Network configuration multi-factory scheduling with batch delivery: A learning-oriented simulated annealing approach

Abstract

This paper introduces the network configuration multi-factory scheduling with the batch delivery problem (NCMFSBDP) in supply chain (SC). There is a set of given jobs that are transported among factories through a sufficient number of batches with limited capacity. In the assumed SC network, some in-process jobs may have the same processing routes and can be delivered together within the same batch to reduce the transportation costs. On the other hand, the batch delivery decision may cause an increase in the tardiness and holding costs. Therefore, in such a complicated network, the aim of NCMFSBDP is to find the optimum scheduling and batch delivery solutions to minimize the total transportation, tardiness and holding costs. The proposed problem is formulated as a mixed-integer programming model. Due to the NP-hardness of the problem, in addition to the standard simulated annealing (SA) and the cloud theory-based SA, this paper develops a learning-oriented SA based on Q-learning algorithm to deal with the large-sized problems, quickly. Then, a novel hybrid cloud theory-based learning-oriented SA (HCLSA) is proposed. The algorithms are examined dealing with several randomly generated test instances versus genetic algorithm. According to the statistical analysis, it is shown that the proposed HCLSA is superior to the other examined algorithms in terms of both solution’s quality and execution time. Finally, some analytical insights are conducted about the effects of reinforcement learning on controlling parameters of SA during the search process.