in the context of renewable energy selection. The potential renewable energy projects are clustered in order to aid the decision making process and preferential weight sensitivity methods are employed. Conclusions are drawn for optimistic and pessimistic scenarios in the context of UK marine renewable energy planning.

The production planning system can provide satisfaction to the manufacture with the desire target and also with the available raw materials. In achieving the target of goals also face a situation of uncertainty (fuzzy). The aims of this study is proposed the model of fuzzy goal programming approach to optimize production planning system. In this model obtaining maximizing profit and revenue with consider minimize costs of labor cost, raw materials cost, time machine production, and also inventory cost. The numerical example is illustrate that the fuzzy goal programming model can optimize optimize production and profit according desired of decision maker.

Many scientific fields, such as engineering, data analytics, and deep learning, focus on optimization. Optimization problems are classified into two types based on the number of optimized objective functions: single objective and multi objective optimization problems. In this paper, a comparative review since 2000 using one of the deterministic and stochastic modelling approaches called goal programming (GP) and genetic algorithm (GA) in multi-objective optimization problem is discussed. This study gives a prime review of the application of GP and GA in various criteria of project portfolio selection problem. GP is a method for solving large-scale multi-objective optimization problems to assist decision makers in finding solutions that satisfy several competing goals. GA on the other hand are global meta-heuristic search algorithms that are used to provide approximation or optimal solutions to large-scale optimization problems. Of the 23 articles considered in this review showed that, from more than 100 projects, GA proved near optimal, feasible solution and efficient frontier in projects ranking, projects interaction and a preferred decision support tool of project portfolio selection. In addition, the two models select projects on risk-based approach, but GA proved to be more effective in terms of number of projects proposed, central processing unit (CPU) time and accuracy. The review concludes that, in multi-objective optimization model for project portfolio selection problems on a large-scale, very large or complex problems and less CPU time, GA is more effective than GP in multi-objective optimization problems. The review also showed gaps in previous studies of GP and GA application on project portfolio selection problem (PPSP). This review will aid scholars and demanding practitioners in gaining a broader understanding of goal programming and genetic algorithms in the context of project portfolio selection problems.

Hızlı ve dinamik bir çalışma temposu olan savunma sanayinde insan gücüne olan ihtiyaç gün geçtikçe artmaktadır. Firmalar hızlı ve titiz üretim yaparken, iş güvenliği önlemlerini almaları, iş sağlığına önem vermeleri ve personelin verimli çalışabileceği ortamları ergonomik olarak doğru planlaması firmanın sektörde gelişmesine yardımcı olmaktadır. Ergonomik riskler göz önüne alınarak yapılan çizelgelemeler meslek hastalıklarını ve iş kazalarını azaltmakta, gerekli işçilik maliyetlerini azaltmakta ve çalışanların verimliliğini arttırmaktadır. Bu çalışmada savunma sanayisinin alt tedarikçisi olan bir firmada, toplam 60 personel için ergonomik personel çizelgeleme problemini gerçekleştirilmiştir. Çalışmada operatörlerin makinelerdeki yetkinlikleri ve özel istekleri tespit edilerek matematiksel model kurulmuştur. Daha sonra departmanlardaki görevler belirlenmiş ve görevlerin ergonomik risk değerleri REBA metodu ile hesaplanmıştır. Elde edilen risk değerlerini kullanarak görevlere atanmadaki toplam risk puanını en küçükleyecek matematiksel model kurulmuştur ve iki matematiksel model ILOG CPLEX Studio IDE programında çözülüp, çalışanlar için ergonomik riskler de göz önünde bulundurularak aylık çalışma çizelgeleri elde edilmiştir., The need for manpower in the defense industry, which has a fast and dynamic work pace, is increasing day by day. While companies are producing fast and meticulously, taking occupational safety precautions, giving importance to occupational health and ergonomically correct planning of the environments where the personnel can work efficiently help the company to develop in the sector. Scheduling, taking into account ergonomic risks, reduces occupational diseases and work accidents, reduces the necessary labor costs and increases the productivity of the employees. In this study, the ergonomic personnel scheduling problem was carried out for a total of 60 personnel in a company that is a sub-supplier of the defense industry. In the study, the competencies and special requests of the operators on the machines were determined and a mathematical model was established. Then, the tasks in the departments were determined and the ergonomic risk values of the tasks were calculated with the REBA method. Using the obtained risk values, a mathematical model was established to minimize the total risk score in assignment to tasks, and two mathematical models were solved in the ILOG CPLEX Studio IDE program, and monthly work schedules were obtained considering ergonomic risks for the employees.

The Diet Optimization Problem is a classical Linear Programming problem which finds, from a set of available food commodities, the most affordable diet that meets an individual’s nutritional requirements. It is, however, not always possible to create a feasible diet, as certain nutritional requirements are difficult to be met (e.g. iron for adolescent females). In that case, Goal Programming is used to minimize nutritional deviations in order to obtain a near feasible diet. The cost of the diet is, however, often overlooked or neglected. Therefore, we focus in our paper on finding all tradeoffs between the cost and nutritional adequacy of a diet. To do so, we present a bi-objective approach based on the NISE method that is able to find all efficient tradeoffs between two linear objectives. To the best of our knowledge, this approach bridges the current gap between diet optimization and multicriteria optimization, as we are the first to address how to obtain all efficient trade-offs of two linear objectives in a dietary context. In order to show what type of insights can be gained from this approach, two analyses are presented using data collected in Ebonyi, Nigeria. In the first analysis a diet with a restriction on the exact energy intake is considered where all nutrient intakes except energy are allowed to deviate from their prescription. This analysis is especially helpful when one needs to make decisions in case there is a restrictive budget or when a nutritionally adequate diet is either unaffordable or unattainable. The second analysis only relaxes the exact energy intake, where the other nutrients are kept within their requirements, to investigate how the energy intake affects the cost of a diet. Here, we describe in what situations the so-called more-for-less paradox takes place, which can be induced by the exact energy intake.

The Diet Optimization Problem is a classical Linear Programming problem whichfinds, from a set of available food commodities, the most affordable diet that meets an individual’s nutritional requirements. It is, however, not always possible to create a feasible diet, as certain nutritional requirements are difficult to be met (e.g. iron for adolescent females). In that case, Goal Programming is used to minimize nutritional deviations in order to obtain a near feasible diet. The cost of the diet is, however, often overlooked or neglected. Therefore, we focus in our paper on finding all tradeoffs between the cost and nutritional adequacy of a diet. To do so, we present a bi-objective approach based on the NISE method that is able to find all efficient tradeoffs between two linear objectives. To the best of our knowledge, this approach bridges the current gap between diet optimization and multicriteria optimization, as we are the first to address how to obtain all efficient trade-offs of two linear objectives in a dietary context. In order to show what type of insights can be gained from this approach, two analyses are presented using data collected in Ebonyi, Nigeria. In the first analysis a diet with a restriction on the exact energy intake is considered where all nutrient intakes except energy are allowed to deviate from their prescription. This analysis is especially helpful when one needs to make decisions in case there is a restrictive budget or when anutritionally adequate diet is either unaffordable or unattainable. The second analysis only relaxes the exact energy intake, where the other nutrients are kept within their requirements, to investigate how the energy intake affects the cost of a diet. Here, we describe in what situations the so-called more-for-less paradox takes place, which can be induced by the exact energy intake.

The Diet Optimization Problem is a classical Linear Programming problem which finds, from a set of available food commodities, the most affordable diet that meets an individual’s nutritional requirements. It is, however, not always possible to create a feasible diet, as certain nutritional requirements are difficult to be met (e.g. iron for adolescent females). In that case, Goal Programming is used to minimize nutritional deviations in order to obtain a near feasible diet. The cost of the diet is, however, often overlooked or neglected. Therefore, we focus in our paper on finding all tradeoffs between the cost and nutritional adequacy of a diet. To do so, we present a bi-objective approach based on the NISE method that is able to find all efficient tradeoffs between two linear objectives. To the best of our knowledge, this approach bridges the current gap between diet optimization and multicriteria optimization, as we are the first to address how to obtain all efficient trade-offs of two linear objectives in a dietary context. In order to show what type of insights can be gained from this approach, two analyses are presented using data collected in Ebonyi, Nigeria. In the first analysis a diet with a restriction on the exact energy intake is considered where all nutrient intakes except energy are allowed to deviate from their prescription. This analysis is especially helpful when one needs to make decisions in case there is a restrictive budget or when a nutritionally adequate diet is either unaffordable or unattainable. The second analysis only relaxes the exact energy intake, where the other nutrients are kept within their requirements, to investigate how the energy intake affects the cost of a diet. Here, we describe in what situations the so-called more-for-less paradox takes place, which can be induced by the exact energy intake.

Objectives The Police Districting Problem concerns the definition of patrol districts that distribute police resources in a territory in such a way that high-risk areas receive more patrolling time than low-risk areas, according to a principle of territorial fairness. This results in patrolling configurations that are efficient and effective at controlling crime but that, at the same time, might exacerbate racial disparity in police stops and arrests. In this paper, an Equitable Police Districting Problem that combines crime-reduction effectiveness with racial fairness is proposed. The capability of this model in designing patrolling configurations that find a balance between territorial and racial fairness is assessed. Also, the trade-off between these two criteria is analyzed. Methods The Equitable Police Districting Problem is defined as a mixed-integer program. The objective function is formulated using Compromise Programming and Goal Programming. The model is validated on a real-world case study on the Central District of Madrid, Spain, and its solutions are compared to standard patrolling configurations currently used by the police. Results A trade-off between racial fairness and crime control is detected. However, the experiments show that including the proposed racial criterion in the optimization of patrol districts greatly improves racial fairness with limited detriment to the policing effectiveness. Also, the model produces solutions that dominate the patrolling configurations currently in use by the police. Conclusions The results show that the model successfully provides a quantitative evaluation of the trade-off between the criteria and is capable of defining patrolling configurations that are efficient in terms of both racial and territorial fairness.

Complex agricultural problems concern many countries, as a result of competing economic and environmental objectives. In this work we model three common agricultural problems through optimization techniques: a water-scarce area with overexploited surface and groundwater resources due to over-pumping for irrigation (Greece); an area facing water quality deterioration caused by agriculture (Canada); and an intensified animal farming area facing environmental degradation and increased greenhouse gases emissions (Ireland). Multiple goals are considered to optimize farmers’ welfare and environmental sustainability. The proposed approaches are new applications for each case-study, providing useful insights for most countries facing similar problems.

Nowadays, the transportation problem is a multiobjective decision-making problem. It involves deciding to determine the ideal transportation setup that matches the decision maker’s preferences while taking into account competing objectives/criteria such as transportation cost, transportation time, and environmental and social concerns. This study presents a general framework of the multiobjective fractional transportation problem (MOFTP) to deal with such complex scenarios. This paper’s major goal is to propose a solution methodology to solve the MOFTP based on a neutrosophic goal programming (NGP) approach. By obtaining the optimal compromise solution using three memberships, namely, truth membership, indeterminacy membership, and falsity membership, the suggested technique gives a novel insight into solving the MOFTP. A real-world problem such as selling wind turbine blades’ problem and a numerical example are used to demonstrate the efficacy and superiority of the proposed method.

With the increase in the number of patients and activity of hospitals, the issue of hospital waste management (HWM) is becoming more and more challenging and worrying. In addition to financial losses, there will be irreparable damage to the ecosystem and environment which will create many problems for people (because the job of some people in the area is livestock and agriculture and they have a lot to do with their surroundings). It also doubles the need to pay attention to the issue of sustainable development (simultaneous attention to social, economic and environmental dimensions) in waste management. Moreover, the climatic and geographical conditions and lack of proper waste management in this area lead to major problems. Therefore, in this research, by developing a novel multi-objective mixed integer linear programming model, HWM is addressed in the hospitals of Sari, Iran. The aim is to design an HWM network considering sustainability, resiliency and uncertainty. In order to deal with uncertainty, a robust fuzzy programming approach is employed, and then an improved goal programming technique and Lp-metric method is proposed to solve the model. It was revealed that goal programming outperforms the Lp-metric method in terms of all objectives. Furthermore, the obtained results demonstrate the applicability and efficiency of the proposed methodology to design an efficient sustainable HWM network.

PurposeDespite unanimity in the literature that eco-innovation (EI) leads to sustainable development, evidence remains limited on measuring EI efficiency with the Malmquist productivity index (MPI). In conventional data envelopment analysis (DEA) models, decision-making units (DMUs) are inclined to assign more favorable weights, even zero, to the inputs and outputs to maximize their own efficiency. This paper aims to overcome this shortcoming by developing a common set of weights (CSW). Design/methodology/approachUsing goal programming, this study develops a CSW model to evaluate the EI efficiency of the organization for economic co-operation and development (OECD) countries and track their changes with MPI during 2010–2018. FindingsAchieving a complete ranking of DMUs, findings show the higher discrimination power of the proposed CSW compared with the original DEA models. Furthermore, results reveal that Iceland, Latvia and Luxembourg are the only OECD countries that have incessantly improved their EI productivity (MPI > 1) from 2010 to 2018. On the other hand, Japan is the OECD country that has experienced the highest yearly EI efficiency during 2010–2018. This paper also found that Iceland has the highest MPI over 2010–2018. Practical implicationsMore investment in environmental research and development (R&D) projects instead of generic R&D enables OECD members to realize more opportunities for sustainable development through minimizing energy use and environmental pollution in any form of waste and greenhouse gas emissions. Originality/valueIn addition to developing a novel common weights model for DEA-MPI to measure and evaluate the EI of OECD countries, this paper develops a CSW model by including the undesirable outputs for EI analysis.

Fog Computing continues to extend its usage by solving cloud computing challenges about Internet of Things (IoT). Fog nodes as a processing resource, can perform tasks generated by IoT devices. IoT as a client are concerned with the timely execution of their tasks and also lower cost services, and on the other hand, they are looking for a secured task execution. In this paper, we propose a multi-objective simulated annealing (MOSA) algorithm to allocate tasks securely on the fog and cloud nodes based on deadline constraints. The Goal Programming Approach (GPA) is applied to find a compromised solution which will satisfy multiple goals. Also, regarding the distribution of IoT tasks between fog and cloud nodes, a new goal is created called access level and scheduling based on client demand. Simulation results in four low, normal, medium, and high load scenarios showing that the proposed algorithm is on average 9.5% more efficient in terms of service delay time, 87% in terms of access level control and 49.8% in terms of deadline compared to multi-objective Particle Swarm Optimization (MOPSO), multi-objective Tabu Search (MOTS), and multi-objective Moth-Flame optimization (MOMF). Also, in terms of service cost, it has obtained acceptable results close to the average of other algorithms.

This paper determines the optimal communication by the policymakers in the wake of the Covid-19 crisis. The authors have developed a conceptual framework for optimal communication from the availabl...

Despite the multi-objective nature of the nurse rostering problem (NRP), most NRP formulations employ a single evaluation function that minimizes the weighted sum of constraint violations. When solving the NRP in practice, the focus should be on obtaining compromise solutions: those with appropriate trade-offs between different constraints. Due to the real-world characteristics of the problem, appropriate trade-offs may vary substantially across instances, and quantifying these trade-offs does not necessarily translate well into a single evaluation function. This paper introduces a new multi-objective approach for the NRP that promotes controlled trade-offs and guides the solver towards acceptable compromise solutions. The method consists of two phases. The first phase quantifies the characteristics of acceptable compromise solutions by estimating acceptance thresholds that implicitly incorporate trade-offs. This quantification is performed automatically by drawing upon the instance at hand and identifying appropriate trade-offs. The second phase solves the NRP by employing these acceptance thresholds in a lexicographic goal programming framework. By automatically estimating instance-specific acceptance thresholds, we not only require minimal information from the user but also obtain a realistic prediction for solution quality. A case study shows that the methodology produces rosters with little or no deviations from acceptance thresholds, within only a few minutes. Furthermore, this methodology provides the user with clear reasoning behind the trade-offs made, as opposed to methods employing a single evaluation function.

For the Internet of Things (IoT) that have low processing power, fog computing can play an essential role as a processing resource to execute their tasks. Timely execution of the service and the optimal service cost are two major challenges of task scheduling in fog computing. In this article, a privacy architecture is proposed for task scheduling in IoT, and based on this architecture, a multi-objective algorithm is presented to minimize the service time and service cost. We simulate the problem with four scenarios: easy, medium, semi-hard, and hard, and then compare it with other multi-objective algorithms. Since the proposed algorithm is multi-objective, the goal programming approach (GPA) is used to choose one possible solution. The simulation results show that our proposed algorithm has better performance and higher convergence speed to the optimal solution than other algorithms while considering the privacy requirements of IoT devices.

In group decision-making with multiplicative reciprocal paired comparison matrices (MRPCMs), existing research uses iterative procedures or optimisation models to improve consistency of individual ...

Portfolio selection can be regarded as a type of multi-objective decision problem. However, traditional solution methods rarely discussed the decision maker’s nonsatisfaction and hesitation degrees with regard to multiple objectives and they require many extra binary variables, which lead to tedious computational burden. Based on the above, the aim of this paper is to develop a new and unified intuitionistic fuzzy multi-objective linear programming (IFMOLP) model for such portfolio selection problems. The nonmembership functions are constructed by the pessimistic, optimistic, and mixed approaches so as to perfect the traditional intuitionistic fizzy (IF) inequalities and IF theory. The decision maker’s hesitation degrees with regard to multiple objectives are represented by using IF inequalities, and the new IFMOLP model based on IF inequalities is proposed. The IFMOLP problems are solved by the S-shaped membership functions without extra binary variables required by the piecewise-linear method. Finally, the portfolio selection model under IF environments based on IFMOLP is established, and a real example is analyzed to demonstrate its validity and superiority. The developed unified IFMOLP model and method can not only effectively solve multi-objective decision problems with nonsatisfaction and hesitation degrees but also remarkably reduce the complexity of the nondeterministic polynomial-hard problems.

The utility of a service or product can be considered as an aggregation of utilities of multiple attributes, that the consumers consider while choosing a product. The two methods used to generate a linear utility function are the logarithmic goal programming model (LGPM) and the conjoint analysis method (CAM). In these two methodologies, the procedures used to collect data and generate the utility function differ significantly. This is possibly the first study to compare the two methods for determining the utility function of a product (here vehicle insurance policy). For this study we will be collecting the data from the same set of respondents (customers) for the same set of five different brands of the product (vehicle insurance policy) available in the market. The similarities and differences among LGPM and CAM approaches are examined to provide useful insights in terms of consistency in consumer behaviour while prioritizing their choices for a product. The study addresses if the priority order of consumer choices for a product remains the same or changes if the methodology changes. Moreover, we apply a multinomial logit choice model to derive a choice probability of the brands available in the market using both approaches.

This article aims at the evaluation and improvement of the development of higher education. First of all, according to the theory and hypothesis, the comprehensive evaluation index system of higher education development level is established. The TOPSIS and the combination weighting method based on goal programming are used to measure the scores of higher education development levels of 19 major countries in the world in the recent 20 years, and the horizontal and vertical comparative analysis is carried out. Then, according to the practical feasibility, Turkey is selected as the target country to propose a higher education development and improvement policy by 2030, and the causal effect of our proposed policy is estimated using the synthetic control method. This study can provide a reference for other countries in the world to improve their higher education level.

Background Occupational health and safety (OHS) is a significant interest of all governments to prevent workplace hazards. Although appropriate legislation and regulations are essentials for the protection of workers, they are solely not enough. Application of them in practice should be secured by an efficient inspection system. Fundamental components of an inspection system are inspectors and their audit tasks. Maintaining the fair balanced task assignment among inspectors strictly enhances the efficiency of the overall system. Methods This study proposes a two-phased goal programming approach for OHS inspectors' task assignments and presents a case study. Results The solution approach gives the balanced assignment of inspectors to the workplaces in different cities of the country in the planning period. The obtained schedule takes into account the distances covered by the work places and the number of the workplaces' employees to be audited and pays attention to the human factors by considering the preferences of the inspectors. The comparisons between the obtained optimal schedule and the implemented one that is produced manually show that the approach not only maintains the technical requirements of the problem, but also provides social and physical balance to the task assignment. Conclusion Both the approach and the application study are expected to offer fruitful inspirations in the area of safety management and policy and they provide a good guide for social policy and organizational aspects in the field of OHS inspectors' task assignment.

Decision-making for shelter location-allocation influences the success of disaster response and affects the security of victims. This paper proposes a multi-objective optimization model for determining shelter location-allocation in response to humanitarian relief logistics. Three objective functions are formulated to improve both efficiency and effectiveness. The first objective is to minimize total costs, including fixed costs for opening the shelters, transportation costs, and service costs. The second objective is to minimize the total time for evacuating victims from all affected areas to allocated shelters. The third objective is to minimize the number of shelters required to provide thorough service to victims. The Epsilon Constraint method (EC) and Goal Programming (GP) are employed for solving the proposed model. The applicability of the proposed model is validated through a case study of flooding in Surat Thani, Thailand. The Pareto efficiency obtained from solving the proposed model is compared with current shelter location-allocation plans determined by the government sector. The comparisons reveal that the results obtained from solving the proposed model outperform current shelter location-allocation plans. Furthermore, the results of this study could provide an advantage to decision-makers considering appropriate strategies for disaster response.