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Jun qing Li

Other affiliations: Shandong Normal University
Bio: Jun qing Li is an academic researcher from Southeast University. The author has contributed to research in topics: Stability (learning theory) & Local search (optimization). The author has an hindex of 1, co-authored 1 publications receiving 76 citations. Previous affiliations of Jun qing Li include Shandong Normal University.

Papers
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TL;DR: The experimental results show that the EIWO algorithm can find equal or better optimal solution compared with other algorithms, and the convergence ability, stability and robustness are verified.

92 citations


Cited by
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TL;DR: A hybrid artificial bee colony (ABC) algorithm to solve a parallel batching distributed flow-shop problem (DFSP) with deteriorating jobs is proposed, which is favorably compared against several algorithms in terms of both solution quality and population diversity.
Abstract: In this article, we propose a hybrid artificial bee colony (ABC) algorithm to solve a parallel batching distributed flow-shop problem (DFSP) with deteriorating jobs. In the considered problem, there are two stages as follows: 1) in the first stage, a DFSP is studied and 2) after the first stage has been completed, each job is transferred and assembled in the second stage, where the parallel batching constraint is investigated. In the two stages, the deteriorating job constraint is considered. In the proposed algorithm, first, two types of problem-specific heuristics are proposed, namely, the batch assignment and the right-shifting heuristics, which can substantially improve the makespan. Next, the encoding and decoding approaches are developed according to the problem constraints and objectives. Five types of local search operators are designed for the distributed flow shop and parallel batching stages. In addition, a novel scout bee heuristic that considers the useful information that is collected by the global and local best solutions is investigated, which can enhance searching performance. Finally, based on several well-known benchmarks and realistic industrial instances and via comprehensive computational comparison and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several algorithms in terms of both solution quality and population diversity.

123 citations

Journal ArticleDOI
TL;DR: Six new mixed integer linear programming (MILP) models with turning Off/On strategy are proposed based on two different modeling ideas namely idle time variable and idle energy variable to help the enterprises rationalize production so as to reduce energy consumption and costs.

114 citations

Journal ArticleDOI
TL;DR: A novel multi-objective cellular grey wolf optimizer (MOCGWO) is proposed to address the hybrid flowshop scheduling problem by integrating the merits of cellular automata for diversification and variable neighborhood search for intensification, which balances exploration and exploitation.

105 citations

Journal ArticleDOI
TL;DR: Three variants of the discrete invasive weed optimization (DIWO) for the DAPFSP with total flowtime criterion are presented and it is shown that among the proposed algorithms, HDIWO is the best one.
Abstract: Distributed assembly permutation flowshop scheduling problem (DAPFSP) has important applications in modern assembly systems. In this paper, we present three variants of the discrete invasive weed optimization (DIWO) for the DAPFSP with total flowtime criterion. For solving such a problem, we present a two-level representation that consists of a product permutation and a number of job sequences. We introduce neighbourhood operators for both the product permutation and job sequences. We design effective local search procedures respectively for product-permutation-based neighbourhood and job-sequence-based neighbourhood. By combining the problem-specific knowledge and the idea of invasive weed optimization, we present three DIWO-based algorithms: a two-level discrete invasive weed optimization (TDIWO), a discrete invasive weed optimization with hybrid search operators (HDIWO), and a HDIWO with selection probability. The algorithms explore the two neighbourhoods in quite a different way. We calibrate the presented DIWO algorithms by means of the design of experimental method, and carry out a comprehensive computational campaign based on the 810 benchmark instances in the literature. The numerical experiments show that the presented DIWO algorithms perform significantly better than the other competing algorithms in the literature. Among the proposed algorithms, HDIWO is the best one.

90 citations

Journal ArticleDOI
TL;DR: The flexible task scheduling problem in a cloud computing system is studied and solved by a hybrid discrete artificial bee colony (ABC) algorithm, where the considered problem is firstly modeled as a hybrid flowshop scheduling (HFS) problem.
Abstract: In this study, the flexible task scheduling problem in a cloud computing system is studied and solved by a hybrid discrete artificial bee colony (ABC) algorithm, where the considered problem is firstly modeled as a hybrid flowshop scheduling (HFS) problem. Both a single objective and multiple objectives are considered. In multiple objective HFS problems, three objectives, i.e., minimization of the maximum completion time, maximum device workload, and total workloads of all devices, are considered simultaneously. Two different kinds of HFS are considered, i.e., HFS with identical parallel machines and HFS with unrelated machines. In the proposed algorithm, three types of artificial bees are included as in the classical ABC algorithm, i.e., the employed bee, the onlooker bee, and the scout bee. Each solution is represented as an integer string. To consider the problem features, several different types of perturbation structures are investigated to enhance the searching abilities. An improved version of the adaptive perturbation structure is embedded in the proposed algorithm to balance the exploitation and exploration ability. A simple but efficient selection and updated approach are applied to enhance the exploitation process. To further improve the exploitation abilities, a deep-exploitation operator is designed. An improved scout bee employed with different local search methods for the best food source or the abandoned solution is designed and can increase the convergence ability of the proposed algorithm. The proposed algorithm is tested on sets of the well-known benchmark instances, and the performance of the proposed algorithm is verified.

82 citations