Browsing by Author "Huang, Ying-Ying"

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    A New Heuristic for PCBs Grouping Problem with Setup Times
    (IEEE Computer Society help@computer.org, 2020) Jiangping Huang; Quanke Pan; M. Fatih Tasgetiren; Yingying Huang; Tasgetiren, M Fatih; Huang, Ying-Ying; Pan, Quan-Ke; Huang, Jiang-Ping; J. Fu , J. Sun
    In this paper we present a new heuristic to divide a batch of printed circuit boards (PCBs) into subgroups to save the setup time for loading and unloading components from the assembly machine. In the heuristic we propose several concepts about similarity to make the number of groups as few as possible. To better show the relationship between the PCB types and the component types of a group we introduce a new solution representation. In addition considering the characteristics of the PCBs grouping problem (PGP) a method for pairing PCBs is presented. With the PCB pairs an iterative scheme is applied to start a new group. We try the rest PCBs one by one according to the similarity between it and the PCB group. Finally the experiments and comparisons show the good performance of the proposed heuristic. © 2020 Elsevier B.V. All rights reserved.
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    Citation - Scopus: 3
    An iterated local search algorithm for distributed assembly permutation flowshop problem
    (IEEE, 2020) Ying-Ying Huang; Quan-ke Pan; XiaoLu Hu; Mehmet Fatih Tasgetiren; Jiang-ping Huang; Tasgetiren, M. Fatih; Huang, Ying-Ying; Pan, Quan-ke; Hu, XiaoLu; Huang, Jiang-ping; J Fu; J Sun
    Nowadays the distributed assembly permutation flowshop problem (DAPFSP) has important applications in practice. In this paper we propose a group iterated local search (gILS) algorithm to solve the problem with total flowtime (TF) criterion. We use the heuristic method based on a ascending order which is originated from the NEH. In order to simplify and optimize the algorithm we introduce two kinds of local search methods based on products and jobs respectively. In addition considering the diversity of search area we propose a probabilistic random selection based on the TF value and the number of iterations to determine the optimized solution. Acceptance criterion is a simple comparison to determine whether a new solution is acceptable or not. Finally we calculate 180 instances with our proposed algorithm and compare the results with those from the recent effective algorithms. The results verify the superiority of the presented gILS algorithm.