تعداد نشریات | 27 |
تعداد شمارهها | 565 |
تعداد مقالات | 5,815 |
تعداد مشاهده مقاله | 8,126,728 |
تعداد دریافت فایل اصل مقاله | 5,442,732 |
MULTI-OBJECTIVE ROUTING AND SCHEDULING IN FLEXIBLE MANUFACTURING SYSTEMS UNDER UNCERTAINTY | ||
Iranian Journal of Fuzzy Systems | ||
مقاله 4، دوره 14، شماره 2، تیر 2017، صفحه 45-77 اصل مقاله (579.4 K) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22111/ijfs.2017.3133 | ||
نویسندگان | ||
Ahmad Mehrabian؛ Reza Tavakkoli-Moghaddam ![]() ![]() | ||
Department of Industrial Engineering, South-Tehran Branch, Islamic Azad University, Tehran, Iran | ||
چکیده | ||
The efficiency of transportation system management plays an important role in the planning and operation efficiency of flexible manufacturing systems. Automated Guided Vehicles (AGV) are part of diversified and advanced techniques in the field of material transportation which have many applications today and act as an intermediary between operating and storage equipment and are routed and controlled by an intelligent computer system. In this study, a two-objective mathematical programming model is presented to integrate flow shop scheduling and routing AVGs in a flexible manufacturing system. In real-life problems parameters like demand, due dates and processing times are always uncertain. Therefore, in order to solve a realistic problem, foregoing parameters are considered as fuzzy in our proposed model. Subsequently, to solve fuzzy mathematical programming model, one of the most effective technique in the literature is used. To solve the problem studied, two meta-heuristic algorithms of Non-dominated Sorting Genetic Algorithm-II (NSGAII) and multi-objective particle swarm optimization (MOPSO) are offered that the accuracy of mathematical models and efficiency of algorithms provided are assessed through numerical examples. | ||
کلیدواژهها | ||
Scheduling؛ Routing؛ Automated guided vehicle؛ meta-heuristic algorithm؛ Flexible manufacturing | ||
مراجع | ||
[1] A. Asef-Vaziri, N. G. Hall and R. George, The signicance of deterministic empty vehicle trips in the design of a unidirectional loop ow path, Computers & Operations Research, 35(5) (2008), 1546-1561. [2] W. X. Bing, The application of analytic process of resource in an AGV scheduling, Computers & industrial engineering, 35(1) (1998), 169-172. [3] J. Blazewicz, R. E. Burkard, G. Finke and G. J. Woeginger, Vehicle scheduling in two-cycle exible manufacturing systems, Mathematical and computer modelling, 20(2) (1994), 19-31. [4] I. A. Chaudhry, S. Mahmood and M. Shami, Simultaneous scheduling of machines and au- tomated guided vehicles in exible manufacturing systems using genetic algorithms, Journal of Central South University of Technology, 18(5) (2011), 1473-1486. [5] C. A. C. Coello, G. T. Pulido and M. S. Lechuga, Handling multiple objectives with particle swarm optimization, Evolutionary Computation, IEEE Transactions on, 8(3) (2004), 256- 279. [6] A. I. Correa, A. Langevin and L. M. Rousseau, Scheduling and routing of automated guided vehicles: A hybrid approach, Computers & operations research, 34(6) (2007), 1688-1707. [7] K. Deb, Multi-objective optimization using evolutionary algorithms, John Wiley & Sons, 16 (2001). [8] M. Desrochers, J. Desrosiers and M. Solomon, A new optimization algorithm for the vehicle routing problem with time windows, Operations research, 40(2) (1992), 342-354. [9] G. Desaulniers, A. Langevin, D. Riopel and B. Villeneuve, Dispatching and con ict-free routing of automated guided vehicles: An exact approach, International Journal of Flexible Manufacturing Systems, 15(4) (2003), 309-331. [10] M. E. Dogan and I. E. Grossmann, A decomposition method for the simultaneous planning and scheduling of single-stage continuous multiproduct plants, Industrial & engineering chemistry research, 45(1) (2006), 299-315. [11] I. G. Drobouchevitch and V. A.Strusevich, Heuristics for the two-stage job shop scheduling problem with a bottleneck machine, European journal of operational research,123(2) (2000), 229-240. [12] G. El Khayat, A. Langevin and D. Riopel, Integrated production and material handling scheduling using mathematical programming and constraint programming, European Journal of Operational Research, 175(3) (2006), 1818-1832. [13] H. Fazlollahtabar, B. Rezaie, and H. Kalantari, Mathematical programming approach to op- timize material ow in an AGV-based exible jobshop manufacturing system with perfor- mance analysis, The International Journal of Advanced Manufacturing Technology, 51(9-12) (2010), 1149-1158. [14] H. Fazlollahtabar and M. Saidi-Mehrabad, Methodologies to optimize automated guided ve- hicle scheduling and routing problems: a review study, Journal of Intelligent & Robotic Systems,77(3-4) (2013), 525-545. [15] H. Fazlollahtabar, M. Saidi-Mehrabad and J. Balakrishnan, Mathematical optimization for earliness/tardiness minimization in a multiple automated guided vehicle manufacturing sys- tem via integrated heuristic algorithms, Robotics and Autonomous Systems, 2015. [16] M. L. Fisher, K. O. ornsten and O. B. Madsen, Vehicle routing with time windows: Two optimization algorithms, Operations Research, 45(3) (1997), 488-492. [17] N. Gans and G. Van Ryzin, Dynamic vehicle dispatching: Optimal heavy trac performance and practical insights, Operations Research, 47(5) (1999), 675-692. [18] M. Gen, R. Cheng, and L. Lin, Advanced planning and scheduling models, Network Models and Optimization: Multiobjective Genetic Algorithm Approach, (2008), 297-417. [19] H. H. Gotting, Automation and Steering of Vehicles in ports, Port Technology International, 10 (2000), 101-111. [20] A. L. Guirida and R. Nagi, Fuzzy set theory applications in production management re- search: a literature survey, J. Intell. Manuf., 9 (1998), 39-56. [21] R. Hamana, M. Konishi and J. Imai, Simultaneous optimization of production and transporta- tion planning by using logic cut algorithm, Memoirs of the Faculty of Engineering, Okayama University, 41(1) (2007), 31-43. [22] M. Hamzeei, R. Z. Farahani and H. Rashidi-Bejgan, An exact and a simulated annealing algorithm for simultaneously determining ow path and the location of P/D stations in bidi- rectional path, Journal of Manufacturing Systems, 32(4) (2013), 648-654. [23] S. Hartmann, A general framework for scheduling equipment and manpower at container terminals, Springer Berlin Heidelberg, (2005), 207-230. [24] C. F. Hsueh, A simulation study of a bi-directional load-exchangeable automated guided ve- hicle system, Computers & Industrial Engineering, 58(4) (2010), 594-601. [25] O. R. Ilic, Analysis of the number of automated guided vehicles required in exible manu- facturing systems, The International Journal of Advanced Manufacturing Technology, 9(6) (1994), 382-389. [26] N. Jawahar, P. Aravindan, S. G. Ponnambalam and R. K Suresh, AGV schedule integrated with production in exible manufacturing systems,The International Journal of Advanced Manufacturing Technology, 14(6) (1998), 428-440. [27] J. Jerald, P. Asokan, R. Saravanan and A. D. C. Rani, Simultaneous scheduling of parts and automated guided vehicles in an FMS environment using adaptive genetic algorithm, The International Journal of Advanced Manufacturing Technology, 29(5-6) (2006), 584-589. [28] M. Jimenez, M. Arenas, A. Bilbao and M. V. Rodriguez, Linear programming with fuzzy parameters: an interactive method resolution, European Journal of Operational Research, 177 (2007), 1599-1609. [29] N. Kohl, J. Desrosiers, O. B. Madsen, M. M. Solomon and F. Soumis, 2-path cuts for the vehicle routing problem with time windows, Transportation Science, 33(1) (1999), 101-116. [30] P. Lacomme, A. Moukrim and N. Tchernev, Rsolution conjointe de problmes dordon- nancement et de la gestion du chariot loguid: couplage Branch and Bound-simulation a evenements discrets, Congres ROADEF, (2000), 128-129. [31] A. Langevin, D. Lauzon and D. Riopel, Dispatching, routing, and scheduling of two auto- mated guided vehicles in a exible manufacturing system, International Journal of Flexible Manufacturing Systems, 8(3) (1996), 247-262. [32] T. E. Liang, Application of interactive possibilistic linear programming to aggregate pro- duction planning with multiple imprecise objectives, Production Planning & Control, 18(7) (2007), 548-560. [33] W. A. Lodwick and K. D. Jamison, Theoretical and semantic distinctions of fuzzy, possi- bilistic, and mixed fuzzy/possibilistic optimization, Fuzzy Sets and Systems, 158(17) (2007), 1861-1872. [34] F. Mahmoodi, C. T. Mosier and J. R. Morgan, The eects of scheduling rules and rout- ing exibility on the performance of a random exible manufacturing system, International journal of exible manufacturing systems, 11(3) (1999), 271-289. [35] P. J. M. Meersmans, Optimization of container handling systems, Erasmus School of Economics (ESE)., 2002. [36] P. J. Meersmans and A. P. Wagelmans, Eective algorithms for integrated scheduling of handling equipment at automated container terminals, 2001. [37] C. Moon and Y. Seo, Evolutionary algorithm for advanced process planning and scheduling in a multi-plant, Computers & Industrial Engineering,48(2) (2005), 311-325. [38] C. Moon, Y. Seo, Y. Yun and M. Gen, Adaptive genetic algorithm for advanced planning in manufacturing supply chain, Journal of Intelligent Manufacturing, 17(4) (2006), 509-522. [39] T. Muller, Automated Guided Vehicles, IFS (Publications) Ltd./Springer-Verlag, UK/Berlin, 1983. [40] M. Nageswararao, K. N. Rao and G. Rangajanardhana, Integration of strategic tactical and operational level planning of scheduling in FMS by metaheuristic algorithm, International Journal of Advanced Engineering Research and Studies, I (II), 2012. [41] T. Nishi, Y. Hiranaka and I. E. Grossmann, A bilevel decomposition algorithm for simultane- ous production scheduling and con ict-free routing for automated guided vehicles, Computers & Operations Research, 38(5) (2011), 876-888. [42] M. A. Parra, A. B. Terol, B. P. Gladish and M. V. Rodriguez Uria, Solving a multiobjective possibilistic problem through compromise programming, European Journal of Operational Research, 164 (2005), 748-759. [43] M. L. Pinedo, Scheduling: theory, algorithms, and systems, Springer Science & Business Media. [44] M. S. Pishvaee and S. A. Torabi, A possibilistic programming approach for closed-loop supply chain network design under uncertainty, Fuzzy Sets and Systems, 161 (2010), 26682683. [45] L. Qiu and W. J. Hsu, A bi-directional path layout for con ict-free routing of AGVs, International Journal of Production Research, 39(10) (2001), 2177-2195. [46] S. Rajotia, K. Shanker and J. L. Batra, A semi-dynamic time window constrained routeing strategy in an AGV system, International Journal of Production Research, 36(1) (1998a), 35-50. [47] S. Rajotia, K. Shanker and J. L. Batra, Determination of optimal AGV eet size for an FMS, International Journal of Production Research, 36(5) (1998b), 1177-1198. [48] H. Rashidi and E. P. Tsang, A complete and an incomplete algorithm for automated guided vehicle scheduling in container terminals, Computers & Mathematics with Applications, 61(3) (2011), 630-641. [49] M. Saidi-Mehrabad, S. Dehnavi-Arani, F. Evazabadian and V. Mahmoodian, An Ant Colony Algorithm (ACA) for solving the new integrated model of job shop scheduling and con ict-free routing of AGVs, Computers & Industrial Engineering, 2015. [50] A. Salehipour, H. Kazemipoor and L. M. Naeini, Locating workstations in tandem automated guided vehicle systems, The International Journal of Advanced Manufacturing Technology, 52(1-4) (2011), 321-328. [51] A. J. Sanchez-Salmeron, R. Lopez-Tarazon, R. Guzman-Diana and C. Ricolfe-Viala, An inter- machine material handling system for micro-manufacturing based on using a standard car- rier, The International Journal of Advanced Manufacturing Technology, 47(9-12) (2010), 937-943. [52] B. R. Sarker and S. S. Gurav, Route planning for automated guided vehicles in a manufac- turing facility, International journal of production research, 43(21) (2005), 4659-4683. [53] M. Savelsbergh and M. Sol, DRIVE: Dynamic routing of independent vehicles, Operations Research, 46(4) (1998), 474-490. [54] D. Sinriech and L. Palni, Scheduling pickup and deliveries in a multiple-load discrete carrier environment, IIE transactions, 30(11) (1998), 1035-1047. [55] D. Sinriech and J. Kotlarski, A dynamic scheduling algorithm for a multiple-load multiple- carrier system, International Journal of Production Research, 40(5) (2002), 1065-1080. [56] D. Spensieri, J. S. Carlson, F. Ekstedt, and R. Bohlin, An iterative approach for collision free routing and scheduling in multirobot stations, IEEE Transactions on Automation Science and Engineering, 13(2) (2016), 950-962. [57] G. Taguchi, System of experimental design; engineering methods to optimize quality and minimize costs, No. 04; QA279, T3, (1987). [58] E. Taillard, Benchmarks for basic scheduling problems, European Journal of Operational Research, 64(2) (1993), 278-285. [59] J. A.Tompkins, J. A. White, Y. A. Bozer, and J. M. A. Tanchoco, Facilities planning, John Wiley & Sons, 2010. [60] Y. Tanaka, T. Nishi and M. Inuiguchi, Dynamic optimization of simultaneous dispatching and con ict-free routing for automated guided vehicles-Petri net decomposition approach, Journal of Advanced Mechanical Design, Systems, and Manufacturing, 4(3) (2010), 701-715. [61] P. Udhayakumar and S. Kumanan, Task scheduling of AGV in FMS using non-traditional optimization techniques, International Journal of Simulation Modelling, 9(1) (2010), 28-39. [62] G. Ulusoy, F. Sivrikaya-Serifo^glu and Bilge, U. A genetic algorithm approach to the simul- taneous scheduling of machines and automated guided vehicles, Computers & Operations Research, 24(4) (1997), 335-351. [63] B. Vahdani, Vehicle positioning in cell manufacturing systems via robust optimization, Applied Soft Computing, 24 (2014), 78-85. [64] M. C. Van der Heijden, M. Ebben, N. Gademann, and A. van Harten, Scheduling vehicles in automated transportation systems Algorithms and case study, OR spectrum,24(1) (2002a), 31-58. [65] M. C. Van der Heijden, A. Van Harten, M. J. R. Ebben, Y. A. Saanen, E. C. Valentin, and A. Verbraeck, Using simulation to design an automated underground system for transporting freight around Schiphol Airport, Interfaces, 32(4) (2002b), 1-19. [66] B. Veeravalli, G. Rajesh and N. Viswanadham, Design and analysis of optimal material distribution policies in exible manufacturing systems using a single AGV, International journal of production research, 40(12) (2002), 2937-2954. [67] I. F. Vis and I. Harika, Comparison of vehicle types at an automated container terminal, OR Spectrum, 26(1) (2004), 117-143. [68] I. F. Vis, Survey of research in the design and control of automated guided vehicle systems, European Journal of Operational Research, 170(3) (2006), 677-709. [69] K. Vivaldini, L. F. Rocha, N. J. Martarelli, M. Becker and A. P. Moreira, Integrated tasks assignment and routing for the estimation of the optimal number of AGVS, The International Journal of Advanced Manufacturing Technology, 82(1-4) (2016), 719-736. [70] N. Wu, and M. Zhou, Modeling and deadlock control of automated guided vehicle systems, Mechatronics, IEEE/ASME Transactions on,9(1) (2004), 50-57. [71] C. H. Yang, Y. S. Choi and T. Y. Ha, Simulation-based performance evaluation of transport vehicles at automated container terminals, Or Spectrum,26(2) (2004), 149-170. [72] J. W. Yoo, E. S. Sim, C. Cao and J. W. Park, An algorithm for deadlock avoidance in an AGV System, The International Journal of Advanced Manufacturing Technology, 26(5-6) (2005), 659-668. [73] M. B. ZA Remba, A. Obuchowicz, Z. A. Banaszak and K. J. Jed Rzejek, A max-algebra approach to the robust distributed control of repetitive AGV systems, International journal of production research, 35(10) (1997), 2667-2688. | ||
آمار تعداد مشاهده مقاله: 2,978 تعداد دریافت فایل اصل مقاله: 1,717 |