Multi-Objective Genetic Algorithm for an Integrated Inspection Allocation and Flow Shop Scheduling with Sequence Dependent Setup Time
One of the most critical elements in manufacturing is production scheduling. Hence, any improvement in the scheduling system has significantly impacts time, quality, productivity, flexibility, and total cost. Improving these factors can easily increase the customer satisfaction. Multi-objective flow-shop scheduling with sequence-dependent setup time is considered as an NP-hard problem (nondeterministic polynomial time). Integrating inspection allocation with flow-shop scheduling problem results in a complex scheduling problem. For this degree of complexity, genetic algorithm is an excellent choice. Adding many inspection points in the production line can help to avoid reject items. However, the operation cost and productivity will be impacted negatively. The main objectives in this thesis are minimizing the inspection cost, minimizing the cost of processing defective items, and minimizing penalty cost by using genetic algorithm. Moreover, establishing a balance between cost and quality is extremely important in modern manufacturing management to avoid waste according to lean manufacturing methodology.