In the semiconductor back-end manufacturing, the device test central processing unit (CPU) is most costly and is typically the bottleneck machine at the test plant. A multihead tester contains a CPU and several test heads, each of which can be connected to a handler that processes one lot of the same device. The residence time of a lot is closely related to the product mix on test heads, which increases the complexity of this problem. It is critical for the test scheduling problem to reduce CPU's idle time and to increase tester utilization. In this paper, a multihead tester scheduling problem is formulated as an identical parallel machine scheduling problem with the objective of minimizing makespan. A heuristic grouping method is developed to obtain a good initial solution in a short time. Three metaheuristic techniques, using lot-specific and configuration-specific information, are proposed to receive a near-optimum and are compared to traditional approaches. Computational experiments show that a tabu search with lot-specific information outperforms all other competing approaches.
A cross-docking system is a kind of facility design for the purpose of enhancing the time efficiency of a distribution center. In this study, we attempt to consider a cross-docking system without temporary storage and obtain great system performance by addressing the scheduling problem of inbound and outbound trucks, in which the total operation time of trucks is minimized. In order to reduce computational efforts, three hybrid metaheuristic approaches based on particle swarm optimization, simulated annealing, and a variable neighborhood search are proposed. By the computational experiments, the three optimized approaches are analyzed and compared with each other. The experimental results show that all of these three approaches can obtain pretty good solutions, even in the large-scale examples. Moreover, one of these approaches—a hybrid metaheuristic with particle swarm optimization and a variable neighborhood search—can usually obtain the best solutions.
Because of time and cost constraints, item picking plays a major role in warehouse operations. Considering diversified orders and a constant warehouse design, deciding how to combine each batch and picker route effectively is a challenge in warehouse management. In this study, we focus on the evaluation of order-batching strategies for a single picker facing multiple orders with the objective of minimizing the total traveling distance. We propose two-stage simulated annealing and variable neighborhood search algorithms to solve the combined problem. The orders are first merged into batches, followed by determining the sequence in each batch. The computational analysis revealed that the best-fit-decreasing (BFD) batch ordering strategy in the two-stage algorithms, the variable neighborhood search algorithm, obtained superior solutions to those of the simulated annealing algorithm.
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