This paper considers simultaneously two areas of facility location and assortment planning in a competitive environment. In fact, a chain store that has competitors in the market locates a new facility. As there are different products in the market that can substitute with each other, it is intended to determine the best product assortment as well. An integer nonlinear programming problem is proposed to model the mentioned subject. For solving the model, the problem is reformulated as a mixed integer linear programming one. Therefore, a MIP solver software can be used for solving the small- and medium-size problems. For large-scale problems, a firefly algorithm is designed for obtaining a satisfactory solution. By using the proposed model, it is numerically shown that, in addition to the optimal location, it is also necessary to determine simultaneously the best product assortment for the new store. Actually, comparison results reveal that the location significantly affects the assortment scenarios for the new store. In other words, the selection of new store locations may lead to loss of large profit if the assortment planning is neglected.

In current paper, a novel, green, efficient and recyclable magnetic nanocatalyst (α-Fe₂O₃@MoS₂@Ni) was prepared and characterized by spectroscopic and microscopic techniques (FT-IR, XRD, SEM, EDX, TEM and VSM). α-Fe₂O₃@MoS₂@Ni magnetic nanoparticles (MNPs) were utilized for preparation of 1,2,4-triazolidine-3-thiones and spiro-triazole hybrid series. In this method, a series of triazole derivatives prepared from the reaction between thiosemicarbazide or semicarbazide with diverse isatine derivatives or various arylaldehydes and ketones at room temperature in water as solvent with great yields and short reaction times. α-Fe₂O₃@MoS₂@Ni MNPs was simply removed from the reaction mixture and was reused for five times without any notable changes in its catalytic activity.

An examination of two areas of promotion and assortment planning in an environment is attempted in this paper. Sales promotion is a marketing strategy used by retailers to increase sales and profits by retaining customers and preventing them from switching to their competitors. Various products are available on the market that can substitute each other, so the best product assortment must be determined as well. In order to model the above subject, a nonlinear integer programming problem is proposed. Model solution involves rephrasing the problem as mixed integer linear programming. Small- and medium-sized problems can therefore be solved using MIP solver software. Firefly algorithms are designed to solve large-scale problems. According to the numerical results, determining the best product assortment for stores must also be done simultaneously with finding the optimal promotion. As a matter of fact, the promotion of the products significantly affects the assortment scenarios for the stores. Consequently, the selection of the promotional discount may result in large profit losses if the assortment planning is not taken into consideration. In order to assess the importance and sensitivity of the model parameters, a sensitivity analysis is conducted. The sensitivity analysis demonstrates that the model is able to respond to changes in market demand and competition, and provides an effective tool for chain stores to optimize their promotion and assortment strategies. To further validate the effectiveness of the model, a case study is conducted in Tehran, Iran. The results of the case study demonstrate the ability of the model to effectively optimize promotion and assortment strategies in real-world settings. Overall, the proposed model provides a valuable tool for chain stores to optimize their promotion and assortment strategies, and improve their market competitiveness.

Supply chains are vulnerable to various disruption risks that can adversely affect their overall performance and objectives. This study addresses the challenge of designing a resilient and environmentally sustainable mixed open and closed-loop supply chain network that can withstand both operational and disruption risks. The research employs a bi-objective stochastic mathematical model to examine the balance between environmental sustainability and profitability within the SC. To mitigate the impact of disruptions, several resilience strategies are incorporated into the model, significantly reducing their adverse effects. Due to the inherent complexity of the problem, the study introduces a novel hybrid metaheuristic algorithm that combines ant colony optimization with teaching and learning-based optimization, named ACO-TLBO. Additionally, two other enhanced hybrid metaheuristics are proposed. The performance of these solution methods is assessed through various test problems, using specific performance metrics for comparison. Results reveal that the ACO-TLBO algorithm excels in generating high-quality, non-dominated solutions. The model's practical applicability is demonstrated through a case study in the tire industry, validating its effectiveness. The findings indicate that the proposed resilience strategies are crucial for minimizing the negative impacts of disruptions on SC objectives. Furthermore, the results underscore the importance of resilience in maintaining both sustainability and profitability within supply chains.

The issue of integrated scheduling and sequencing operation of unloading and loading equipment in container ports has been one of the most important issues concerning time efficiency. In addition, with the emergence of green harbor concepts, the inclusion of criteria for minimizing energy consumption, fuel and emission reduction are among the other issues that have been noticed by planners in the field of energy efficiency. Furthermore, due to the complexity and scope of activities of a container terminal, uncertainty in operational parameters such as transportation time, time of readiness and entry of work into the system and the velocity of the transportation fleet are inevitable in this operational environment. Therefore, this research with the aim of sharing trucks among loading and unloading equipment, proposes a robust multi-objective integer programming model for the synchronized scheduling of truck operations with other handling equipment to decrease the fuel consumption of trucks and the flow time of containers, considering the uncertainty in operational parameters as fuzzy numbers. To find the Pareto solutions for this model, the ε-Constraint technique is employed. Finally, the performance of the model in deterministic and uncertain modes is evaluated, compared and analyzed employing the inputs gathered from Shahid Rajaei port. The findings demonstrate that using this model will result in a substantial decrease in both fuel consumption and flow time of containers in comparison to the current procedure. Additionally, results will demonstrate the extent to which the terminal's fuel and time consumption will increase under conditions of uncertainty in operational parameters when the optimal plans derived from the robust model are implemented.

Social dilemmas describe conflict situations between immediate self-interest and longer-term collective interests. In these situations, it is better that all players work together to attain a common goal, but individuals may threaten the best payoff of the group by free-riding. Human behavior in a pandemic is one example of a social dilemma but wait-and-see games and relying on herd immunity to get a free ride generates a threat of continuing the pandemic. This study aims to use probabilistic incentives given by a third party as a mechanism to inhibit free-riding behavior by promoting cooperation in the volunteer dilemma game. For more realistic human behavior simulation, we use an agent-based model of network topology. When the parameters of the problem change gradually, an abrupt jump in the cooperation rate may happen and lead to a significant shift in the outcome. Catastrophe theory is a valuable approach to survey these nonlinear changes. This study tries to give some managerial insights to the decision-makers to find the minimum level of necessary effort in which the cooperation dominates the defection.

Hospital hazards and human errors pose a significant and complex problem, with rising incidents and irreversible consequences. Managing laboratory errors and risks is vital due to the presence of chemicals, electrical equipment, and the involvement of students, professors, and staff. The high value of laboratory equipment further underscores the need for robust risk management strategies. To address these challenges, researchers have explored the Failure Mode and Effects Analysis (FMEA) method for risk identification and assessment in healthcare settings. However, recognizing its limitations, this study aims to prioritize and evaluate laboratory errors using an integrated approach that combines the Best-Worst Method (BWM) and Complex Proportional Assessment with a Fuzzy Spherical Environment (CoCoSo-FSE). By applying the BWM, criteria such as severity, detectability, and occurrence probability are weighted to account for the nature of laboratory errors. The CoCoSo-FSE is then employed to evaluate and prioritize 18 identified laboratory errors, reducing uncertainty and enhancing decision-making. The fuzzy spherical set is used to address uncertainties by providing a flexible framework for decision-makers to define membership functions in specific spherical regions, enhancing the representation of knowledge and decision-making information. The proposed approach is compared with other decision-making methods, namely MOORA and COPRAS, demonstrating reliable ranking results. Sensitivity analysis confirms the stability of the approach's ranking when adjusting the flexibility parameter. This integrated approach offers a reliable and robust decision-making technique for managing laboratory errors, providing valuable insights to enhance laboratory safety and risk management for stakeholders, managers, and policymakers.

Supply chain management (SCM) that is Lean, Agile, Resilient, and Green (LARG) are required for competitiveness in today's complex, high-demand market. SCM must consider LARG paradigms concurrently, a rarely investigated topic. This study provides a comprehensive review of publications that combine all four LARG principles in engineering, business, and management domains. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, two Scopus and Google Scholar databases were exhaustively examined. Thirty-two manuscripts were selected for a comprehensive review. The year of publication, document type, countries, authors, journals, keywords, and topics was analyzed from 2000 to 2023. Also, each paper's methodology, central topic, findings, limitations, and future recommendations were outlined. Consequently, the current systematic literature review (SLR) revealed that the proposed topic is in its infancy, with promising prospects. By emphasizing the findings of this study, managers and businesses can increase consumer satisfaction and reduce costs.

This work presents a comprehensive synthesis method for the development of a new hyperbranched unsaturated polyester resin (HUPR). The resin is synthesized by modifying dehydrated castor oil fatty acid (DCOFA) with methyl methacrylate (MMA) and styrene, dicyclopentadiene-maleate (DCPD-M), and trimethylol propane diallyl ether (TMPDE). The synthesis process involves the radical addition reaction of DCOFA with MMA and styrene, followed by the addition of DCPD-M and TMPDE to form a HUPR. The resulting resin exhibits enhanced properties, making it a promising candidate for car body putty in the polymer industry.

Hub location is crucial for resilient and uninterrupted supply chain operations, particularly during disruptions or unforeseen events. In this paper, we propose a resilience hub location framework for Third Party Logistics (3PL) companies with two key objectives: optimizing demand flows and establishing a resilient network capable of with-standing sudden disruptions. The study aims to identify the key criteria that contribute to the successful implementation of the resilient center. The proposed structure utilizes a two-phase decision-making methodology. The first phase presents a new Multi-Criteria Decision-Making (MCDM) approach called SWARA-EDAS method that evaluates and ranks potential locations based on resiliency criteria. The second phase proposes an optimization model to determine the optimal hub location. To illustrate the approach, a real-world case study of a 3PL company in Tehran is included. Due to the absence of precise demand data in the case study, a novel clustering approach is proposed to estimate the demand flow. Each cluster can be considered as a distinct demand point, and a clustering analysis involving 122 regions within Tehran is conducted, taking into account various factors such as population, economic index, accessibility to the Internet, and the number of business units. To enhance the resiliency of the network, mobile distribution centers are also deployed. These mobile centers not only provide flexibility but also serve as backup capabilities in the event of a disruption or failure at the fixed hub. The proposed structure offers practical in-sights for 3PL companies seeking to implement a resilient network structure.