IJE TRANSACTIONS C: Aspects Vol. 29, No. 12 (December 2016) 1691-1703    Article in Press

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N. Nahavandi and M. Abbasian
( Received: March 18, 2016 – Accepted in Revised Form: June 14, 2016 )

Abstract    Nowadays energy saving is one of the crucial aspects in decisions. One of the approaches in this case is efficient use of resources in the industrial systems. Studies in real manufacturing systems indicating that one or more machines may also act as the Bottleneck Resource/ Resources (BR). On the other hand according to the Theory of Constraints (TOC), the efficient use of resources in manufacturing systems is limited by the capacity of the BR(s). Hence, in order to improveing such systems performance, the BR(s) should be identified and assessed and improved the using capacity of such resources to the greatest extent possible. Studies indicating that Bottleneck Resource Detection (BRD) problem in the “Multi-Objective and the Dynamic conditions” of job-shop is an important issue which has not been studied in the previous literature due to its computational complexity. Hence the development of an efficient approach to identify and assess BRs in Multi-objective Dynamic Job Shop (MODJS) has been considered as the subject of this paper. In this article, a BRD method based on the Taguchi method for MODJS (TM-MODJS) has been developed. The mentioned method takes the objectives of the MODJS as estimated indices and carries out typical and finite number of experiments by combining different suitable dispatching rules to detect BR(s) which have the greatest effect on the estimated index. Comparing the results indicates effectiveness of the developed method especially in scheduling results in a reasonable time.


Keywords    Energy Saving, Multi-Objective Dynamic Job Shop (MODJS), Thory of Constraint (TOC), Bottleneck Resource (BR), Bottleneck Resource Detection (BRD).


چکیده    امروزه صرفه‌جویی در انرژی یکی از وجوه اساسی در تصمیم‌گیری‌ها محسوب می‌شود. یکی از راه‌کارهای مهم در این زمینه، بهره‌برداری کارآ از منابع تولیدی در محیط‌های صنعتی است. مطالعات انجام‌گرفته در سیستم‌های ساخت و تولید واقعی حاکی از این است که در این محیط‌ها و در اغلب مواقع، یک یا مجموعه‌ای از ماشین‌ها به عنوان گلوگاه (BR) عمل می‌نمایند. از سویی دیگر بر اساس تئوری محدودیت‌ها (TOC) بهره‌گیری کارآمد از منابع تولیدی در سیستم‌های تولیدی بر اساس ظرفیت منبع/ منابع گلوگاهی محدود می‌شود. از اینرو بمنظور بهبود عملکرد چنین سیستم‌هایی، بایستی اقدام به شناسایی، ارزیابی و بهبود عملکرد (تا حد ممکن) چنین منابع ارزشمندی نمود. مطالعات حاکی از این است که مسئله شناسایی منابع گلوگاهی در محیط‌های کارکارگاهی پویای چندهدفی، از جمله مسائلی است که به دلیل پیچیدگی محاسباتی کمتر در ادبیات مورد بررسی قرار گرفته‌اند. از اینرو توسعۀ رویکردی کارآمد بمنظور شناسایی گلوگاه(ها) در محیط‌های کارکارگاهی پویای چندهدفی، هدف این تحقیق علمی می‌باشد. در این مقاله، یک روش شناسایی منابع گلوگاهی مبتنی بر روش تاگوچی برای محیط‌های کارکارگاهی پویای چندهدفی (تحت عنوان TM-MODJS) توسعه داده شده است. روش مذکور اهداف مسئله را به عنوان شاخص برآوردی درنظر گرفته و آزمایشات محدود و نمونه‌ای را با ترکیب قواعد توزیعی مختلف برای حل مسئله شناسایی منابع گلوگاهی در محیط‌های کارکارگاهی پویای چندهدفی، ارائه می‌دهد. مقایسۀ نتایج روش پیشنهادی حاکی از کارآمدی بالای آن از بُعد معیارهایی نظیر نرخ بهبود در نتایج زمان‌بندی در یک زمان معقول است.


1.      Zhang, R. and Wu, C., "Bottleneck machine identification method based on constraint transformation for job shop scheduling with genetic algorithm", Information Sciences,  Vol. 188, (2012), 236-252.

2.      Gupta, M., Ko, H.-J. and Min, H., "Toc-based performance measures and five focusing steps in a job-shop manufacturing environment", International Journal of Production Research,  Vol. 40, No. 4, (2002), 907-930.

3.      Hinckeldeyn, J., Dekkers, R., Altfeld, N. and Kreutzfeldt, J., "Expanding bottleneck management from manufacturing to product design and engineering processes", Computers & Industrial Engineering,  Vol. 76, (2014), 415-428.

4.      Lima, E., Chwif, L. and Barreto, M.R.P., "Metodology for selecting the best suitable bottleneck detection method", in Proceedings of the 40th conference on winter simulation, Winter Simulation Conference., (2008), 1746-1751.

5.      Roser, C., Nakano, M. and Tanaka, M., "A practical bottleneck detection method", in Proceedings of the 33nd conference on Winter simulation, IEEE Computer Society., (2001), 949-953.

6.      Roser, C., Nakano, M. and Tanaka, M., "Productivity improvement: Shifting bottleneck detection", in Proceedings of the 34th conference on Winter simulation: exploring new frontiers, Winter Simulation Conference., (2002), 1079-1086.

7.      Zhai, Y., Sun, S., Wang, J. and Niu, G., "Job shop bottleneck detection based on orthogonal experiment", Computers & Industrial Engineering,  Vol. 61, No. 3, (2011), 872-880.

8.      Yan, Z., Hanyu, G. and Yugeng, X., "Modified bottleneck-based heuristic for large-scale job-shop scheduling problems with a single bottleneck", Journal of Systems Engineering and Electronics,  Vol. 18, No. 3, (2007), 556-565.

9.      Tay, J.C. and Ho, N.B., "Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems", Computers & Industrial Engineering,  Vol. 54, No. 3, (2008), 453-473.

10.    Sengupta, S., Das, K. and VanTil, R.P., "A new method for bottleneck detection", in Proceedings of the 40th conference on Winter simulation, Winter Simulation Conference., (2008), 1741-1745.

11.    Zhang, R. and Wu, C., "Bottleneck identification procedures for the job shop scheduling problem with applications to genetic algorithms", The International Journal of Advanced Manufacturing Technology,  Vol. 42, No. 11-12, (2009), 1153-1164.

12.    Li, L., Chang, Q. and Ni, J., "Data driven bottleneck detection of manufacturing systems", International Journal of Production Research,  Vol. 47, No. 18, (2009), 5019-5036.

13.    Kasemset, C. and Kachitvichyanukul, V., "Bi-level multi-objective mathematical model for job-shop scheduling: The application of theory of constraints", International Journal of Production Research,  Vol. 48, No. 20, (2010), 6137-6154.

14.    Zhai, Y., Sun, S., Wang, J. and Guo, S., "A heuristic algorithm for large-scale job shop scheduling based on operation decomposition using bottleneck machine", in Management and Service Science (MASS), International Conference on, IEEE., (2010), 1-4.

15.    Zhai, Y., Sun, S., Wang, J. and Wang, M., "An effective bottleneck detection method for job shop", in Computing, Control and Industrial Engineering (CCIE), International Conference on, IEEE. Vol. 2, , (2010), 198-201.

16.    Abbasian, M. and Nahavandi, N., "Minimization flow time in a flexible dynamic job shop with parallel machines", Engineering Department of Industrial Engineering, Master of Science Thesis,  (2009).

17.    Abbasian, M. and Nahavandi, N., "Solving multi-objective flexible dynamic job-shop scheduling problem with improved genetic algorithm", International Journal of Industrial Engineering of Production Research,  Vol. 3, No. 21, (2011).

18.    Scholz-Reiter, B., Hildebrandt, T. and Tan, Y., "Effective and efficient scheduling of dynamic job shops—combining the shifting bottleneck procedure with variable neighbourhood search", CIRP Annals-Manufacturing Technology,  Vol. 62, No. 1, (2013), 423-426.

19.    Pinedo, M. and Singer, M., "A shifting bottleneck heuristic for minimizing the total weighted tardiness in a job shop", Naval Research Logistics,  Vol. 46, No. 1, (1999), 1-17.

20.    Georgiadis, P. and Politou, A., "Dynamic drum-buffer-rope approach for production planning and control in capacitated flow-shop manufacturing systems", Computers & Industrial Engineering,  Vol. 65, No. 4, (2013), 689-703.

21.    Glock, C.H. and Jaber, M.Y., "Learning effects and the phenomenon of moving bottlenecks in a two-stage production system", Applied Mathematical Modelling,  Vol. 37, No. 18, (2013), 8617-8628.

22.    Abbasian, M., Eslami, H. and Fazlollahtabar, H., "Applying an intelligent dynamic genetic algorithm for solving a multi-objective flexible job shop scheduling problem with maintenance considerations", Journal of Applied & Computational Mathematics,  Vol. 4, No. 4, (2015).

23.    Fraley, S., Oom, M., Terrien, B. and Date, J., "Design of experiments via taguchi methods: Orthogonal arrays", The Michigan chemical process dynamic and controls open text book, USA,  Vol. 2, No. 3, (2006), 4-12.

24.    Haupt, R., "A survey of priority rule-based scheduling", Operations-Research-Spektrum,  Vol. 11, No. 1, (1989), 3-16.

25.    Singh, M.R. and Mahapatra, S., "A quantum behaved particle swarm optimization for flexible job shop scheduling", Computers & Industrial Engineering,  Vol. 93, (2016), 36-44.

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