Mathematical modeling for relocation of terminal facilities in location problems
Subject Areas : Application of soft computing in engineering sciences
1 -
Keywords: Simulated Annealing, Migratory Bird Optimization, Terminal Facilities, Optimization, Taboo Search,
Abstract :
The main goal of terminal facility layout is to place parking lots, areas and different units within predefined limits in such a way as to minimize the cost of moving passengers and employees. Especially in ‘large-scale terminals containing several different specialized departments, it is important for the efficiency of the terminal that the interaction units are located close together. Today, meta-heuristic algorithms are often used to solve optimization problems such as facility layout. Organized using three meta-heuristic algorithms which are Migratory Bird Optimization (MBO), Taboo Search (TS) and Simulated Annealing (SA), the results were compared with the existing parking scheme. As a result, the MBO and SA meta-heuristic algorithms have provided similar best results, which improve the efficiency of the existing parking scheme by approximately 58%.’.
References:
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[1] J. Tompkins, J. White, W. Bozer, J. Tanchoco, Facilities Planning, John Wiley & Sons, New York, USA, 2003.
[2] Sarma, Sisira, Demand for outpatient healthcare, Appl. Health Econ. Health Policy 7 (4) (2009) 265–277.
[3] R.S. Amaral, A new lower bound for the single row facility layout problem, Discrete Appl. Math. 157 (2009) 183–190
[4] M. Mohammadi, K. Forghani, A novel approach for considering layout problem in cellular manufacturing systems with alternative processing routings and subcontracting approach, Appl. Math. Model. 38 (14) (2014) 3624–3640.
[5] A. Barbosa-Povoa, R. Mateus, A. Novais, Optimal two-dimensional layout of industrial facilities, Int. J. Prod. Res. 39 (12) (2001) 2567–2593.
[6] S.P. Singh, R.R. Sharma, A review of different approaches to the facility layout problems, Int. J. Adv. Manuf. Technol. 30 (5–6) (2006) 425–433.
[7] B. Montreuil, A modeling framework for integrating layout design and flow
network design, in: Proceedings of the Material Handling Research Colloquium, 1990, pp. 43–58.
[8] T. Lacksonen, Pre-processing for static and dynamic facility layout problems, Int. J. Prod. Res. 35 (1997) 1095–1106.
[9] F. Azadivar, J. Wang, Facility layout optimization using simulation and genetic algorithms, Int. J. Prod. Res. 38 (17) (2000) 4369–4383.
[10] Y.H. Lee, M.H. Lee, A shape-based block layout approach to facility layout problems using hybrid genetic algorithm, Comput. Ind. Eng. 42 (2) (2002) 237–248.
[11] E. Shayan, A. Chittilappilly, Genetic algorithm for facilities layout problems based on slicing tree structure, Int. J. Prod. Res. 42 (2) (2002) 237–248.
[12] R. Sahin, O. Turkbey, A new hybrid heuristic algorithm for the multi objective facility layout problem, J. Faculty Eng. Archit. Gazi Univ. 25 (1) (2010) 119– 130.
[13] M.Y. Cheng, L.C. Lien, A hybrid AI-based particle bee algorithm for facility layout optimization, Eng. Comput. 28 (1) (2012) 57–69.
[14] Q. Luo, Q. Su, J. Le, L. Lu, in: 10th International Conference on Service Systems and Service Management, IEEE, 2013, pp. 224–227.
[15] Y.Y. Ileri, The Importance of Layout Organization in Hospital Management Efficiency: A Model in S.U. Medical Faculty Hospital, Selcuk University, 2013.
[16] D.T.T. Huyen, N.T. Binh, T.M. Tuan, T.Q. Trung, N.G. Nhu, N. Dey, L.H. Son, Analyzing trends in hospital-cost payments of patients using ARIMA and GIS: case study at the hanoi medical university hospital, Vietnam, J. Med. Imaging
Health Inf. 7 (2) (2017) 421–429.
[17] M.E. Aydin, T.C. Fogarty, A distributed evolutionary simulated annealing algorithm for combinatorial optimisation problems, J. Heuristics 10 (3) (2004) 269–292.
[18] A. Kaveh, P. Sharafi, Charged system search algorithm for minimax and minisum facility layout problems, Asian J. Civ. Eng. 6 (12) (2011) 703–718.
[19] A. Kaveh, M.A.A. Shakouri, M.S. Zolfaghari, An adapted harmony search based algorithm for facility layout optimization, Int. J. Civ. Eng. 1 (10) (2012) 37–42.
[20] K.Y. Chan, M.E. Aydin, T.C. Fogarty, Main effect fine-tuning of the mutation operator and the neighbourhood function for uncapacitated facility location problems, Soft. Comput. 10 (11) (2006) 1075–1090.
[21] J. Yang, M.E. Aydin, J. Zhang, C. Maple, UMTS base station location planning: a mathematical model and heuristic optimisation algorithms, IET Commun. 1 (5) (2007) 1007–1014.
[22] E. Duman, M. Uysal, A.F. Alkaya, Migrating birds optimization: a new metaheuristic approach and its performance on quadratic assignment problem, Inf. Sci. 217 (2012) 65–77.
[23] V. Tongur, E. Ülker, Migrating birds optimization for flow shop sequencing problem, J. Comput. Commun. 2 (04) (2014) 142.
[24] A. Sioud, C. Gagné, Enhanced migrating birds optimization algorithm for the permutation flow shop problem with sequence dependent setup times, Eur. J. Oper. Res. 264 (1) (2018) 66–73.
[25] B. Zhang, Q.K. Pan, L. Gao, X.L. Zhang, H.Y. Sang, J.Q. Li, An effective modified migrating birds optimization for hybrid flowshop scheduling problem with lot streaming, Appl. Soft Comput. 52 (2017) 14–27.
[26] T. Meng, Q.K. Pan, J.Q. Li, H.Y. Sang, An improved migrating birds optimization for an integrated lot-streaming flow shop scheduling problem, Swarm Evol. Comput. 38 (2018) 64–78.
[27] Q.K. Pan, Y. Dong, An improved migrating birds optimisation for a hybrid flowshop scheduling with total flowtime minimisation, Inf. Sci. 277 (2014) 643–655.
[28] K.Z. Gao, P.N. Suganthan, T.J. Chua, in: An Enhanced Migrating Birds Optimization Algorithm for No-Wait Flow Shop Scheduling Problem, IEEE, 2013, pp. 9–13.
[29] E. Duman, I. Elikucuk, Solving credit card fraud detection problem by the new metaheuristics migrating birds optimization62–71, International Work- Conference on Artificial Neural Networks, Springer, Berlin, Heidelberg, 2013.
[30] E. Ulker, V. Tongur, Migrating birds optimization (MBO) algorithm to solve knapsack problem, Proc. Comput. Sci. 111 (2017) 71–76.
[31] V. Tongur, E. Ülker, The analysis of migrating birds optimization algorithm with neighborhood operator on traveling salesman problem, in: Intelligent and Evolutionary Systems, Springer, Cham, 2016, pp. 227–237.
[32] V. Tongur, E. Ülker, PSO-based improved multi-flocks migrating birds optimization (IMFMBO) algorithm for solution of discrete problems, Soft. Comput. 23 (14) (2019) 5469–5484.
[33] M. Hacibeyoglu, K. Alaykiran, A.M. Acilar, V. Tongur, E. Ulker, A Comparative Analysis of metaheuristic approaches for multidimensional two-way number partitioning problem, Arabian J. Sci. Eng. 43 (12) (2018) 7499–7520.
[34] Z. Li, M.N. Janardhanan, A.S. Ashour, N. Dey, Mathematical models and migrating birds optimization for robotic U-shaped assembly line balancing problem, Neural Comput. Appl. (2019) 1–17.
[35] F. Glover, Tabu search part I., ORSA J. Comput. 1 (3) (1989) 190–206.
[36] F.A.T. Montané, D.G. Roberto, A tabu search algorithm for the vehicle routing problem with simultaneous pick-up and delivery service, Comput. Oper. Res. 33 (3) (2006) 595–619.
[37] J. Grabowski, W. Mieczyslaw, A very fast tabu search algorithm for the permutation flow shop problem with makespan criterion, Comput. Oper. Res. 31 (11) (2004) 1891–1909.
[38] C.N. Fiechter, A parallel tabu search algorithm for large traveling salesman problems, Discrete Appl. Math. 51 (3) (1994) 243–267.
[39] S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, Optimization by simulated annealing, Am. Assoc. Adv. Sci. 220 (4598) (1983) 671–680.
[40] N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, E. Teller, Equation of state calculations by fast computing machines, J. Chem. Phys. 21 (6) (1953) 1087–1092.
[41] H. Bagherlou, A. Ghaffari, A routing protocol for vehicular ad hoc networks using simulated annealing algorithm and neural networks, J. Supercomput. 74 (6) (2018) 2528–2552.
[42] L. Wei, Z. Zhang, D. Zhang, S.C. Leung, A simulated annealing algorithm for the capacitated vehicle routing problem with two-dimensional loading constraints, Eur. J. Oper. Res. 265 (3) (2018) 843–859.
[43] K. Karagul, Y. Sahin, E. Aydemir, A. Oral, A simulated annealing algorithm based solution method for a green vehicle routing problem with fuel consumption, in: Lean and Green Supply Chain Management, Springer, Cham, 2019, pp. 161–187.
[44] M.M. Mafarja, S. Mirjalili, Hybrid whale optimization algorithm with simulated annealing for feature selection, Neurocomputing 260 (2017) 302– 312.
[45] S.H. Zhan, J. Lin, Z.J. Zhang, Y.W. Zhong, List-based simulated annealing algorithm for traveling salesman problem, Comput. Intell. Neurosci. 2016 (2016) 8.
[46] S. Kulturel-Konak, A. Konak, A large-scale hybrid simulated annealing algorithm for cyclic facility layout problems, Eng. Optim. 47 (7) (2015) 963– 978.
[47] N. Shivasankaran, P.S. Kumar, K.V. Raja, Hybrid sorting immune simulated annealing algorithm for flexible job shop scheduling, Int. J. Comput. Intell. Syst. 8 (3) (2015) 455–466.
[48] S. García, D. Molina, M. Lozano, F. Herrera, A study on the use of nonparametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization, J. Heuristics 15 (6) (2009) 617.
