The Delivery Vehicle Scheduling Considering Agents' Perception Satisfaction toward Waiting Time

doi:10.3969/j.issn.1007-7375.e16-3301

Abstract

Abstract:

To enhance customer satisfaction and reduce transportation costs, a scientific and reasonable express vehicle scheduling scheme is formulated. Taking the customer as behavior subject, and firstly, combining realistic situation and cognitive psychology, the customer's perception of waiting time is analyzed. Customer waiting time perception satisfaction function is built by incorporating the fuzzy theory into the prospect theory; secondly, the problem can be described by a triplet method α/β/γ, and perception satisfaction can be obtained with maximizing average customer waiting time and minimizing the transportation mileage of the express vehicle. Then, Plant Growth Simulation Algorithm (PGSA) is designed to solve this scheduling model. Finally, combined with the actual data, the MATLAB software is used to solve the numerical simulation, and the results show that:comparing with Saving Algorithm, PGSA achieved the value of the objective function by an increase of 0.287 6, and that the effectiveness of the algorithm is verified, and that specifically, the balanced optimization of the interests of customers and logistics enterprises is realized.

Key words: perception toward waiting time, delivery vehicles, prospect theory, plant growth simulation algorithm, saving mileage algorithm, optimal scheduling

CLC Number:

F406.2

HUANG Yiping, YANG Qin, LI Qing, ZHANG Wei. The Delivery Vehicle Scheduling Considering Agents' Perception Satisfaction toward Waiting Time[J]. Industrial Engineering Journal , 2017, 20(4): 31-36,48.

References

[1] CHANG T S, YEN H M. City-courier routing and scheduling problems[J]. European Journal of Operational Research, 2012, 223(2): 489-498.
[2] JANSSENS J, SÖRENSEN K, CATTRYSSE D, et al. Multi-objective microzone-based vehicle routing for courier companies: From tactical to operational planning[J]. European Journal of Operational Research, 2015, 242(1): 222-231.
[3] 侯玲娟, 周泓. 基于离散差分进化算法的随机车辆路径问题[J]. 工业工程, 2014,17(3): 101-107.HOU Lingjuan, ZHOU Hong. A novel discrete differential evolution algorithm for stochastic VRPSPD[J]. Industrial Engineering Journal, 2014, 17(3): 101-107.
[4] AVCI M, SEYDA T. An adaptive local search algorithm for vehicle routing problem with simultaneous and mixed pickups and deliveries[J]. Computers & Industrial Engineering, 2015, 83(3): 15-29.
[5] GULCZYNSKI D, GOLDEN B, WASIL E. The multi-depot split delivery vehicle routing problem: an integer programming-based heuristic, new test problems, and computational results[J]. Computers & Industrial Engineering, 2011, 61(3): 794-804.
[6] 于宝琴, 武淑萍, 杜广伟. 网购快递物流服务系统测评的枝模型仿真[J]. 中国管理科学, 2014, 22(12): 72-78.YU Baoqing, WU Shuping, DU Guangwei. Branch model simulation for express logistics system evaluation under online shopping[J]. Chinese Journal of Management Science, 2014, 22(12): 72-78.
[7] MCNABB M E, WEIR J D, HILL R R, et al. Testing local search move operators on the vehicle routing problem with split deliveries and time windows[J]. Computers & Operations Research, 2015, 56(6): 93-109.
[8] LIN C K Y. A vehicle routing problem with pickup and delivery time windows, and coordination of transportable resources[J]. Computers & Operations Research, 2011, 38(11): 1596-1609.
[9] SALANI M, VACCA I. Branch and price for the vehicle routing problem with discrete split deliveries and time windows[J]. European Journal of Operational Research, 2011, 213(3): 470-477.
[10] CHOPRA S, LOVEJOY W, YANO C. Five decades of operations management and the prospects ahead[J]. Management Science, 2004, 50(1): 8-14.
[11] KAHNEMAN D, TVERSKY A. Prospect theory: an analysis of decision under risk[J]. Econometrica, 1979, 47(2): 263-291.
[12] KAHNEMAN D, TVERSKY A. Choices, values, and frames [M]. New York: Cambridge University Press, 2000.
[13] CAI W, YANG W W, CHEN X Q. A global optimization algorithm based on plant growth theory: plant growth optimization[J]. Intelligent Computation Technology and Automation, 2008, 1(11): 1194-1199.
[14] WANG C H, CHENG H Z H. Transmission network optimal planning based on plant growth simulation algorithm[J]. European Transactions on Electrical Power, 2009, 19(2): 291-301.
[15] 曹庆奎, 刘新雨, 任向阳. 基于模拟植物生长算法的车辆调度问题[J]. 系统工程理论与实践, 2015, 35(6): 1449-1456.CAO Qingkun, LIU Xinyu, REN Xiangyang. Vehicle scheduling problem based on plant growth simulation algorithm[J]. Systems Engineering--Theory & Practice, 2015, 35(6): 1449-1456.
[16] 陈刚, 张锦, 彭永涛. 震后应急物资敏捷保障体系模型及算法[J]. 自然灾害学报, 2013, 22(3): 47-53.CHEN Gang, ZHANG Jin, PENG Yongtao. Agile security system model and algorithm for post-earthquake urgent relief provisions[J]. Journal of Natural Disasters, 2013, 22(3): 47-53.