|
|
Journal of Zhejiang University SCIENCE C
ISSN 1869-1951(Print), 1869-196x(Online), Monthly
2014 Vol.15 No.12 P.1138-1146
Fuzzy cost-profit tradeoff model for locating a vehicle inspection station considering regional constraints
Abstract: Facility location allocation (FLA) is one of the important issues in the logistics and transportation fields. In practice, since customer demands, allocations, and even locations of customers and facilities are usually changing, the FLA problem features uncertainty. To account for this uncertainty, some researchers have addressed the fuzzy profit and cost issues of FLA. However, a decision-maker needs to reach a specific profit, minimizing the cost to target customers. To handle this issue it is essential to propose an effective fuzzy cost-profit tradeoff approach of FLA. Moreover, some regional constraints can greatly influence FLA. By taking a vehicle inspection station as a typical automotive service enterprise example, and combined with the credibility measure of fuzzy set theory, this work presents new fuzzy cost-profit tradeoff FLA models with regional constraints. A hybrid algorithm integrating fuzzy simulation and genetic algorithms (GA) is proposed to solve the proposed models. Some numerical examples are given to illustrate the proposed models and the effectiveness of the proposed algorithm.
Key words: Cost-profit tradeoff, Credibility theory, Fuzzy simulation, Fuzzy programming, Genetic algorithm
Chinese Summary <54> 考虑区域约束的车辆检测站选址模糊费用—利润均衡模型
创新要点：考虑到汽车检测站选址即网点布局的不确定性，为更切合地描述实际情况，引入检测车辆数量为模糊变量的汽车检测站模糊选址问题，以充分反映专家评估的偏见。另外，考虑到自然环境限制或政策限定等因素，构建了保证投资商获得一定利润、且检测用户总运输费用最低的均衡模型。
方法提亮：建立反映选址实际情况的模糊费用—利润均衡模型，提出应用融合模糊模拟和遗传算法的混合智能算法进行求解分析。
重要结论：求解结果表明所提方法不仅很好地描述了专家评估的偏见，且和传统的确定性求解方法结果基本一致，说明所构建的模型有效。
关键词组：
Recommended Papers Related to this topic:
References:
[1]Ani, O.B., Xu, H., Shen, Y.P., et al., 2013. Modeling and multiobjective optimization of traction performance for autonomous wheeled mobile robot in rough terrain. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 14(1):11-29.
[2]Arabani, B., Farahani, R.Z., 2012. Facility location dynamics: an overview of classifications and applications. Comput. Ind. Eng., 62(1):408-420.
[3]Arish, S., Amiri, S., Noori, K., 2014. FICA: fuzzy imperialist competitive algorithm. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 15(5):363-371.
[4]Arostegui, M.A.Jr., Kadipasaoglu, S.N., Khumawala, B.M., 2006. An empirical comparison of tabu search, simulated annealing, and genetic algorithms for facilities location problems. Int. J. Prod. Econ., 103(2):742-754.
[5]Badri, M.A., 1999. Combining the analytic hierarchy process and goal programming for global facility location-allocation problem. Int. J. Prod. Econ., 62(3):237-248.
[6]Church, R., ReVelle, C., 1974. The maximal covering location problem. Papers Reg. Sci. Assoc., 32(1):101-118.
[7]Dai, J.Y., 2010. Study on Optimization Problem for Vehicle Detection Station Network Layout. MS Thesis, Jilin University, China (in Chinese).
[8]Daskin, M.S., 1995. Network and Discrete Location: Models, Algorithms, and Applications. Wiley, New York, NY.
[9]Drezner, Z., Hamacher, H., 2002. Facility Location: Applications and Theory. Springer-Verlag, Berlin.
[10]Ernst, A.T., Krichnamoorthy, M., 1999. Solution algorithms for the capacitated single allocation hub location problem. Ann. Oper. Res., 86:141-159.
[11]Escavy, J.I., Herrero, M.J., 2013. The use of location-allocation techniques for exploration targeting of high place-value industrial minerals: a market-based prospectivity study of the Spanish gypsum resources. Ore Geol. Rev., 53:504-516.
[12]Farahani, R.Z., Hekmatfar, M., 2009. Facility Location: Concepts, Models, Algorithms and Case Studies. Physica-Verlag, Heidelberg.
[13]Farahani, R.Z., SteadieSeifi, M., Asgari, N., 2010. Multiple criteria facility location problems: a survey. Appl. Math. Model., 34(7):1689-1709.
[14]Farahani, R.Z., Asgari, N., Heidari, N., et al., 2012. Covering problems in facility location: a review. Comput. Ind. Eng., 62(1):368-407.
[15]Gong, D., Gen, M., Xu, W., et al., 1995. Hybrid evolutionary method for obstacle location-allocation problem. Int. J. Comput. Ind. Eng., 29(1-4):525-530.
[16]Huang, Y., Lin, L., 2014. Designing a location update strategy for free-moving and network-constrained objects with varying velocity. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 15(8):675-686.
[17]Ke, H., Liu, B., 2010. Fuzzy project scheduling problem and its hybrid intelligent algorithm. Appl. Math. Model., 34(2):301-308.
[18]Klose, A., Drexl, A., 2005. Facility location models for distribution system design. Eur. J. Oper. Res., 162(1):4-29.
[19]Kuenne, R.E., Soland, R.M., 1972. Exact and approximate solutions to the multisource Weber problem. Math. Program., 3(1):193-209.
[20]Li, Z., Tian, G.D., Cheng, G., et al., 2014. An integrated cultural particle swarm algorithm for multi-objective reliability-based design optimization. Proc. Inst. Mech. Eng. C, 228(7):1185-1196.
[21]Liu, B., 2004. Uncertainty Theory: an Introduction to Its Axiomatic Foundations. Springer-Verlag, Berlin.
[22]Liu, B., 2006. A survey of credibility theory. Fuzzy Optim. Dec. Making, 5(4):387-408.
[23]Liu, B., 2010. Uncertainty Theory: a Branch of Mathematics for Modeling Human Uncertainty. Springer, Berlin.
[24]Liu, B., Liu, Y.K., 2002. Expected value of fuzzy variable and fuzzy expected value models. IEEE Trans. Fuzzy Syst., 10(4):445-450.
[25]Logendran, R., Terrell, M.P., 1988. Uncapacitated plant location-allocation problems with price sensitive stochastic demands. Comput. Oper. Res., 15(2):189-198.
[26]Lu, Z.Q., Bostel, N., 2007. A facility location model for logistics systems including reverse flows: the case of remanufacturing activities. Comput. Oper. Res., 34(2):299-323.
[27]Min, H., Melachrinoudis, E., Wu, X., 1997. Dynamic expansion and location of an airport: a multiple objective approach. Transp. Res. Part A, 31(5):403-417.
[28]Mousavi, S.M., Niaki, S.T.A., 2013. Capacitated location allocation problem with stochastic location and fuzzy demand: a hybrid algorithm. Appl. Math. Model., 37(7):5109-5119.
[29]Murray, A.T., Church, R.L., 1996. Applying simulated annealing to location-planning models. J. Heur., 2(1):31-53.
[30]Nickel, S., Puetro, J., 2005. Location Theory: a Unified Approach. Springer-Verlag Berlin Heidelberg.
[31]O’Kelly, M.E., 1987. A quadratic integer program for the location of interacting hub facilities. Eur. J. Oper. Res., 32(3):393-404.
[32]Qiang, T.G., Tian, G.D., Chu, J.W., et al., 2013. Location analysis of vehicle inspection station based on NN-GA. Adv. Inf. Sci. Serv. Sci., 5(4):622-629.
[33]Rajagopalan, H.K., Saydam, C., Xiao, J., 2008. A multi-period set covering location model for dynamic redeployment of ambulances. Comput. Oper. Res., 35(3):814-826.
[34]Sahin, G., Sural, H., 2007. A review of hierarchical facility location models. Comput. Oper. Res., 34(8):2310-2331.
[35]Taaffe, K., Geunes, J., Romeijn, H.E., 2010. Supply capacity acquisition and allocation with uncertain customer demands. Eur. J. Oper. Res., 204(2):263-273.
[36]Tian, G.D., Liu, Y., 2014. Energy-efficient models of sustainable location for a vehicle inspection station with emission constraints. IEEE Trans. Autom. Sci. Eng.,
[37]Tian, G.D., Chu, J.W., Liu, Y.M., et al., 2011. Expected energy analysis for industrial process planning problem with fuzzy time parameters. Comput. Chem. Eng., 35(12):2905-2912.
[38]Tian, G.D., Zhou, M.C., Chu, J.W., et al., 2012. Probability evaluation models of product disassembly cost subject to random removal time and different removal labor cost. IEEE Trans. Autom. Sci. Eng., 9(2):288-295.
[39]Tian, G.D., Chu, J.W., Hu, H.S., et al., 2014a. Technology innovation system and its integrated structure for automotive components remanufacturing industry development in China. J. Cleaner Prod., 85(15):419-432.
[40]Tian, G.D., Zhou, M.C., Chu, J.W., et al., 2014b. Stochastic cost-profit tradeoff model for locating an automotive service enterprise. IEEE Trans. Autom. Sci. Eng., (99):1-8.
[41]Vasko, F.J., Newhart, D.D., Stott, K.L., et al., 2003. A large-scale application of the partial coverage uncapacitated facility location problem problem. J. Oper. Res. Soc., 54(1):11-20.
[42]Wang, K.J., Makond, B., Liu, S.Y., 2011. Location and allocation decisions in a two-echelon supply chain with stochastic demand: a genetic-algorithm based solution. Expert Syst. Appl., 38(5):6125-6131.
[43]Wang, S., Watada, J., 2010. Recourse-based facility location problems in hybrid uncertain environment. IEEE Trans. Syst. Man Cybern. B, 40(4):1176-1187.
[44]Wang, S., Watada, J., 2012. A hybrid modified PSO approach to VaR-based facility location problems with variable capacity in fuzzy random uncertainty. Inf. Sci., 192:3-18.
[45]Wang, S., Watada, J., Pedrycz, W., 2009. Value-at-risk-based two-stage fuzzy facility location problems. IEEE Trans. Ind. Inf., 5(4):465-482.
[46]Wen, M., Iwamura, K., 2008. Fuzzy facility location-allocation problem under the Hurwicz criterion. Eur. J. Oper. Res., 184(2):627-635.
[47]Zhou, J., Liu, B., 2003. New stochastic models for capacitated location-allocation problem. Comput. Ind. Eng., 45(1):111-125.
[48]Zhou, J., Liu, B., 2007. Modeling capacitated location-allocation problem with fuzzy demands. Comput. Ind. Eng., 53(3):454-468.
Open peer comments: Debate/Discuss/Question/Opinion
<1>
DOI:
10.1631/jzus.C1400116
CLC number:
TP271
Download Full Text:
Downloaded:
3821
Download summary:
<Click Here>Downloaded:
2903Clicked:
9424
Cited:
5
On-line Access:
2024-08-27
Received:
2023-10-17
Revision Accepted:
2024-05-08
Crosschecked:
2014-11-09
Tel: +86-571-87952276; Fax: +86-571-87952331; E-mail: jzus@zju.edu.cn
Copyright © 2000~ Journal of Zhejiang University-SCIENCE