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The Physical Internet (PI) offers an innovative approach to logistics networks, focussing on operational, digital, and physical interconnectivity through encapsulation, interfaces, and protocols. However, PI network can be complex to manage due to its dynamic structure and the need for routing protocols that adapt to continually evolving circumstances. This paper proposes a dynamic and reactive routing protocol for a PI sub-network, leveraging well-known Internet techniques, namely 'Route Request' and 'Route Reply'. The objective is to enable dynamic, self-starting, multi-hop routing among nodes, ensuring continuous connectivity even amidst network disruptions. Moreover, the protocol incorporates reactive assignment algorithms to address disruptions, such as product shortages and resource unavailability. Additionally, the protocol is innovative in considering each container and resource's preferences and local knowledge of the system's known state. To include preferences in decision-making, we incorporate the Technique for Order Preference by Similarity to Ideal Solution and goal programming. Finally, we use multi-agent simulation and real order data from two major French retail chains to evaluate the protocol's performance. Results showed improved routing robustness and efficiency amidst disruptions compared to state-of-the-art methods. Various scenarios have been examined to yield promising insights for future research and practical applications in the realm of the PI. [ABSTRACT FROM AUTHOR]

In Yao et al. [Yao CL, Wang SQ, Tammer C. Construction of vector-valued weak separation functions with applications to conjugate duality in vector optimization; 2024. Submitted.], based on the idea of image space analysis and abstract convexity, we proposed a uniform framework of conjugate duality for the constrained vector optimization problems. This paper aims to apply this framework to linear vector optimizations. With the aid of a special collection of separation functions, a set-valued dual problem, which was also derived by Heyde, Löhne, Tammer in [Heyde F, Löhne A, Tammer C. The attainment of the solution of the dual program in vertices for vectorial linear programs. In: Barichard V, Ehrgott M, Gandibleux X, T'Kindt V, editors. Multiobjective programming and goal programming. 2009. p. 13–24. (Lecture Notes in Economics and Mathematical Systems; 618).; Heyde F, Löhne A, Tammer C. Set-valued duality theory for multiple objective linear programs and application to mathematical finance. Math Meth Oper Res. 2009;69:159–179.], is obtained by this approach. Within the uniform framework, we provide a new way to construct this dual model, and to investigate the duality theory of it. In this method, the strong duality is established by the separation of sets in the image space and the generalized saddle points. [ABSTRACT FROM AUTHOR]

This paper proposes a non-oriented, non-radial Inverse Data Envelopment Analysis (InvDEA) approach for target setting. The changes of any input and output dimensions may be fixed. In addition to the corresponding multiobjective optimisation model, the approach includes a prior step that checks the feasibility of the input and output changes that are fixed and revises them if necessary. This Pre-InvDEA model uses Goal Programming (GP) and guarantees the feasibility of those changes. The approach also includes a Post-InvDEA multiobjective optimisation model to optimise the input and output changes given in order to guarantee that the computed target is efficient. The overall target-setting approach is conceived as an interactive process so that the three steps (Pre-InvDEA, InvDEA, and Post-InvDEA) may be run more than once until a satisfactory target is determined. The application of the proposed approach to computing efficient targets for Spanish universities is presented. [ABSTRACT FROM AUTHOR]

In the modern world, effective and successful supply chain management is among the most significant strategies for all organizations and public/private companies. Nowadays, recommending new tools and attitudes to improve supply chain efficiency is a challenging topic in organizational management. Another important goal of organizational management is to allocate costs logically and conveniently. Even though there are numerous studies on fixed costs and many tools for addressing this issue, data envelopment analysis (DEA) is a powerful technique for fair allocation. Therefore, the present study aimed to investigate the allocation of fixed costs in four-level supply chains using DEA models. The proposed model was examined by implementing a common set of weights and cost allocations according to the size of the decision-making unit. Additionally, the goal programming technique was applied to minimize the deviation between efficient allocation and size-based allocation. The proposed model is linear and provides a unique fixed allocation for each stage of the supply chain. Given that this model is always feasible, it can always provide an optimal allocation for each stage of the supply chain in different industries. In this paper, we focused on the allocation of personnel costs in a cement supply chain in Iran. The results show that the allocation amount to the customer stage (fourth stage) of the supply chain is much higher than other stages and the lowest allocation is obtained in the supplier stage (first stage). [ABSTRACT FROM AUTHOR]

Disasters that occur unexpectedly cause many material and moral losses. Türkiye is an earthquake zone that frequently experiences earthquakes as a result of the convergence of the plates. The period in which earthquakes will occur is unknown, but the damage caused by the necessary planning can be reduced. Disaster management, which is a dynamic and versatile process, is a system that requires effective organization to minimize losses and damages. In the response phase of disaster management, search, and rescue teams work against time pressure by undertaking complex tasks in their live-focused debris work in multiple and scattered disaster areas. In the 6 February 2023 Kahramanmaraş earthquakes, it has been seen how important it is to dispatch the teams to the regions quickly. In this study, the problem of scheduling 380 Disaster and Emergency Management Presidency (AFAD) search and rescue teams to be dispatched to 8 areas where destruction was experienced was handled by considering the earthquake scenario in a province located in a risky region. The purpose of the problem is to dispatch the appropriate number of teams to the correct disaster areas. The mathematical model created with the goal programming method is solved with the IBM ILOG optimization program. According to the solution results obtained, the optimal assignment and schedule of the teams are created by providing the targeted constraints. [ABSTRACT FROM AUTHOR]

Pilots are small-scale initial experiments that are intended to guide the design of future, larger studies, with a view to increasing their effectiveness. In this statistical primer we highlight five common mistakes that limit the utility of pilot studies and provide practical guidance to avoid such errors and increase their effectiveness. The common thread connecting these mistakes is insufficient planning and over-interpretation of the results. This approach compromises the ultimate goals of the research programme and the future experimental cascade. In support of our view that over-interpretation is an error, we present a simple simulation to demonstrate that pilots will generally generate an inaccurate estimate of the variability of the biological endpoint under study and that frequent under-estimation will lead to inconclusive and unethical subsequent experiments. We argue that well planned pilots are an important part of the research cascade and still need to be implemented to a high standard. [ABSTRACT FROM AUTHOR]

Increasing port productivity will affect increasing port income. To be able to achieve this, Resources already in place must be optimized. One option cannot adequately address the complexity of factors affecting port productivity. A method called multiple objectives can assist decision-makers in logically allocating resources by using models for numerous distinct purposes at once. Several objectives must be achieved, including maximizing revenue, minimizing environmental costs, and maximizing the number of workers to design a non-commercial port business optimization model. These goals contain different or even contradictory aspects. For this reason, we need a method that can provide an optimal solution: a meeting point (trade-off) of these goals. The goal programming method can solve problems optimally with more than one (multi) goal objective. The research results show that the product combination resulting from goal programming optimization is more profitable than what the company has done so far. Optimization results fulfil aspects of sustainable port management, namely economic, social, and environmental elements. With this optimization method, port managers can optimize their ports in a balanced way between seeking profits, advancing job growth and minimizing environmental impacts, thus leading to sustainable port management. [ABSTRACT FROM AUTHOR]

In recent years, a relatively novel paradigm known as sustainable development has been introduced in response to concerns regarding the adverse impact of industrial activities on the environment and society. Managers in the food sector have been attempting to incorporate the principle of sustainable development in their supply chains owing to the paramount importance of social and environmental considerations in creating a competitive advantage for food products. To this end, we propose a multi-objective linear mathematical model considering the three dimensions of sustainability, i.e. economic, environmental, and social, to design a sustainable food supply chain. Given today's volatile business environment, we employ a robust optimization model by incorporating Conditional Value-at-Risk into the configuration of two-stage stochastic programming to tackle uncertainty and take up a risk-averse strategy in supply chain design. The model aims to identify the optimal production and delivery times of the products, investigate the effects of their perishability characteristic on inventory decisions, and assess the financial and environmental advantages of transportation decisions to improve the sustainability of logistics operations. A novel version of fuzzy goal programming approach is applied to solve the proposed model. Next, the applicability of the proposed model and its solution method is verified based on computational experiments on a real-world case study of a processed food company. Lastly, conflicts between the sustainability aspects are examined, and several sensitivity analyses on risk-aversion parameters are performed to provide managerial insights for industry executives seeking to optimize their network concerning sustainability issues and well-performance under worst-case scenarios. [ABSTRACT FROM AUTHOR]

In agriculture, crop planning and land distribution have been important research subjects. The distribution of land involves several multi-functional tasks, such as maximizing output and profit and minimizing costs. These functions are influenced by a variety of uncertain elements, including yield, crop price, and indeterminate factors like seed growth and suitable fertilizer. In order to address this problem, other researchers have used fuzzy and intuitionistic fuzzy optimization approaches, which did not include the indeterminacy membership functions. However, the neutrosophic optimization technique addresses the problem by using individual truth, falsity, and indeterminacy membership functions. So, to improve the optimal solution, the Neutrosophic Goal Programming (NGP) problem with hexagonal intuitionistic parameters is employed in this study. The membership functions for truth, indeterminacy, and falsity are constructed using hyperbolic, exponential, and linear membership functions. Minimizing the under deviations of truth, over deviations of indeterminacy, and falsity yields the NGP achievement function, which is used to attain optimal expenditure, production, and profit under the constraints of labour, land, food requirements, and water. Bio-inspired computing has been a major research topic in recent years. Optimization is mostly accomplished through the use of bio-inspired algorithms, which draw inspiration from natural behaviour. Bio-inspired algorithms are highly efficient in exploring large solution spaces, and helps to manage trade-offs between various goals, and providing the global optimal solution. Consequently, bio-inspired algorithms such as Grey Wolf Optimization (GWO), Social Group Optimization (SGO), and Particle Swarm Optimization (PSO) are employed in the current work to determine the global optimal solutions for the NGP achievement function. The data for the study was collected from the medium-sized farmers in Ariyalur District, Tamil Nadu, India. To illustrate the uniqueness and application of the developed method, the optimal solutions of the suggested method are compared with Zimmermann, Angelov, and Torabi techniques. The proposed technique demonstrates that the bioinspired algorithms' optimal solution to the neutrosophic goal is superior to the existing approaches. [ABSTRACT FROM AUTHOR]

This study discusses the shift scheduling problem of security personnel, considering personnel preferences for 43 stations on four lines of the Ankara metro, which carries more than 10 million passengers monthly. Firstly, 751 security personnel have been distributed to four lines of the Ankara metro according to personnel needs. A survey is conducted among the personnel at the stations on each line, and they are asked to rank the stations they want to work at according to their preferences. The station preferences of the personnel are listed with increasing scoring in parallel with the order of preference. A goal programming model has been used to assign personnel to stations, considering personnel needs, operating rules, and personnel preference scoring at the stations. The main objective of the model is to minimize the personnel-preferred station scoring. As a result of the solution of the mathematical model solved separately for four lines, 24.23% of the personnel for the M1 (Kızılay-Batıkent) line, 27.94% of the personnel for the M2 (Kızılay-Koru) line, 23.32% of the personnel for the M3 (Batıkent-Sincan) line, and 35.85% of the personnel for the M4 (Keçiören-Şehitler) line, are assigned to their first three preferences. Moreover, an important balance is achieved between personnel preferences. Few studies are in the literature on railway security personnel scheduling, and no studies that consider personnel preferences have been found in this field. This study, which considers personnel preferences, contributes to the literature. [ABSTRACT FROM AUTHOR]

Goal programming (GP) is one of the widely used and effective method of solving real-world multi-objective decision-making problems. The term "multi-objective transportation problem" (MOTP) covers a specific class of vector maximum (minimum) linear programming problem that typically has multiple, competing, and incompatible objective functions. In this article, GP and weighted goal programming (WGP) are summarized in MOTP. Furthermore, its advantages over GP are illustrated by a theorem. Additionally, by utilizing WGP, a solution procedure is articulated for MOTP. Finally, an application on screen-panels of mobile devices is presented to explore the applicability. The paper ends with a conclusion and including an outlook to future studies, whereby we address internal and external transportation, and view the internal one with a broader understanding of logistics. [ABSTRACT FROM AUTHOR]

In real-life optimization problems, due to some uncertainty and hesitancy, it might be challenging for the decision-makers to identify the precise values of the parameters. To define this uncertainty, neutrosophic set theory is an extension of the crisp, fuzzy and the intuitionistic fuzzy set theory in which along with the incomplete information, indeterminate and inconsistent information is also handled. Considering these advantages, a model for the multi-objective fixed-charge transportation problem with the single-valued trapezoidal neutrosophic numbers is formulated here. To derive the Pareto-optimal solution for the presented model, neutrosophic goal programming approach is proposed which is rooted in the principles of maximizing the degree of satisfaction, maximizing the degree of indeterminacy and minimizing the degree of dissatisfaction. For demonstrating the applicability of the proposed approach, a numerical problem within the same framework is solved. Furthermore, the same problem is also solved using the fuzzy programming and intuitionistic fuzzy programming approach and a comparative analysis is conducted. The results demonstrate that the proposed neutrosophic goal programming approach consistently provides better Pareto-optimal solution as compared to the existing approaches. Finally, the implications of this research and outline avenues for the future research are also discussed at the last. [ABSTRACT FROM AUTHOR]

Purpose: As supply chain excellence matters, designing an appropriate health-care supply chain is a great consideration to the health-care providers worldwide. Therefore, the purpose of this paper is to benchmark several potential health-care supply chains to design an efficient and effective one in the presence of mixed data. Design/methodology/approach: To achieve this objective, this research illustrates a hybrid algorithm based on data envelopment analysis (DEA) and goal programming (GP) for designing real-world health-care supply chains with mixed data. A DEA model along with a data aggregation is suggested to evaluate the performance of several potential configurations of the health-care supply chains. As part of the proposed approach, a GP model is conducted for dimensioning the supply chains under assessment by finding the level of the original variables (inputs and outputs) that characterize these supply chains. Findings: This paper presents an algorithm for modeling health-care supply chains exclusively designed to handle crisp and interval data simultaneously. Research limitations/implications: The outcome of this study will assist the health-care decision-makers in comparing their supply chains against peers and dimensioning their resources to achieve a given level of productions. Practical implications: A real application to design a real-life pharmaceutical supply chain for the public ministry of health in Morocco is given to support the usefulness of the proposed algorithm. Originality/value: The novelty of this paper comes from the development of a hybrid approach based on DEA and GP to design an appropriate real-life health-care supply chain in the presence of mixed data. This approach definitely contributes to assist health-care decision-makers design an efficient and effective supply chain in today's competitive word. [ABSTRACT FROM AUTHOR]

Abstract In this study a real case multi-objective material and supplier selection problem in cardboard box production industries is studied. This problem for the first time optimizes the objective functions such as total wastage amounts remained from all raw sheets, total costs of the system including purchasing cost and transportation cost (including fixed and variable costs) of the raw sheets, and total overplus of produced cardboard boxes. To be closer to the real situations, as a novelty, the problem is formulated in belief-degree-based uncertain environment with normal distribution where this type of uncertainty applies the ideas of experts. A solution approach including two steps is proposed to solve the problem. In the first step, the proposed uncertain formulation is converted to a crisp form using a typical chance constrained programming scheme. In the second step, a new goal programming approach containing a piecewise penalty function is developed in order to solve the obtained multi-objective crisp formulation. In this approach, based on the ideas of experts, multiple goals are considered with different penalty values. A case study from cardboard box industries is considered to evaluate the proposed formulations and solution approach. According to the obtained results, the proposed solution approach is compared to similar approaches of the literature and its efficiency is studied.

A shift system implementation is usually required in scheduling security services which also causes important managerial challenges due to the necessity of the 7 days-24 hours continuous nature of the corresponding services. In the construction of a shift schedule, it is also necessary to consider some criteria about the humanitarian issues, such as the equality of not only the number of workdays but also the number of shifts for each person as well as the ones imposed by the corresponding labor law legislation usually about the maximum number of workdays and shift transitions. Construction of an ideal schedule for security services under the aforementioned constraints is thus a challenging task, especially for the large-sized real-life problems due to the increases in both the number of people and the length of the planning horizon. An idea to deal with the curse of dimensionality in the shiftscheduling problem is dividing a planning horizon into shorter periods, which might reduce problem size significantly; however, it also requires a flexible approach for relating the solutions of the consecutive periods. Such a flexible approach for relating the corresponding schedules of the consecutive periods is proposed in this study where we propose an integer-goal programming formulation that efficiently handles the issue. We also illustrate its performance on a real-life problem involving the construction of the monthly schedules in a year for a security team of a hundred people. It is noted from the computational experiments that the proposed formulation is able to construct the corresponding monthly schedules in a few minutes, implying its potential for use real-life shift scheduling problems. [ABSTRACT FROM AUTHOR]

The university course timetabling is a tactical level problem that almost all academic departments encounter before each semester. Nowadays, in order to meet some standards, set by educational accreditation agencies within the scope of higher education quality assurance, it is required to generate efficient course timetables, which ensure optimal distribution of basic capabilities. Actually, these capabilities should be first specified based on the learning outcomes defined by lecturers for each course that are also associated with program outcomes and are aimed to be acquired by all of the students. Based on this motivation, a multi-objective mixed-integer non-linear programming model is developed for a novel capability-based course timetabling problem. Its validity and practicality are tested on a real-life application in an Industrial Engineering Department. When the balanced solutions provided by compromise and fuzzy goal programming techniques were compared with the existing schedules of the previous years, it was revealed that significant improvements could be achieved in terms of several conflicting objectives (i.e., optimal capability distribution over the timetable, acquisition of maximum number/variety of different capabilities by the students, meeting expectations of lecturers by minimizing total temporal difference between the periods his/her courses are assigned, total penalty cost related to soft constraints). [ABSTRACT FROM AUTHOR]

Computational thinking (CT), an essential competency for comprehending and addressing intricate issues in the digital world, has been incorporated into curriculum planning as a goal for programming education. This study introduced flow design into programming curricula to investigate its impact on undergraduates 'CT skills during pair work. Two types of flow design approaches, construct-by-self flow design (CBS-FD) and construct-on-scaffold flow design (COS-FD), were proposed to determine which approach better enhances students' CT skills. Seventy-six first-year undergraduates participated, including thirty in the CBS group and thirty-six in the COS group. Evaluations made from the results and processes of programming tasks were employed to describe computational performance and computational practices, respectively. Data gathered from CT skill surveys were thoroughly analyzed to gain a deeper understanding of computational perspectives. Our findings highlighted that COS-FD significantly improved participants' computational performance compared with CBS-FD. The COS groups fostered an engaging, sharing atmosphere, while CBS groups spent more energy on negotiating the manipulation of flow design. Moreover, both COS-FD and CBS-FD proved beneficial in enhancing participants' computational perspectives, with the COS groups better improving their algorithm thinking. The study presents valuable perspectives on the design and implementation of collaborative programming activities within curriculum education. [ABSTRACT FROM AUTHOR]

Integrating inventory with production–distribution operations has critical importance realized by several companies' top management a long time before; however, challenging. The management has to decide where to shift products, which demand to fulfil, from which distribution centre (s), how much inventory to keep and a host of critical and complex decisions. As the organization increase in size and complexity, more robust optimization tools required to help analyze and comprehend these complex decisions. In this study, inventory, production and distribution problems with planned shortages, back-ordered quantity and delay in replenishment studied under uncertainty. A multi-objective multi-product model for minimizing the inventory, production and shipment costs and delivery time developed for the integrated system. The study considers the model's different input parameters under a fuzzy and stochastic environment. The fuzziness converted to deterministic using interval type-2 trapezoidal fuzzy number. The randomness follows the Maxwell–Boltzmann distribution in which the deterministic form obtained using chance-constrained programming. The developed integrated model illustrated a numerical example in a manufacturing firm and solved using three robust goal programming variants. The results indicated inventory, production and transshipment costs are 99% minimized with 100% delivery time achievement. [ABSTRACT FROM AUTHOR]

In this research, we focus on the multi-mode resource-constrained multi-project scheduling problem from the perspective of consultants who attend design-bid-build contracts and put their efforts to optimize the client and contractors' profits simultaneously. Each contractor may have a different level of knowledge and experience to perform a project. Therefore, the contractor selection to execute a project may affect the quality and satisfaction level of the project. This study aims to schedule activities of a certain number of projects with multiple execution modes and pre-specified relations. A multi-objective mixed-integer linear programming model is proposed to maximize satisfaction in contractor selection, minimize the total cost of projects and earliness and tardiness penalties, and minimize the total completion time of projects subject to resource constraints. It is assumed that activity durations are uncertain; thereby the globalized robust counterpart of the formulation is presented such that the normal range of the perturbation is the intersection of a box and a polyhedral. The proposed multi-objective model is solved by the multi-choice goal programming approach. This problem is computationally complex and the Benders decomposition (BD) method is utilized to solve it. Numerical experiments on different instances prove the quality of the proposed BD method and illustrate that the proposed model is capable of selecting a proper contractor and multi-project scheduling. [ABSTRACT FROM AUTHOR]

This paper presents an optimization approach for supply chain management that incorporates goal programming (GP), dependent chance constraints (DCC), and the hunger games search algorithm (HGSA). The model acknowledges uncertainty by embedding uncertain parameters that promote resilience and efficiency. It focuses on minimizing costs while maximizing on-time deliveries and optimizing key decision variables such as production setups, quantities, inventory levels, and backorders. Extensive simulations and numerical results confirm the model's effectiveness in providing robust solutions to dynamically changing supply chain problems when compared to conventional models. However, the integrated model introduces substantial computational complexity, which may pose challenges in large-scale real-world applications. Additionally, the model's reliance on precise probabilistic and fuzzy parameters may limit its applicability in environments with insufficient or imprecise data. Despite these limitations, the proposed approach has the potential to significantly enhance supply chain resilience and efficiency, offering valuable insights for both academia and industry. [ABSTRACT FROM AUTHOR]

Introduction: Approval of the first long‐acting injectable antiretroviral therapy (LAI ART) medication heralded a new era of HIV treatment. However, the years since approval have been marked by implementation challenges. The "Accelerating Implementation of Multilevel Strategies to Advance Long‐Acting Injectable for Underserved Populations (ALAI UP Project)" aims to accelerate the systematic and equitable delivery of LAI ART. Methods: We coded and analysed implementation barriers according to the Consolidated Framework for Implementation Research (CFIR) domains, desired resources and programme goals from questionnaire short‐answer responses by clinics across the United States responding to ALAI UP's solicitation to participate in the project between November 2022 and January 2023. Results: Thirty‐eight clinics responded to ALAI UP's solicitation. The characteristics of LAI ART as an innovation (cost, complexity of procurement, dosing interval, limited eligibility) precipitated and interacted with barriers in other CFIR domains. Barriers included obtaining coverage for the cost of medication (27/38 clinics) (outer setting); need for new workflows and staffing (12/38) and/or systems to support injection scheduling/coordination (16/38), transportation and expanded clinic hours (13/38) (inner setting); and patient (10/38) and provider (7/38) education (individuals). To support implementation, applicants sought: technical assistance to develop protocols and workflows (18/38), specifically strategies to address payor challenges (8/38); additional staff for care coordination and benefits navigation (17/38); opportunities to share experiences with other implementing clinics (12/38); patient‐facing materials to educate and increase demand (7/38); and support engaging communities (6/38). Clinics' LAI ART programme goals varied. Most prioritized delivering LAI ART to their most marginalized patients struggling to achieve viral suppression on oral therapy, despite awareness that current US Food and Drug Administration approval is only for virally suppressed patients. The goal for LAI ART reach after 1 year of implementation ranged from ≤10% of patients with HIV on LAI ART (17/38) to ≥50% of patients (2/38). Conclusions: Diverse clinic types are interested in offering LAI ART and most aspire to use LAI ART to support their most vulnerable patients sustain viral suppression. Dedicated resources centred on equity and relevant to context and population are needed to support implementation. Otherwise, the introduction of LAI ART risks exacerbating, not ameliorating, health disparities. [ABSTRACT FROM AUTHOR]

With the Covid-19 epidemic, which first appeared in China in December 2019 and affected the world in a short time, the importance of health is increasing day by day. The increasing importance of health in combating he epidemic causes an increase in the need for hospitals and health personnel. The health personnel that have the main role in this struggle are nurses. When nurses are mentally and physically healthy, they are better equipped to serve and care for patients. In this study, a goal programming model has been developed in order to make November 2020 shift schedule of 50 nurses working in 4 clinics in Ankara City Hospital's General Hospital building. While developing the model; hospital rules, laws, Covid-19 measures were taken into consideration. In addition, extra precautions have been taken for the special conditions of nurses and for nurses who are in the high risk group for the transmission of the virus. The optimal result has been achieved by writing the developed model into the ILOG CPLEX Studio IDE program. [ABSTRACT FROM AUTHOR]

Effective contractor selection is crucial for successful execution of construction projects. In contrast to the conventional lowest-bid approach prevalent in the public sector, this study focuses on developing a framework that minimizes time and cost overruns by considering diverse criteria for contractor selection. A variety of machine learning models, including multi-linear regression, random forest, Support Vector Machine, and Artificial Neural Network, have been employed, with multi-linear regression proving to be the most effective, achieving the lowest Mean Squared Error of 0.00003366. To determine the final order allocation, a multi-objective mathematical model was utilized to optimize conflicting criteria, such as time and cost overruns, sustainability, risk, and safety aspects related to shortlisted contractors. The findings highlight the significance of specific selection criteria, such as turnover, experience in similar projects, qualification of staff, technology utilization, client satisfaction, accident records, available bid capacity, and socioeconomic factors. This study emphasizes a three-phase decision-making framework for contractor selection and order allocation, particularly in public construction projects, with a focus on sustainability. By adopting this approach, government agencies can enhance infrastructure projects and minimize overruns through optimization and analytical tools, which aligns with the Gati-Shakti scheme of the Indian government. It is recommended that clients embrace a holistic approach to contractor selection, considering both technical and non-technical factors, to ensure successful project outcomes. [ABSTRACT FROM AUTHOR]

This paper introduces a novel two-phase framework for designing a proactive–reactive scheduling model in the multi-mode resource-constrained project scheduling problem under disruptions. The proactive phase involves constructing a resilient baseline scheduling model using a mixed-integer linear programming model. This phase contributes to a multi-objective model that minimizes the project completion time and total project cost while maximizing resilience criteria. In this context, resilience refers to allocating float time to project activities to protect their start and finish times against future disruptions as much as possible. The reactive phase involves a bi-objective mathematical model that mitigates the impact of disruptions through preempt-repeat, preempt-resume, and activity-crashing strategies. Real-world projects involve many uncertain parameters that can negatively affect the optimization of rescheduling problems if overlooked. Therefore, for the first time, a scenario-based robust optimization approach is proposed to cope with the uncertainty of the reactive phase. Additionally, a novel hybrid multi-objective method based on goal programming is introduced to solve the proposed multi-objective model. Finally, to demonstrate the capability of the proposed approach, an oil and gas project in Iran is regarded as a real case study. The results indicate that the negative impact of disruptions on the makespan and total cost of the project can be largely mitigated by considering resilience criteria in the proactive phase and preempt-repeat, preempt-resume, and activity-crashing strategies in the reactive phase. [ABSTRACT FROM AUTHOR]

Internal audit is an independent function that provides objective assurance and advisory services to business partners in the form of projects. As the skilled human resources, internal auditors play a significant role in ensuring audit quality and thus should be managed properly. However, there are only very few studies on auditor-project assignment problems at the operational level. Also, many studies on general team formation problems neglect the real-life implications of simplifying mathematical models to enable practitioners to understand and implement. To bridge these gaps, this study presents a new integrated two-phase approach to assign internal auditors to multiple projects. In the designed decision support model, a harmonious level index is firstly proposed, measuring the suitability between auditor capability and project characteristics using a simplified fuzzy filter ranking method. Then, Min–Max multi-choice goal programming (MMCGP) model provides the assignment solution that balances internal auditors' multi-dimensional fit to the projects and their preferences, and thus optimizes the productivity of internal audit projects and staff's job satisfaction. A real case study is presented to illustrate the effectiveness of the proposed model and its solution. The comparison results further show that the proposed model outperforms the manual operations and the exiting approaches in the literature. This research extends the existing literature on auditor assignment problem by developing a flexible decision support tool and a practical classification of auditor competency. The MMCGP approach also provides an ideal staff assignment solution by setting multiple aspiration levels for each goal and balancing the unwanted deviations among goals. [ABSTRACT FROM AUTHOR]

Disaster management includes efforts to reduce the negative effects of a disaster. This management system has various challenges. Teams need to be sent to disaster areas as soon as possible to reduce losses and damages. Turkey, which frequently experiences earthquake disasters, has recently experienced the 2023 Kahramanmaraş earthquakes. After the recent earthquakes, the Great Marmara Earthquake, which scientists emphasize the most, has started to be discussed. It is said that Istanbul will be affected very much. Possible earthquake scenarios with a magnitude of 7.5 have been produced. In this study, Adalar district, which will be affected by the Great Marmara Earthquake, is considered and the problem of assigning teams to very heavily damaged buildings is discussed. For the continuation of the study, the data in the Istanbul Province Adalar District Possible Earthquake Loss Estimates Booklet is used. The 413 severely damaged buildings in the 5 neighborhoods where the destruction will occur and the number of building floors in the neighborhoods are taken into consideration. A mathematical model was created for the problem with the goal programming method. The model was solved with the Cplex solver of IBM ILOG optimization program. Thus, the optimal team assignment was realized. [ABSTRACT FROM AUTHOR]

The global water resource crisis caused by population growth, and increasing water demand has led to using treated wastewater in many regions. Effective distribution of wastewater treatment across various consumer sectors is crucial for sustainable environmental practices and resource management. Wastewater Allocation (WA) is a multi-criteria decision-making problem, and this study used modified VIKOR (VIekriterijumsko KOmpromisno Rangiranje) method in the combination with GP (Goal Programming) technique to optimize WA in Iran. The environmental effects were evaluated using Leopold Matrix results. The proposed approach resulted in an allocation of 6912 m3/day for urban green space irrigation 10,368 m3/day for the agricultural irrigation, and 2592 m3/day for industrial water demand at the Shahreza wastewater treatment plant. The research shows that better recommendations for resource allocation may be developed by combining quantitative assessment, optimization, and mathematical models. This approach has proven successful in maximizing the reuse of wastewater for a variety of uses. [ABSTRACT FROM AUTHOR]

The aim of this study is to investigate the fair allocation of multi‐objective cooperative games (MOCGs) using a preemptive goal programming (PGP). First, this research establishes the PGP model, where the priority factors of PGP describe the importance degrees of different objectives in MOCGs, the positive and negative deviations of PGP present the relation between the overall payoff and the worth of a coalition, and the weighting factors of negative deviations of PGP express the coalition weights of MOCGs. Second, we investigate the relationships between the solutions of the PGP model and the cores of MOCGs, including the preference core, preference p$p$‐core, and dominance core. Our analysis shows that the optimal solution of PGP belongs to the preference core, and the satisfactory solution of PGP satisfying certain conditions belongs to the dominance core or preference p$p$‐core. Finally, we demonstrate the practical and managerial relevance of our results by applying our findings to a multi‐objective linear production to produce certain kinds of goods using different resources for multi‐firms with varying objectives and illustrate them numerically. [ABSTRACT FROM AUTHOR]

Predicting the exact outcome of a real-life problem, which may occur in various fields like the industrial or healthcare sector, is challenging. Due to high information uncertainty and complicated factors influencing the industrial sector, traditional data-driven prediction approaches can hardly reflect real changes in practical situations. Fuzzy programming is a powerful prediction reasoning and risk assessment model for uncertain environments. This article mainly explores and applies a modified form of fuzzy programming, namely the Fuzzy Linear Fractional Programming Problem (FLFPP), having the coefficients of the objectives and constraints as Triangular Fuzzy Numbers (TFNs). The FLFPP is converted into an equivalent crisp Multi-Objective Linear Fractional Programming Problem (MOLFPP) and solved individually to associate an aspiration level. Then, by applying the Fuzzy Goal Programming (FGP) technique, the maximum degree of each membership goal is obtained by minimizing the negative deviational variables. We carry out two industrial application simulations in a hypothetical industrial scenario. Our study shows that the proposed model is practical and applicable to the uncertain practical environment to realize the prediction, and the results obtained are compared with those of the existing methods. [ABSTRACT FROM AUTHOR]

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In this study, we address the challenge of balancing sustainability and decision-maker preferences in regional development location selections. We propose a multi-criteria decision-making framework combining the Analytical Hierarchy Process (AHP) and Fuzzy Goal Programming (FGP) to evaluate potential sites. AHP is utilized to prioritize criteria, incorporating both quantitative and qualitative factors, while FGP allows for the accommodation of uncertainty and conflicting goals. Our findings reveal that this integrated approach provides a robust, systematic method for identifying optimal locations that align with both sustainability goals and stakeholder priorities. The analysis revealed the following satisfaction levels: Price 61.11%, Quality 80.4%, Delivery Time 79.3%, Carbon Emission 91.76%, and Preference 51.18%. The findings emphasize the complex process of selecting vendors within the palm oil supply chain. The implications of this research suggest enhanced decision-making efficiency and effectiveness in regional planning, promoting sustainable development practices. [ABSTRACT FROM AUTHOR]

In this study, we address the difficult group decision-making situations in which the preference data are represented by linguistic variables using the dynamic programming approach. Making decisions with exact numbers is difficult for decision-makers due to the ambiguity and complexity of reality. Since they cannot be computed directly, the language variables were encoded using single and interval neutrosophic sets. In a real-world context, neutrosophic sets are used to manage indeterminacy. Then, new models of similarity and distance are developed to quantify the relations between interval and single neutrosophic sets. Next, an interval-based clustering approach based on dynamic programming is proposed and used to group the decision-makers. In addition, a novel method is provided to calculate interval weights for decision-makers and clusters, considering both the cluster centre and group size. Next, a centroid-based ranking method is used to compare and rank the possibilities, and the effectiveness of the proposed strategy is demonstrated through illustrative trials. The purpose of the comparisons and discussions is to demonstrate its superiority. [ABSTRACT FROM AUTHOR]

Water resource allocation systems typically involve multi-level decision-making, with each level having distinct goals and interests, while being influenced by various factors such as social, economic, environmental, and policy planning. The decision-making in water resource allocation systems is characterized by complex uncertainty factors and dynamic changes. In light of this, this study integrates stochastic chance-constrained programming, dynamic programming, bi-level programming, goal programming, and water rights trading to construct a bi-level objective programming model of water resource uncertainty based on water rights trading. The model not only effectively represents the random uncertainty, dynamic characteristics, interests of decision-making levels, and planning requirements of policies in water resource allocation systems but also utilizes market mechanisms to enable compensated transfer of water rights, fully leveraging the role of water rights marketization in water resource allocation. Taking the Yehe River Irrigation District in Hebei Province of China as an illustrative case study, the specific allocation scheme of each stage under the guaranteed rate of 50% in 2025 and the water rights trading results of each sub-region are obtained. Compared with the bi-level objective programming model of water resources uncertainty without water rights trading, the results show that the water consumption per CNY ten thousand GDP(WG)of the irrigation district decreased by 3.42%, and the economic benefits of Luquan District, Jingxing County, Pingshan County, and Yuanshi County in each sub-region increased by 19.17%, 7.19%, 15.11%, and 4.94%, respectively. This improves regional water use efficiency and economic benefits and provides a scientific basis for regional water resource allocation. [ABSTRACT FROM AUTHOR]

Research and enquiry (R&E) is an integral part of veterinary training. It is a foundation of evidence‐based practice. In the University of Sydney Doctor of Veterinary Medicine degree R&E culminates in a cap‐stone experience in Year 3: a 'professionally focused project', a student‐driven and academic supported individual research project. The project provides an authentic experience within a veterinary discipline. Students work with an academic advisor who provides guidance for developing and achieving meaningful educational and professional goals. Successful advising depends upon a shared understanding of, and commitment to, the advising process by students, advisors and the university. The R&E mission can be broadly defined as ‐ veterinarians recognise that evidence‐based approach to practice, which is based on the scientific method, leads to the generation of new knowledge that underpins the veterinary medical profession. [ABSTRACT FROM AUTHOR]

Background: Improving the performance of marine terminals is one of the major concerns of both researchers and decision-makers in the maritime transportation sector. The problem of container storage planning and the berth allocation problem (BAP) are the two mainstays of optimizing port operations. Methods: In this work, we address these two issues, proposing two mathematical models that operate sequentially and are applicable to both static and dynamic cases. The first developed model is a mixed-integer linear problem model aimed at minimizing vessel traffic time in the port. The second model developed is a multi-objective optimization model based on goal programming (GP) to minimize both container transfer time and the number of storage areas (minimizing container dispersion). Results: The robustness of the proposed models has been proven through a benchmark with tests using data from the literature and real port data, based on the IBM ILOG CPLEX 12.5 solver. Conclusions: The two developed mathematical models allowed the both minimization of the transfer time and the number of used storage areas, whatever the number of operations handling companies (OHCs) operating in the seaport and for both static and dynamic cases. We propose, as prospects for this work, the development of a heuristic model to deal with the major instances relating to the case of large ports. [ABSTRACT FROM AUTHOR]

In this case study, the authors describe a targeted programme which facilitates the widespread undertaking of training in Universal Design across their institution. This programme is a key part of the strategic whole institution approach to inclusion pioneered in and adopted by University College Dublin (UCD). The programme creates and empowers faculty to be local leaders in inclusion and Universal Design, and provides them with the structure, support and a community of practice in order to achieve the programme goals. The community of practice has been crucial to the success of the programme, particularly the partnership element. Since the inception of the programme, the number of colleagues completing training in Universal Design has increased threefold and has greatly raised the institutional awareness of the need for adopting Universal Design practices and created a sense of urgency behind it. Flexibility of the programme structure, the funding streams and leadership buy-in are recognised as vital ingredients for implementation. [ABSTRACT FROM AUTHOR]

We developed a DEA-based resource re-allocation model based on environmental DEA technology for organizations with a central decision-making environment. The proposed model considered a weak disposability axiom for undesirable outputs and combined Data Envelopment Analysis (DEA) with Multiple-Objective Programing (MOP). The objective was to find the appropriate re-allocation model in order to save energy and reduce environmental pollution, so that the next steps could be taken toward improvement. Given that reducing the inputs and outputs of inefficient units is sometimes not achievable and does not seem logical, for the reduction in the values to be logical and achievable, we divided the Decision-Making Units (DMUs) into different levels of efficient frontier using the context-dependent DEA technique. For this purpose, the model was designed to move the DMUs from the current frontier to the efficient frontier of the previous layer, which has better efficiency conditions, or keep them on their own frontier. In addition, the opinion of the central decision maker regarding the amount of reduction in the inputs and outputs was expressed using Goal Programing (GP) in a way that does not make the model infeasible. By implementing the model in 8 regions of the world, suggestions were made regarding the amounts of energy saving and CO2 pollution reduction based on the conditions determined by the central decision maker aiming improve the efficiency of inefficient units in the next step. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Management & Economics Research is the property of Journal of Management & Economics Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research paper develops an algorithm to solve a neutrosophic linear fractional optimization problem, where the cost of the objective functions, the technology coefficients, and the resources are neutrosophic numbers (triangular and trapezoidal numbers). Two defuzzification methods are used here to convert the neutrosophic linear fractional optimization problem into a multi-objective linear fractional optimization problem (MOLFOP). The fractional objective function is defuzzified using the component-wise optimization method, and the linear constraints are defuzzified using the aggregation ranking function strategy. Hence, the MOLFOP is then solved using the fuzzy goal programming method. In order to further explain our suggested algorithm, two numerical examples are solved at the end, and the outcomes obtained by our method are compared with the outcomes obtained using the other algorithm. [ABSTRACT FROM AUTHOR]

In this study, the shift scheduling problem of the personnel who maintain the 324 trains operating on the lines of the Ankara Metro, which carry approximately 10 million passengers per month, is discussed. In the problem, daily personnel needs and personnel qualifications are evaluated together according to the fault prediction. Firstly, a total of 1721 fault data for 181 days is analyzed, and the prediction is made according to 8 prediction methods. Then, a 30-day fault prediction is made with the prediction method, which has the least error rate. According to the number of faults obtained as a result of the prediction, the daily personnel requirement is determined. Then, according to the type of fault, the personnel are classified as electronics, electromechanics, and mechanics. It is aimed at distributing the personnel evenly to the shifts. The goal programming model is used to solve the problem, allowing us to add more than one goal to the model. When the solution results of the problem are compared with the current schedule, it is seen that the monthly total working hours are distributed equally, and the shifts are distributed fairly, and a necessary balance is achieved in the shifts according to the personnel qualifications. It is thought that this study, in which personnel qualifications and fault prediction are evaluated together, will contribute to the literature on railway maintenance personnel scheduling. [ABSTRACT FROM AUTHOR]

Multi-objective transportation problems (MOTPs) are mathematical optimization problems that involve simultaneously considering multiple, often conflicting objectives in transportation planning. Unlike traditional transportation problems, which typically focus on minimizing a single objective such as cost or distance, MOTPs aim to balance multiple objectives to find the optimal solution. These problems appear in various real-world applications such as logistics, supply chain management, and transportation, where decision-makers need to consider multiple criteria when designing transportation networks, routing vehicles, or scheduling deliveries. The primary challenge lies in the uncertainty in real-world transportation scenarios, where logistics involve factors beyond cost and distance. We investigated a multi-choice solid fractional multi-objective transportation problem (MCSF-MOTP) based on supply, demand, and conveyance capacity, where the coefficients of the objective functions were of the multi-choice type due to uncertainty. To address this uncertainty, the proposed model employed the Newton divided difference interpolation polynomial method, and the suitability of this model was validated through a numerical illustration employing a ranking approach. [ABSTRACT FROM AUTHOR]