Vehicle Scheduling Model for Fresh Agriculture Products Pickup with Uncertain Demands

Abstract:

A chance-constrained vehicle scheduling model for fresh agriculture products pickup with uncertain demands is proposed in this paper. The uncertain measure that vehicle loading will not exceed capacity constraint is presented in the model because of the uncertainty of demands. Based on uncertainty theory, when the demands are some special uncertain variables with uncertainty distribution such as linear, zigzag and normal uncertain distribution etc., the model can be transformed to a deterministic form and solved by genetic algorithm. When the demands are general uncertain variables, a hybrid genetic algorithm with uncertain simulation is presented to obtain the optimal solution. At last, to illustrate the effective of the model and algorithm, and to analyze the impact of parameters on model solution, an experiment is provided.

• Periodical: Advanced Materials Research (Volume 974)
• Pages: 282-287
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.974.282
• Online since: June 2014
• Authors: Li Xia Rong*, Huan Bin Sha
• Keywords: Fresh Agriculture Products, Hybrid Genetic Algorithm, Uncertain Variable, Vehicle Scheduling Model

References

[1] N. Li, S. Wang, Y. Li: A Hybrid Approach of GA and ACO for VRP. Journal of Computational Information Systems. Vol. 7 (2011), p.4939.

[2] K. Wang, C. Ye: The Competitive Decision Algorithm for the Vehicle Routing Problem with Simultaneous Delivery and Pickup. Journal of Computational Information Systems. Vol. 9 (2013), p.3189.

[3] C. D. J. Waters: Vehicle-scheduling problems with uncertainty and omitted customers. Journal of the Operational Research Society. Vol. 40 (1989), p.1099.

DOI: 10.2307/2582919

[4] M. Dror, G. Laporte, P. Trudreau: Vehicle routing with stochastic demands: Properties and solution frameworks. Transportation science. Vol. 23 (1989), p.166.

DOI: 10.1287/trsc.23.3.166

[5] C. Bastian, K.A. Rinnooy: The stochastic vehicle routing problem revisited. European Journal of Operational Research. Vol. 56 (1992), p.407.

DOI: 10.1016/0377-2217(92)90323-2

[6] B.D. Liu, K.K. Lai: Stochastic programming models for vehicle routing problems. Focus on computational neurobiology. Nova Science Publishers, Inc. Vol. 1 (2004), p.13.

[7] R. Cheng, M. Gen, T. Tozawa: Vehicle routing problem with fuzzy due-time using genetic algorithm. Japanese Journal Fuzzy Theoy System. Vol. 7 (1995), p.1050.

DOI: 10.3156/jfuzzy.7.5_1050

[8] Y. Zheng, B. Liu: Fuzzy vehicle routing model with credibility measure and its hybrid intelligent algorithm. Applied Mathematics and Computation. Vol. 176 (2006), p.673.

DOI: 10.1016/j.amc.2005.10.013

[9] J.Y. Zhang, J. Li: A hybrid genetic algorithm of fuzzy vehicle routing problem. Journal of Engineering Management. Vol. 19 (2005), p.23, In Chinese.

[10] E.B. Cao, M.Y. Lai: Vehicle routing problem study with fuzzy demands. System Engineering. Vol. 25 (2007), p.14, In Chinese.

[11] L.X. Rong: Fuzzy simulation of vehicle routing problem under condition of fuzzy demand. Computer Engineering Application. Vol. 46 (2010), p.209, In Chinese.

[12] C.I. Hsu, S.F. Hung, H.C. Li: Vehicle routing problem with time-windows for perishable food dilivery. Journal of Food Engineering. Vol. 80 (2007), p.465.

DOI: 10.1016/j.jfoodeng.2006.05.029

[13] A. Osvald, L.Z. Stirn: A vehicle routing algorithm for the distribution of fresh vegetables and dimilar perishable food. Journal of food engineering. Vol. 85 (2008), p.285.

DOI: 10.1016/j.jfoodeng.2007.07.008

[14] H.K. Chen, C.F. Hsuch, M.S. Chang: Production scheduling and vehicle routing with time windows for perishable food products. Computers&operations research. Vol. 36 (2009), p.2311.

DOI: 10.1016/j.cor.2008.09.010

[15] B. Liu: Uncertainty theory. Springer-Verlag. Berlin, (2007).