Journal of Industrial Engineering and Management Studies

Abstract Operations management in service organizations has become a significant focus for researchers and decision-makers in recent years. Accordingly, scheduling problems, which are the process of allocating resources within a specific planning horizon, are fundamental to every service system. Corporations need to satisfy some recurring service requirements in such systems where the efficient allocation of resources and effective time management are vital for improving operational processes. This problem, known as multi-period service scheduling, includes customers with periodic demands for specific services. By investigating the related study, no research has been found that surveyed the different visit patterns of customers. This is the first study to provide a mathematical model considering customers' preferences concerning various visit patterns. Despite its complicated structure, the problem is formulated as a new Pure Integer Linear Programming (PILP), minimizing the total number of operators required during the planning horizon. This study uses a numerical example and a real case study to confirm the validity of the proposed model. The practical implications of this research are significant, as it presents a model that can effectively solve real-world, large-scale problems with reasonable computing time and full compliance with all constraints, thereby improving operational efficiency and customer satisfaction.

Abstract In this research, the integrated sourcing and inventory policy problem in a pharmaceutical distribution company is investigated. In order to select the superior solution, a new tool is introduced. Sourcing is one of the most critical issues in pharmaceutical industry. In addition, drug inventory shortages can cause irreparable humanitarian crises. However, only a limited number of studies has been focused on integrated sourcing and inventory policy of drugs so far. In real-world problems, it is difficult to calculate the exact cost of inventory shortage such as company reputation and humanitarian crises. To overcome this obstacle, in this study, the number of shortage is considered as a separate objective. Likewise, demands of the distributors and breakdowns of suppliers are stochastic, and due to the complicated nature of the problem is difficult to calculate the objective function by using classic methods. So, simulation is used for estimating the objectives of the problem. It’s been proved that the problem of this study is NP-Hard. Therefore, a metaheuristic multi-objective particle swarm optimization (MOPSO) method is used to find the optimal solution. To test the reliability of the model and the proposed algorithm, a real drug distributing problem is used and after estimating a Pareto front, the best answer is chosen by The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method.

Abstract This paper considers the job scheduling problem in virtual manufacturing cells (VMCs) with the goal of minimizing two objectives namely, makespan and total travelling distance. To solve this problem two algorithms are proposed: traditional non-dominated sorting genetic algorithm (NSGA-II) and knowledge-based non-dominated sorting genetic algorithm (KBNSGA-II). The difference between these algorithms is that, KBNSGA-II has an additional learning module. Finally, we draw an analogy between the results obtained from algorithms applied to various test problems. The superiority of our KBNSGA-II, based on set coverage and mean ideal distance metrics, is inferred from results.

Abstract Make-to-order is a production strategy in which manufacturing starts only after a customer's order is received; in other words, it is a pull-type supply chain operation since manufacturing is carried out as soon as the demand is confirmed. This paper studies the order acceptance problem with weighted tardiness penalties in permutation flow shop scheduling with MTO production strategy, the objective function of which is to maximize the total net profit of the accepted orders. The problem is formulated as an integer-programming (IP) model, and a cloud-based simulated annealing (CSA) algorithm is developed to solve the problem. Based on the number of candidate orders the firm receives, fifteen problems are generated. Each problem is regarded as an experiment, which is conducted five times to compare the efficiency of the proposed CSA algorithm to the one of simulated annealing (SA) algorithm previously suggested for the problem. The experimental results testify to the improvement in objective function values yielded by CSA algorithm in comparison with the ones produced by the formerly proposed SA algorithm.