Browsing by Author "Li, Yuan-Zhen"

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Citation - WoS: 53
Citation - Scopus: 62
A green scheduling algorithm for the distributed flowshop problem
(Elsevier Ltd, 2021) Yuanzhen Li; Quanke Pan; Kaizhou Gao; M. Fatih Tasgetiren; Biao Zhang; Junqing Li; Tasgetiren, M. Fatih; Li, Jun-Qing; Li, Yuan-Zhen; Pan, Quan-Ke; Gao, Kai-Zhou; Zhang, Biao
In recent years sustainable development and green manufacturing have attracted widespread attention to environmental problems becoming increasingly serious. Meanwhile affected by the intensification of market competition and economic globalization distributed manufacturing systems have become increasingly common. This paper addresses the energy-efficient scheduling of the distributed permutation flowshop (EEDPFSP) with the criteria of minimizing both total flow time and total energy consumption. Considering the distributed and multi-objective optimization complexity an improved NSGAII algorithm (INSGAII) is proposed. First we analyze the problem-specific characteristics and designed new operators based on the knowledge of the problem. Second four constructive heuristic algorithms are proposed to produce high-quality initial solutions. Third inspired by the artificial bee colony algorithm we propose a new colony generation method using the operators designed. Fourth a local intensification is designed for exploiting better non-dominated solutions. The influence of parameter settings is investigated by experiments to determine the optimal parameter configuration of the INSGAII. Finally a large number of computational tests and comparisons have been carried out to verify the effectiveness of the proposed INSGAII in solving EEDPFSP. © 2021 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.