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A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops

INFORMATION SCIENCES 2011年第16期181卷 3459-3475页

作者： Tasgetiren, M. Fatih Pan, Quan-Ke Suganthan, P. N. Chen, Angela H-L Liaocheng Univ Coll Comp Sci Liaocheng 252059 Peoples R China Yasar Univ Dept Ind Engn Izmir Turkey Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Nanya Inst Technol Dept Finance Tao Yuan 320 Taiwan

Obtaining an optimal solution for a permutation flowshop scheduling problem with the total flowtime criterion in a reasonable computational timeframe using traditional approaches and optimization tools has been a challenge. This paper presents a discrete artificial bee colony algorithm hybridized with a variant of iterated greedy algorithms to find the permutation that gives the smallest total flowtime. iterated greedy algorithms are comprised of local search procedures based on insertion and swap neighborhood structures. In the same context, we also consider a discrete differential evolution algorithm from our previous work. The performance of the proposed algorithms is tested on the well-known benchmark suite of Taillard. The highly effective performance of the discrete artificial bee colony and hybrid differential evolution algorithms is compared against the best performing algorithms from the existing literature in terms of both solution quality and CPU times. Ultimately, 44 out of the 90 best known solutions provided very recently by the best performing estimation of distribution and genetic local search algorithms are further improved by the proposed algorithms with short-term searches. The solutions known to be the best to date are reported for the benchmark suite of Taillard with long-term searches, as well. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Permutation flowshop scheduling problem iterated greedy algorithm Discrete differential evolution algorithm Discrete artificial bee colony algorithm Estimation of distribution algorithm Genetic local search

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Effective heuristics and metaheuristics to minimise total tardiness for the distributed permutation flowshop scheduling problem

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2021年第23期59卷 7266-7282页

作者： Khare, Ankit Agrawal, Sunil Indian Inst Informat Technol Design & Mfg PDPM Jabalpur Madhya Pradesh India

During recent years, the distributed permutation flowshop scheduling problem (DPFSP) has become a very active area of research. However, minimising total tardiness in DPFSP, a very essential and relevant objective for today's customer-orientated market, has not been studied much. In this paper, we address the DPFSP with the total tardiness criterion. We present a mixed-integer linear programming model, two heuristics, hybrid discrete Harris hawks optimisation and an enhanced variant of iterated greedy algorithm to solve the considered problem. Problem-specific knowledge is explored and effective technologies, such as path relinking and random sub-sequence/single-point local search, are employed to improve the presented algorithms. The operators and parameters of the algorithms are analysed and calibrated using the design of experiments. To evaluate the performance, the well-known benchmark problem set of Naderi and Ruiz for DPFSP is extended with due dates. We compare the presented algorithms against seven other well-known meta-heuristics from the literature. Statistically sound results demonstrate the effectiveness of the presented algorithms for the considered problem.

关键词： Distributed scheduling total tardiness mixed integer linear programming Harris hawks optimisation algorithm iterated greedy algorithm path relinking

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Metaheuristics for two-stage flow-shop assembly problem with a truncation learning function

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ENGINEERING OPTIMIZATION 2021年第5期53卷 843-866页

作者： Wu, Chin-Chia Zhang, Xingong Azzouz, Ameni Shen, Wei-Lun Cheng, Shuenn-Ren Hsu, Peng-Hsiang Lin, Win-Chin Feng Chia Univ Dept Stat Taichung Taiwan Chongqing Normal Univ Coll Math Sci Chongqing Peoples R China Univ Tunis Inst Superieur Gest SMART Lab Tunis Tunisia Cheng Shiu Univ Grad Inst Business Adm Kaohsiung Taiwan Univ Kang Ning Dept Business Adm Taipei Taiwan

This study examines a two-stage three-machine flow-shop assembly scheduling model in which job processing time is considered as a mixed function of a controlled truncation parameter with a sum-of-processing-times-based learning effect. However, the truncation function is very limited in the two-stage flow-shop assembly scheduling settings. To overcome this limitation, this study investigates a two-stage three-machine flow-shop assembly problem with a truncation learning function where the makespan criterion (completion of the last job) is minimized. Given that the proposed model is NP hard, dominance rules, lemmas and a lower bound are derived and applied to the branch-and-bound method. A dynamic differential evolution algorithm, a hybrid greedy iterated algorithm and a genetic algorithm are also proposed for searching approximate solutions. Results obtained from test experiments validate the performance of all the proposed algorithms.

关键词： Two-stage assembly dynamic differential evolution algorithm genetic algorithm 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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METSM: Multiobjective energy-efficient task scheduling model for an edge heterogeneous multiprocessor system

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 152卷 207-223页

作者： Jiang, Qiangqiang Xin, Xu Yao, Libo Chen, Bo Harbin Inst Technol Inst Space Sci & Appl Technol Univ Town ShenzhenTaoyuan St Shenzhen 518055 Guangdong Peoples R China Hong Kong Polytech Univ Fac Business Dept Logist & Maritime Studies Hong Kong 999077 Peoples R China Naval Aviat Univ Inst Informat Fus 188 Erma Rd Yantai 264000 Shandong Peoples R China

ABS T R A C T Along with the growth of computing requirements, edge devices, such as satellites and unmanned aerial vehicles, are equipped with heterogeneous multiprocessor systems to cope with complicated missions, including attitude control, signal processing, and objective detection. Concerning the extremely limited power on these devices, reducing the energy and time overhead of task execution evolves into a crucial challenge. Therefore, a multiobjective energy-efficient task scheduling technique (METSM) is proposed. First, a mathematical model is established for the energy-efficient task scheduling problem on edge heterogeneous multiprocessor systems. In this model, both makespan and total energy consumption are the optimization objectives. The decision variables include the task execution sequence, processor assignment, and dynamic voltage and frequency scaling level for each processor. Second, a problem-specific algorithm, namely, iterated greedy-based multiobjective optimizer (IMO), is proposed. Specifically, destruction-reconstruction and local search are redesigned for optimizing task scheduling and resource allocation. Considering local optima avoidance, a probabilistic mutation operation is developed. In addition, multiobjective-oriented strategies of optimal solution selection and acceptance criteria are adopted to accelerate the convergence. Finally, our proposed IMO is compared with several of the latest algorithms through multiple performance metrics. The experimental results show that IMO can obtain optimal Pareto fronts among several multiobjective methods. Savings of approximately 10% and 12% in time and power consumption, respectively, can be achieved by IMO. Moreover, in comparison with classic list-based heuristics for solving test cases, while IMO maintains a similar makespan, energy is reduced by nearly 90%.

关键词： Heterogeneous multiprocessor system Task scheduling Energy efficiency Multiobjective optimization iterated greedy algorithm Edge device

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A Job Sequence Optimization Approach for Parallel Machine Scheduling Problem in Printing Manufacturing Systems

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IEEE ACCESS 2024年 12卷 63462-63476页

作者： Li, Huailin Zheng, Yingying Sun, Bangyong Du, Bin Xian Univ Technol Fac Printing Packaging Engn & Digital Media Techno Xian 710048 Peoples R China Shaanxi Prov Key Lab Printing & Packaging Engn Xian 710048 Peoples R China

This paper investigated a parallel machine scheduling problem in the printing manufacturing systems due to operational requirements on the color-batching of the printed matter and sequential requirements on the sequence adherence of a printing press. Resequencing printing jobs as color-oriented batches reduced the costs of color changes and operational costs for printing shop. Also, post press workshop required printing shops to print jobs with minimal makespan so that high sequence adherence with its demand is assured. Based on real-world applications, we investigated two contradictory objectives-color change costs and minimal makespan-in a parallel high-fidelity printing press scheduling environment. A job sequence optimization approach is proposed. Moving interpolation algorithm and color sequence mapping algorithm are designed to reduce frequency of replacing ink. Base on iterative greedy algorithm, color sequence job groups with the same or similar color sequence are scheduled to the printing presses. Three experiments are conducted and showed that the proposed approach has good feasibility and effectiveness, and convergence in solving minimum completion time. Therefore, the proposed approach can be used in printing shops to improve scheduling efficiency and reduce production costs.

关键词： Printing manufacturing systems parallel machine scheduling problem minimal makespan color sequence comparison iterated greedy algorithm

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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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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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Managing Uncertainty and Disruptions in Resource Constrained Project Scheduling Problems: A Real-Time Reactive Approach

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IEEE ACCESS 2021年第99期9卷 45562-45586页

作者： Rahman, Md. Humyun Fuad Chakrabortty, Ripon K. Ryan, Michael J. UNSW Canberra ADFA Sch Engn & Informat Technol Capabil Syst Ctr Campbell ACT 2600 Australia

Resource constrained project scheduling problems (RCPSPs) are complex optimization problems that aim to minimize project completion time after considering limited resources and precedence-related activities with known durations. However, due to the dynamic nature of real-world applications, activity durations are vulnerable to change. In addition, resource demands along with resource availability at any stage of a project can also vary due to disruptions, which compel practitioners to re-think existing scheduling methodologies. Consequently, to mitigate the conjoint effect of those uncertainties and/or disruptions, this paper proposes a real-time reactive scheduling approach. To deal with uncertain activity durations, a chance constrained based approach is followed, which is later solved by an advanced meta-heuristic based approaches called IGFBIS and IGFBID. After solving an exhaustive list of stochastic RCPSP instances, this paper proves the efficacy of the proposed approaches, which is proven to be more useful to mitigate uncertainty or disruption while executing real-life projects.

关键词： Uncertainty Job shop scheduling Schedules Risk management Heuristic algorithms Electric breakdown Dynamic scheduling Scheduling real-life projects project scheduling with uncertainty and disruption iterated greedy algorithm chance constrained approach

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Accelerated methods for total tardiness minimisation in no-wait flowshops

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2015年第4期53卷 1002-1018页

作者： Ding, Jianya Song, Shiji Zhang, Rui Gupta, Jatinder N. D. Wu, Cheng Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Nanchang Univ Sch Econ & Management Nanchang Peoples R China Univ Alabama Coll Business Adm Huntsville AL 35899 USA

For the minimisation of total tardiness in no-wait flowshops, objective incremental properties are investigated in this paper to speed up the evaluation of candidate solutions. To explore the properties, we introduce a new concept of sensitive jobs and identify through experiments that the proportion of such jobs is very small. Instead of evaluating the tardiness of each job, we focus on the evaluation of sensitive jobs which will help to reduce the computational efforts. With these properties, the time complexity of the NEH-insertion procedure is reduced from [GRAPHICS] to approximately [GRAPHICS] in average. Then, an accelerated NEH algorithm and an accelerated iterated greedy algorithm are designed for the problem. Since the NEH-insertion procedure constitutes the main computational burden for both algorithms, these algorithms will benefit directly from the speedup. Numerical computations show that the accelerated algorithms perform 10-40 times faster than the original algorithms on the middle- and large-sized instances. In addition, comparisons show that the proposed algorithms perform more efficiently and effectively than the existing heuristics and meta-heuristics.

关键词： no-wait flowshop NEH-insertion total tardiness iterated greedy algorithm objective incremental properties

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