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discrete differential evolution algorithm for distributed blocking flowshop scheduling with makespan criterion

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2018年 76卷 96-107页

作者： Zhang, Guanghui Xing, Keyi Cao, Feng Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Syst Engn Inst 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China Guizhou Inst Technol Sch Sci Guiyang 550003 Guizhou Peoples R China

This paper deals with a distributed blocking flowshop scheduling problem, which tries to solve the blocking flowshop scheduling in distributed manufacturing environment. The optimization objective is to find a suitable schedule, consisting of assigning jobs to at least two factories and sequencing the jobs assigned to each factory, to make the maximum completion time or makespan minimization. Two different mathematical models are proposed, and in view of the NP-hardness of the problem, a novel hybrid discrete differential evolution (DDE) algorithm is established. First, the problem solution is represented as several job permutations, each of which denotes the partial schedule at a certain factory. Second, four widely applied heuristics are generalized to the distributed environment for providing better initial solutions. Third, both operators of mutation and crossover are redesigned to perform the DDE directly based on the discrete permutations, and a biased section operator is used to increase the diversity of the searching information. Meanwhile, an effective local search based on distributed characteristics and an elitist retain strategy are integrated into the DDE framework to stress both local exploitation and global exploration. Taking into account the time cost, an effective speed-up technique is designed to enhance the algorithmic efficiency. In the experimental section, the parameters of DDE are calibrated by the Taguchi method. Experimental results derived from a wealth of test instances have demonstrated the algorithmic effectiveness, which further concludes that the proposed DDE algorithm is a suitable alternative approach for solving the problem under consideration.

关键词： Scheduling Distributed blocking flowshop Makespan Mathematical model discrete differential evolution algorithm

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A discrete differential evolution algorithm for the permutation flowshop scheduling problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2008年第4期55卷 795-816页

作者： Pan, Quan-Ke Tasgetiren, Mehmet Fatih Liang, Yun-Chia Sultan Qaboos Univ Dept Operat Management & Business Stat Muscat Oman Liaocheng Univ Coll Comp Sci Liaocheng Peoples R China Yuan Ze Univ Dept Ind Engn & Management Chungli 320 Taiwan

Very recently, Pan et al. [Proceedings of the 9th Annual Conference on Genetic and evolutionary Computation, GECC007, pp. 126-33] presented a new and novel discrete differential evolution algorithm for the permutation flowshop scheduling problem with the makespan criterion. On the other hand, the iterated greedy algorithm is proposed by [Ruiz, R.. & Stutzle, T. (2007). A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research, 177(3), 2033-49] for the permutation flowshop scheduling problem with the makespan criterion. However, both algorithms are not applied to the permutation flowshop scheduling problem with the total flowtime criterion. Based on their excellent performance with the makespan criterion, we extend both algorithms in this paper to the total flowtime objective. Furthermore, we propose a new and novel referenced local search procedure hybridized with both algorithms to further improve the solution quality. The referenced local search exploits the space based on reference positions taken from a reference solution in the hope of finding better positions for jobs when performing insertion operation. Computational results show that both algorithms with the referenced local search are either better or highly competitive to all the existing approaches in the literature for both objectives of makespan and total flowtime. Especially for the total flowtime criterion, their performance is superior to the particle swarm optimization algorithms proposed by [Tasgetiren, M. F., Liang, Y. -C., Sevkli, M., Gencyilmaz, G. (2007). Particle swarm optimization algorithm for makespan and total flowtime minimization in permutation flowshop sequencing problem. European journal of Operational Research, 177(3), 1930-47] and [Jarboui, B., Ibrahim, S., Siarry, P., Rebai, A. (2007). A combinatorial particle swarm optimisation for solving permutation flowshop problems. Computers & Industrial Engineeri

关键词： Permutation flowshop Scheduling Iterated greedy algorithm discrete differential evolution algorithm discrete particle swarm optimization Referenced local search

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An efficient discrete differential evolution algorithm based on community structure for influence maximization

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APPLIED INTELLIGENCE 2022年第11期52卷 12497-12515页

作者： Li, Huan Zhang, Ruisheng Liu, Xin Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Gansu Peoples R China

As one of the main contents of influence analysis, influence maximization is selecting a group of influential nodes with specified size in a given network to form a seed node set, and the influence spread cascaded by the selected seed node set can be maximized under a given propagation model. The research of influence maximization is helpful to understand social network and viral marketing. How to develop an effective algorithm to solve this problem in large-scale networks is still a challenge. In this paper, a discrete differential evolution algorithm based on community structure (CDDE) is proposed. At first, the fast Louvain algorithm is used to detect the community structure. On this basis, significant communities are defined and candidate nodes are extracted from each significant community. And then, an improved discrete differential evolution algorithm is proposed to obtain influential nodes. Furthermore, a population initialization strategy based on candidate nodes is designed, and the candidate nodes are also used to accelerate the discrete evolution process of the population. Experimental results on six real-world social networks show that the proposed CDDE is competitive with the comparison algorithms in terms of effectiveness and efficiency, and achieves comparable influence spread to CELF.

关键词： Social network Influence maximization Community structure discrete differential evolution algorithm

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A discrete differential evolution algorithm for the single machine total weighted tardiness problem with sequence dependent setup times

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COMPUTERS & OPERATIONS RESEARCH 2009年第6期36卷 1900-1915页

作者： Tasgetiren, M. Fatih Pan, Quan-Ke Liang, Yun-Chia Sultan Qaboos Univ Dept Operat Management & Business Stat Muscat Oman Liaocheng Univ Coll Comp Sci Liaocheng 252059 Shandong Peoples R China

This paper is concerned with solving the single machine total weighted tardiness problem with sequence dependent setup times by a discrete differential evolution algorithm developed by the authors recently. Its performance is enhanced by employing different population initialization schemes based on some constructive heuristics such as the well-known NEH and the greedy randomized adaptive search procedure (GRASP) as well as some priority rules such as the earliest weighted due date (EWDD) and the apparent tardiness cost with setups (ATCS). Additional performance enhancement is further achieved by the inclusion of a referenced local search (RLS) in the algorithm together with the use of destruction and construction (DC) procedure when obtaining the mutant population. Furthermore, to facilitate the greedy job insertion into a partial solution which will be employed in the NEH. GRASP, DC heuristics as well as in the RLS local search, some newly designed speed-up methods are presented for the insertion move for the first time in the literature. They are novel contributions of this paper to the single machine tardiness related scheduling problems with sequence dependent setup times. To evaluate its performance, the discrete differential evolution algorithm is tested on a set of benchmark instances from the literature. Through the analyses of experimental results, its highly effective performance with substantial margins both in solution quality and CPU time is shown against the best performing algorithms from the literature, in particular, against the very recent newly designed particle swarm and ant colony optimization algorithms of Anghinolfi and Paolucci [A new discrete particle swarm optimization approach for the single machine total weighted tardiness scheduling problem with sequence dependent setup times. European journal of Operational Research 2007;doi:10.1016/***.2007.10.044] and Anghinolfi and Paolucci [A new ant colony optimization approach for the single m

关键词： discrete differential evolution algorithm Single machine total weighted tardiness problem with sequence dependent setup times Iterated greedy algorithm Greedy randomized adaptive search procedure

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An effective discrete differential evolution algorithm for scheduling uniform parallel batch processing machines with non-identical capacities and arbitrary job sizes

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2016年第0期179卷 1-11页

作者： Zhou, Shengchao Liu, Ming Chen, Huaping Li, Xueping Univ Sci & Technol China Sch Management Hefei 230026 Anhui Peoples R China Nankai Univ Sch Finance Tianjin 300071 Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Anhui Peoples R China Univ Tennessee Dept Ind & Syst Engn Knoxville TN 37996 USA

Batch processing machines (BPMs) simultaneously process multiple jobs in a batch, which are commonly used in many industrial systems. This paper studies the scheduling problem of uniform parallel batch processing machines with arbitrary job sizes. These batch processing machines have non-identical capacities and different speeds. The objective is to minimize the makespan (or maximize the machine utilization). We formulate this problem as a mixed integer programming model. Since the problem is strongly NP-hard, an effective differential evolution-based hybrid algorithm is proposed for solving large-scale problems. Firstly, in this algorithm, individuals in the population are represented as discrete job sequences, and novel mutation and crossover operators are designed based on this representation. Next, a heuristic is developed to form batches and schedule the resulting batches on the uniform parallel machines. Then, the performance of the proposed algorithm is evaluated by comparing its results to a commercial solver (CPLEX), a random keys genetic algorithm (RKGA) and a particle swarm optimization (PSO) algorithm. Experimental results demonstrate the superiority of the proposed algorithm in terms of solution quality and robustness, especially for large-scale instances. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Scheduling Batch processing machines (BPMs) Uniform parallel machines Non-identical capacities discrete differential evolution algorithm

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Hybrid discrete differential evolution algorithm for biobjective cyclic hoist scheduling with reentrance

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COMPUTERS & OPERATIONS RESEARCH 2016年第0期76卷 155-166页

作者： Yan, Pengyu Wang, Guanhua Che, Ada Li, Yaoyiran Univ Elect Sci & Technol China Sch Management & Econ Chengdu 610054 Peoples R China Northwestern Polytech Univ Sch Management Xian 710072 Peoples R China

Cyclic hoist scheduling problems in automated electroplating lines and surface processing shops attract many attentions and interests both from practitioners and researchers. In such systems, parts are transported from a workstation to another by a material handling hoist. The existing literature mainly addressed how to find an optimal cyclic schedule to minimize the cycle time that measures the productivity of the lines. The material handling cost is an important factor that needs to be considered in practice but seldom addressed in the literature. This study focuses on a biobjective cyclic hoist scheduling problem to minimize the cycle time and the material handling cost simultaneously. We consider the reentrant workstations that are usually encountered in real-life lines but inevitably make the part flow more complicated. The problem is formulated as a biobjective linear programming model with a given hoist move sequence and transformed into finding a set of Pareto optimal hoist move sequences with respect to the bicriteria. To obtain the Pareto optimal or near-optimal front, a hybrid discrete differential evolution (DDE) algorithm is proposed. In this hybrid evolutional algorithm, the population is divided into several subpopulations according to the maximal work-in-process (WIP) level of the system and the sizes of subpopulations are dynamically adjusted to balance the exploration and exploitation of the search. We propose a constructive heuristic to generate initial subpopulations with different WIP levels, hybrid mutation and crossover operators, an evaluation method that can tackle infeasible individuals and a one-to-one greedy tabu selection method. Computational results on both benchmark instances and randomly generated instances show that our proposed hybrid DDE algorithm outperforms the basic DDE algorithm and can solve larger-size instances than the existing e-constraint method. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Hoist scheduling Cyclic scheduling Reentrant workstation discrete differential evolution algorithm Biobjective optimization

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An interactive approach based on a discrete differential evolution algorithm for a class of integer bilevel programming problems

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2016年第10期47卷 2330-2341页

作者： Li, Hong Zhang, Li Jiao, Yong-Chang Xidian Univ Sch Math & Stat Xian Peoples R China Xidian Univ Sch Elect Engn Xian Peoples R China

This paper presents an interactive approach based on a discrete differential evolution algorithm to solve a class of integer bilevel programming problems, in which integer decision variables are controlled by an upper-level decision maker and real-value or continuous decision variables are controlled by a lower-level decision maker. Using the Karush--Kuhn-Tucker optimality conditions in the lower-level programming, the original discrete bilevel formulation can be converted into a discrete single-level nonlinear programming problem with the complementarity constraints, and then the smoothing technique is applied to deal with the complementarity constraints. Finally, a discrete single-level nonlinear programming problem is obtained, and solved by an interactive approach. In each iteration, for each given upper-level discrete variable, a system of nonlinear equations including the lower-level variables and Lagrange multipliers is solved first, and then a discrete nonlinear programming problem only with inequality constraints is handled by using a discrete differential evolution algorithm. Simulation results show the effectiveness of the proposed approach.

关键词： interactive approach discrete differential evolution algorithm discrete bilevel programming problem smoothing function constraint-handling technique

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SELECTIVE ENSEMBLE USING discrete differential evolution algorithm FOR SHORT-TERM LOAD FORECASTING

SELECTIVE ENSEMBLE USING DISCRETE DIFFERENTIAL EVOLUTION ALG...

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International Conference on Machine Learning and Cybernetics

作者： Li, Yan Wang, Dong-Feng Han, Pu N China Elect Power Univ Sch Control Sci & Engn Baoding 071003 Peoples R China

ISBN: (纸本)9781424447053

In order to further improve the accuracy of short-term load forecasting, a selective neural network ensemble method using discrete differential evolution algorithm is proposed. Firstly, the individual vectors in differential evolution algorithm are dispersed. Secondly, a group of RBF neural networks with larger difference are trained independently and a binary bit string in multi-dimensional space with the value of 0 or 1 is used to describe all the possible neural network integrations. Lastly, part of individual networks is optimized selected to ensemble and an entropy method is used to determine the integrated weighted coefficient of component neural networks according to the variability of prediction error sequences. The experiments show that the proposed approach has higher accuracy and stability.

关键词： discrete differential evolution algorithm RBF neural network Selective ensemble Entropy weighted method Short-term load forecasting

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A Hybrid discrete differential evolution algorithm for Dynamic Scheduling in Robotic Cells 13

A Hybrid Discrete Differential Evolution Algorithm for Dynam...

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13th International Conference on Service Systems and Service Management (ICSSSM)

作者： Ma, Kaiyuan Yan, Pengyu Dai, Wenqiang Univ Elect Sci & Technol China Sch Management & Econ Chengdu Peoples R China

ISBN: (纸本)9781509028429

This paper addresses a dynamic scheduling problem in robotic cells, where a computer-controlled robot is responsible for handling materials between workstations. In such automated manufacturing systems, efficiently scheduling transportations of the material plays an important role in improving the throughput of the system. We consider an uncertain setting where more than one new jobs arrive at the system and need to be scheduled immediately. To reduce the disturbance occurred by frequently rescheduling or adjusting the existing schedule, we keep the existing schedule unchanged and insert the new jobs' processing operations and transportations into the available(idle) time intervals of the workstations and the robot, respectively. To get an optimal new schedule, the problem is formulated as a mixed integer programming model with the objective that minimizes the total completion time of the new jobs. As the NP-hard nature of the problem, a hybrid discrete differential evolution (DDE) algorithm is proposed to search for a near-optimal solution within a reasonable computational time. In the hybrid DDE, a extension heuristic method is developed to generate a local optimal solution provided that a new jobs inserting sequence is given, and the DDE is applied to search for an optimal or near-optimal new jobs inserting sequence. The computational results indicate that the hybrid DDE runs effectively to search for the optimal or near-optimal new schedules.

关键词： material handling robotic cells dynamic scheduling new jobs discrete differential evolution algorithm

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A novel discrete differential evolution algorithm for SVRPSPD

A novel discrete differential evolution algorithm for SVRPSP...

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International Conference on Sensors, Measurement and Intelligent Materials (ICSMIM 2012)

作者： Hou, Lingjuan Hou, Zhijiang Tianjin Normal Univ Sch Management Tianjin 300387 Peoples R China Tianjin Univ Technol Lib Tianjin 300387 Peoples R China

ISBN: (纸本)9783037856529

Aiming at the stochastic vehicle routing problems with simultaneous pickups and deliveries, a novel discrete differential evolution algorithm is proposed for routes optimization. The algorithm can directly be used for the discrete domain by special design. Computational simulations and comparisons based on a medium-sized problem of SVRPSPD is provided. Results demonstrate that the proposed algorithm obtains better results than the basic differential evolution algorithm and the existing genetic algorithm.

关键词： SVRPSPD bitwise operator discrete differential evolution algorithm

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