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A model of investment in projects with uncertain construction time is presented in which both the cash-flows upon completion and construction progress are modeled as diffusions. The decision maker has three sequential options: (i) to start construction, (ii) to abandon the project during construction, and (ii) to start operations or abandon after completion. Existence of a solution and some of its qualitative properties are proved. A Markov-chain approximation is used to numerically approximate the solution is introduced. The model is applied to a real-world railway project for which it is found that it does not represent value for money (VfM) and is unlikely to do so in the next decade. A comparative statics analysis reveals that the presence of an abandonment option can increase project value substantially and can speed up investment. Finally, it is argued that the commonly-used benefit-to-cost ratio (BCR) is not an appropriate measure to summarize a project’s VfM. Instead, the value ratio (VR) is suggested as an alternative.

This paper introduces an interactive approach to support multi-criteria decision analysis of project portfolios. In high-scale strategic decision domains, scientific studies suggest that the Decision Maker (DM) can find help by using many-objective optimisation methods, which are supposed to provide values in the decision variables that generate high-quality solutions. Even so, DMs usually wish to explore the possibility of reaching some levels of benefits in some objectives. Consequently, they should repeatedly run the optimisation method. However, this approach cannot perform well – in an interactive way – for large instances under the presence of many objective functions. We present a mathematical model that is based on compromise programming and fuzzy outranking to aid DMs in analysing multi-criteria project portfolios on the fly. This approach allows relaxing the problem of rapidly optimising portfolios while preserving the beneficial properties of the DM’s preferences expressed by outranking relations. Our model supports the decision analysis on two instance benchmarks: for the first one, a better compromise solution was generated 84% of the runs; for the second one, this ranged from 93% to 97%. Our model was also applied to a real-world problem involving social projects, obtaining satisfactory results.

The weight and reliability of an individual assessment are two important concepts considered in the evidential reasoning (ER) approach. Through analyzing the existing studies on the combination of individual assessments with both their weights and reliabilities considered in the ER context, their deficiencies are identified in accordance with two principles. One principle is developed in the situation where a specific individual assessment is fully unreliable and the other is developed in the situation where all individual assessments are fully reliable. To address the deficiencies, this paper proposes a new method. In the method, a combination process that takes into account both the weights and reliabilities of individual assessments simultaneously is developed to generate the overall assessment. It is theoretically proven that the combination process satisfies the two principles. Three ways are designed to help a decision maker to flexibly provide individual assessments and determine their reliabilities. A strategic project evaluation problem for an enterprise located in Changzhou, Jiangsu, China is analyzed using the proposed method as a case study to demonstrate its validity and applicability. These are highlighted by its comparison with two existing methods.

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.

Differences in interest in decision making are one of the things that must be facilitated in decision support applications. The most basic difference of interest in decision making is the difference in the weight of the importance of each criterion used. Each decision maker has their own interest in the criteria used. If these differences can be facilitated properly, the resulting decision recommendations can be optimal, in this case more in line with the interests of the users. The model is designed using the Analytical Hierarchy Process (AHP) and Simple Additive Weight (SAW) methods, using 5 criteria. The final result of the AHP method is in the form of criteria weights that are in accordance with the interests of decision makers and in accordance with the consistency of the comparisons that have been given. The resulting weight will be used in the final calculation of SAW, namely in the calculation of alternative weights. By using AHP, the weight of the criteria becomes more subjective according to the interests of decision makers. Thus, the resulting alternative recommendations become more optimal because they are in accordance with the needs of decision makers.

International audience; In this paper, we consider the problem of allocation of Emergency Vehicles (EVs) to Rescue Centers (RCs). The objective is to improve the operational response of the Fire and Rescue Services (SDIS) in France. More precisely, we focus on the mid-term management, and on the process of redeployment of EVs in RCs according to the evolution of requirements. At this level of decision, we do not have information on accidents. We only used the available information on the simultaneity of requirements. Based on the Hurwicz criterion, we develop a mathematical model and an iterative algorithm to solve it. The proposed approach takes into account both the uncertainty on the occurrence sequence of accidents, and the attitude towards optimization of the decision maker (DM). An illustration from the DM’s point of view is presented. It shows that this uncertainty significantly impacts the deployment of EVs. Results show that the proposed approach has an efficient resolution time for real size problems.

Along with the development of the era of demand, to always be fast in doing everything, I can't be avoided from the name of transportation. Transportation is a necessity for everyone alive. The car is one of the negotiated land transportation that is chosen by the people. People who work in the service sector own various types of cars to meet the needs of society. To get a car that meets expectations, of course, the rental tenant / car rental owner must have information about the car. Of the many car choices we can make decisions based on feelings. However, we had difficulty making a decision. Therefore we need a decision support system to help consumers decide which car to rent based on the criteria they want. TOPSIS is a method that can be used in the decision making process. And the products used are Toyota Cars 2000-2014. This method can be used to determine whether the car will be rented according to the qualifications required or not. This is because the method is a step-by-step assessment, both qualitative and quantitative. And TOPSIS is a method of taking into account the tolerance limits for the validity of various criteria chosen by the decision maker.

Choquet Expected Utility framework for decision making under uncertainty is compared with the Expected Utility framework in a scenario involving rare events with potentially catastrophic consequences. The scenario involves detection of suspected criminal activity in a surveillance system. The results of simulation studies show the advantages of using the Choquet framework to model pessimistic attitude of decision maker. The Choquet approach detects threats earlier and with higher probability of detection when compared to Expected Utility approach. The paper outlines the relevant decision theory, provides details of simulation design, and experimental results.

Several logistics problems involving disaster relief have attracted growing research interest in the recent decade. A commonality among these problems is that demand–supply incongruence is always observed in post-disaster logistics operations due to the limited relief supplies and the randomly increasing relief demands. In such situations, the decision maker fails to meet the total relief demands simultaneously. This study employs a novel decision-making framework, where the traditional disaster relief logistics actions (e.g., vehicles routing and relief allocation) are replaced by periodic, sequential actions involving demand point location and assignment. A sequential approach allows the decision maker, in the face of every demand-point, to decide whether to locate and assign it to relief suppliers immediately or later, which sequentially influences the decision in the next period. A dynamic optimization model is built and solved by using particle swarm optimization algorithm. The results of a case study indicate the advantages of the sequential approach.

This paper analyses the literature to identify ideas which may be applied to decentralized real-time railway traffic management. This system represents a new way for dealing with railway traffic perturbations in absence of a central decision maker. Specifically, we are interested in identifying techniques that may constitute suitable automatic mechanisms for the emergence of an effective system behaviour. In this literature review, we discuss the possibility of exploiting the existing research works on other transport modes. The analysis of these works makes it clear that real-time railway traffic management is very peculiar. Hence, we consider different approaches: hierarchical self organization, task allocation, reinforcement learning, consensus, auction and coopetition techniques. Some promising possibilities emerge, which we analyse proposing ideas for modelling decentralized real-time railway traffic management.

The article presents the possibility of using the PVM-VSI (Preference Vector Method - Vector Space of Increments) method in supporting the decision problem process, in which we choose one of several decision variants. However, these variants are assessed according to various criteria. Therefore, we need to have knowledge about the options under consideration. This knowledge is not always quantified. It is also important that the assessment of variants of the decision problem under consideration is independent of the preferences and opinions of the decision-maker, i.e. relatively objective. The article uses the PVM-VSI method [1] to support decisions related to the choice of an electric family car. The ranking is intended to indicate which car meets the usability requirements best defined by the decision maker, while meeting its visual preferences both in terms of appearance and interior.

Offshore wind power has been an important force to promote energy transformation. To build an advanced offshore wind farm, wind turbine selection requires the decision maker to explore new relevant criteria and evaluate alternatives with respect to decision criteria with assigning importance weightings to the criteria. In this paper, we devise a novel hybrid multi-criteria decision-making (MCDM) approach for offshore wind turbine selection by reconstructing analytic network process (ANP) and entropy-weight method (EWM). Based on the assigned weightings for ANP and EWM, the best alternative can be selected. For the decision-making model, five main criteria are specifically subdivided into 22 sub-criteria. The method is then applied to deal with the quantitative and qualitative inputs and the interrelation of criteria as arising from decision-making process. The results show that the proposed method can effectively select the optimal one from four different types of alternative offshore wind turbines.

In a manufacturing facility, the decision maker considers customer satisfaction as an essential priority in which customer due dates should be met. The cell loading, scheduling and the total manpower are considered among the main decisions in a manufacturing system. Three performance measures are studied in this paper and they are the number of tardy jobs, total manpower, and average flow time. Two approaches are proposed to handle such performance measures. In the first approach, lexicographical analysis is proposed to achieve the objectives, when the performance measures are not equally important. A three-phase methodology is introduced. In the first phase, a mathematical model has been used to maximize the output rate by allocating manpower to operations for several manpower levels. In the second phase, another mathematical model is developed based on the results of the first phase to minimize total manpower such that no job is tardy (i.e., the number of tardy jobs ( N T = 0)). In the third phase, a mathematical model is also proposed to minimize the average flow time based on the results obtained in the first and the second phases. The second approach allows varying weights for minimizing average flow time and total manpower subject to zero tardy jobs due to considerations for customer satisfaction. Thus, a two-phase methodology is followed, and a bi-objective fuzzy mathematical model is developed. Two-phase methodology produced similar results to three-phase methodology for certain values of weights assigned to performance measures. Three-phase methodology was efficient for lexicographical analysis. Furthermore, the number of cells in the system is affected by the nature of due dates. The number of cells and total manpower increase when the due dates are tight and decrease when the due dates are relaxed.

Many domains require the use of sophisticated simulators to adequately model the effect of chosen alternatives on the decision maker’s value. Decision support in such complex systems brings unique challenges around efficiency, because simulating each combination of inputs can be time-consuming. In this paper, we conduct a value of information (VOI) analysis to study whether one should purchase information about critical input uncertainties in such complex systems. We propose a novel computational approach, where Gaussian processes are used to model the decision maker’s profit as a function of different alternatives and uncertainties. Under this modeling assumption, the expected improvement of the profit is analytically available, which we use to approximate the VOI effectively over batches of simulations, thus avoiding too many computer-intensive evaluations of the system. We illustrate the proposed computational approach with an offshore wind farm maintenance application, where the decision maker relies on outputs from large-scale simulations to determine the optimal vessel fleet mix and number of personnel for operation and maintenance. Such computer-intensive simulations mimic long-term energy production under different input conditions. It is often not possible to explore all the alternatives exhaustively, and one must therefore guide the simulations to run on promising alternatives. We conduct a VOI analysis to study whether one should purchase information about failure rates of wind turbines for this application. The proposed methodologies are general and apply to other domains involving expensive simulators.

The main goal of this research is to help decision maker in prioritizing transportation problems. Although numerous criteria were identified, the critical problems of transportation is less investigated. In order to fill this gap, this study was carried out to prioritizing the critical problems of transportation. The approach developed in this study implements fuzzy ANP to determine the weight for transportation problem criteria and sub criteria. As a result, the highest rank sub criteria was identified, that is “Professionalism” (0.203), followed by “Personal vehicle preferences” (0.190) and “E-ticket” (0.145) . Although prioritize of transportation problems were identified, future research needed to develop the alternative strategy for developing transportation improvement strategies.

Currently, when planning and monitoring the implementation of the development strategy of the Russian oil industry, several target indicators are used in the areas of industry development. The presence of several indicators related to various tactical tasks of the development of the oil industry, with the limited capabilities of the decision maker, does not allow to generalize these data without the use of special tools. These reasons make it necessary to use an integrated indicator in the strategic management of the Russian oil industry, which will allow us to assess the dynamics of the complex state of the Russian oil industry in time, as well as conduct international comparisons. The paper proposes a methodology for evaluating such an integral indicator using methods of hierarchy analysis, a weighted sum of criteria, cluster analysis and the Delphi method. The integral indicator is a synthetic indicator obtained by collapsing the local indicators of the oil industry (quantitatively characterizing the degree of solution of tactical tasks), taking into account the weights of these indicators, as well as the significance of tactical tasks relative to the strategic goal. To evaluate the weight of the criteria system, the hierarchy analysis method is used. The folding of the criteria system is carried out using the method of weighted sum of criteria. Target values of local indicators are estimated using cluster analysis and the Delphi method.

The paper presents a Multiple Criteria Analysis (MCA) of trams, based on customers’ specifications and open bid regulations in different countries, such as: Czech Republic, France, Germany, Hungary, Poland and Turkey. We investigate the costumers’ specifications concerning trams and we try to respond whether they can be applied in a universal assessment procedure for trams to be used in urban, public transportation systems, based on the principles of Multiple Criteria Decision Making/ Aiding (MCDM/A). The analysis of trams is formulated as a multiple criteria ranking problem. The proposed paradigm includes: the definition of the group decision maker – DM and stakeholders, selection of variants (trams), formulation of the consistent family of criteria, recognition and modeling of the DM and stakeholders’ interests/ preferences, performance of the computational experiments, generation of the ranking of trams. We perform a series of computational experiments with an application of selected multiple criteria ranking methods (ELECTRE III/IV, AHP). The generated rankings result in a comprehensive analysis of the quality of trams in different countries and the suitability of selected MCDM/A methods for the evaluation of trams.

One application of Data Envelopment Analysis (DEA) is the resource allocation and target setting among homogeneous Decision Making Units (DMUs). In this paper, we assume that all units are under the supervision and control of a central decision making unit, for instance chain stores, banks, schools, etc. The aim is to allocate available resources among units in a way that the so-called organisational overall ”virtual profit” is maximized. Our method is highly flexible in decision making to achieve the goals of the Decision Maker (DM). The resulting production plans maintain the following characteristics: (1) the virtual profit of each unit is calculated with a common set of weights; (2) the selected weights for calculating the virtual profit prevent the virtual profit of the system from getting worse; (3) the virtual profits of less profitable units are improved as much as possible. The proposed method is illustrated with a simple numerical example and a real life application.

Planning problems in healthcare systems have received greater attention in the last decade, especially because of the concerns recently raised about the scattering of surgical interventions among a wide number of different facilities that can undermine the quality of the outcome due to the volume-outcome association. In this paper, an approach to plan the amount of surgical interventions that a facility has to perform to assure a low adjusted mortality rate is proposed. The approach explicitly takes into account the existing interaction among patients’ choices and decision makers’ planning decisions. The first objective of the proposed approach is to find a solution able to reach quality in health outcomes and patients’ adherence. The second objective is to investigate the difference among solutions that are identified as optimal by either only one of the actors’ perspective, i.e., decision makers and patients, or by considering both the perspectives simultaneously. Following these objectives, the proposed approach is applied to a case study on Italian colon cancer interventions performed in 2014. Results confirm a variation in the hospital planned volumes when considering patients’ behaviour together with the policy maker plan, especially due to personal preferences and lack of information about hospital quality.

This paper develops a hybrid multiattribute decision‐making methodology for ranking project proposals (PPs) through a judicious usage of historical data of completed projects to determine attribute weights, enabling elimination of problems associated with projected data such as cost and schedule overruns of real‐world projects. The weights generated from data envelopment analysis are explicitly utilized for ranking PPs while allowing subjectivity to be ingeniously incorporated into the decision process. The new approach is implemented for ranking 25 PPs, and the rankings it yields are found indifferent to the decision maker's attitude, which ascertains the robustness of the ranking methodology.

The large amount and different types of data and knowledge generated within the Additive Manufacturing (AM) value chain are highly challenging in terms of management and organization. Understanding the interconnections between all these immaterial corpuses is important for decision making and process optimization issues. Moreover, AM has more parameters than conventional manufacturing processes, and many of these parameters are difficult to assess and monitor. Therefore, it becomes important to develop computer-based solutions that are able to aid the decision maker and to support the management of all information along the AM value chain. In this paper, a knowledge-based decision support framework using ontological models and mechanisms is proposed for the above objective. Cost estimation is conducted as an application of the proposed framework.

Prior work on fairness in machine learning has focused on settings where all the information needed about each individual is readily available. However, in many applications, further information may be acquired at a cost. For example, when assessing a customer's creditworthiness, a bank initially has access to a limited set of information but progressively improves the assessment by acquiring additional information before making a final decision. In such settings, we posit that a fair decision maker may want to ensure that decisions for all individuals are made with similar expected error rate, even if the features acquired for the individuals are different. We show that a set of carefully chosen confidence thresholds can not only effectively redistribute an information budget according to each individual's needs, but also serve to address individual and group fairness concerns simultaneously. Finally, using two public datasets, we confirm the effectiveness of our methods and investigate the limitations.

A product network comprises a vast number of goods which are linked to one another. This paper investigates decision making in this new environment by using revealed preference techniques. A decision maker will search within the network to uncover available goods. Due to the constraint imposed by the network structure and the starting point, the decision maker might not discover all available goods. We illustrate how one can deduce both the decision maker’s preference and her product network from observed behavior. We also consider an extension of the model where the decision maker might terminate the search before exhausting all the options (limited search).

Developing environmental conservation plans involves assessing trade-offs between the benefits and costs of conservation. The benefits of conservation can be established with ecological inventories or estimated based on previously collected information. Conducting ecological inventories can be costly, and the additional information may not justify these costs. To clarify the value of these inventories, we investigate the multiple criteria value of information associated with the acquisition of improved ecological data. This information can be useful when informing the decision maker to acquire better information. We extend the concept of the value of information to a multiple criteria perspective. We consider value of information for both monetary and biodiversity criteria and do not assume any fixed budget limits. Two illustrative cases are used describe this method of evaluating the multiple criteria value of information. In the first case, we numerically evaluate the multiple criteria value of information for a single forest stand. In the second case, we present a forest planning case with four stands that describes the complex interactions between the decision maker’s preference information and the potential inventory options available. These example cases highlight the importance of examining the trade-offs when making conservation decisions. We provide a definition for the multiple criteria value of information and demonstrate the potential application when conservation issues conflict with monetary issues.

This paper puts forward a framework that assists managers to prepare action plans using attributes of Kano’s Model and to order them by utilizing multi-criteria decision analysis. The frame...

In group decision making problems, it is almost impossible to have a homogeneous group of decision makers whose experiences, attitudes, knowledge are the same or similar. Therefore, it is required to determine the weights of decision makers to reflect their relative importance or contribution to the problem. Decision maker weights show the importance or reliability of decision makers in solving the particular problem. The studies on determining the weights of the decision makers are limited. Besides, there is no comprehensive literature review or survey related to the determination of decision makers’ weight among the limited numbers of studies. Therefore, in this study, the literature on deriving decision makers’ weights is reviewed to present the state-of-the-art in the group decision making environment. Subsequently, a new classification system is proposed. Objective methods for deriving decision makers' weights are classified into five categories: Similarity-based approaches, index-based approaches, clustering-based approaches, integrated approaches, and other approaches. The literature review and analysis of the studies are conducted based on these categories; moreover, challenges and potential research directions are identified. According to the analysis of fifty-five papers, the interest in the topic increases dramatically after 2011. The highest percentage of the studies fell into the similarity-based approaches.

Multi Attribute Decision Making (MADM) is an asset to provide solutions for our todays' complex issues and problems. The fact of the matter is that the main source of information in many MADMs is a panel of experts. However, in some cases, there is a possibility of lack of knowledge by the panel to rank or weight one or a few particular criterion/criteria for the decision making. Therefore, the decision maker needs an altered source of information to complete the decision making process. Hence, WSM (Weighted Sum Method) by means of the most popular MADM techniques is selected; and as a prior aim of this article, a modified version of the WSM is proposed as a solution for multiple criteria decision makers by way of a solution for the cases when there is a need for another source of information to rank or weight the particular criterion/criteria. The modified WSM is presented in five stages. The validity, through feasibility, of the modified WSM is tested and verified in a numerical example. Additionally, following this article, future researches could use the same approach for modification of other MADMs to deal with two or more sources of information.

This paper studies the impact of two decision makers’ interaction with conflicts on the efficiencies of the system. We start with a general principal-agent framework where the principal and the agent make decisions independently and the principal has a contradictive objective to that of the agent. We develop data envelopment analysis (DEA) models in the principal’s and the agent’s perspectives respectively. Non-cooperation between the principal and the agent is discussed to illustrate how one decision maker affects the other and the corresponding efficiency and incentive contract of the system. In addition, cooperation of the two parties is also analyzed to better derive how the performance of the system is influenced by the parties and their interactions as well. Then, this study illustrates the proposed models and effective incentive contracts by applying them to the efficiency evaluations of 22 China listed electric power companies.

In actual decision making process, the final decision result is often affected by decision maker’s psychological behavior, however, for the multiple attribute decision making (MADM) problem in which attributes values are expressed with trapezoidal intuitionistic fuzzy numbers (TIFNs), there is few literature considering the decision maker's behavior factors in decision making process. For this case, this paper first proposes a new distance measure of TIFNs and a new ranking method which considers decision maker’s attitude behavior, and then develops an extended TODIM decision making method. Finally an example is given to illustrate the validity and practicability of the proposed method.

This paper addresses the multi-period facility location problem during the market expansion stage, where the decision maker plans to add a certain number of new facilities in each period to gradual...

The aim of this paper is to make a proposal for an easy–to–use approach to the evaluation of customer satisfaction in restaurants. In order to provide a reliable way to collect respondents’ real attitudes, an approach based on the use of smaller number of evaluation criteria and interactive questionnaire created in a spreadsheet file is proposed in this paper, whereby an easy-to-understand and simple-to-use procedure is proposed for determining weights of criteria. In addition to the said, the proposed approach applies the simplified SERVQUAL-based approach, for which reason a simplified version of the Weighted Sum Method based on the decision maker’s Preferred Levels of Performances is used for the final ranking of the alternatives. The usability of the proposed approach is considered in the case study intended for the evaluation of traditional restaurants in the city of Zajecar.

When defining criteria that have an influence on traffic accessibility, it is necessary to take into account the specificity of a concrete urban unit. A large number of authors base own research on a small number of traffic access criteria using one of the decision-making methods. The methods on which research can be based are multi-criteria decision-making in combination with fuzzy logic and rough numbers that give relevant results and are widely applied in all fields of science today. Thanks to these methods, for problems where is a large number of solutions in which certain criteria are formed, a valid solution can be reached. When using these methods, it is necessary to emphasize that there is a certain degree of subjectivity of the decision maker, but this can be minimized using fuzzy or rough numbers. The aim of this paper is to compare the significance of particular criteria using the Fuzzy AHP method and the Rough AHP method, which would show differences in the values of weight significance criteria and their ranking.

Proper handling of preferences in multiobjective evolutionary algorithms is essential for the algorithms' success in real-life applications. While there has been a tremendous work addressing preferences in evolutionary algorithms, the issue of the exact interpretation of decision maker's (DM) preferences and how it affects the performance of evolutionary algorithms has received little attention. One interpretation of preferences that has received significant attention lately by the AI community and is believed to be exercised naturally by decision makers is the Ceteris Paribus (all else being equal) interpretation. In this paper, we adopt the notion of Ceteris Paribus as an interpretation for the DM preferences and incorporate it in a constrained multiobjective problem known as virtual machine placement (VMP). VMP is an essential multiobjective problem in the design and operation of cloud data centers concerned about placing each virtual machine to a physical machine (a server) in the data center. We propose a variant of the NSGA-II that promotes Ceteris Paribus preferred solutions and evaluate its applicability. Our experiment results show that this variant was able to return preferred solutions at almost no extra cost when compared to NSGA-II.

Several studies have found that students' academic achievement is as much determined by the socioeconomic composition of their school as their own socioeconomic status. In this paper we provide a methodology for assigning students to schools so as to balance the socioeconomic compositions of the schools while taking into consideration the total travel distance. Our technique utilizes a biobjective general 0–1 fractional program that is linearized into a mixed 0–1 linear program that can be submitted directly to a standard optimization package. We show how a parametrized model could be utilized to provide a spectrum of different possible assignments so that a decision maker can decide how to balance socioeconomic factors with transportation costs. As a test case for our approach we analyze data from the Greenville County School District in Greenville, South Carolina.

Task scheduling within the deadline is more challenging due to different task parameters: start time, execution time, deadline and number of virtual machines (VMs). The OpenNebula platform supports Backfilling algorithm to schedule deadline based tasks. If there are similar types of tasks, then the scheduling performance should be enhanced. In this work, we propose, implementation of Multi-criteria Decision Making (MCDM) using VIKOR method to the existing backfilling algorithm. The VIKOR method is implemented as a decision maker to resolve conflicts among similar tasks. The mechanism finds the best ideal task among the similar tasks and ranks them for scheduling. Therefore, a number of tasks are scheduled. The performance is evaluated by analyzing the number of tasks scheduled, resource utilization and task rejection, which is quite better in comparison to the existing algorithm. Keywords: Deadline sensitive task, Scheduling, Backfilling, MCDA/MCDM, VIKOR

We study the sequential resource allocation problem where a decision maker repeatedly allocates budgets between resources. Motivating examples include allocating limited computing time or wireless spectrum bands to multiple users (i.e., resources). At each timestep, the decision maker should distribute its available budgets among different resources to maximize the expected reward, or equivalently to minimize the cumulative regret. In doing so, the decision maker should learn the value of the resources allocated for each user from feedback on each user's received reward. For example, users may send messages of different urgency over wireless spectrum bands; the reward generated by allocating spectrum to a user then depends on the message's urgency. We assume each user's reward follows a random process that is initially unknown. We design combinatorial multi-armed bandit algorithms to solve this problem with discrete or continuous budgets. We prove the proposed algorithms achieve logarithmic regrets under semi-bandit feedback.

Measuring the level of sustainability taking into account many contributing aspects is a challenge. In this paper, we apply a multiple criteria decision aiding framework, namely, the hierarchical-SMAA-PROMETHEE method, to assess the environmental, social, and economic sustainability of 20 European cities in the period going from 2012 to 2015. The application of the method is innovative for the following reasons: (i) it permits to study the sustainability of the mentioned cities not only comprehensively but also considering separately particular macro-criteria, providing in this way more specific information on their weak and strong points; (ii) the use of PROMETHEE and, in particular, of PROMETHEE II, avoids the compensation between different and heterogeneous criteria, that is arbitrarily assumed in value function aggregation models; finally, (iii) thanks to the application of the Stochastic Multicriteria Acceptability Analysis, the method provides more robust recommendations than a method based on a single instance of the considered preference model compatible with few preference information items provided by the Decision Maker.

This study aims to jointly optimise regular and demand responsive transit (DRT) services, which can offer opportunities for leveraging on their respective advantages. An optimisation model with the objective of minimising the total travel time of passengers and the total fleet size is proposed. The terminal bus stops of regular bus lines, the service area of the DRT, and the fleet size of both regular and DRT are optimised simultaneously. A rule-based optimisation preparation step is added to the proposed model to obtain a reasonable design scheme and to reduce the computational load. The model is solved using a tailored boundary-start-based two-step heuristic algorithm. The performance of the mixed network is affected by the preference of the decision maker and the operation mode adopted for the DRT service. A reduction in the operational level of the DRT results in a considerable increase in the travel time of DRT passengers.

In the present article the Analytic Hierarchy Process (AHP) method, which has an important place among multi-criteria decision making methods, will be used in the solution. In addition, the group decision making will be applied as it will be beneficial to the result that there will be more than one decision maker. The method will be handled with the Interval Type-2 Fuzzy Sets (IT2FSs) that have high success in handling uncertainties.

We provide an adversarial risk analysis framework for batch acceptance problems in which a decision maker relies exclusively on the size of the batch to accept or reject its admission to a system, albeit being aware of the presence of an opponent. The adversary acts as a data-fiddler attacker perturbing the observations perceived by the decision maker through injecting faulty items and/or modifying the existing items to faulty ones. We develop optimal policies against this combined attack strategy and illustrate the methodology with a review spam example., The work of DRI is supported by the AXA-ICMAT Chair on Adversarial Risk Analysis and the Spanish Ministry of Science program MTM2017-86875-C3-1-R AEI/FEDER, UE. FR also acknowledges the contribution of the Community of Madrid through its Chair of Excellence programme. JGO’s research has been financed by the Spanish Ministry of Economy and Competitiveness under FPI SO grant agreement BES-2015-072892. This work has also been partially supported by the Spanish Ministry of Economy and Competitiveness through the “Severo Ochoa” Program for Centers of Excellence in R&D (SEV-2015-0554) and project MTM2015-72907-EXP, as well as by the US National Science Foundation through grant DMS-163851 and the BBVA Foundation project “Adversarial Machine Learning: Methods, Computations and Applications to Malware, Fake News and Autonomous Vehicles (AMALFI)”.

The newsvendor problem captures the trade-off between ordering decisions, stocking costs and customer service level when the demand distribution is known. Nonetheless, in real case scenarios, it is unlikely that the decision maker knows the true demand distribution and its parameters, encouraging the use of datasets for empirical solutions that will achieve more precise results and reduce misleading decisions. Motivated by the availability of large amount of quality datasets, advances in machine learning algorithms and enhancement of computational power, the development of data-driven approaches has been emerging over the recent years. However, it is still unclear in which settings these data-driven solutions outperform the traditional model-based methods. In this paper, a systematic literature review is conducted for the descriptive analysis and classification of the most relevant studies that addressed the newsvendor problem and its variations under the data-driven approaches. The methods developed to solve the problems with unknown demand distribution are categorized and assessed. For each category, our paper discusses the relevant publications in detail and how they evidence the data-driven performance better. By identifying the gaps in the available literature, the future research directions are suggested.

Selections, exams, and tests for knowledge assessment frequently require the participant be present on a facility to take a presential test. In 2017, 6.7 Million participants took the biggest Brazilian exam: Exame Nacional do Ensino Médio (ENEM). Logistics is a fundamental key to the execution of these events, responsible to select the locations, allocate each participant in one of a list of possible preselect facilities, and hire temporary staff to apply the tests. Deciding the locations that will be used is a crucial step that determines the two major logistical costs (staff and rent), and a bad decision will directly impact the global costs of the event. Hence, the goal of this work is to elaborate mathematical models using Lingo 17.0 to assist the decision makers to decide which locations to use, including the designation where the participant will take the test, first to minimalize the costs and then maximize the participant service level. The resulting models include multiple complex and specific constraints solved extremely fast and focused on not only providing the best solution, but also automating a manual and time-consuming process. In addition, a trade-off curve between cost and participant service level assists the decision maker to select the best option for different scenarios.

A Dynamic Goal Programming Diet Menu Plan (DGPDMP) model for patients undergoing Hemodialysis has previously been formulated and validated [1]. The problem was validated and the obtained menus guarantees all the patient nutritional requirements. However, it is recommended that the decision maker (DM)(patient in accordance with the dietician guidelines) can deal with a model that consist of multiple and conflicting goals to be simultaneously pursued even if the objectives have different importance and can communicate the objectives' relative importance by determining a level of satisfaction in accordance to deviations associated to nutritional goals. The development of the Weighted DGPDMP model (WDGPDMP) is hereby presented in this work and we explicitly introduce the structure of the DM's preferences into the developed model.

Decisions are taken under three varieties of environments: (a) decision making under conditions of certainty (b) decision making under conditions of uncertainty, where the decision maker does not have enough data or knowledge to permit him set probabilities to the different states of nature (c) Decision making under conditions of risk, where the decision maker possesses enough information to permit him assign probabilities to any of possible states of nature. A few examples showing the application of the different criteria that may be used for infrastructure are given. A four step strategy (1) feasible alternatives (2) expected future events (3) payoff table (4) optimum decision criterion helps the infrastructure supplier.

A start-up company has no history and contains low profit at the beginning. It is risky to invest in them. So, one of the biggest tasks of a Start-up Company is to raise a significant amount of money for further improvement in the market. PROMETHEE is a much simpler ranking technique compared to other methods for multi-criteria analysis. GAIA exposes key aspects of the decision making problem and helps the decision maker in making choices and finalizing the decision. This paper engages with the application of PROMETHEE-GAIA methodology. It helps in ranking of alternatives for investing in Start-up Company based on four various criteria. It begins with a general overview of multi criteria issues and emphasizes that the multi criteria problem cannot be treated without additional information regarding the priorities of decision makers. The analysis is dealing with the PROMETHEE-GAIA technique, which shows the over complex figure of the relation between criteria and alternatives in the Gaia plane.