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The purpose of this study is to develop a holistic optimization model for an integrated sustainable fleet planning and closed-loop supply chain (CLSC) network design problem under uncertainty.

A novel mixed-integer programming model that is able to consider interactions between vehicle fleet planning and CLSC network design problems is first developed. Uncertainties of the product demand and return fractions of the end-of-life products are handled by a chance-constrained stochastic program. Several Pareto optimal solutions are generated for the conflicting sustainability objectives via compromise and fuzzy goal programming (FGP) approaches.

The proposed model is tested on a real-life lead/acid battery recovery system. By using the proposed model, sustainable fleet plans that provide a smaller fleet size, fewer empty vehicle repositions, minimal CO₂ emissions, maximal vehicle safety ratings and minimal injury/illness incidence rate of transport accidents are generated. Furthermore, an environmentally and socially conscious CLSC network with maximal job creation in the less developed regions, minimal lost days resulting from the work's damages during manufacturing/recycling operations and maximal collection/recovery of end-of-life products is also designed.

Unlike the classical network design models, vehicle fleet planning decisions such as fleet sizing/composition, fleet assignment, vehicle inventory control, empty repositioning, etc. are also considered while designing a sustainable CLSC network. In addition to sustainability indicators in the network design, sustainability factors in fleet management are also handled. To the best of the authors' knowledge, there is no similar paper in the literature that proposes such a holistic optimization model for integrated sustainable fleet planning and CLSC network design.

The purpose of this study is to develop a new parallel metaheuristic algorithm for solving unconstrained continuous optimization problems.

The proposed method brings several metaheuristic algorithms together to form a coalition under Weighted Superposition Attraction-Repulsion Algorithm (WSAR) in a parallel computing environment. The proposed approach runs different single solution based metaheuristic algorithms in parallel and employs WSAR (which is a recently developed and proposed swarm intelligence based optimizer) as controller.

The proposed approach is tested against the latest well-known unconstrained continuous optimization problems (CEC2020). The obtained results are compared with some other optimization algorithms. The results of the comparison prove the efficiency of the proposed method.

This study aims to combine different metaheuristic algorithms in order to provide a satisfactory performance on solving the optimization problems by benefiting their diverse characteristics. In addition, the run time is shortened by parallel execution. The proposed approach can be applied to any type of optimization problems by its problem-independent structure.

The purpose of this paper is to present how complexity on retail supply chains should be recognized and its relationship with the performance. Different supply chain structures and planning horizons have been analyzed to support practitioners taking action on the short, mid and long terms. Confronted complexity in the supply chain has been categorized as system, perceived and value adding. This would also help practitioners to understand the sources of the complexity and if the complexity is useful for the system or not.

Three different retail supply chain scenarios – each concentrating on different planning horizons – have been simulated on system dynamics software STELLA. Using the new classification scheme for complexity and suggested performance metrics, a multi-perspective analysis has been performed on the STELLA output.

The results and the methodology can be easily applicable in practice to support decision-making process and to answer “what-if” type scenario analysis on systems design and configuration. Using the selected complexity metrics, complexity of the system considering time factor – static and dynamic – and different information levels – system, perceived and value adding – has been evaluated. Used complexity metrics indicate the problematic areas in the systems to be distinguished.

This paper uses system dynamics modeling in retail supply chains to derive insight about dynamic behavior and to represent the complex interactions and a new classification scheme for system complexity.

In the real estate business, identifying the ideal property for a user poses a difficult task due to the many factors involved in the decision-making process. Moreover, users often struggle to find platforms that facilitate effective communication of their preferences. To tackle this issue, a web-based data-driven recommendation system has been devised for the real estate business.

The process of identifying the most suitable rental property for a user hinges greatly on how the user prioritizes each criterion and the analysis of unstructured data. In this research, a novel recommendation system for house rentals is developed by utilizing the Weighted Hierarchical Fuzzy Axiomatic Design (WFAD) approach. Techniques for extracting pertinent information from unstructured house descriptions are employed. The user’s preferences are captured through an interactive web application equipped with a map feature to highlight key locations.

Data on various available rental properties are gathered using web scraping techniques. The efficacy of the proposed rental house recommendation system is demonstrated through multiple case studies. It is observed that the developed system provides more informed and reliable decisions.

First time in the related literature, we applied the weighted fuzzy axiomatic design procedure (WFAD) to the product recommendation problem and developed a comprehensive web-based system for recommending rental houses based on it in the real estate business.

The purpose of this paper is to analyze previous models of the concept of ranking accuracy within the weighted aggregated sum product assessment (WASPAS) method and make necessary refinements.

This paper presents a correct combination of the weighted sum model (WSM) and weighted product model (WPM), which is usually performed on an ad hoc basis in the literature.

One of the reasons of rarely conducting ranking accuracy analysis might be that some of the reported equations in the literature are confusing, and hence, accurate partial derivatives cannot be calculated. In this study, all of the necessary formulations are re-derived and necessary modifications are proposed.

A corrected WASPAS equation for optimal combination parameters is derived. Two examples are used to validate the formulations, and software implementation is provided. Because multiple attribute decision-making (MADM) has gained widespread attention from both the academia and industry, the findings of this paper help decision makers fully capitalize the concept of ranking accuracy and avoid possible confusions regarding the equations reported in the literature.

WASPAS is a relatively new MADM method and has enjoyed a visible position in the MADM literature. In addition to its simplicity, the WASPAS method utilizes the concept of ranking accuracy by combining the well-known WSM and WPM. This combination realized via an optimization criterion brings unique opportunities for decision makers such as evaluating confidence intervals for relative significance of alternatives and reducing estimated variance of ranking results. Despite its crucial importance, the combination of WSM and WPM is usually performed on an ad hoc basis in the literature. In this study, all of the necessary formulations are re-derived and necessary modifications are proposed along with clarifying examples.

Usage of learning management systems (LMSs) has become widespread with the disruption of face-to-face educations after the COVID-19 pandemic. There are several software products, usually named as LMS to enable and support distance education. However, selection of a suitable LMS is a complex multiple criteria decision making (MCDM) problem that requires consideration of many criteria and inputs from different parties like students, academicians, education managers, etc. Usability evaluation of LMS is one of the critical steps in deciding which LMS system to be adapted. There are several studies related to usability evaluation of LMS in the literature, but utilization of MCDM methods and real life case studies are very rare. Based on this motivation, perceived usability evaluation of SAKAI-LMS that is in use at an academic department is performed by employing axiomatic design procedure (ADP). This paper aims to discuss the aforementioned issues.

ADP is considered as a suitable MCDM method for perceived usability evaluation as it allows an easy approach to data fusion and setting performance targets for decision makers. A questionnaire is developed to collect data from three types of system users about predetermined usability criteria and their importance. After detailed statistical analyses and weighting criteria via analytical hierarch process (AHP), ADP is carried out to evaluate usability of the LMS.

It is found that the proposed ADP based approach is easy to apply in practical circumstances and able to quantify perceived usability of the LMSs.

The proposed approach provides an easy and practical evaluation of perceived usability of the LMSs for decision makers who are responsible for the implementation of LMSs. The developed novel and practical MCDM-based perceived usability approach for LMS in this study has been verified through a real life case study at an academic department. Perceived usability results, therefore, reflects only the views of this focus group and are not generalizable.

First time in the literature, a comprehensive ADP based MCDM approach is proposed based on the analyses of the related literature and information gathered from the system users.

Various approaches and algorithms have been proposed since the 1950s to solve the assembly line (AL) balancing problem. These methods have established an AL configuration from the beginning. However, a prebalanced AL may have to be rebalanced in real life for many reasons, such as changes in the cycle time, production demand, product features or task operation times. This problem has increasingly attracted the interest of scientists in recent years. This study aims to offer a detailed review of the assembly line rebalancing problems (ALRBPs) to provide a better insight into the theoretical and practical applications of ALRBPs.

A structured database search was conducted, and 41 ALRBP papers published between 2005 and 2022 were classified based on the problem structure, objective functions, problem constraints, reasons for rebalancing, solution approaches and type of data used for solution evaluation. Finally, future research directions were identified and recommended.

Single model, straight lines with deterministic task times were the most studied type of the ALRBPs. Eighteen percent of the studies solved worker assignment problems together with ALRBP. Product demand and cycle time changes were the leading causes of the rebalancing need. Furthermore, seven future research opportunities were suggested.

Although there are many review studies on AL balancing problems, to the best of the authors’ knowledge, there have been no attempts to review the studies on ALRBPs.

In today's very competitive, dynamic and unpredictable manufacturing environments it is critical to improve manufacturing performance in order to be able to compete. Responsiveness and agility become important characteristics of manufacturing systems and organizations. Manufacturing systems must be designed optimally by taking into account responsiveness and agility related measures in order to improve effectiveness and performance. One of the important enablers of performance improvement is flexibility. It is a known fact that flexibility has a positive effect on the manufacturing system performance if it is properly utilized by the control system (usually scheduling). However, the relationship between flexibility and manufacturing system performance through scheduling is not entirely explored in the previous literature. The purpose of this paper is to investigate the effects of process plan and machine flexibilities on the scheduling performance of manufacturing job‐shops.

Effects of process plan and machine flexibilities on the scheduling performance of manufacturing job‐shops are analyzed at different flexibility levels by using the grammar‐based flexible job shop scheduling system that is developed by Baykasoğlu et al.. Three different flexibility levels are defined for process plans and machines. Four different problem sizes are evaluated according to “makespan” “machine load balance” and “mean waiting times of jobs”. Performance differences among “process plan” and “machine flexibility” levels are determined and statistically analyzed through Taguchi experimental design methodology.

It is found out after detailed analysis that the effect of machine flexibility on job shop performance is higher than the process plan flexibility. It is also figured out that after a certain level of machine flexibility, the speed of scheduling performance improvement decreases considerably.

The paper presents the interaction between flexibility and scheduling performance of manufacturing job‐shops. The findings should be taken into account while designing scheduling systems for job shops that have flexible processing capabilities.

The purpose of this paper is to develop a new multi-objective optimization procedure for crashworthiness optimization of thin-walled structures especially circular tubes with functionally graded thickness.

The proposed optimization approach is based on finite element analyses for construction of sample design space and verification; gene-expression programming (GEP) for generating algebraic equations (meta-models) to compute objective functions values (peak crash force and specific energy absorption) for design parameters; multi-objective genetic algorithms for generating design parameters alternatives and determining optimal combination of them. The authors have also utilized linear and non-linear least square regression meta-models as a benchmark for GEP.

It is shown that the proposed approach is able to generate Pareto optimal designs which are in a very good agreement with the actual results.

The paper presents the application of a genetic programming-based method, namely, GEP first time in the literature. The proposed approach can be used to all kinds of related crashworthiness problems.

The purpose of this paper is to describe the characteristics of complexity and how a retail supply chain can contain complexity in itself. A case has been provided to show the measurement of complexity with/without information sharing and the relation of complexity with the performance measures. Quantification of the complexity will help the practitioners to take strategic actions.

System dynamics simulation has been used to model the retail supply chain with and without information sharing and data visibility. Entropy-based metric used for quantification and comparison of complexity based on the outputs of the models. Performance measures proposed for the retail supply chains to understand the effect of data visibility.

Paper provides insight about the complexity of retail supply chain perspective. Using system dynamics modelling can be a useful way to perform what-if type analysis before business process changes. Including both complexity and performance measures can be useful to understand if the complexity is good or bad for the business and if it is in manageable amount.

Paper can encourage the future research on retail supply chains.

Approach can be useful to analyse what-if type analysis in practice easily. It can support strategic decision making process.

Combines retail supply chain with complexity and performance measurement.