Modeling of Spare Parts Supply Route Optimization with Hard Time Windows

doi:10.23940/ijpe.19.02.p11.464474

Abstract

Abstract:

In view of the spare parts supply route problem with hard time windows in transportation, an optimal model that aims to minimize the total delivery time is put forward. In this paper, the supply route optimization problem with the shortest transportation distance as the decision target is discussed in detail. Taking the shortest total transportation distance of all vehicles as the optimization goal, a mathematical analytical model thatc onsiders the hard time window requirements of each customer is established. Based on the improved ant colony algorithm, the algorithm flow of solving the vehicle routing problem with hard time windows is designed to solve the difficulty of the model. In addition, the feasibility of the improved algorithm is verified by a case considering the actual terrain. The results show that the improved algorithm can quickly find the solution of VRPTW and provide an effective delivery plan for decision makers.

Key words: spare parts supply, route optimization, modeling, hard time windows, ant colony algorithm

Ming Han, Yabin Wang, and Zhonghua Cheng. Modeling of Spare Parts Supply Route Optimization with Hard Time Windows [J]. Int J Performability Eng, 2019, 15(2): 464-474.

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Table 1

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References 19

1	J. Brito, A. Expósito, J.A.M. Pérez,“Bi-Objective Discrete PSO for Service-Oriented VRPTW,”Advances in Evolutionary and Deterministic Methods for Design,Vol. 36,pp. 445-460,2015 doi: 10.1007/978-3-319-11541-2_29
2	B. Ge, J. H.Han, Z. Wei, L. Cheng, Y. Han,“Dynamic Hybrid Ant Colony Optimization Algorithm for Vehicle Routing Problem with Time Windows,”Pattern Recognition and Artificial Intelligence,Vol.28,No.7,pp.641-650,July 2015 doi: 10.16451/j.cnki.issn1003-6059.201507008
3	Y. M. Guo, D. W. Hu, X. Chen, “Solution of Emergency Logistics Open-Loop Vehicle Routing Problem with Time Windows based on Improved Ant Colony Algorithm,”Journal of Chang’an University (Natural Science Edition), Vol. 37, No. 6, pp.105-112, November 2017
4	K. Ilker, E. Seval, A. Asli, O. Nursel, “A Memory Structure Adapted Simulated Annealing Algorithm for a Green Vehicle Routing Problem,”Environmental Science and Pollution Research, Vol. 22, No. 5,2015 doi: 10.1007/s11356-014-3253-5 pmid: 25056743
5	K. N.Androutsopoulos and K. G. Zografos ,“An Integrated Modelling Approach for the Bicriterion Vehicle Routing and Scheduling Problem with Environmental Considerations,”Transportation Research Part C: Emerging Technologies, Vol. 82, pp.180-209, 2017 doi: 10.1016/j.trc.2017.06.013
6	P. V.Silvestrin and M. Ritt ,“An Iterated Tabu Search for the Multi-Compartment Vehicle Routing Problem,”Computers and Operations Research, Vol. 81, pp.192-202, 2017 doi: 10.1016/j.cor.2016.12.023
7	A. Utamima, K. R. Pradina, N. S. Dini, H. Studiawan, “Distribution Route Optimization of Gallon Water using Genetic Algorithm and Tabu Search,”Procedia Computer Science, Vol. 72, pp.503-510, 2015 doi: 10.1016/j.procs.2015.12.132
8	T.Y. Wu, J. H. Xu, J. Y. Liu, L. Zan, “Improved Genetic Algorithm for Vehicle Routing Problem with Hard Time Window,” Systems Engineering and Electronics, Vol. 36, No. 4, pp.708-713, April 2014
9	H. Yousefi, R. T. Moghaddam, M. T. BOliaei,M. Mohammadi, and A. Mozaffari,“Solving a Bi-Objective Vehicle Routing Problem under Uncertainty by a Revised Multi-Choice Goal Programming Approach,” International Journal of Industrial Engineering Computation, Vol. 8, No. 3, pp.283-302, 2017
10	A. Alvarez and P. Munari,“An Exact Hybrid Method for the Vehicle Routing Problem with Time Windows and Multiple Deliverymen,”Computers and Operations Research, Vol. 83, pp.1-12, 2017 doi: 10.1016/j.cor.2017.02.001
11	J. C. Paz, M. G. Echeverri, J. W. Escobar, “The Multi-Depot Electric Vehicle Location Routing Problem with Time Windows”, International Journal of Industrial Engineering Computations, Vol. 9, No. 1, pp.123-136, 2018
12	F. Errico, G. Desaulniers, M. Gendreau, W. Rei, L.M. Rousseau, “A Priori Optimization with Recourse for the Vehicle Routing Problem with Hard Time Windows and Stochastic Service Times”, European Journal of Operational Research, Vol. 249, No. 1, pp.55-66, 2016 doi: 10.1016/j.ejor.2015.07.027
13	R. C. Funes, M. A. S. Aguilar, and V. Boyer,“Multi-Depot Periodic Vehicle Routing Problem with Due Dates and Time Windows,” Journal of the Operational Research Society, Vol. 69, No. 2, pp.296-306, 2018 doi: 10.1057/s41274-017-0206-7
14	S. Gao, “The Research of Vehicle Routing Problems with Time Windows based on Electric Vehicle, ” Dalian Maritime University, 2015
15	D. M. Miranda and S. V.Conceição,“The Vehicle Routing Problem with Hard Time Windows and Stochastic Travel and Service Time,”Expert Systems with Applications,Vol. 64,pp. 104-116,2016 doi: 10.1016/j.eswa.2016.07.022
16	V. A.Nguyen, J. Jiang, K.M. Teo, “Satisficing Measure Approach for Vehicle Routing Problem with Time Windows under Uncertainty,”European Journal of Operational Research,Vol.248,No. 2,pp. 404-414,2016 doi: 10.1016/j.ejor.2015.07.041
17	R. Spliet and A.F. Gabor,“The Time Window Assignment Vehicle Routing Problem,”Transportation Science,Vol.49,No. 4,pp. 721-731,2015
18	L. J.Tan, F.Y. Lin, H. Wang,“BFO Optimization Algorithms for Vehicle Routing Problem with Time Windows,”Applied Mechanics and Materials,Vol.543-547,pp. 1884-1887,2014 doi: 10.4028/www.scientific.net/AMM.543-547.1884
19	Y. B. Wang, J. M. Zhao, X. S. Jia, Y. Tian, “Spare Parts Allocation Optimization in a Multi-Echelon Support System based on Multi-Objective Particle Swarm Optimization Method,”Maintenance and Reliability, Vol.16, No. 1, pp.29-36, 2014

No.	CSR	Coordinate (km,km,m)	Demand	Deadline	No.	CSR	Coordinate (km,km,m)	Demand	Deadline
1	FP	(5.6,10.2,6.4)	7	9:00	21	RN	(7.7,10.6,6.8)	5	8:50
2	FQ	(3.7,7.8,6.4)	5	9:10	22	RJ	(15.6,10.8,7.9)	3	10:45
3	FG	(12.8,10.8,7.2)	2	10:30	23	RS	(19.9,9.9,7.7)	2	10:55
4	FM	(13.1,12.9,7.1)	4	10:35	24	AX	(26.2,16.2,8.9)	7	11:25
5	TX	(10.9,8.7,7.1)	2	10:05	25	AN	(32.1,17.6,8.1)	6	11:35
6	TY	(9.7,5.6,6.9)	3	9:40	26	AJ	(7.8,31.2,8.1)	4	16:40
7	TG	(36.5,19.8,9.2)	2	11:45	27	AD	(7.9,40.1,8.6)	3	16:10
8	TF	(49.8,37.9,11.1)	5	14:35	28	AT	(4.6,16.7,6.2)	5	10:00
9	BG	(70..2,68.9,6.4)	2	13:50	29	AR	(23.1,9.8,6.7)	5	11:05
10	BT	(50.6,37.5,10.5)	3	14:30	30	AZ	(74.5,27.8,7.3)	4	12:50
11	BM	(26.5,11.6,10.3)	3	11:15	31	AX	(42.1,42.5,9.9)	8	14:45
12	BA	(10.8,11.3,8.9)	5	10:20	32	XK	(3.7,36.9,86)	5	16:30
13	JD	(7.6,43.7,6.5)	3	16:20	33	XJ	(4.6,16.7,6.2)	3	8:30
14	JR	(27.8,22.8,9.3)	9	15:25	34	XC	(5.9,17.8,6.5)	2	8:40
15	JX	(32.1,22.1,9..5)	2	15:15	35	XX	(17.3,17.8,8.3)	4	15:40
16	ZW	(11.3,10.9,7.6)	3	10:15	36	XN	(61.8,17.3,7.7)	2	12:30
17	ZM	(81.2,24.5,8.8)	7	13:00	37	XB	(37.8,15.7,75)	3	11:55
18	ZB	(46.2,6.1,6.9)	4	12:10	38	XF	(9.8,7.9,6.3)	5	9:55
19	ZT	(43.9,27.8,7.2)	4	15:00	39	XD	(7.5,1.8,5.9)	4	9:30
20	RY	(7.5,29.6,6.8)	6	16:45	Sum			161

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