Scheduling customized orders: A case study at BEST transformers company

Abstract

Stochastic operation times make job-shop scheduling harder at companies which work based on project type labor-intensive production in a dynamic environment. The operation times are not known before production and change based on the orders' technical specifications. In performing required operations with the aim of producing a final product, scheduling is required for different purposes such as minimizing makespan, maximizing resource utilization, etc. This is important as it enables companies to meet customer demands by due date and reduce the labor cost on the finalized product. In this study, an order scheduling algorithm is proposed for nearly optimizing average makespan for several waiting orders in a transformer company's core production workshop considering dynamical production environment. The proposed algorithm adopts the technical order specifications, computes the stochastic operation times making use of simulation, and schedule orders using one of the widely used meta-heuristics, namely genetic algorithm. The objective is to determine the entry sequence of the waiting orders to the core production workshop for minimizing their average makespan which directly influences the resource utilization, efficiency, and labor costs.