چكيده به لاتين
Abstract:
This thesis aims to minimize the number of weighted tardy jobs besides of the sum of delivery costs in a two-stage assembly flow-shop problem with considering a batch delivery system. In the real world, manufacturers often keep completed jobs to deliver in batches. Under such circumstances, sending several jobs in batches may result in reducing delivery costs; nevertheless, it may add to other scheduling-related objective functions such as minimizing the number of tardy jobs, which is often used to rate managers’ performance in many manufacturing environments. Therefore, the proposed model tries to account both aspects and find a trade-off between them to hold the total costs low. In literature review, minimizing the number of weighted tardy jobs is known as an NP-Hard problem, so the problem is defined in this paper with the additional term which shows that the total delivery cost of the system remains at least NP-hard. In this study, we present a mixed-integer linear programming (MILP) model to solve the problem. As this is an MILP model, the commercial solver (the CPLEX solver) is not guaranteed to find the optimal solution for large-size problems at a reasonable amount of time. Accordingly, we present a meta-heuristic method based on genetic algorithm with the hierarchical decision making (GA) and the performance of the proposed GA is examined versus another GA with the different structure. Besides, the proposed model is applied in a furniture Factory which has the same condition as described in the model. Using the model, the results showed that the factory costs improve by 12.79 and 13.20 and 40.55 percent in the several experiments.
Keywords: Two-stage Assembly Flow-shop, Tardy Jobs, Batch Delivery System, Genetic Algorithm
