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A modified teaching learning metaheuristic algorithm with opposite-based learning for permutation flow-shop scheduling problem

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EVOLUTIONARY INTELLIGENCE 2022年第1期15卷 57-79页

作者： Balande, Umesh Shrimankar, Deepti Visvesvaraya Natl Inst Technol Nagpur 440010 Maharashtra India

Teaching-Learning-Based Optimization is one of the well-known metaheuristic algorithm in the research industry. Recently, various population-based algorithms have been developed for solving optimization problems. In this paper, a random scale factor approach is proposed to modify the simple TLBO algorithm. Modified Teaching-Learning-Based Optimization with Opposite-Based-Learning algorithm is applied to solve the permutation flow-shop-scheduling problem with the purpose of minimizing the makespan. The OBL approach is used to enhance the quality of the initial population and convergence speed. PFSSP is used extensively for solving scheduling problem, which belongs to the category of NP-hard optimization problems. First, MTLBO is developed to effectively determine the PFSSP using the Largest Order Value rule-based random key, so that individual job schedules are converted into discrete schedules. Second, new initial populations are generated in MTLBO using the Nawaz-Enscore-Ham heuristic mechanism. Finally, the local exploitation ability is enhanced in the MTLBO using effective swap, insert and inverse structures. The performance of proposed algorithm is validated using ten benchmark functions and the Wilcoxon rank test. The computational results and comparisons indicate that the proposed algorithm outperformed over five well-known datasets such as Carlier, Reeves, Heller, Taillards and VRF benchmark test functions, compared to other metaheuristic algorithms. Thep-value indicated the significance and superiority of the proposed algorithm over other metaheuristic algorithms.

关键词： Evolutionary algorithms Opposite-based learning permutation flow-shop scheduling problem Teaching-learning-based optimization

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An extended continuous estimation of distribution algorithm for solving the permutation flow-shop scheduling problem

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ENGINEERING OPTIMIZATION 2017年第11期49卷 1868-1889页

作者： Shao, Zhongshi Pi, Dechang Shao, Weishi Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Jiangsu Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing Jiangsu Peoples R China

This article proposes an extended continuous estimation of distribution algorithm (ECEDA) to solve the permutation flow-shop scheduling problem (PFSP). In ECEDA, to make a continuous estimation of distribution algorithm (EDA) suitable for the PFSP, the largest order value rule is applied to convert continuous vectors to discrete job permutations. A probabilistic model based on a mixed Gaussian and Cauchy distribution is built to maintain the exploration ability of the EDA. Two effective local search methods, i.e. revolver-based variable neighbourhood search and Henon chaotic-based local search, are designed and incorporated into the EDA to enhance the local exploitation. The parameters of the proposed ECEDA are calibrated by means of a design of experiments approach. Simulation results and comparisons based on some benchmark instances show the efficiency of the proposed algorithm for solving the PFSP.

关键词： Extended continuous estimation of distribution algorithm local search hybrid algorithm permutation flow-shop scheduling problem

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An Effective DE-EDA for permutation flow-shop scheduling problem

An Effective DE-EDA for Permutation Flow-shop Scheduling Pro...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Li, Zuo-cheng Guo, Qingxin Tang, Lixin Northeastern Univ Inst Ind Engn & Logist Optimizat Shenyang Peoples R China

ISBN: (纸本)9781509006229

Aiming at the permutation flow-shop scheduling problem (PFSSP) with makespan criterion, a combination algorithm based on differential evolution (DE) and estimation of distribution algorithm (EDA), namely DE-EDA, is proposed. Firstly, DE-EDA combines the probability-dependent macro information extracted by EDA and the individual-dependent micro information obtained by DE to execute the exploration, which is helpful in guiding the global search to explore promising solutions. Secondly, in order to make DE well suited to solve PFSSP, a convert rule named smallest-ranked-value (SRV) is designed to generate the discrete job permutations from the continuous values. Thirdly, a sequence-learning-based Bayes posterior probability is presented to estimate EDA's probability model and sample new solutions, so that the global information of promising search regions can be learned precisely. In addition, a simple but effective two-stage local search is embedded into DE-EDA to perform the exploitation, and thereafter numerous potential solution(s) with relative better fitness can be exploited in some narrow search regions. Finally, simulation experiments and comparisons based on 29 well-known benchmark instances demonstrate the effectiveness of the proposed DE-EDA.

关键词： differential evolution estimation of distribution algorithm probability model permutation flow-shop scheduling problem makespan

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A revised discrete particle swarm optimization algorithm for permutation flow-shop scheduling problem

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SOFT COMPUTING 2014年第11期18卷 2271-2282页

作者： Chen, Chun-Lung Huang, Shin-Ying Tzeng, Yeu-Ruey Chen, Chuen-Lung Takming Univ Sci & Technol Dept Accounting Informat Taipei 114 Taiwan Acad Sinica Res Ctr Informat Technol Innovat Taipei 115 Taiwan Chinese Culture Univ Dept Comp Sci & Informat Engn Taipei 111 Taiwan Natl Chengchi Univ Dept Management Informat Syst Taipei 116 Taiwan

This research proposes a revised discrete particle swarm optimization (RDPSO) to solve the permutation flow-shop scheduling problem with the objective of minimizing makespan (PFSP-makespan). The candidate problem is one of the most studied NP-complete scheduling problems. RDPSO proposes new particle swarm learning strategies to thoroughly study how to properly apply the global best solution and the personal best solution to guide the search of RDPSO. A new filtered local search is developed to filter the solution regions that have been reviewed and guide the search to new solution regions in order to keep the search from premature convergence. Computational experiments on Taillard's benchmark problem sets demonstrate that RDPSO significantly outperforms all the existing PSO algorithms.

关键词： permutation flow-shop scheduling problem Particle swarm optimization Makespan

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Invasive Weed Optimization Algorithm for Solving permutation flow-shop scheduling problem

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2013年第3期10卷 708-713页

作者： Chen, Huan Zhou, Yongquan He, Sucai Ouyang, Xinxin Guo, Peigang Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Peoples R China Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China

An Invasive Weed Optimization (IWO) scheduling algorithm for solving permutation flow-shop scheduling problem (PFSP) is proposed. The Most Position Value (MPV) method is used to coding the weed individuals so that fitness values can be calculated. Then, the global exploration capacity of IWO is used to select the best fitness value and its corresponding processing sequence of job by evaluating the fitness of individuals. The results of 6 PFSP benchmarks compared with other algorithms show that PFSP can be effectively solved by IWO with adaptability and robustness.

关键词： permutation flow-shop scheduling problem Invasive Weed Optimization Most Position Value Method Global Exploration Capacity

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A DISCRETE BUTTERFLY-INSPIRED OPTIMIZATION ALGORITHM FOR SOLVING permutation flow-shop scheduling problemS

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NEURAL NETWORK WORLD 2020年第4期30卷 211-229页

作者： Qi, X. Yuan, Z. Han, X. Liu, S. Shenyang Univ Sch Mech Engn Shenyang 110044 Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China

permutation flow-shop scheduling problem (PFSP) which exists in many manufacturing systems is a classic combinatorial optimization problem. Studies have shown that the PFSP including more than three machines belongs to the NP-hard problems and is difficult to solve. Based on a new bio-inspired algorithm - Artificial Butterfly Optimization (ABO) algorithm, this paper presents a Discrete Artificial Butterfly Optimization (DABO) algorithm to find the permutation that gives the smallest completion time or the smallest total flow time. The performance of the proposed algorithm is tested on well-known benchmark suites of Car, Reeves and Taillard. The experimental results show that the proposed algorithm is able to provide very promising and competitive results on most benchmark functions. The DABO algorithm is then employed for one production optimization problem.

关键词： Artificial Butterfly Optimization artificial bee colony algorithm particle swarm optimization permutation flow-shop scheduling problem

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Deterministic Method for Input Sequence Modification in NEH-Based Algorithms

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IEEE ACCESS 2024年 12卷 68940-68953页

作者： Puka, Radoslaw Skalna, Iwona Lamasz, Bartosz Duda, Jerzy Stawowy, Adam AGH Univ Krakow Fac Management PL-30059 Krakow Poland

scheduling of production jobs falls into the area of planning, which, according to Henri Fayol's conception, is one of the basic functions of management. The permutation flow-shop scheduling problem (PFSP) with makespan criterion is one of the most studied scheduling problems in the area of scheduling theory and applications. The most-known polynomial complexity method for solving this complex problem is the Nawaz-Enscore-Ham (NEH) deterministic constructive algorithm. The subject literature shows that the results of NEH strongly depend on the input sequence of jobs. In this paper, we propose a new method to build the input sequence of jobs for NEH-based heuristics. The proposed Turn-off-Machine (ToM) method and its generalized version ToM+ (which has the feature to produce a set of input sequences that can be used in population-based optimization methods) compute the total processing time of jobs by virtually "turning off" one machine. The ToM+ method is one of a few deterministic methods for modifying the input sequence, and is the first one that modifies the input sequence based on individual machine processing times. Extensive numerical experiments on standard Taillard and VRF benchmarks show the good efficiency of the proposed method in solving PSFP with makespan criterion. The method improved the performance (measured using ARPD) of the NEH-based algorithms by up to nearly 35%. Moreover, by combining ToM+ method, SM alpha P+, N-list, and vN-list technique, it was possible to improve the results of the original NEH algorithm by up to nearly 50% (the method outperformed most of the NEH-based methods). This confirms that creating an adequate input sequence is of great importance for the performance of NEH-based algorithms.

关键词： Heuristics NEH ToM method N-list technique permutation flow-shop scheduling problem makespan

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A Novel Water Wave Optimization based Memetic Algorithm for flow-shop scheduling

A Novel Water Wave Optimization based Memetic Algorithm for ...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Yun, Xin Feng, Xiaoyi Lyu, Xin Wang, Shouyang Liu, Bo Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China

ISBN: (纸本)9781509006229

Recently, Water Wave Optimization (WWO) has been presented as an optimization approach which takes inspiration from shallow wave model considering wind forcing, nonlinear wave interaction, and frictional dissipation. In contrast to WWO dedicated for continuous optimization problems, this paper extends WWO for solving combinatorial permutation flow-shop scheduling problem (PFSSP). In particular, three fundamental search operators in WWO, i.e., propagation, refraction, and breaking have been re-formulated to make WWO suitable for solving combinatorial optimization problem. To enhance the searching performance and efficiency, an improved NEH based algorithm has been applied. Simulation results based on well-known benchmarks and comparisons indicate the competitive advantage of the proposed WWO based memetic algorithm in which the global exploration and the local exploitation are well balanced, providing satisfactory solutions over the state-of-the-art (meta) heuristics such as genetic algorithm for PFSSP.

关键词： Memetic algorithm water wave optimization NEH permutation flow-shop scheduling problem

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A mathematical formulation and anarchic society optimisation algorithms for integrated scheduling of processing and transportation operations in a flow-shop environment

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2015年第19期53卷 5988-6006页

作者： Ahmadi-Javid, Amir Hooshangi-Tabrizi, Pedram Amirkabir Univ Technol Dept Ind Engn Tehran Iran

This paper addresses a permutation flow-shop scheduling problem where there are a finite number of transporters to carry jobs from each machine to its subsequent machine. The problem is first formulated as a mixed-integer linear programme, and then two anarchic society optimisation (ASO) algorithms are developed to solve large-sized instances of the problem. The numerical experience shows that the ASO algorithms are considerably effective and efficient. Finally, a sensitivity analysis is carried out to study the performance of the manufacturing system versus the transportation times and the number of transporters.

关键词： integrated scheduling permutation flow-shop scheduling problem transportation mixed-integer linear programme (MILP) swarm intelligence anarchic society optimisation (ASO) particle swarm optimisation (PSO)

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