A bicriterian flow shop scheduling using artificial neural network

doi:10.1007/S00170-005-0135-5

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A bicriterian flow shop scheduling using artificial neural network

Journal Article•DOI•

A bicriterian flow shop scheduling using artificial neural network

A. Noorul Haq¹, T. Radha Ramanan¹•Institutions (1)

National Institute of Technology, Tiruchirappalli¹

11 Sep 2006-The International Journal of Advanced Manufacturing Technology (Springer-Verlag)-Vol. 30, Iss: 11, pp 1132-1138

TL;DR: The quality of the measure of performance is better when ANN approach is used than obtained by constructive or improvement heuristics, and the system’s efficiency increases with increasing numbers of training exemplars.

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Abstract: This paper considers the sequencing of jobs that arrive in a flow shop in different combinations over time. Artificial neural network (ANN) uses its acquired sequencing knowledge in making the future sequencing decisions. The paper focuses on scheduling for a flow shop with ‘m’ machines and ‘n’ jobs. The authors have used the heuristics proposed by Campbell et al.(1970, A heuristic algorithm for n-jobs m-machines sequencing problem) to find a sequence and makespan (MS). Then a pair wise interchange of jobs is made to find the optimal MS and total flow time (TFT). The obtained sequence is used for giving training to the neural network and a matrix called neural network master matrix (NNMM) is constructed, which is the basic knowledge of the neurons obtained after training. From the matrix, interpretations are made to determine the optimum sequence for the jobs that arrive in the future over a period of time. The results obtained by the ANN are compared with a constructive heuristics and an improvement heuristics. The results show that the quality of the measure of performance is better when ANN approach is used than obtained by constructive or improvement heuristics. It is found that the system’s efficiency (i.e., obtaining the optimal MS and TFT) increases with increasing numbers of training exemplars.

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Journal Article•DOI•

Multi-objective permutation flow shop scheduling problem: Literature review, classification and current trends

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Mehmet Mutlu Yenisey¹, Betul Yagmahan²•Institutions (2)

Istanbul Technical University¹, Uludağ University²

01 Jun 2014-Omega-international Journal of Management Science

TL;DR: A brief literature review of the contributions to MOFSP is provided and areas of opportunity for future research are identified.

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Abstract: The flow shop scheduling problem is finding a sequence given n jobs with same order at m machines according to certain performance measure(s). The job can be processed on at most one machine; meanwhile one machine can process at most one job. The most common objective for this problem is makespan. However, many real-world scheduling problems are multi-objective by nature. Over the years there have been several approaches used to deal with the multi-objective flow shop scheduling problems (MOFSP). Hence, in this study, we provide a brief literature review of the contributions to MOFSP and identify areas of opportunity for future research.

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191 citations

Journal Article•DOI•

Multi-objective production scheduling: a survey

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Deming Lei¹•Institutions (1)

Wuhan University of Technology¹

01 Aug 2009-The International Journal of Advanced Manufacturing Technology

TL;DR: In this article, the authors provide an extensive review of the literature on the scheduling problems with multiple objectives in the past 13 years and present some problems receiving less attention than the others.

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Abstract: The real life scheduling problems often have several conflicting objectives. The solutions of these problems can provide deeper insights to the decision maker than those of single-objective problems. However, the literature of multi-objective scheduling is notably sparser than that of single-objective scheduling. Since the survey paper on multi-objective and bi-objective scheduling was conducted by Nagar et al. in 1995, there has been an increasing interest in multi-objective production scheduling, especially in multi-objective deterministic problem. The goal of this paper was to provide an extensive review of the literature on the scheduling problems with multiple objectives in the past 13 years. This paper also presents some problems receiving less attention than the others.

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128 citations

Cites background from "A bicriterian flow shop scheduling ..."

...... Guner [63] FSSP Two TS, heuristic Setup times Eren and Guner [64] FSSP Two TS, heuristic Learning effect Eren and Guner [65] FSSP Three TS, heuristic, EDD NO Tavakkoli-Moghaddam et al. [67] FSSP Two HMOIA NO Tavakkoli-Moghaddam et al. [68] FSSP Two MOIA NO Li and Zhang [69] FSSP Two ACO NO Yagmahan and Yenisey [70] FSSP Two ACO NO T’kindt et al. [71] FSSP Two ACO NO Ruiz and Allahverdi [72] FSSP Two GA NO Noorul Haq and Rahda ......
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...Noorul Haq and Rahda Ramanan [ 73 ]d emonstrated that neural network approach has very promising properties for real world flow shop sequencing problems....
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Journal Article•DOI•

Multi-objective optimization algorithms for flow shop scheduling problem: a review and prospects

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Yi Sun¹, Chaoyong Zhang¹, Liang Gao¹, Xiaojuan Wang¹•Institutions (1)

Huazhong University of Science and Technology¹

01 Jul 2011-The International Journal of Advanced Manufacturing Technology

TL;DR: A broad description and the complexity of MFSP is introduced, a taxonomy of multi-objective optimizations and an analysis of the publications are presented, and it is noteworthy that heuristic and meta-heuristic methods and hybrid procedures are proven much more useful than other methods in large and complex situations.

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Abstract: Since multi-objective flow shop scheduling problem (MFSP) plays a key role in practical scheduling, there has been an increasing interest in MFSP according to the literature. However, there still have been wide gaps between theories and practical applications, and the review research of multi-objective optimization algorithms in MFSP (objectives > 2) field is relatively scarce. In view of this, this paper provides a comprehensive review of both former and the state-of-the-art approaches on MFSP. Firstly, we introduce a broad description and the complexity of MFSP. Secondly, a taxonomy of multi-objective optimizations and an analysis of the publications on MFSP are presented. It is noteworthy that heuristic and meta-heuristic methods and hybrid procedures are proven much more useful than other methods in large and complex situations. Finally, future research trends and challenges in this field are proposed and analyzed. Our survey shows that algorithms developed for MFSP continues to attract significant research interest from both theoretical and practical perspectives.

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95 citations

Cites background from "A bicriterian flow shop scheduling ..."

...Fm||Cmax,T ANN Noorul Haq and Rahda Ramanan (2006) [104]...
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...The results show that the B&B method proposed here is quite effective in handing MFSP. Lin and Wu [39] paid special attention to Table 1 (continued) Problem Algorithm Reference Fm||Cmax, Fave, I PSO Sha and Lin (2009) [85] Fm|prmu|Cmax, Tmax MOHDE Qian et al. (2006) [26] Fm|nwt|(Cmax, Tmax),(Isum, NT) MADE Qian et al. (2009a) [87] Fm|block|Cmax, Tmax HDE Qian et al. (2009b) [88] Fm|nwt|Cmax, Tmax DE Pan et al. (2009) [89] HFm|prmu|Cmax, F QDEA Zheng andYamashiro (2010) [90] Fm||Cmax,D,Wave,A IA Yang et al. (2002) [92] Fm|nwt|Cave,w,Tave,w HMOIA Moghaddam et al. (2007) [27] Fm|nwt|Cave,w,Tave,w MOIA Moghaddam et al. (2008) [93] Fm|nwt|Cave,w, Eave,w FMOLP Javadi et al. (2008) [112] HFm(PM)||NT, Zpih_cost A lexicographic approach Sawik (2007) [110] Fm||Cmax, F Heuristics Rajendran (1995) [95] F2||Cmax, F Heuristics Gupta et al. (2001) [16] Fm||Cmax, F Heuristics Framinan et al. (2002) [97] Fm||Cmax, F Heuristic Allahverdi (2003) [98] Fm||Cmax, Tmax,F Heuristic Arroyo and Armentano (2004) [100] Fm||Cmax, Tmax Heuristic Allahverdi (2004) [99] Fm||Cmax, T Heuristics Ravindran et al. (2005) [22] Fm|prum|Cmax/T Heuristics Framinan and Leisten (2006) [101] HFm(RM)|SDST|Cmax, NT Heuristic Jungwattanakit et al. (2006) [113] HFm(PM)||Ew, Tw, Ww Meta-heuristics Janiak (2007) [111] HFm(QM)|SDST|Cmax,E,T MO-hybrid meta-heuristic Behnamian et al. (2009) [108] HF2||C,Fave forward and backward simulation Zhang et al. (2002) [106] F2||(F,Cmax), (T,Cmax) Various methods, Weighted functions Gupta et al. (2002) [102] Fm||Cmax,T ANN Noorul Haq and Rahda Ramanan (2006) [104] HF2||(Cmax,Zcost),(Cmax,Zcost,NT,Zq) EA Wei et al. (2006) [107] Fm||Cmax,Tw EMEA Shi and Zhou(2007) [105] Fm|stch|Cmax, T indicator-based EA Figueira et al. (2010) [109] Fm|prmu|many LS Geiger(2007) [103] Fm|nwt|Cave,w, Tave,w MOSS Rahimi-Vahed et al. (2008) [114] Fm|prmu|Eave,w,Tave,w SFLA Rahimi-Vahed et al. (2009) [115] HFm(PM)|SDST|Isum, Cw, Zdue GRASP Davoudpour and Ashrafi (2009) [137] Fm||Cmax,Tw EM Naderi et al. (2010) [117] HFm|SDST|Cmax,Zcost MOHM Behnamian and Fatemi Ghomi (2010) [116] two-machine FSP with the objectives of the makespan and total completion time....
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...Haq and Ramanan [104] described a bi-criteria FSP using artificial neural network (ANN)....
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Journal Article•DOI•

Single-machine-based production scheduling model integrated preventive maintenance planning

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Ershun Pan¹, Wenzhu Liao¹, Lifeng Xi¹•Institutions (1)

Shanghai Jiao Tong University¹

05 Feb 2010-The International Journal of Advanced Manufacturing Technology

TL;DR: In this paper, the authors proposed an integrated scheduling model by incorporating both production scheduling and preventive maintenance planning for a single-machine problem with the objective of minimizing the maximum weighted tardiness.

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Abstract: Manufacturing and production plants operate physical machine that will deteriorate with increased usage and time. Maintenance planning which can keep machines in good operation is thus required for smooth production. However, in previous research, production scheduling and maintenance planning are usually performed individually and not studied as an integrated model. In order to balance the trade-offs between them, this study proposes an integrated scheduling model by incorporating both production scheduling and preventive maintenance planning for a single-machine problem with the objective of minimizing the maximum weighted tardiness. In this model, a variable maintenance time subjected to machine degradation is considered. Finally, a numerical example using this improved production scheduling model is shown. The computational results prove its efficiency.

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93 citations

Cites methods from "A bicriterian flow shop scheduling ..."

...They focused on scheduling for a flow-shop with ‘m’ machines and ‘n’ jobs, and used artificial neural network with acquired scheduling knowledge in making the future sequencing decisions [ 17 ]....
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Journal Article•DOI•

Digital twin-driven cyber-physical production system towards smart shop-floor

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Zhang Haijun¹, Zhang Guohui¹, Yan Qiong¹•Institutions (1)

Zhengzhou University¹

01 Nov 2019-Journal of Ambient Intelligence and Humanized Computing

TL;DR: A new architecture of CPPS is proposed, which is composed of five layers (physical layer, network layer, database layer, model layer, application layer, and the opportunities to use DT for the CPPS to support job scheduling during normal operation are addressed.

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Abstract: Smart manufacturing is the core in the 4th industrial revolution. Smart shop-floor is one of the basic units of smart manufacturing. With the development of the advanced technologies (e.g. cloud computing, internet of things, model-based definition, advanced simulation, artificial intelligence), a larger number of virtual shop-floors are being built. However, it is very important that how to realize the intelligent interconnection and interaction between physical shop-floors and virtual ones. Digital twin (DT) is one of the key technologies associated to the cyber-physical system. In this paper, we present our vision on the cyber-physical production system (CPPS) towards smart shop-floor at scale via DT. This paper firstly explores a product manufacturing digital twin (PMDT), which focuses on the production phase in smart shop-floor. The proposed PMDT consists of five models: Product Definition Model (PDM), Geometric and Shape Model (GSM), Manufacturing Attribute Model (MAM), Behavior and Rule Model (BRM) and Data Fusion Model (DFM). And then based on PMDT, this paper proposes a new architecture of CPPS, which is composed of five layers (physical layer, network layer, database layer, model layer, application layer). Finally, this paper addresses the opportunities to use DT for the CPPS to support job scheduling during normal operation. Furthermore, the related further work and suggestions are also discussed.

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74 citations

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References

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Journal Article•DOI•

Optimal two- and three-stage production schedules with setup times included

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Selmer Martin Johnson¹•Institutions (1)

RAND Corporation¹

01 Mar 1954-Naval Research Logistics Quarterly

TL;DR: A simple decision rule is obtained in this paper for the optimal scheduling of the production so that the total elapsed time is a minimum.

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Abstract: Each of a collection of items are to be produced on two machines (or stages). Each machine can handle only one item at a time and each item must be processed through machine one and then through machine two. The setup time plus work time for each item for each machine is known. A simple decision rule is obtained in this paper for the optimal scheduling of the production so that the total elapsed time is a minimum. A three-machine problem is also discussed and solved for a restricted case.

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3,082 citations

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Jacek M. Zurada

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Abstract: Jacek M. Zurada received his MS and Ph.D. degrees (with distinction) in electrical engineering from the Technical University of Gdansk, Poland. Since 1989 he has been a Professor with the Electrical and Computer Engineering Department at the University of Louisville, Kentucky. He was Department Chair from 2004 to 2006. He has published over 350 journal and conference papers in the areas of neural networks, computational intelligence, data mining, image processing and VLSI circuits. INTRODUCTION TO ARTIFICIAL NEURAL SYSTEMS

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Introduction to sequencing and scheduling

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TL;DR: In this article, the authors present an introduction to Sequencing and Scheduling in the context of the Operational Research Society (ORS) and the International Journal of Distributed Sensor Networks (ILS).

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Journal Article•DOI•

A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem

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Muhammad Nawaz¹, E. Emory Enscore², Inyong Ham²•Institutions (2)

University College of Engineering¹, Pennsylvania State University²

01 Jan 1983-Omega-international Journal of Management Science

TL;DR: A simple algorithm is presented in this paper, which produces very good sequences in comparison with existing heuristics, and performs especially well on large flow-shop problems in both the static and dynamic sequencing environments.

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Abstract: In a general flow-shop situation, where all the jobs must pass through all the machines in the same order, certain heuristic algorithms propose that the jobs with higher total process time should be given higher priority than the jobs with less total process time. Based on this premise, a simple algorithm is presented in this paper, which produces very good sequences in comparison with existing heuristics. The results of the proposed algorithm have been compared with the results from 15 other algorithms in an independent study by Park [13], who shows that the proposed algorithm performs especially well on large flow-shop problems in both the static and dynamic sequencing environments.

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2,255 citations

Journal Article•DOI•

A Heuristic Algorithm for the n Job, m Machine Sequencing Problem

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Herbert G. Campbell¹, Richard A. Dudek¹, Milton L. Smith¹•Institutions (1)

Texas Tech University¹

01 Jun 1970-Management Science

TL;DR: A simple algorithm for the solution of very large sequence problems without the use of a computer that produces approximate solutions to the n job, m machine sequencing problem where no passing is considered and the criterion is minimum total elapsed time.

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Abstract: This paper describes a simple algorithm for the solution of very large sequence problems without the use of a computer. It produces approximate solutions to the n job, m machine sequencing problem where no passing is considered and the criterion is minimum total elapsed time. Up to m-1 sequences may be found.

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921 citations