Multi-objective optimization of flexible fuzzy job shop scheduling problem under neutrosophic environment

Abstract

Real-world business decisions often aim to maximize total productivity or handle the most assignments in the least time, which involves tackling a job-shop scheduling problem (JSSP). However, in real-world business problems, factors such as the preparation time for tasks on machines, completion times of assignments, and processing plans of operations often go undetected. To address this challenge, the proposed study focuses on the flexible job shop scheduling problem (FJSSP), aiming to increase overall productivity and effectively manage a majority of jobs. The proposed study identifies two cases of operations in machine tool production workshops, considering uncertain transportation and preparation times. By incorporating two different types of immediate predecessor activities, the FJSSP is updated to become a multi-objective FJSSP. To manage uncertain transportation and preparation times, the study operates within a single-valued neutrosophic fuzzy (SVNF) environment. Furthermore, the proposed study integrates sequence-dependent setup durations in SVN form and process plan flexibility into advanced FJSSP optimization problems. The primary objective is to reduce makespan and balance machine workloads. An SVNFGP model is developed for both problems, with minor modifications to accommodate the specific requirements. Finally, a numerical illustration is provided for both cases, and the effects of separable and non-separable setup times on performance criteria are statistically evaluated.