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This paper considers a joint transshipment, markdown, and clearance sales decisions of multiple products for a fast-fashion retailer that owns and operates a large network of retail stores. This problem is motivated by the logistics operations of the largest apparel retailer in Turkey, namely LC Waikiki. Particularly, the emphasis is on the following questions: How should the products be re-distributed among retail stores after a few weeks of demand observation? What should be the price level for each product? And, finally, which products should be marked for clearance sales? To answer these questions, a mathematical model is proposed considering the business rules and the practices of the retailer encounters. We develop a Bender-Decomposition-based heuristic to find upper bounds and a simulated annealing-based metaheuristic to find incumbent solutions, both of which have proven to be quite effective. A Covering-Cut-Bundle approach is used to accelerate the convergence of the Bender-Decomposition algorithm. We have also conducted a set of experiments to uncover the impact of business rules on the retailer's operations and the effectiveness of joint markdown and transshipment decisions. [ABSTRACT FROM AUTHOR]

During COVID-19, blood demand exceeded pre-pandemic levels due to reduced donations, causing shortages. Given the severe shortage, it's crucial to optimise blood use, prevent shortages, minimise wastage, and reduce unnecessary transfusions in all hospitalised patients. Designing a reliable blood supply chain network (BSCN) is an effective solution, especially for COVID-19 patients. This strategic decision significantly impacts emergency management performance. An efficient and reliable blood supply chain requires the consideration of multiple factors, including scarceness and perishability of blood, simultaneously. However, existing studies have not addressed all relevant factors in an integrated blood supply chain, and this paper aims to bridge this gap. Furthermore, an efficient Benders Decomposition based heuristic approach is proposed to solve the model. The solution approach has been compared with a set of commonly used meta-heuristic algorithms, including the red deer algorithm (RDA), tree growth algorithm (TGA), and genetic algorithm (GA). The outcomes illustrate that the proposed heuristic approach can solve small and large-size problems in significantly less CPU time than the other proposed solution approaches. For large-size problems, it can reduce the average CPU time by about 80% compared to TGA, about 80% compared to GA, and about 83% compared to RDA. A real case study has been implemented to validate the proposed mathematical model and solution method. The sensitivity analysis has been conducted to validate the significance of the model's parameters; consequently, several managerial insights have been derived. [ABSTRACT FROM AUTHOR]

In response to the challenges posed by economic globalisation and increasing customer demands, enterprises are compelled to adapt and refine their production models and operational objectives, which motivates this paper to address the integrated production scheduling and vehicle routing problem in the distributed manufacturing environment (IPSVRP-DME). The objective is to simultaneously minimise both total energy consumption and total earliness/tardiness. Initially, a mixed integer programming model is proposed to address small-scale problems using Gurobi. Considering the NP-hardness of the problem, a novel Self-adapted Gaussian Mutation-based Arithmetic Optimiser Algorithm (SGMAOA) is developed to handle medium-scale and large-scale instances. To address the complexity of decision-making, an innovative two-level encoding method that encompasses four decision dimensions is introduced. Additionally, an incremental repair strategy is devised to rectify infeasible solutions caused by unreasonable delivery batching, while a time relaxation strategy is proposed to further enhance service levels without compromising energy consumption. Comparative experiments are conducted to demonstrate the effectiveness of SGMAOA, which is benchmarked against five prominent metaheuristics. In addition, a specific example is applied for the discussion of managerial applications and to illustrate the practicality of the proposed model and solution method. [ABSTRACT FROM AUTHOR]

Due to the severity of resource consumption and uncertainty in orders, new challenges have arisen for production scheduling in enterprises. This paper studies the scheduling problem of minimising the total weighted tardiness for jobs with fuzzy processing times and due dates on identical parallel machines with resource constraint. To address this research problem, we first propose methods to calculate the upper bound of resource consumption and the maximum number of machines that can be used, effectively reducing the search space and improving the algorithm's efficiency. Secondly, a local search algorithm based on job swapping is proposed to enhance the algorithm's performance. Then, repair algorithms based on job removal and job swapping are designed to repair infeasible solutions. Finally, we propose a fuzzy genetic algorithm (FGALS) to solve the problem based on the above elements. Through extensive simulation experiments, the effectiveness and efficiency of the FGALS algorithm are verified by comparing it with commercial solver Gurobi and several meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

Ship maintenance service optimisation is of great significance for improving the competitiveness of shipbuilding enterprises. In this paper, we investigate a ship maintenance service scheduling problem considering the deteriorating maintenance time, distributed maintenance tasks, and limited maintenance teams. The objective is to minimise the service span. First, we construct an initial mixed-integer programming model for the studied problem. Then, through the property analysis of the problem with a single maintenance team, an exact scheduling algorithm is proposed. In addition, the lower bound of the problem with multiple maintenance teams is derived. A scheduled rule is developed to obtain the lower bound for the problem. Based on the property analysis, the original mixed-integer programming model is simplified to an improved mathematical programming model. Since the studied problem is NP-hard, this paper proposes two heuristic algorithms and an integrated metaheuristic algorithm based on the variable neighbourhood search to obtain approximate optimal solutions in a reasonable time. In computational experiments, the two models can solve problems on small scale, while metaheuristics can find approximately optimal solutions in each problem category. Moreover, the computational results validate the performance of the proposed integrated metaheuristic in terms of convergence and stability. [ABSTRACT FROM AUTHOR]

This study proposed multiple revised variable neighborhood search (VNS) approaches applying the greedy concept to solve a nurse scheduling problem (NSP). In this paper, we developed three greedy-neighbourhood-swapping mechanisms (greedy-2-exchange, greedy-3-exchange, and greedy-4-exchange) to conduct local searches based on one-, two-, or three-neighbourhood structures that accounted for constraints imposed by government and hospital regulations. The greedy-neighbourhood-swapping mechanisms were used to identify medical staff members with the highest soft-constraint (e.g. nurses' preferences) violation weights on a given day who then swapped their shifts with others. To validate the proposed VNS approaches, we also conducted a case study. Based on the testing instances, all of the proposed VNS approaches generated optimal or near-optimal solutions, and the differences between them were small. The optimal number of the neighbourhood structures was determined to be two, confirming that a larger number of neighbourhoods in a neighbourhood structure would not necessarily be associated with more easily escaping local optima. Furthermore, the resulting outcomes supported the conclusion that the proposed modified VNS approaches generated better schedules for the medical staff members of hospitals than the compared meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]