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 The purpose of this systematic review is to identify and categorize the application of soft operations research methods in healthcare settings. A systematic review was conducted to identify published papers on the application of soft operations research methods in the healthcare setting, using Google Scholar, Scopus, PubMed, Emerald, Elsevier, Web of Science, and ProQuest databases through December 2021. A total of 69 papers met our selection criteria for the systematic review. Soft operations research methods were used in a wide range of healthcare fields, including healthcare management, health informatics, e-health, and medical education, for identifying requirements, problem-solving, system design and implementation, process improvement, policymaking, knowledge management, and managing resilience, and marketing. This study contained restrictions on access to the full text of some articles and dissertations that had little impact on the study’s quality. The present study demonstrates the use of soft operations research methods in various areas of the healthcare system to better understand problematical situations. This paper can help to use soft operations research methods further in the healthcare problems, especially in the design and implementation of e-health and emerging new technology.

Abstract Aggregate production planning (APP) determines the optimal production plan for the medium term planning horizon. The purpose of the APP is effective utilization of existing capacities through facing the fluctuations in demand. Recently, fuzzy approaches have been applied for APP focusing on vague nature of cost parameters. Considering the importance of coping with customer demand in different periods at different and variable rates, in this research, demand is considered fuzzy and the APP decisions modeled through a bi-objective LP model optimizing production and workforce level costs. The APP decisions are taken in two rounds, First The fuzzy model is transformed to a crisp goal programming counterpart and in the second round as the principal contribution of this paper, the APP decisions for rest of the horizon are updated based on actual demand occurred during starting periods. By generating several sample problems and using the Lingo, the validity of the proposed model is shown.