Browsing by Author "Tasgetiren, M Fatih"

Now showing 1 - 3 of 3

Citation - Scopus: 1
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.
Citation - WoS: 57
Citation - Scopus: 67
An Adaptive Iterated Greedy algorithm for distributed mixed no-idle permutation flowshop scheduling problems
(Elsevier B.V., 2021) Yuanzhen Li; Quanke Pan; Junqing Li; Liang Gao; M. Fatih Tasgetiren; Li, Jun-Qing; Tasgetiren, M Fatih; Li, Yuan-Zhen; Gao, Liang; Pan, Quan-Ke
Distributed flow shop scheduling is a very interesting research topic. This paper studies the distributed permutation flow shop scheduling problem with mixed no-idle constraints which have important applications in practice. The optimization goal is to minimize total flowtime. A mixed-integer linear programming model is presented and an Adaptive Iterated Greedy (AIG) algorithm with the sample length changing according to the search process is designed. A restart strategy is also introduced to escape from local optima. Additionally to further improve the performance of the algorithm swap-based local search methods and acceleration algorithms for swap neighborhoods are proposed. Referenced Local Search (RLS) which shows better performance in solving scheduling problems is also used in our algorithm. In the destruction stage the job to be removed is selected according to the degree of influence on the total flowtime. In the initialization and construction phase when a job is inserted the jobs before and after the insertion position are removed and re-inserted into a better position to improve the algorithm search performance. A detailed design experiment is carried out to determine the best parameter configuration. Finally large-scale experiments show that the proposed AIG algorithm is the best-performing one among all the algorithms in comparison. © 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 3
Citation - Scopus: 2
NEH-Based heuristics for the distributed blocking flowshop with makespan criterion
(IEEE Computer Society help@computer.org, 2020) Shuai Chen; Quanke Pan; Xiaolu Hu; M. Fatih Tasgetiren; Tasgetiren, M Fatih; Pan, Quan-Ke; Hu, XiaoLu; Chen, Shuai; J. Fu , J. Sun
The distributed blocking flowshop scheduling problem (DBFSP) that is an important generalization of the traditional blocking flowshop scheduling problem (BFSP) in which the blocking constraint has to be considered. The NEH heuristic is regarded as the best constructive heuristic for the permutation flowshop scheduling problem. Naderi and Ruiz proposed the NEH2 for the distributed permutation flowshop scheduling problem by adding a factory assignment rule to the Nawaz-Enscore-Ham (NEH) heuristic. In this paper a priority rule is first presented for generating initial sequence for the NEH2. Then the proposed rescheduling operator is applied in NEH insertion procedure. Additionally we generated a sequence containing all job twice and then the solution for the problem is constructed by applying the NEH insertion procedure on each element of the sequence. Based on the above ideas we proposed three NEH-based heuristics namely NEH2E NEH2Een and NEH2EE. Computational results demonstrate that the proposed heuristics perform significantly better than the original NEH2. © 2020 Elsevier B.V. All rights reserved.