| Shop production scheduling is an essential aspect of both manufacturing system and process industry. Nowadays, due to growing scarcity of resources, fierce competition between corporations and various demands from customers, effective scheduling techniques are crucial for improving production efficiency. This directly affects the allocation of productive resources and is related to the enterprise's survival and future. However, as a complex combinatorial optimization problem with many variations, shop production scheduling is strong nondeterministic polynomial time (NP)-complete and has drawn increasing concern from researchers in recent years.The main contributions of this paper are summarized as follows:1. A discrete quantum-behaved particle swarm optimization (DQPSO) approach is developed and applied to shop scheduling problem. This algorithm combines the principle of quantum-PSO and crossover and mutation operator in GA, which makes it applicable for searching in combinatorial space directly. In addition, in order to update solutions with certain quality and diversity, NEH heuristic and VNS local search are also incorporated into this algorithm. The proposed DQPSO algorithm has been tested on standard flow-shop and job-shop benchmark examples and compared with some recently proposed algorithms. The results show that the proposed approach is very efficient and easy to implement.2. Shop scheduling problems usually have large amounts of variables. Complicated mathematical formulation and makespan are frequently used in the literature. In order to improve the computational efficiency in no-wait flow-shop scheduling with mutation operation of DQPSO, a speed-up method is proposed in this study, which reduces the computing complexity from O(mn) to O(3m).3. DQPSO algorithms are applied to industrial examples of flexible job-shop scheduling. The simulation results show that resolutions acquired by DQPSO are effective.At last, conclusions and future research topics are given. |
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