A New Approach Generating Robust and Stable Schedules in m-Machine Flow Shop Scheduling Problems: A Case Study

This paper considers a scheduling problem with uncertain processing times and machine breakdowns in industriall/office workplaces and solves it via a novel robust optimization method. In the traditional robust optimization, the solution robustness is maintained only for a specific set of scenarios, which may worsen the situation  for new scenarios. Thus, a two-stage predictive algorithm is proposed to efficiently handle the uncertainties and find robust and stable solutions. The first stage creates robust solutions and ensures their stability in the new scenarios. The second stage proposes a novel stability measure to proactively offset the effects of the machine breakdowns of the former stage. Moreover, a tri-component measure based on efficiency, robustness, and stability is proposed which aims to create a realistic schedule to satisfy the customers, manufacturers, and the staff. To meet the customer’s requirements, the robustness measure is defined based on the tardiness and the delivery dates of jobs. Finally, the proposed algorithm is applied to a case study, and the findings are compared with the empirical data. The results emphasize the superiority of the proposed technique in satisfying the customers, staff, and increasing the profitability and accountability of the company.