Fuzzy order acceptance and scheduling on identical parallel machines

This thesis is a study about simultaneous fuzzy order acceptance and scheduling in identical parallel machines. The research problem is about scheduling orders in a make-to-order system with capacity constraints. Due to the limited capacity, some orders can be produced beyond their due date. So, the manufacturer has to pay or discount this delay cost. The manufacturer has to select a subset of orders to have the maximum net profit. The tardiness of orders or outsourcing penalty cost decrease the revenue of orders. The orders coming from the customer are characterized by their revenue, outsourcing penalty cost, weighted delay cost, fuzzy due date, fuzzy processing times, and fuzzy sequence-dependent setup times. OAS on single machine problem is strongly NP-Hard, as a result, FOAS-IPM is also a strongly NP-Hard problem. In this study, first, mixed-integer linear programming is formulated to solve the problem. Next, two genetic algorithms with the name of the GADOC and GATPC algorithms are proposed to solve the large size instances. The performance of the algorithms is compared concerning the total revenue. With extensive computational tests, the proposed algorithms produce and provide high-quality feasible solutions even in big-size instances.