A Flexible Job-Shop Scheduling Problem: A Case Study of VICT Company

The Flexible Job-shop Scheduling Problem (FJSP) is a modification of the classical Job-shop Scheduling Problem (JSP). In FJSP, jobs consist of multiple operations, each of which can be processed on one of several available workers. The core objective of this paper is to find optimized methods to mitigate the total completion time of workers to perform orders early, leading to directly reducing the cost. This paper is inspired by a real situation observed in the production schedule of the VICT Garment Factory in Vietnam. To increase machine utilization or decrease idle time, the mathematical model is developed to minimize the make span and give the optimized production schedule for the flexible job-shop scheduling problem. The worker processing time of each operation in jobs is collected and integrated with the mixed integer linear programming (MILP) model to give the optimized result through CPLEX software. Additionally, a metaheuristic – a genetic algorithm (GA) – is developed to efficiently address large problems by Python code. When the problem is solved, sensitivity analysis is conducted to evaluate methods. Hence, the company could make plans and potential strategies to operate the business.