Browsing by Author "Li, Jun-Qing"

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Citation - WoS: 232
Citation - Scopus: 270
A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities
(Elsevier Science Inc, 2014) Junqing Li; Quanke Pan; M. Fatih Tasgetiren; Li, Jun-Qing; Tasgetiren, M. Fatih; Pan, Quan-Ke
This paper presents a novel discrete artificial bee colony (DABC) algorithm for solving the multi-objective flexible job shop scheduling problem with maintenance activities. Performance criteria considered are the maximum completion time so called makespan the total workload of machines and the workload of the critical machine. Unlike the original ABC algorithm the proposed DABC algorithm presents a unique solution representation where a food source is represented by two discrete vectors and tabu search (TS) is applied to each food source to generate neighboring food sources for the employed bees onlooker bees and scout bees. An efficient initialization scheme is introduced to construct the initial population with a certain level of quality and diversity. A self-adaptive strategy is adopted to enable the DABC algorithm with learning ability for producing neighboring solutions in different promising regions whereas an external Pareto archive set is designed to record the non-dominated solutions found so far. Furthermore a novel decoding method is also presented to tackle maintenance activities in schedules generated. The proposed DABC algorithm is tested on a set of the well-known benchmark instances from the existing literature. Through a detailed analysis of experimental results the highly effective and efficient performance of the proposed DABC algorithm is shown against the best performing algorithms from the literature. © 2013 Elsevier Inc. © 2014 Elsevier B.V. All rights reserved.
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.