M.r. Amin Naseri, I. Nakhaee, and M. A. Beheshti Nia,
Volume 26, Issue 2 (1-2008)
Abstract
In this paper, the problem of batch scheduling in a flexible flow shop environment is studied. It is assumed that machines in some stages are able to process a number of jobs simultaneously. The applications of this problem can be found in various industries including spring and wire manufacturing and in auto industry. A mixed integer programming formulation of the problem is presented and it is shown that the problem is NP-Hard. Three heuristics will then be developed to solve the problem and a lower bound is also developed for evaluating the performance of the proposed heuristics. Results show that heuristic H3 gives better results compared to the others.
N. Fattahi, M. Reisi-Nafchi, G. Moslehi,
Volume 39, Issue 1 (8-2020)
Abstract
Scheduling in production environments is used as a competitive tool to improve efficiency and respond to customer requests. In this paper, a scheduling problem is investigated in a three-stage flexible flowshop environment with the consideration of blocking and batch processing. This problem has been inspired by the charging and packaging line of a large battery manufacturer. In this environment, the first and third stages involve a single processor machine, and the second one consists of m identical parallel batch processing machines. The objective is to minimize the total weighted tardiness of the received orders.Given the lack of consideration of this problem in the literature, first, a mathematical programming model is presented for the problem. Also, due to the NP-hardness of the problem, a variable neighborhood search algorithm and a memetic algorithm are developed to solve it. The computational results show that the variable neighborhood search algorithm can solve instances up to 1200 orders and 15 machines with an average deviation of about 1.9%, relative to the best solution of the two algorithms, and the memetic algorithm can solve instances up to 1200 orders and 15 machines with an average deviation of about 7.8%, as compared e to the best solution of the two algorithms. In general, computational results show the better performance of the variable neighborhood search algorithm in comparison to the memetic algorithm.
