Morteza Karimi; Tahmoores Sohrabi; Hasan Mehrmanesh
Abstract
In this study, the problem of simultaneous determination of order acceptance, scheduling and batch delivery considering sequence-dependent setup and capacity constraint has been presented. This problem is a combination of the three problems of order acceptance, scheduling and batch delivery. The most ...
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In this study, the problem of simultaneous determination of order acceptance, scheduling and batch delivery considering sequence-dependent setup and capacity constraint has been presented. This problem is a combination of the three problems of order acceptance, scheduling and batch delivery. The most important innovation of this research is the simultaneous optimization of profits and the total weighted earliness and tardiness as two conflicting objectives in the problem of combining order, scheduling and batch delivery. Another innovation of this research is the use of multi-objective Grey Wolf Optimization (GWO) algorithm, which has not been used in studies of this field so far. It has also been shown that the multi-objective Grey Wolf Optimization algorithm is comparable to the exact solution methods. The second part of the numerical results compares the results of the ε-constraint method, NSGA-II and the multi-objective Grey Wolf Optimization algorithm. The results of this section show that by increasing the scale of the problem, the efficiency of the multi- objective Grey Wolf Optimization algorithm is better displayed, and in general, this method has a significant advantage relative to NSGA-II and ε-constraint in terms of DM, SNS and NPS indicators. Also, the solving time of this method is very shorter than that of the ε-constraint. Therefore, from a managerial point of view, a tool called the multi-objective Grey Wolf Optimization algorithm can be used as an efficient tool for supply and production managers, which is able to provide several optimal solutions with different profits, earliness and tardiness.
M. Zandieh; M.M. Asgari Tehrani
Volume 1, Issue 1 , November 2014, , Pages 1-19
Abstract
Make-to-order is a production strategy in which manufacturing starts only after a customer's order is received; in other words, it is a pull-type supply chain operation since manufacturing is carried out as soon as the demand is confirmed. This paper studies the order acceptance problem with weighted ...
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Make-to-order is a production strategy in which manufacturing starts only after a customer's order is received; in other words, it is a pull-type supply chain operation since manufacturing is carried out as soon as the demand is confirmed. This paper studies the order acceptance problem with weighted tardiness penalties in permutation flow shop scheduling with MTO production strategy, the objective function of which is to maximize the total net profit of the accepted orders. The problem is formulated as an integer-programming (IP) model, and a cloud-based simulated annealing (CSA) algorithm is developed to solve the problem. Based on the number of candidate orders the firm receives, fifteen problems are generated. Each problem is regarded as an experiment, which is conducted five times to compare the efficiency of the proposed CSA algorithm to the one of simulated annealing (SA) algorithm previously suggested for the problem. The experimental results testify to the improvement in objective function values yielded by CSA algorithm in comparison with the ones produced by the formerly proposed SA algorithm.