Farklı üretim ortamlarında dinamik parti büyüklüğü belirleme problemlerinin modellenmesi ve çözümü

This thesis addresses common lot sizing and scheduling problems that are encountered in the industries. Among these problems, General Lot Sizing and Scheduling Problem (GLSP) has received a lot of attention from researchers. The first aim of this Ph.D. study is to propose novel Simulated Annealing (SA) based hybrid approaches for solving the GLSP. First, a mixed-integer programming (MIP) model for the GLSP is given in order to solve smaller size problems. Afterwards, a matheuristic algorithm that integrates SA algorithm and MIP model is devised for solving larger size problems. The proposed matheuristic approach decomposes the GLSP into subproblems. The second aim of this Ph.D. study is to solve a practical production issue in the paper bag industry that contains the problem of capacitated lot sizing and scheduling with cutting plan assignment. A MIP model is proposed for modeling and solving the problem. To handle the industrial case, a problem-based matheuristic approach that resolves the lot sizing and assignment problems separately is provided based on this mathematical formulation. A similar approach is also proposed for the extended version which several real life applications such as existence backordered and defective items, external procurement, and sequence dependent setup times. The last aim of this Ph.D. study is to solve a practical production issue in the pool equipment manufacturer industry that contains the problem of the multi-machine capacitated lot sizing and scheduling problem with machine eligibility constraints. The considered problem is also formulated as a multi-objective MIP model. Computational experiments indicate that the constructed model meets the practical requirements for the pool equipment manufacturer industry.