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A Variable Block Insertion Heuristic for Solving Permutation Flow Shop Scheduling Problem with Makespan Criterion

algorithmS 2019年第5期12卷 100-100页

作者： Kizilay, Damla Tasgetiren, Mehmet Fatih Pan, Quan-Ke Gao, Liang Yasar Univ Dept Ind Engn TR-35100 Izmir Turkey Istinye Univ Dept Ind & Syst Engn TR-34010 Istanbul Turkey Huazhong Univ Sci & Technol Dept Ind & Mfg Syst Engn Wuhan 430074 Hubei Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China

In this paper, we propose a variable block insertion heuristic (VBIH) algorithm to solve the permutation flow shop scheduling problem (PFSP). The VBIH algorithm removes a block of jobs from the current solution. It applies an insertion local search to the partial solution. Then, it inserts the block into all possible positions in the partial solution sequentially. It chooses the best one amongst those solutions from block insertion moves. Finally, again an insertion local search is applied to the complete solution. If the new solution obtained is better than the current solution, it replaces the current solution with the new one. As long as it improves, it retains the same block size. Otherwise, the block size is incremented by one and a simulated annealing-based acceptance criterion is employed to accept the new solution in order to escape from local minima. This process is repeated until the block size reaches its maximum size. To verify the computational results, mixed integer programming (MIP) and constraint programming (CP) models are developed and solved using very recent small VRF benchmark suite. Optimal solutions are found for 108 out of 240 instances. Extensive computational results on the VRF large benchmark suite show that the proposed algorithm outperforms two variants of the iterated greedy algorithm. 236 out of 240 instances of large VRF benchmark suite are further improved for the first time in this paper. Ultimately, we run Taillard's benchmark suite and compare the algorithms. In addition to the above, three instances of Taillard's benchmark suite are also further improved for the first time in this paper since 1993.

关键词： heuristic optimization block insertion heuristic flow shop scheduling iterated greedy algorithm constraint programming mixed integer programming

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A Lexicographic Bi-objective Scheduling Problem From Steel Production Systems

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IFAC-PapersOnLine 2020年第5期53卷 158-163页

作者： Ziyan Zhao MengChu Zhou Shixin Liu Xiwang Guo Haoyue Liu State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Northeastern University Shenyang 110819 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ 07102 USA College of Computer and Communication Engineering Liaoning Shihua University Fushun 113001 China

Wire rod and bar rolling is an essential process in a steel production system (SPS). This work considers a lexicographic bi-objective scheduling problem originated from it. As a medium process in SPS, both the impacts of its upstream process, i.e., release time constraints, and the requirements of its downstream process, i.e., due time constraints, on SPS performance, must be considered. The concerned problem aims to minimize the number of tardy tasks as a main objective and total setup time as a secondary one. It is solved via a two-stage method. The first-stage problem is handled with an off-the-shelf optimization software, and the second one with iterated greedy algorithms (IGAs). We compare the performance of IGAs under different strategies and select the best one to solve it. By comparing the experimental results an exact single-objective method’s, this paper shows that the proposed method can solve the problem fast with excellent results in terms of the number of tardy tasks and setup time, i.e., the secondary objective, on average. Its usage can result in significant performance improvement in steel production over the existing practice.

关键词： Batch process batch scheduling iterated greedy algorithm lexicographic bi-objective optimization steel production systems

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Scheduling the distributed assembly flowshop problem to minimize the makespan

Scheduling the distributed assembly flowshop problem to mini...

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International Conference on ENTERprise Information Systems (CENTERIS) / International Conference on Project MANagement (ProjMAN) / International Conference on Health and Social Care Information Systems and Technologies (HCist)

作者： Ochi, Hanadi Driss, Olfa Belkahla Inst Super Gest Tunis 41 Ave Liberte Tunis Tunisia Univ Manouba Ecole Super Commerce Tunis Tunis Tunisia Univ Manoouba COSMOS Lab ENSI Manouba Tunisia

In this paper, we studied the distributed assembly permutation flowshop scheduling problem (DAPFSP) with the makespan minimization as objective. We proposed iterated greedy-based approach that is labelled the bounded-search iterated greedy algorithm BSIG. In addition, four local search methods are designed to improve solution quality. The computational results show the effectiveness of BSIG in solving the DAPFSP specially with small instances where BSIG outperforms most of the existing algorithms. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the CENTERIS -International Conference on ENTERprise Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health and Social Care Information Systems and Technologies.

关键词： distributed assembly permutation flowshop scheduling problem (DAPFSP) iterated greedy algorithm optimization makespan

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Fast heuristics for minimizing the makespan in non-permutation flow shops

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COMPUTERS & OPERATIONS RESEARCH 2018年 100卷 230-243页

作者： Benavides, Alexander J. Ritt, Marcus Univ Nacl San Agustin Arequipa Escuela Profes Ciencia Comp Ave Venezuela S-N Area Ingn Arequipa Peru Univ Fed Rio Grande do Sul Inst Informat Ave Bento Gonalves 9500 BR-91501970 Porto Alegre RS Brazil

We introduce a new permutation representation for non-permutation schedules, and show how the acceleration technique of Taillard can be extended to it. We propose three new heuristics for the non permutation flow shop scheduling problem with makespan minimization: a constructive heuristic with the same time complexity as the well-known NEH heuristic, a non-permutation insertion local search with a time complexity O(n(2)m) for evaluating a neighbourhood on n jobs and m machines, the same as for the permutation insertion local search, and a reduced-neighbourhood best-improvement non permutation local search with a time complexity of O(nm) per neighbourhood. These heuristics are combined into iterated greedy algorithms for the permutation and non-permutation flow shop scheduling problem. In extensive computational experiments we find that our iterated greedy algorithms produce better non-permutation schedules than other methods for the permutation and non-permutation flow shop scheduling problem, in the same computational time. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Scheduling Metaheuristics Flow shop Non-permutation schedules Makespan iterated greedy algorithm

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A discrete Water Wave Optimization algorithm for no-wait flow shop scheduling problem

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 91卷 347-363页

作者： Zhao, Fuqing Liu, Huan Zhang, Yi Ma, Weimin Zhang, Chuck Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Gansu Peoples R China Xijin Univ Sch Mech Engn Xian 710123 Shaanxi Peoples R China Tongji Univ Sch Econ & Management Shanghai 200092 Peoples R China Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

In this paper, a discrete Water Wave Optimization algorithm (DWWO) is proposed to solve the no-wait flowshop scheduling problem (NWFSP) with respect to the makespan criterion. Inspired by the shallow water wave theory, the original Water Wave Optimization (WWO) is constructed for global optimization problems with propagation, refraction and breaking operators. The operators to adapt to the combinatorial optimization problems are redefined. A dynamic iterated greedy algorithm with a changing removing size is employed as the propagation operator to enhance the exploration ability. In refraction operator, a crossover strategy is employed by DWWO to avoid the algorithm falling into local optima. To improve the exploitation ability of local search, an insertion-based local search scheme which is utilized as breaking operator, is applied to search for a better solution around the current optimal solution. A ruling out inferior solution operator is also introduced to improve the convergence speed. The global convergence performance of the DWWO is analyzed with the Markov model. In addition, the computational results based on well-known benchmarks and statistical performance comparisons are presented. Experimental results demonstrate the effectiveness and efficiency of the proposed DWWO algorithm for solving NWFSP. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Water Wave Optimization (WWO) iterated greedy algorithm No-wait flow shop scheduling problem Makespan

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Bicriterion total flowtime and maximum tardiness minimization for an order scheduling problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 117卷 152-163页

作者： Wu, Chin-Chia Liu, Shang-Chia Lin, Tzu-Yun Yang, Tzu-Hsuan Chung, I-Hong Lin, Win-Chin Feng Chia Univ Dept Stat Taichung 40724 Taiwan Fujen Catholic Univ Dept Business Adm Hsinpei City Taiwan

An order scheduling problem arises in numerous production scheduling environments. Makespan, mean flow time, and mean tardiness are the most commonly discussed and studied measurable criteria in the research community. Although the order scheduling model with a single objective has been widely studied, it is at odds with real-life scheduling practices. In practice, a typical manager must optimize multiple objectives. A search of the literature revealed that no articles had addressed the issue of optimizing an order scheduling problem with multiple objectives. Therefore, an order scheduling model to minimize the linear sum of the total flowtime and the maximum tardiness is introduced in this study. Specifically, several dominance relations and a lower bound are derived to expedite the search for the optimal solution. Three modified heuristics are proposed for finding near-optimal solutions. A hybrid iterated greedy algorithm and a particle swarm colony algorithm are proposed to solve this problem. Finally, a computational experiment is conducted to evaluate the performances of all proposed algorithms.

关键词： Order scheduling iterated greedy algorithm Particle swarm optimization algorithm Total completion time Maximum tardiness

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Scheduling automated transport vehicles for material distribution systems

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APPLIED SOFT COMPUTING 2019年 82卷 105552-000页

作者： Rahman, Humyun Fuad Nielsen, Izabela Univ New South Wales Sch Engn & IT Canberra ACT Australia Aalborg Univ Dept Mat & Prod Aalborg Denmark

The rise of advanced manufacturing technology has led to enhanced, efficient material handling equipment in manufacturing and container terminal environments. In order to exploit the full potential of advanced material handling equipment in real-world industrial environments, novel scheduling approaches capable of ensuring integrated operations for multiple automated transport vehicles need to be developed. This research develops a methodology for scheduling automated transport vehicles to ensure the smooth flow of materials in production and container terminal environments. The procedure consists of a mixed-integer programming model and two meta-heuristic-based algorithms that are proposed to achieve quality schedules within a reasonable amount of time. The obtained results show a significant reduction in the earliness or lateness of material delivery tasks and an improvement in operational performance, demonstrating that the proposed approaches are capable of ensuring smooth material distribution by scheduling automated transport vehicles in an integrated manner. (C) 2019 Elsevier B.V. All rights reserved.

关键词： ATV scheduling Material distribution Genetic algorithm iterated greedy algorithm

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New meta-heuristic for dynamic scheduling in permutation flowshop with new order arrival

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2018年第5-8期98卷 1817-1830页

作者： Liu, Weibo Jin, Yan Price, Mark China Univ Petr Acad Chinese Energy Strategy Beijing 102249 Peoples R China Queens Univ Belfast Sch Mech & Aerosp Engn Ashby Bldg Belfast BT9 5AH Antrim North Ireland

Permutation flowshop scheduling problem (PFSP) has attracted lots of attention from both academia and industry as it finds many applications especially for today's mass customization. The PFSP is proved NP-hard, and the dynamic uncertainties such as stochastic new order arrivals significantly increase the problem complexity and difficulty. Many enterprises often struggle to make decisions on accepting new orders and setting due dates for them due to lack of effective scheduling methods. To fill in the knowledge gap, this paper is to propose a new meta-heuristic algorithm which is based on a new enhanced destruction and construction method and a novel repair method while adopting the architecture of the iterated greedy algorithm. Statistical tests were conducted and results show that the new algorithm outperforms existing ones.

关键词： Dynamic scheduling New order Permutation flowshop iterated greedy algorithm Order mixing

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Scheduling the distributed assembly flowshop problem to minimize the makespan

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Procedia Computer Science 2019年 164卷 471-477页

作者： Hanadi Ochi Olfa Belkahla Driss Institut Supérieur de Gestion de Tunis 41 Avenue de liberté Cité Bouchoucha Bardo Tunis Tunisia Ecole Supérieure de Commerce de Tunis Université de La Manouba Tunis Tunisia COSMOS laboratory ENSI Université de La Manoouba

关键词： distributed assembly permutation flowshop scheduling problem (DAPFSP) iterated greedy algorithm optimization makespan

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Green Permutation Flowshop Scheduling: A Trade- off- Between Energy Consumption and Total Flow Time 14th

Green Permutation Flowshop Scheduling: A Trade- off- Between...

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14th International Conference on Intelligent Computing (ICIC)

作者： Oztop, Hande Tasgetiren, M. Fatih Eliiyi, Deniz Tursel Pan, Quan-Ke Yasar Univ Dept Ind Engn Bornova Turkey Yasar Univ Dept Int Logist Management Bornova Turkey Huazhong Univ Sci & Technol State Key Lab Wuhan Hubei Peoples R China

ISBN: (纸本)9783319959573;9783319959566

Permutation flow shop scheduling problem (PFSP) is a well-known problem in the scheduling literature. Even though many multi-objective PFSPs are presented in the literature with the objectives related to production efficiency and customer satisfaction, studies considering energy consumption and environmental effects in scheduling is very seldom. In this paper, the trade-off between total energy consumption (TEC) and total flow time is investigated in a PFSP environment, where the machines are assumed to operate at varying speed levels. A multi-objective mixed integer linear programming model is proposed based on a speed-scaling strategy. Due to the NP-complete nature of the problem, an efficient multi-objective iterated greedy (IGALL) algorithm is also developed. The performance of IGALL is compared with model performance in terms of quality and cardinality of the solutions.

关键词： Permutation Flowshop Scheduling Energy efficient scheduling Multi-objective optimization iterated greedy algorithm Heuristic optimization

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