A Mathematical Programming Model for Flow Shop Scheduling Problems for Considering Just in Time Production

In this paper, we consider a flow shop scheduling problem with bypass consideration for minimizing the sum of earliness and tardiness costs. We propose a new mathematical modeling to formulate this problem. There are several constraints which are involved in our modeling such as the due date of jobs, the job ready times, the earliness and the tardiness cost of jobs, and so on. We apply adapted genetic algorithm based on bypass consideration to solve the problem. The basic parameters of this meta-heuristic are briefly discussed in this paper. Also a computational experiment is conducted to evaluate the performance of the implemented methods. The implemented algorithm can be used to solve large scale flow shop scheduling problem with bypass effectively

A Mathematical Programming Model for Flow Shop Scheduling Problems for Considering Just in Time Production

In this paper, we consider a flow shop scheduling problem with bypass consideration for minimizing the sum of earliness and tardiness costs. We propose a new mathematical modeling to formulate this problem. There are several constraints which are involved in our modeling such as the due date of jobs, the job ready times, the earliness and the tardiness cost of jobs, and so on. We apply adapted genetic algorithm based on bypass consideration to solve the problem. The basic parameters of this meta-heuristic are briefly discussed in this paper. Also a computational experiment is conducted to evaluate the performance of the implemented methods. The implemented algorithm can be used to solve large scale flow shop scheduling problem with bypass effectively

A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty

Manufacturers need to satisfy consumer demands in order to compete in the real world. This requires the efficient operation of a supply chain planning. In this research we consider a supply chain including multiple suppliers, multiple manufacturers and multiple customers, addressing a multi-site, multi-period, multi-product aggregate production planning (APP) problem under uncertainty. First a new robust multi-objective mixed integer nonlinear programming model is proposed to deal with APP considering two conflicting objectives simultaneously, as well as the uncertain nature of the supply chain. Cost parameters of the supply chain and demand fluctuations are subject to uncertainty. Then the problem transformed into a multi-objective linear one. The first objective function aims to minimize total losses of supply chain including production cost, hiring, firing and training cost, raw material and end product inventory holding cost, transportation and shortage cost. The second objective function considers customer satisfaction through minimizing sum of the maximum amount of shortages among the customers' zones in all periods. Working levels, workers productivity, overtime, subcontracting, storage capacity and lead time are also considered. Finally, the proposed model is solved as a single-objective mixed integer programming model applying the LP-metrics method. The practicability of the proposed model is demonstrated through its application in solving an APP problem in an industrial case study. The results indicate that the proposed model can provide a promising approach to fulfill an efficient production planning in a supply chain.Aggregate production planning Robust multi-objective optimization Uncertainty Supply chain

Aggregate production planning in a green supply chain by considering flexible lead times and multi breakpoint purchase and shortage cost functions.

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