Multiobjective mathematical models and solution approaches for heterogeneous fixed fleet vehicle routing problems

Melis Alpaslan Takan; Refail Kasimbeyli

doi:10.3934/jimo.2020059

Previous Article
The approximation algorithm based on seeding method for functional $ k $-means problem^†
JIMO Home
This Issue
Next Article
Two-echelon trade credit with default risk in an EOQ model for deteriorating items under dynamic demand

doi: 10.3934/jimo.2020059

Multiobjective mathematical models and solution approaches for heterogeneous fixed fleet vehicle routing problems

Melis Alpaslan Takan and Refail Kasimbeyli ^,

Department of Industrial Engineering, Eskisehir Technical University, Eskisehir, Turkey

^* Corresponding author: Refail Kasimbeyli

Received January 2019 Revised December 2019 Published March 2020

In this paper, we study three types of heterogeneous fixed fleet vehicle routing problems, which are capacitated vehicle routing problem, open vehicle routing problem and split delivery vehicle routing problem. We propose new multiobjective linear binary and mixed integer programming models for these problems, where the first objective is the minimization of a total routing and usage costs for vehicles, and the second one is the vehicle type minimization, respectively. The proposed mathematical models are all illustrated on test problems, which are investigated in two groups: small-sized problems and the large-sized ones. The small-sized test problems are first scalarized by using the weighted sum scalarization method, and then GAMS software is used to compute efficient solutions. The large-sized test problems are solved by utilizing the tabu search algorithm.

Keywords: Vehicle routing problem, multiobjective optimization, tabu search algorithm.

Mathematics Subject Classification: Primary: 90C08, 90C11; Secondary: 90C29.

Citation: Melis Alpaslan Takan, Refail Kasimbeyli. Multiobjective mathematical models and solution approaches for heterogeneous fixed fleet vehicle routing problems. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2020059

References:

[1]	C. Archetti, M. G. Speranza and A. Hertz, Tabu search algorithm for the split delivery vehicle routing problem, Transportation Science, 40 (2006), 64-73. doi: 10.1287/trsc.1040.0103. Google Scholar
[2]	M. C. Bolduc, G. Laporte, J. Renaud and F. F. Boctor, A tabu search heuristic for the split delivery vehicle routing problem with production and demand calendars, European Journal of Operational Research, 202 (2010), 122-130. doi: 10.1016/j.ejor.2009.05.008. Google Scholar
[3]	J. Brandão, A tabu search algorithm for the open vehicle routing problem, European Journal of Operational Research, 157 (2004), 552-564. doi: 10.1016/S0377-2217(03)00238-8. Google Scholar
[4]	P. Belfiore and H. T. Y. Yoshizaki, Scatter search for a real-life heterogeneous fleet vehicle routing problem with time windows and split deliveries in Brazil, European Journal of Operational Research, 199 (2009), 750-758. doi: 10.1016/j.ejor.2008.08.003. Google Scholar
[5]	P. Chen, B. Golden, X. Wang and E. Wasil, A novel approach to solve the split delivery vehicle routing problem, International Transactions in Operational Research, 24 (2017), 27-41. doi: 10.1111/itor.12250. Google Scholar
[6]	G. B. Dantzig and J. H. Ramser, The truck dispatching problem, Management Science, 6 (1959/60), 80-91. doi: 10.1287/mnsc.6.1.80. Google Scholar
[7]	M. Dror and P. Trudeau, Savings by split delivery routing, Transportation Science, 23 (1989), 141-145. doi: 10.1287/trsc.23.2.141. Google Scholar
[8]	M. Dror, G. Laporte and P. Trudeau, Vehicle routing with split deliveries, Discrete Applied Mathematics, 50 (1994), 239-254. doi: 10.1016/0166-218X(92)00172-I. Google Scholar
[9]	J. Euchi and H. Chabchoub, A hybrid tabu search to solve the heterogeneous fixed fleet vehicle routing problem, Logistics Research, 2 (2010), 3-11. doi: 10.1007/s12159-010-0028-3. Google Scholar
[10]	M. L. Fisher and R. Jaikumar, A Decomposition Algorithm for Large-Scale Vehicle Routing, Wharton School, University of Pennsylvania Department of Decision Sciences, Philadelphia, PA, 1978. Google Scholar
[11]	K. Fleszar, I. H. Osman and K. S. Hindi, A variable neighbourhood search algorithm for the open vehicle routing problem, European Journal of Operational Research, 195 (2009), 803-809. doi: 10.1016/j.ejor.2007.06.064. Google Scholar
[12]	R. N. Gasimov, A. Sipahioglu and T. Saraç, A multi-objective programming approach to 1.5-dimensional assortment problem, European Journal of Operational Research, 179 (2007), 64-79. doi: 10.1016/j.ejor.2006.03.016. Google Scholar
[13]	M. Gendreau, G. Laporte, C. Musaraganyi and E. D. Taillard, A tabu search heuristic for the heterogeneous fleet vehicle routing problem, Computers and Operations Research, 26 (1999), 1153-1173. doi: 10.1016/S0305-0548(98)00100-2. Google Scholar
[14]	F. Glover, Future paths for integer programming and links to artificial intelligence, Computers and Operations Research, 13 (1986), 533-549. doi: 10.1016/0305-0548(86)90048-1. Google Scholar
[15]	B. Golden, A. Assad, L. Levy and F. Gheysens, The fleet size and mix vehicle routing problem, Computers and Operations Research, 11 (1984), 49-66. doi: 10.1016/0305-0548(84)90007-8. Google Scholar
[16]	S. C. Ho and D. Haugland, A tabu search heuristic for the vehicle routing problem with time windows and split deliveries, Computers and Operations Research, 31 (2004), 1947-1964. doi: 10.1016/S0305-0548(03)00155-2. Google Scholar
[17]	N. A. Ismayilova, M. Saǧir and R. N. Gasimov, A multiobjective faculty-course-time slot assignment problem with preferences, Mathematical and Computer Modelling, 46 (2007), 1017-1029. doi: 10.1016/j.mcm.2007.03.012. Google Scholar
[18]	M. Jin, K. Liu and B. Eksioglu, A column generation approach for the split delivery vehicle routing problem, Operations Research Letters, 36 (2008), 265-270. doi: 10.1016/j.orl.2007.05.012. Google Scholar
[19]	N. Kasimbeyli, T. Sarac and R. Kasimbeyli, A two-objective mathematical model without cutting patterns for one-dimensional assortment problems, Journal of Computational and Applied Mathematics, 235 (2011), 4663-4674. doi: 10.1016/j.cam.2010.07.019. Google Scholar
[20]	R. Kasimbeyli, A nonlinear cone separation theorem and scalarization in nonconvex vector optimization, SIAM Journal on Optimization, 20 (2009), 1591-1619. doi: 10.1137/070694089. Google Scholar
[21]	R. Kasimbeyli, A conic scalarization method in multi-objective optimization, Journal of Global Optimization, 56 (2013), 279-297. doi: 10.1007/s10898-011-9789-8. Google Scholar
[22]	R. Kasimbeyli, Z. K. Ozturk, N. Kasimbeyli, G. D. Yalcin and B. I. Erdem, Comparison of some scalarization methods in multiobjective optimization: Comparison of scalarization methods, Bulletin of the Malaysian Mathematical Sciences Society, 42 (2019), 1875-1905. doi: 10.1007/s40840-017-0579-4. Google Scholar
[23]	F. Li, B. Golden and E. Wasil, A record-to-record travel algorithm for solving the heterogeneous fleet vehicle routing problem, Computers and Operations Research, 34 (2007), 2734-2742. doi: 10.1016/j.cor.2005.10.015. Google Scholar
[24]	X. Li, S. C. H. Leung and P. Tian, A multi start adaptive memory-based tabu search algorithm for the heterogeneous fixed fleet open vehicle routing problem, Expert Systems with Applications, 39 (2012), 365-374. doi: 10.1016/j.eswa.2011.07.025. Google Scholar
[25]	S. Liu, A hybrid population heuristic for the heterogeneous vehicle routing problems, Transportation Research Part E: Logistics and Transportation Review, 54 (2013), 67-78. doi: 10.1016/j.tre.2013.03.010. Google Scholar
[26]	A. J. Pedraza-Martinez and L. N. Van Wassenhove, Transportation and vehicle fleet management in humanitarian logistics: Challenges for future research, EURO Journal on Transportation and Logistics, 1 (2012), 185-196. doi: 10.1007/s13676-012-0001-1. Google Scholar
[27]	C. E. Miller, A. W. Tucker and R. A. Zemlin, Integer programming formulations and traveling salesman problems, Journal of the Association for Computing Machinery, 7 (1960), 326-329. doi: 10.1145/321043.321046. Google Scholar
[28]	K. Nesbitt and D. Sperling, Fleet purchase behavior: Decision processes and implications for new vehicle technologies and fuels, Transportation Research Part C: Emerging Technologies, 9 (2001), 297-318. doi: 10.1016/S0968-090X(00)00035-8. Google Scholar
[29]	J. Renaud and F. F. Boctor, A sweep-based algorithm for the fleet size and mix vehicle routing problem, European Journal of Operations Research, 140 (2002), 618-628. doi: 10.1016/S0377-2217(01)00237-5. Google Scholar
[30]	L. Schrage, Formulation and structure of more complex/realistic routing and scheduling problems, Networks, 11 (1981), 229-232. doi: 10.1002/net.3230110212. Google Scholar
[31]	E. Taillard, A heuristic column generation method for the heterogeneous fleet VRP, RAIRO - Operations Research, 33 (1999), 1-14. doi: 10.1051/ro:1999101. Google Scholar
[32]	C. D. Tarantilis, C. T. Kiranoudis and V. S. Vassiliadis, A threshold accepting metaheuristic for the heterogeneous fixed fleet vehicle routing problem, European Journal of Operational Research, 152 (2004), 148-158. doi: 10.1016/S0377-2217(02)00669-0. Google Scholar
[33]	O. Ustun and R. Kasimbeyli, Combined forecasts in portfolio optimization: A generalized approach, Computers & Operations Research, 39 (2012), 805–819. doi: 10.1016/j.cor.2010.09.008. Google Scholar
[34]	M. Yousefikhoshbakht, F. Didehvar and F. Rahmati, Solving the heterogeneous fixed fleet open vehicle routing problem by a combined metaheuristic algorithm, International Journal of Production Research, 52 (2014), 2565-2575. doi: 10.1080/00207543.2013.855337. Google Scholar
[35]	S. Yu, C. Ding and K. Zhu, A hybrid GA-TS algorithm for open vehicle routing optimization of coal mines material, Expert Systems with Applications, 38 (2011), 10568-10573. doi: 10.1016/j.eswa.2011.02.108. Google Scholar

show all references

References:

[1]	C. Archetti, M. G. Speranza and A. Hertz, Tabu search algorithm for the split delivery vehicle routing problem, Transportation Science, 40 (2006), 64-73. doi: 10.1287/trsc.1040.0103. Google Scholar
[2]	M. C. Bolduc, G. Laporte, J. Renaud and F. F. Boctor, A tabu search heuristic for the split delivery vehicle routing problem with production and demand calendars, European Journal of Operational Research, 202 (2010), 122-130. doi: 10.1016/j.ejor.2009.05.008. Google Scholar
[3]	J. Brandão, A tabu search algorithm for the open vehicle routing problem, European Journal of Operational Research, 157 (2004), 552-564. doi: 10.1016/S0377-2217(03)00238-8. Google Scholar
[4]	P. Belfiore and H. T. Y. Yoshizaki, Scatter search for a real-life heterogeneous fleet vehicle routing problem with time windows and split deliveries in Brazil, European Journal of Operational Research, 199 (2009), 750-758. doi: 10.1016/j.ejor.2008.08.003. Google Scholar
[5]	P. Chen, B. Golden, X. Wang and E. Wasil, A novel approach to solve the split delivery vehicle routing problem, International Transactions in Operational Research, 24 (2017), 27-41. doi: 10.1111/itor.12250. Google Scholar
[6]	G. B. Dantzig and J. H. Ramser, The truck dispatching problem, Management Science, 6 (1959/60), 80-91. doi: 10.1287/mnsc.6.1.80. Google Scholar
[7]	M. Dror and P. Trudeau, Savings by split delivery routing, Transportation Science, 23 (1989), 141-145. doi: 10.1287/trsc.23.2.141. Google Scholar
[8]	M. Dror, G. Laporte and P. Trudeau, Vehicle routing with split deliveries, Discrete Applied Mathematics, 50 (1994), 239-254. doi: 10.1016/0166-218X(92)00172-I. Google Scholar
[9]	J. Euchi and H. Chabchoub, A hybrid tabu search to solve the heterogeneous fixed fleet vehicle routing problem, Logistics Research, 2 (2010), 3-11. doi: 10.1007/s12159-010-0028-3. Google Scholar
[10]	M. L. Fisher and R. Jaikumar, A Decomposition Algorithm for Large-Scale Vehicle Routing, Wharton School, University of Pennsylvania Department of Decision Sciences, Philadelphia, PA, 1978. Google Scholar
[11]	K. Fleszar, I. H. Osman and K. S. Hindi, A variable neighbourhood search algorithm for the open vehicle routing problem, European Journal of Operational Research, 195 (2009), 803-809. doi: 10.1016/j.ejor.2007.06.064. Google Scholar
[12]	R. N. Gasimov, A. Sipahioglu and T. Saraç, A multi-objective programming approach to 1.5-dimensional assortment problem, European Journal of Operational Research, 179 (2007), 64-79. doi: 10.1016/j.ejor.2006.03.016. Google Scholar
[13]	M. Gendreau, G. Laporte, C. Musaraganyi and E. D. Taillard, A tabu search heuristic for the heterogeneous fleet vehicle routing problem, Computers and Operations Research, 26 (1999), 1153-1173. doi: 10.1016/S0305-0548(98)00100-2. Google Scholar
[14]	F. Glover, Future paths for integer programming and links to artificial intelligence, Computers and Operations Research, 13 (1986), 533-549. doi: 10.1016/0305-0548(86)90048-1. Google Scholar
[15]	B. Golden, A. Assad, L. Levy and F. Gheysens, The fleet size and mix vehicle routing problem, Computers and Operations Research, 11 (1984), 49-66. doi: 10.1016/0305-0548(84)90007-8. Google Scholar
[16]	S. C. Ho and D. Haugland, A tabu search heuristic for the vehicle routing problem with time windows and split deliveries, Computers and Operations Research, 31 (2004), 1947-1964. doi: 10.1016/S0305-0548(03)00155-2. Google Scholar
[17]	N. A. Ismayilova, M. Saǧir and R. N. Gasimov, A multiobjective faculty-course-time slot assignment problem with preferences, Mathematical and Computer Modelling, 46 (2007), 1017-1029. doi: 10.1016/j.mcm.2007.03.012. Google Scholar
[18]	M. Jin, K. Liu and B. Eksioglu, A column generation approach for the split delivery vehicle routing problem, Operations Research Letters, 36 (2008), 265-270. doi: 10.1016/j.orl.2007.05.012. Google Scholar
[19]	N. Kasimbeyli, T. Sarac and R. Kasimbeyli, A two-objective mathematical model without cutting patterns for one-dimensional assortment problems, Journal of Computational and Applied Mathematics, 235 (2011), 4663-4674. doi: 10.1016/j.cam.2010.07.019. Google Scholar
[20]	R. Kasimbeyli, A nonlinear cone separation theorem and scalarization in nonconvex vector optimization, SIAM Journal on Optimization, 20 (2009), 1591-1619. doi: 10.1137/070694089. Google Scholar
[21]	R. Kasimbeyli, A conic scalarization method in multi-objective optimization, Journal of Global Optimization, 56 (2013), 279-297. doi: 10.1007/s10898-011-9789-8. Google Scholar
[22]	R. Kasimbeyli, Z. K. Ozturk, N. Kasimbeyli, G. D. Yalcin and B. I. Erdem, Comparison of some scalarization methods in multiobjective optimization: Comparison of scalarization methods, Bulletin of the Malaysian Mathematical Sciences Society, 42 (2019), 1875-1905. doi: 10.1007/s40840-017-0579-4. Google Scholar
[23]	F. Li, B. Golden and E. Wasil, A record-to-record travel algorithm for solving the heterogeneous fleet vehicle routing problem, Computers and Operations Research, 34 (2007), 2734-2742. doi: 10.1016/j.cor.2005.10.015. Google Scholar
[24]	X. Li, S. C. H. Leung and P. Tian, A multi start adaptive memory-based tabu search algorithm for the heterogeneous fixed fleet open vehicle routing problem, Expert Systems with Applications, 39 (2012), 365-374. doi: 10.1016/j.eswa.2011.07.025. Google Scholar
[25]	S. Liu, A hybrid population heuristic for the heterogeneous vehicle routing problems, Transportation Research Part E: Logistics and Transportation Review, 54 (2013), 67-78. doi: 10.1016/j.tre.2013.03.010. Google Scholar
[26]	A. J. Pedraza-Martinez and L. N. Van Wassenhove, Transportation and vehicle fleet management in humanitarian logistics: Challenges for future research, EURO Journal on Transportation and Logistics, 1 (2012), 185-196. doi: 10.1007/s13676-012-0001-1. Google Scholar
[27]	C. E. Miller, A. W. Tucker and R. A. Zemlin, Integer programming formulations and traveling salesman problems, Journal of the Association for Computing Machinery, 7 (1960), 326-329. doi: 10.1145/321043.321046. Google Scholar
[28]	K. Nesbitt and D. Sperling, Fleet purchase behavior: Decision processes and implications for new vehicle technologies and fuels, Transportation Research Part C: Emerging Technologies, 9 (2001), 297-318. doi: 10.1016/S0968-090X(00)00035-8. Google Scholar
[29]	J. Renaud and F. F. Boctor, A sweep-based algorithm for the fleet size and mix vehicle routing problem, European Journal of Operations Research, 140 (2002), 618-628. doi: 10.1016/S0377-2217(01)00237-5. Google Scholar
[30]	L. Schrage, Formulation and structure of more complex/realistic routing and scheduling problems, Networks, 11 (1981), 229-232. doi: 10.1002/net.3230110212. Google Scholar
[31]	E. Taillard, A heuristic column generation method for the heterogeneous fleet VRP, RAIRO - Operations Research, 33 (1999), 1-14. doi: 10.1051/ro:1999101. Google Scholar
[32]	C. D. Tarantilis, C. T. Kiranoudis and V. S. Vassiliadis, A threshold accepting metaheuristic for the heterogeneous fixed fleet vehicle routing problem, European Journal of Operational Research, 152 (2004), 148-158. doi: 10.1016/S0377-2217(02)00669-0. Google Scholar
[33]	O. Ustun and R. Kasimbeyli, Combined forecasts in portfolio optimization: A generalized approach, Computers & Operations Research, 39 (2012), 805–819. doi: 10.1016/j.cor.2010.09.008. Google Scholar
[34]	M. Yousefikhoshbakht, F. Didehvar and F. Rahmati, Solving the heterogeneous fixed fleet open vehicle routing problem by a combined metaheuristic algorithm, International Journal of Production Research, 52 (2014), 2565-2575. doi: 10.1080/00207543.2013.855337. Google Scholar
[35]	S. Yu, C. Ding and K. Zhu, A hybrid GA-TS algorithm for open vehicle routing optimization of coal mines material, Expert Systems with Applications, 38 (2011), 10568-10573. doi: 10.1016/j.eswa.2011.02.108. Google Scholar

Table 7. Vehicle type data for tabu search algorithm

Problem no	vehicle type	Taillard's original vehicle number data	TK data	vehicle type cost (penalty cost)
13	1	4	10	10
	2	2	10	15
	3	4	10	20
	4	4	10	35
	5	2	10	60
	6	1	10	100
14	1	4	10	100
	2	2	10	150
	3	1	10	290
15	1	4	10	10
	2	3	10	25
	3	2	10	45
16	1	2	10	10
	2	4	10	20
	3	3	10	40
17	1	4	10	10
	2	4	10	22
	3	2	10	40
	4	1	10	70
18	1	4	10	10
	2	4	10	25
	3	2	10	50
	4	2	10	75
	5	1	10	125
	6	1	10	200
19	1	4	10	10
	2	3	10	20
	3	3	10	30
20	1	6	10	10
	2	4	10	23
	3	3	10	35

Table Options

Download as excel

Problem no	vehicle type	Taillard's original vehicle number data	TK data	vehicle type cost (penalty cost)
13	1	4	10	10
	2	2	10	15
	3	4	10	20
	4	4	10	35
	5	2	10	60
	6	1	10	100
14	1	4	10	100
	2	2	10	150
	3	1	10	290
15	1	4	10	10
	2	3	10	25
	3	2	10	45
16	1	2	10	10
	2	4	10	20
	3	3	10	40
17	1	4	10	10
	2	4	10	22
	3	2	10	40
	4	1	10	70
18	1	4	10	10
	2	4	10	25
	3	2	10	50
	4	2	10	75
	5	1	10	125
	6	1	10	200
19	1	4	10	10
	2	3	10	20
	3	3	10	30
20	1	6	10	10
	2	4	10	23
	3	3	10	35

Table 1. GAMS results for the multiobjective heterogeneous fixed fleet capacitated vehicle routing problem

$ w_1 $	$ w_2 $	$ z_1 $	$ z_2 $	Routes	Vehicles
1	9	523.81	1	1-2-10-6-1	vehicle 1 of type 3
				1-4-9-1	vehicle 2 of type 3
				1-5-3-1	vehicle 3 of type 3
				1-7-8-1	vehicle 4 of type 3
2	8	523.81	1	1-3-5-1	vehicle 1 of type 3
				1-6-10-2-1	vehicle 2 of type 3
				1-8-7-1	vehicle 3 of type 3
				1-9-4-1	vehicle 4 of type 3
3	7	523.81	1	1-3-5-1	vehicle 1 of type 3
				1-6-10-2-1	vehicle 2 of type 3
				1-8-7-1	vehicle 3 of type 3
				1-4-9-1	vehicle 4 of type 3
4	6	468.91	2	1-4-3-6-1	vehicle 2 of type 4
				1-5-10-1	vehicle 1 of type 1
				1-7-8-9-2-1	vehicle 1 of type 4
5	5	468.91	2	1-5-10-1	vehicle 1 of type 1
				1-6-3-4-1	vehicle 1 of type 4
				1-7-8-9-2-1	vehicle 2 of type 4
6	4	468.91	2	1-2-9-8-7-1	vehicle 1 of type 4
				1-5-10-1	vehicle 1 of type 1
				1-6-3-4-1	vehicle 2 of type 4
7	3	468.91	2	1-2-9-8-7-1	vehicle 1 of type 4
				1-6-3-4-1	vehicle 2 of type 4
				1-10-5-1	vehicle 1 of type 1
8	2	468.91	2	1-5-10-1	vehicle 1 of type 1
				1-6-3-4-1	vehicle 1 of type 4
				1-7-8-9-2-1	vehicle 2 of type 4
9	1	468.91	2	1-2-9-8-7-1	vehicle 1 of type 4
				1-4-3-6-1	vehicle 2 of type 4
				1-5-10-1	vehicle 1 of type 1

Table Options

Download as excel

$ w_1 $	$ w_2 $	$ z_1 $	$ z_2 $	Routes	Vehicles
1	9	523.81	1	1-2-10-6-1	vehicle 1 of type 3
				1-4-9-1	vehicle 2 of type 3
				1-5-3-1	vehicle 3 of type 3
				1-7-8-1	vehicle 4 of type 3
2	8	523.81	1	1-3-5-1	vehicle 1 of type 3
				1-6-10-2-1	vehicle 2 of type 3
				1-8-7-1	vehicle 3 of type 3
				1-9-4-1	vehicle 4 of type 3
3	7	523.81	1	1-3-5-1	vehicle 1 of type 3
				1-6-10-2-1	vehicle 2 of type 3
				1-8-7-1	vehicle 3 of type 3
				1-4-9-1	vehicle 4 of type 3
4	6	468.91	2	1-4-3-6-1	vehicle 2 of type 4
				1-5-10-1	vehicle 1 of type 1
				1-7-8-9-2-1	vehicle 1 of type 4
5	5	468.91	2	1-5-10-1	vehicle 1 of type 1
				1-6-3-4-1	vehicle 1 of type 4
				1-7-8-9-2-1	vehicle 2 of type 4
6	4	468.91	2	1-2-9-8-7-1	vehicle 1 of type 4
				1-5-10-1	vehicle 1 of type 1
				1-6-3-4-1	vehicle 2 of type 4
7	3	468.91	2	1-2-9-8-7-1	vehicle 1 of type 4
				1-6-3-4-1	vehicle 2 of type 4
				1-10-5-1	vehicle 1 of type 1
8	2	468.91	2	1-5-10-1	vehicle 1 of type 1
				1-6-3-4-1	vehicle 1 of type 4
				1-7-8-9-2-1	vehicle 2 of type 4
9	1	468.91	2	1-2-9-8-7-1	vehicle 1 of type 4
				1-4-3-6-1	vehicle 2 of type 4
				1-5-10-1	vehicle 1 of type 1

Table 2. GAMS results for the single-objective heterogeneous fixed fleet capacitated vehicle routing problem

$ z_2 $	$ z_1 $	Routes	Vehicles
$ z_2\leq 1 $	523.81	1-2-10-6-1	vehicle 1 of type 3
		1-4-9-1	vehicle 2 of type 3
		1-5-3-1	vehicle 3 of type 3
		1-7-8-1	vehicle 4 of type 3
$ z_2\leq 2 $	505.78	1-7-9-4-3-10-6-1	vehicle 1 of type 5
		1-2-8-5-1	vehicle 1 of type 3
$ z_2\leq 3 $	494.54	1-6-5-1	vehicle 1 of type 2
		1-7-8-9-4-3-2-1	vehicle 1 of type 5
		1-10-1	vehicle 1 of type 1
$ z_2\leq 4 $	480.63	1-2-3-10-6-1	vehicle 1 of type 4
		1-4-9-1	vehicle 1 of type 3
		1-7-5-1	vehicle 1 of type 2
		1-8-1	vehicle 1 of type 1
$ z_2\leq 5 $	480.63	1-2-3-10-6-1	vehicle 1 of type 4
		1-4-9-1	vehicle 1 of type 3
		1-7-5-1	vehicle 1 of type 2
		1-8-1	vehicle 1 of type 1

Table Options

Download as excel

$ z_2 $	$ z_1 $	Routes	Vehicles
$ z_2\leq 1 $	523.81	1-2-10-6-1	vehicle 1 of type 3
		1-4-9-1	vehicle 2 of type 3
		1-5-3-1	vehicle 3 of type 3
		1-7-8-1	vehicle 4 of type 3
$ z_2\leq 2 $	505.78	1-7-9-4-3-10-6-1	vehicle 1 of type 5
		1-2-8-5-1	vehicle 1 of type 3
$ z_2\leq 3 $	494.54	1-6-5-1	vehicle 1 of type 2
		1-7-8-9-4-3-2-1	vehicle 1 of type 5
		1-10-1	vehicle 1 of type 1
$ z_2\leq 4 $	480.63	1-2-3-10-6-1	vehicle 1 of type 4
		1-4-9-1	vehicle 1 of type 3
		1-7-5-1	vehicle 1 of type 2
		1-8-1	vehicle 1 of type 1
$ z_2\leq 5 $	480.63	1-2-3-10-6-1	vehicle 1 of type 4
		1-4-9-1	vehicle 1 of type 3
		1-7-5-1	vehicle 1 of type 2
		1-8-1	vehicle 1 of type 1

Table 3. GAMS results for the multiobjective heterogeneous fixed fleet open vehicle routing problem

$ w_1 $	$ w_2 $	$ z_1 $	$ z_2 $	Routes	Vehicles
1	9	408.43	1	1-2-6-10	vehicle 1 of type 3
				1-5-3	vehicle 2 of type 3
				1-7-8	vehicle 3 of type 3
				1-9-4	vehicle 4 of type 3
2	8	408.43	1	1-7-8	vehicle 1 of type 3
				1-2-6-10	vehicle 2 of type 3
				1-5-3	vehicle 3 of type 3
				1-9-4	vehicle 4 of type 3
3	7	408.43	1	1-2-6-10	vehicle 1 of type 3
				1-5-3	vehicle 2 of type 3
				1-7-8	vehicle 3 of type 3
				1-9-4	vehicle 4 of type 3
4	6	352.76	2	1-2-9-8	vehicle 1 of type 4
				1-3-4	vehicle 2 of type 4
				1-5	vehicle 1 of type 1
				1-6	vehicle 3 of type 4
				1-7	vehicle 4 of type 4
				1-10	vehicle 2 of type 1
5	5	352.76	2	1-2-9-8	vehicle 1 of type 4
				1-5	vehicle 1 of type 1
				1-6-3-4	vehicle 2 of type 4
				1-7	vehicle 3 of type 4
				1-10	vehicle 2 of type 1
6	4	352.76	2	1-2-9-8	vehicle 1 of type 4
				1-5	vehicle 1 of type 1
				1-6-3-4	vehicle 2 of type 4
				1-7	vehicle 3 of type 4
				1-10	vehicle 2 of type 1
7	3	352.76	2	1-2-9-8	vehicle 1 of type 4
				1-5	vehicle 1 of type 1
				1-6-3-4	vehicle 2 of type 4
				1-7	vehicle 3 of type 4
				1-10	vehicle 2 of type 1
8	2	352.76	2	1-2-9-8	vehicle 1 of type 4
				1-5	vehicle 1 of type 1
				1-6-3-4	vehicle 2 of type 4
				1-7	vehicle 3 of type 4
				1-10	vehicle 2 of type 1
9	1	347.01	3	1-2-9	vehicle 1 of type 2
				1-5	vehicle 1 of type 1
				1-6-3-4	vehicle 1 of type 4
				1-7	vehicle 2 of type 1
				1-8	vehicle 3 of type 1
				1-10	vehicle 4 of type 1

Table Options

Download as excel

$ w_1 $	$ w_2 $	$ z_1 $	$ z_2 $	Routes	Vehicles
1	9	408.43	1	1-2-6-10	vehicle 1 of type 3
				1-5-3	vehicle 2 of type 3
				1-7-8	vehicle 3 of type 3
				1-9-4	vehicle 4 of type 3
2	8	408.43	1	1-7-8	vehicle 1 of type 3
				1-2-6-10	vehicle 2 of type 3
				1-5-3	vehicle 3 of type 3
				1-9-4	vehicle 4 of type 3
3	7	408.43	1	1-2-6-10	vehicle 1 of type 3
				1-5-3	vehicle 2 of type 3
				1-7-8	vehicle 3 of type 3
				1-9-4	vehicle 4 of type 3
4	6	352.76	2	1-2-9-8	vehicle 1 of type 4
				1-3-4	vehicle 2 of type 4
				1-5	vehicle 1 of type 1
				1-6	vehicle 3 of type 4
				1-7	vehicle 4 of type 4
				1-10	vehicle 2 of type 1
5	5	352.76	2	1-2-9-8	vehicle 1 of type 4
				1-5	vehicle 1 of type 1
				1-6-3-4	vehicle 2 of type 4
				1-7	vehicle 3 of type 4
				1-10	vehicle 2 of type 1
6	4	352.76	2	1-2-9-8	vehicle 1 of type 4
				1-5	vehicle 1 of type 1
				1-6-3-4	vehicle 2 of type 4
				1-7	vehicle 3 of type 4
				1-10	vehicle 2 of type 1
7	3	352.76	2	1-2-9-8	vehicle 1 of type 4
				1-5	vehicle 1 of type 1
				1-6-3-4	vehicle 2 of type 4
				1-7	vehicle 3 of type 4
				1-10	vehicle 2 of type 1
8	2	352.76	2	1-2-9-8	vehicle 1 of type 4
				1-5	vehicle 1 of type 1
				1-6-3-4	vehicle 2 of type 4
				1-7	vehicle 3 of type 4
				1-10	vehicle 2 of type 1
9	1	347.01	3	1-2-9	vehicle 1 of type 2
				1-5	vehicle 1 of type 1
				1-6-3-4	vehicle 1 of type 4
				1-7	vehicle 2 of type 1
				1-8	vehicle 3 of type 1
				1-10	vehicle 4 of type 1

Table 4. Data related to customers for the multiobjective heterogeneous fixed fleet split delivery vehicle routing problem

customer	x coordinate	y coordinate	demand
1	145	215	0
2	151	264	10
3	159	261	8
4	130	254	12
5	128	252	14

Table Options

Download as excel

customer	x coordinate	y coordinate	demand
1	145	215	0
2	151	264	10
3	159	261	8
4	130	254	12
5	128	252	14

Table 5. Data related to vehicles for the multiobjective heterogeneous fixed fleet split delivery vehicle routing problem

Vehicle type	Vehicle capacity	Number of vehicles	Routing Cost	Usage Cost	Type cost
1	10	1	1	20	100
2	15	2	1.1	35	150
3	20	3	1.2	50	200

Table Options

Download as excel

Vehicle type	Vehicle capacity	Number of vehicles	Routing Cost	Usage Cost	Type cost
1	10	1	1	20	100
2	15	2	1.1	35	150
3	20	3	1.2	50	200

Table 6. GAMS results for the multiobjective heterogeneous fixed fleet split delivery vehicle routing problem

$ w_1 $	$ w_2 $	$ z_1 $	$ z_2 $	Routes	Vehicles
1	9	926.40	1	1-4-1	vehicle 1 of type 3
				1-2-3-1	vehicle 2 of type 3
				1-5-1	vehicle 3 of type 3
2	8	926.40	1	1-2-3-1	vehicle 1 of type 3
				1-4-1	vehicle 2 of type 3
				1-5-1	vehicle 3 of type 3
3	7	926.40	1	1-4-1	vehicle 1 of type 3
				1-5-1	vehicle 2 of type 3
				1-2-3-1	vehicle 3 of type 3
4	6	926.40	1	1-4-1	vehicle 1 of type 3
				1-2-3-1	vehicle 2 of type 3
				1-5-1	vehicle 3 of type 3
5	5	812.40	2	1-5(10)-1	vehicle 1 of type 1
				1-2-3-1	vehicle 1 of type 3
				1-5(4)-4-1	vehicle 2 of type 3
6	4	812.40	2	1-5(10)-1	vehicle 1 of type 1
				1-2-3-1	vehicle 1 of type 3
				1-5(4)-4-1	vehicle 2 of type 3
7	3	812.40	2	1-5(10)-1	vehicle 1 of type 1
				1-5(4)-4-1	vehicle 1 of type 3
				1-3-2-1	vehicle 1 of type 3
8	2	770.60	3	1-4(2)-1	vehicle 1 of type 1
				1-5-1	vehicle 1 of type 2
				1-3-2-4(10)-1	vehicle 1 of type 3
9	1	770.60	3	1-4(10)-1	vehicle 1 of type 1
				1-5-1	vehicle 1 of type 2
				1-4(2)-2-3-1	vehicle 1 of type 3

Table Options

Download as excel

$ w_1 $	$ w_2 $	$ z_1 $	$ z_2 $	Routes	Vehicles
1	9	926.40	1	1-4-1	vehicle 1 of type 3
				1-2-3-1	vehicle 2 of type 3
				1-5-1	vehicle 3 of type 3
2	8	926.40	1	1-2-3-1	vehicle 1 of type 3
				1-4-1	vehicle 2 of type 3
				1-5-1	vehicle 3 of type 3
3	7	926.40	1	1-4-1	vehicle 1 of type 3
				1-5-1	vehicle 2 of type 3
				1-2-3-1	vehicle 3 of type 3
4	6	926.40	1	1-4-1	vehicle 1 of type 3
				1-2-3-1	vehicle 2 of type 3
				1-5-1	vehicle 3 of type 3
5	5	812.40	2	1-5(10)-1	vehicle 1 of type 1
				1-2-3-1	vehicle 1 of type 3
				1-5(4)-4-1	vehicle 2 of type 3
6	4	812.40	2	1-5(10)-1	vehicle 1 of type 1
				1-2-3-1	vehicle 1 of type 3
				1-5(4)-4-1	vehicle 2 of type 3
7	3	812.40	2	1-5(10)-1	vehicle 1 of type 1
				1-5(4)-4-1	vehicle 1 of type 3
				1-3-2-1	vehicle 1 of type 3
8	2	770.60	3	1-4(2)-1	vehicle 1 of type 1
				1-5-1	vehicle 1 of type 2
				1-3-2-4(10)-1	vehicle 1 of type 3
9	1	770.60	3	1-4(10)-1	vehicle 1 of type 1
				1-5-1	vehicle 1 of type 2
				1-4(2)-2-3-1	vehicle 1 of type 3

Table 8. Computational results for heterogeneous fixed fleet capacitated VRP, with initial solutions obtained using the NNH algorithm

Pr. no	n	tabu size	tabu tenure	Taillard's data		TK data
Pr. no	n	tabu size	tabu tenure	obj. value	number of vehicles	obj. value	n. of vehicles	vehicle types used
13	50	7	5	4962.84	17	5596.35	6	type 6
13	50	7	10	4962.84	17	5596.35	6	type 6
13	50	10	5	4962.84	17	5596.35	6	type 6
13	50	10	10	4962.84	17	5596.35	6	type 6

14	50	7	5	11717.23	7	15277.12	4	type 3
14	50	7	10	11717.23	7	15277.12	4	type 3
14	50	10	5	11717.23	7	15277.12	4	type 3
14	50	10	10	11717.23	7	15277.12	4	type 3

15	50	7	5	4109.82	9	4411.77	9	type 2
15	50	7	10	4109.82	9	4411.77	9	type 2
15	50	10	5	4109.82	9	4411.77	9	type 2
15	50	10	10	4109.82	9	4411.77	9	type 2

16	50	7	5	4967.63	9	5014.15	6	type 3
16	50	7	10	4967.63	9	5014.15	6	type 3
16	50	10	5	4967.63	9	5014.15	6	type 3
16	50	10	10	4967.63	9	5014.15	6	type 3

17	75	7	5	3581.81	11	3819.1	8	type 3
17	75	7	10	3581.81	11	3819.1	8	type 3
17	75	10	5	3581.81	11	3819.1	8	type 3
17	75	10	10	3581.81	11	3819.1	8	type 3

18	75	7	5	8251.13	14	7535.78	10	type 4
18	75	7	10	8251.13	14	7535.78	10	type 4
18	75	10	5	8251.13	14	7535.78	10	type 4
18	75	10	10	8251.13	14	7535.78	10	type 4

19	100	7	5	12635.68	9	12712.45	8	type 2
19	100	7	10	12635.68	9	12712.45	8	type 2
19	100	10	5	12635.68	9	12712.45	8	type 2
19	100	10	10	12635.68	9	12712.45	8	type 2

20	100	7	5	6982.42	13	8497.79	8	type 3
20	100	7	10	6982.42	13	8497.79	8	type 3
20	100	10	5	6982.42	13	8497.79	8	type 3
20	100	10	10	6982.42	13	8497.79	8	type 3

Table Options

Download as excel

Pr. no	n	tabu size	tabu tenure	Taillard's data		TK data
Pr. no	n	tabu size	tabu tenure	obj. value	number of vehicles	obj. value	n. of vehicles	vehicle types used
13	50	7	5	4962.84	17	5596.35	6	type 6
13	50	7	10	4962.84	17	5596.35	6	type 6
13	50	10	5	4962.84	17	5596.35	6	type 6
13	50	10	10	4962.84	17	5596.35	6	type 6

14	50	7	5	11717.23	7	15277.12	4	type 3
14	50	7	10	11717.23	7	15277.12	4	type 3
14	50	10	5	11717.23	7	15277.12	4	type 3
14	50	10	10	11717.23	7	15277.12	4	type 3

15	50	7	5	4109.82	9	4411.77	9	type 2
15	50	7	10	4109.82	9	4411.77	9	type 2
15	50	10	5	4109.82	9	4411.77	9	type 2
15	50	10	10	4109.82	9	4411.77	9	type 2

16	50	7	5	4967.63	9	5014.15	6	type 3
16	50	7	10	4967.63	9	5014.15	6	type 3
16	50	10	5	4967.63	9	5014.15	6	type 3
16	50	10	10	4967.63	9	5014.15	6	type 3

17	75	7	5	3581.81	11	3819.1	8	type 3
17	75	7	10	3581.81	11	3819.1	8	type 3
17	75	10	5	3581.81	11	3819.1	8	type 3
17	75	10	10	3581.81	11	3819.1	8	type 3

18	75	7	5	8251.13	14	7535.78	10	type 4
18	75	7	10	8251.13	14	7535.78	10	type 4
18	75	10	5	8251.13	14	7535.78	10	type 4
18	75	10	10	8251.13	14	7535.78	10	type 4

19	100	7	5	12635.68	9	12712.45	8	type 2
19	100	7	10	12635.68	9	12712.45	8	type 2
19	100	10	5	12635.68	9	12712.45	8	type 2
19	100	10	10	12635.68	9	12712.45	8	type 2

20	100	7	5	6982.42	13	8497.79	8	type 3
20	100	7	10	6982.42	13	8497.79	8	type 3
20	100	10	5	6982.42	13	8497.79	8	type 3
20	100	10	10	6982.42	13	8497.79	8	type 3

Table 9. Computational results for heterogeneous fixed fleet capacitated VRP, with initial solutions obtained using the RNH algorithm

Pr. no	n	tabu size	tabu tenure	Taillard's data		TK data
Pr. no	n	tabu size	tabu tenure	obj. value	n. of vehicles	obj. value	n. of vehicles	vehicle types used
13	50	7	5	5226.86	17	5390.14	6	type 6
13	50	7	10	5438.5	17	6127.04	6	type 6
13	50	10	5	5322.39	17	6085.09	6	type 6
13	50	10	10	4979.07	17	5859.03	6	type 6

14	50	7	5	11655.46	7	15133.31	4	type 3
14	50	7	10	11747.49	7	15168.62	4	type 3
14	50	10	5	11780.2	7	15392.41	4	type 3
14	50	10	10	11755.49	7	15125.57	4	type 3

15	50	7	5	4108.74	9	4141.99	9	type 2
15	50	7	10	4374.32	9	4149.24	9	type 2
15	50	10	5	3945.91	9	4242.02	9	type 2
15	50	10	10	4175.87	9	4224.99	9	type 2

16	50	7	5	5042.91	9	5146.22	6	type 3
16	50	7	10	4651.12	9	5028.29	6	type 3
16	50	10	5	4471.94	9	5521.87	6	type 3
16	50	10	10	4887.57	9	4845.65	6	type 3

17	75	7	5	3668.52	11	4018.59	8	type 3
17	75	7	10	3170.15	11	3566.41	8	type 3
17	75	10	5	3722.46	11	3694.85	8	type 3
17	75	10	10	3749.49	11	3772.36	8	type 3

18	75	7	5	7012.94	14	6661.09	10	type 4
18	75	7	10	8142.29	14	6977.01	10	type 4
18	75	10	5	7304.11	14	6080.45	10	type 4
18	75	10	10	7370.3	14	6405.93	10	type 4

19	100	7	5	14452.39	9	13337.45	8	type 2
19	100	7	10	13523.82	9	12986.54	8	type 2
19	100	10	5	14516.23	9	15678.2	8	type 2
19	100	10	10	15692.56	9	16994.23	8	type 2

20	100	7	5	7140.26	13	8527.07	8	type 3
20	100	7	10	7267.18	13	8396.23	8	type 3
20	100	10	5	7816.72	13	7918.24	8	type 3
20	100	10	10	7329.74	13	8531.93	8	type 3

Table Options

Download as excel

Pr. no	n	tabu size	tabu tenure	Taillard's data		TK data
Pr. no	n	tabu size	tabu tenure	obj. value	n. of vehicles	obj. value	n. of vehicles	vehicle types used
13	50	7	5	5226.86	17	5390.14	6	type 6
13	50	7	10	5438.5	17	6127.04	6	type 6
13	50	10	5	5322.39	17	6085.09	6	type 6
13	50	10	10	4979.07	17	5859.03	6	type 6

14	50	7	5	11655.46	7	15133.31	4	type 3
14	50	7	10	11747.49	7	15168.62	4	type 3
14	50	10	5	11780.2	7	15392.41	4	type 3
14	50	10	10	11755.49	7	15125.57	4	type 3

15	50	7	5	4108.74	9	4141.99	9	type 2
15	50	7	10	4374.32	9	4149.24	9	type 2
15	50	10	5	3945.91	9	4242.02	9	type 2
15	50	10	10	4175.87	9	4224.99	9	type 2

16	50	7	5	5042.91	9	5146.22	6	type 3
16	50	7	10	4651.12	9	5028.29	6	type 3
16	50	10	5	4471.94	9	5521.87	6	type 3
16	50	10	10	4887.57	9	4845.65	6	type 3

17	75	7	5	3668.52	11	4018.59	8	type 3
17	75	7	10	3170.15	11	3566.41	8	type 3
17	75	10	5	3722.46	11	3694.85	8	type 3
17	75	10	10	3749.49	11	3772.36	8	type 3

18	75	7	5	7012.94	14	6661.09	10	type 4
18	75	7	10	8142.29	14	6977.01	10	type 4
18	75	10	5	7304.11	14	6080.45	10	type 4
18	75	10	10	7370.3	14	6405.93	10	type 4

19	100	7	5	14452.39	9	13337.45	8	type 2
19	100	7	10	13523.82	9	12986.54	8	type 2
19	100	10	5	14516.23	9	15678.2	8	type 2
19	100	10	10	15692.56	9	16994.23	8	type 2

20	100	7	5	7140.26	13	8527.07	8	type 3
20	100	7	10	7267.18	13	8396.23	8	type 3
20	100	10	5	7816.72	13	7918.24	8	type 3
20	100	10	10	7329.74	13	8531.93	8	type 3

Table 10. Computational results for heterogeneous fixed fleet open VRP, with initial solutions obtained using the NNH algorithm

Pr. no	n	tabu size	tabu tenure	Taillard's data		TK data
Pr. no	n	tabu size	tabu tenure	obj. value	n. of vehicles	obj. value	n. of vehicles	vehicle types used
13	50	7	5	4381.93	14	6376.82	6	type 6
13	50	7	10	4386.98	14	6376.82	6	type 6
13	50	10	5	4381.93	14	6376.82	6	type 6
13	50	10	10	4386.98	14	6376.82	6	type 6

14	50	7	5	12609.85	7	10864.35	9	type 1
14	50	7	10	12609.85	7	10864.35	9	type 1
14	50	10	5	12609.85	7	10864.35	9	type 1
14	50	10	10	12609.85	7	10864.35	9	type 1

15	50	7	5	3878.93	9	10054.3	9	type 1
15	50	7	10	3878.93	9	10054.3	9	type1
15	50	10	5	3878.93	9	10054.3	9	type1
15	50	10	10	3878.93	9	10054.3	9	type 1

16	50	7	5	4473.47	9	4845.49	6	type 3
16	50	7	10	4473.47	9	4845.49	6	type 3
16	50	10	5	4473.47	9	4845.49	6	type 3
16	50	10	10	4473.47	9	4845.49	6	type 3

17	75	7	5	3125.69	11	3652.77	8	type 3
17	75	7	10	3125.69	11	3652.77	8	type 3
17	75	10	5	3125.69	11	3652.77	8	type 3
17	75	10	10	3125.69	11	3652.77	8	type 3

18	75	7	5	7803.44	14	6062.63	10	type 4
18	75	7	10	7803.44	14	6062.63	10	type4
18	75	10	5	7803.44	14	6062.63	10	type 4
18	75	10	10	7803.44	14	6062.63	10	type 4

19	100	7	5	12274.38	10	12405.72	8	type 2
19	100	7	10	12274.38	10	12405.72	8	type 2
19	100	10	5	12274.38	10	12405.72	8	type 2
19	100	10	10	12274.38	10	12405.72	8	type 2

20	100	7	5	6782.57	13	8497.79	8	type 3
20	100	7	10	6782.57	13	8497.79	8	type 3
20	100	10	5	6782.57	13	8497.79	8	type 3
20	100	10	10	6782.57	13	8497.79	8	type 3

Table Options

Download as excel

Pr. no	n	tabu size	tabu tenure	Taillard's data		TK data
Pr. no	n	tabu size	tabu tenure	obj. value	n. of vehicles	obj. value	n. of vehicles	vehicle types used
13	50	7	5	4381.93	14	6376.82	6	type 6
13	50	7	10	4386.98	14	6376.82	6	type 6
13	50	10	5	4381.93	14	6376.82	6	type 6
13	50	10	10	4386.98	14	6376.82	6	type 6

14	50	7	5	12609.85	7	10864.35	9	type 1
14	50	7	10	12609.85	7	10864.35	9	type 1
14	50	10	5	12609.85	7	10864.35	9	type 1
14	50	10	10	12609.85	7	10864.35	9	type 1

15	50	7	5	3878.93	9	10054.3	9	type 1
15	50	7	10	3878.93	9	10054.3	9	type1
15	50	10	5	3878.93	9	10054.3	9	type1
15	50	10	10	3878.93	9	10054.3	9	type 1

16	50	7	5	4473.47	9	4845.49	6	type 3
16	50	7	10	4473.47	9	4845.49	6	type 3
16	50	10	5	4473.47	9	4845.49	6	type 3
16	50	10	10	4473.47	9	4845.49	6	type 3

17	75	7	5	3125.69	11	3652.77	8	type 3
17	75	7	10	3125.69	11	3652.77	8	type 3
17	75	10	5	3125.69	11	3652.77	8	type 3
17	75	10	10	3125.69	11	3652.77	8	type 3

18	75	7	5	7803.44	14	6062.63	10	type 4
18	75	7	10	7803.44	14	6062.63	10	type4
18	75	10	5	7803.44	14	6062.63	10	type 4
18	75	10	10	7803.44	14	6062.63	10	type 4

19	100	7	5	12274.38	10	12405.72	8	type 2
19	100	7	10	12274.38	10	12405.72	8	type 2
19	100	10	5	12274.38	10	12405.72	8	type 2
19	100	10	10	12274.38	10	12405.72	8	type 2

20	100	7	5	6782.57	13	8497.79	8	type 3
20	100	7	10	6782.57	13	8497.79	8	type 3
20	100	10	5	6782.57	13	8497.79	8	type 3
20	100	10	10	6782.57	13	8497.79	8	type 3

Table 11. Computational results for heterogeneous fixed fleet open VRP, with initial solutions obtained using the RNH algorithm

Pr. no	n	tabu size	tabu tenure	Taillard's data		TK data
Pr. no	n	tabu size	tabu tenure	obj. value	n. of vehicles	obj. value	n. of vehicles	vehicle types used
13	50	7	5	4100.25	14	6211.45	6	type 6
13	50	7	10	4215.89	14	6544.47	6	type 6
13	50	10	5	4390.47	14	6632.15	6	type 6
13	50	10	10	4412.69	14	6542.12	6	type 6

14	50	7	5	15463.88	7	16250.75	4	type 3
14	50	7	10	16213.24	7	16350.2	4	type 3
14	50	10	5	15478.23	7	16272.54	4	type 3
14	50	10	10	16952.3	7	16897.56	4	type 3

15	50	7	5	3988.45	9	9956.47	9	type 1
15	50	7	10	3995.64	9	10056.23	9	type 1
15	50	10	5	3654.21	9	9854.12	9	type 1
15	50	10	10	3875.46	9	9932.41	9	type 1

16	50	7	5	4852.77	9	4912.55	6	type 3
16	50	7	10	4744.46	9	4753.21	6	type 3
16	50	10	5	4715.23	9	4655.12	6	type 3
16	50	10	10	4879.56	9	4899.52	6	type 3

17	75	7	5	3478.56	11	3678.99	8	type 3
17	75	7	10	3654.61	11	3245.61	8	type 3
17	75	10	5	3541.72	11	3755.17	8	type 3
17	75	10	10	3655.77	11	3547.89	8	type 3

18	75	7	5	7653.45	14	7456.33	10	type 4
18	75	7	10	7664.52	14	7895.41	10	type 4
18	75	10	5	7569.44	14	7754.13	10	type 4
18	75	10	10	8004.56	14	7965.52	10	type 4

19	100	7	5	16542.33	10	17841.22	8	type 2
19	100	7	10	15478.99	10	16984.53	8	type 2
19	100	10	5	14563.01	10	16547.99	8	type 2
19	100	10	10	15879.22	10	15642.33	8	type 2

20	100	7	5	6961.08	13	7918.54	8	type 3
20	100	7	10	6742.13	13	7654.13	8	type 3
20	100	10	5	6830.15	13	7326.58	8	type 3
20	100	10	10	6955.41	13	7456.23	8	type 3

Table Options

Download as excel

Pr. no	n	tabu size	tabu tenure	Taillard's data		TK data
Pr. no	n	tabu size	tabu tenure	obj. value	n. of vehicles	obj. value	n. of vehicles	vehicle types used
13	50	7	5	4100.25	14	6211.45	6	type 6
13	50	7	10	4215.89	14	6544.47	6	type 6
13	50	10	5	4390.47	14	6632.15	6	type 6
13	50	10	10	4412.69	14	6542.12	6	type 6

14	50	7	5	15463.88	7	16250.75	4	type 3
14	50	7	10	16213.24	7	16350.2	4	type 3
14	50	10	5	15478.23	7	16272.54	4	type 3
14	50	10	10	16952.3	7	16897.56	4	type 3

15	50	7	5	3988.45	9	9956.47	9	type 1
15	50	7	10	3995.64	9	10056.23	9	type 1
15	50	10	5	3654.21	9	9854.12	9	type 1
15	50	10	10	3875.46	9	9932.41	9	type 1

16	50	7	5	4852.77	9	4912.55	6	type 3
16	50	7	10	4744.46	9	4753.21	6	type 3
16	50	10	5	4715.23	9	4655.12	6	type 3
16	50	10	10	4879.56	9	4899.52	6	type 3

17	75	7	5	3478.56	11	3678.99	8	type 3
17	75	7	10	3654.61	11	3245.61	8	type 3
17	75	10	5	3541.72	11	3755.17	8	type 3
17	75	10	10	3655.77	11	3547.89	8	type 3

18	75	7	5	7653.45	14	7456.33	10	type 4
18	75	7	10	7664.52	14	7895.41	10	type 4
18	75	10	5	7569.44	14	7754.13	10	type 4
18	75	10	10	8004.56	14	7965.52	10	type 4

19	100	7	5	16542.33	10	17841.22	8	type 2
19	100	7	10	15478.99	10	16984.53	8	type 2
19	100	10	5	14563.01	10	16547.99	8	type 2
19	100	10	10	15879.22	10	15642.33	8	type 2

20	100	7	5	6961.08	13	7918.54	8	type 3
20	100	7	10	6742.13	13	7654.13	8	type 3
20	100	10	5	6830.15	13	7326.58	8	type 3
20	100	10	10	6955.41	13	7456.23	8	type 3

Table 12. Computational results for heterogeneous fixed fleet split delivery VRP, with initial solutions obtained using the NNH algorithm

Pr. no	n	tabu size	tabu tenure	Taillard's data		TK data
Pr. no	n	tabu size	tabu tenure	obj. value	n. of vehicles	obj. value	n. of vehicles	vehicle types used
13	50	7	5	4248.33	17	4539.33	9	type 5
13	50	7	10	4248.33	17	4539.33	9	type 5
13	50	10	5	4236.18	17	4514.64	9	type 5
13	50	10	10	4236.18	17	4514.64	9	type 5

14	50	7	5	12264.91	7	9879.73	9	type 1
14	50	7	10	12264.91	7	9879.73	9	type 1
14	50	10	5	12255.03	7	9869.85	9	type 1
14	50	10	10	12255.03	7	9869.85	9	type 1

15	50	7	5	3750.95	9	3536.62	8	type 2
15	50	7	10	3750.95	9	3536.62	8	type 2
15	50	10	5	3750.95	9	3538.62	8	type 2
15	50	10	10	3750.95	9	3538.62	8	type 2

16	50	7	5	4047.47	9	4317.46	5	type 3
16	50	7	10	4047.47	9	4317.46	5	type 3
16	50	10	5	3988.54	9	4305.2	5	type 3
16	50	10	10	3988.54	9	4305.2	5	type 3

17	75	7	5	2850.35	11	2635.93	7	type 3
17	75	7	10	2850.35	11	2635.93	7	type 3
17	75	10	5	2839.2	11	2612.02	7	type 3
17	75	10	10	2839.2	11	2612.02	7	type 3

18	75	7	5	5121.54	14	5053.58	10	type 4
18	75	7	10	5121.54	14	5053.58	10	type 4
18	75	10	5	5094.1	14	4986.7	10	type 4
18	75	10	10	5094.1	14	4986.7	10	type 4

19	100	7	5	11492	10	11405.7	8	type 2
19	100	7	10	11492	10	11405.7	8	type 2
19	100	10	5	11484.76	10	11411	8	type 2
19	100	10	10	11484.76	10	11411	8	type 2

20	100	7	5	5606.26	13	6630.8	8	type 3
20	100	7	10	5606.26	13	6630.8	8	type 3
20	100	10	5	5541.51	13	6652.7	8	type 3
20	100	10	10	5541.51	13	6652.7	8	type 3

Table Options

Download as excel

Pr. no	n	tabu size	tabu tenure	Taillard's data		TK data
Pr. no	n	tabu size	tabu tenure	obj. value	n. of vehicles	obj. value	n. of vehicles	vehicle types used
13	50	7	5	4248.33	17	4539.33	9	type 5
13	50	7	10	4248.33	17	4539.33	9	type 5
13	50	10	5	4236.18	17	4514.64	9	type 5
13	50	10	10	4236.18	17	4514.64	9	type 5

14	50	7	5	12264.91	7	9879.73	9	type 1
14	50	7	10	12264.91	7	9879.73	9	type 1
14	50	10	5	12255.03	7	9869.85	9	type 1
14	50	10	10	12255.03	7	9869.85	9	type 1

15	50	7	5	3750.95	9	3536.62	8	type 2
15	50	7	10	3750.95	9	3536.62	8	type 2
15	50	10	5	3750.95	9	3538.62	8	type 2
15	50	10	10	3750.95	9	3538.62	8	type 2

16	50	7	5	4047.47	9	4317.46	5	type 3
16	50	7	10	4047.47	9	4317.46	5	type 3
16	50	10	5	3988.54	9	4305.2	5	type 3
16	50	10	10	3988.54	9	4305.2	5	type 3

17	75	7	5	2850.35	11	2635.93	7	type 3
17	75	7	10	2850.35	11	2635.93	7	type 3
17	75	10	5	2839.2	11	2612.02	7	type 3
17	75	10	10	2839.2	11	2612.02	7	type 3

18	75	7	5	5121.54	14	5053.58	10	type 4
18	75	7	10	5121.54	14	5053.58	10	type 4
18	75	10	5	5094.1	14	4986.7	10	type 4
18	75	10	10	5094.1	14	4986.7	10	type 4

19	100	7	5	11492	10	11405.7	8	type 2
19	100	7	10	11492	10	11405.7	8	type 2
19	100	10	5	11484.76	10	11411	8	type 2
19	100	10	10	11484.76	10	11411	8	type 2

20	100	7	5	5606.26	13	6630.8	8	type 3
20	100	7	10	5606.26	13	6630.8	8	type 3
20	100	10	5	5541.51	13	6652.7	8	type 3
20	100	10	10	5541.51	13	6652.7	8	type 3

[1]	Yasmine Cherfaoui, Mustapha Moulaï. Biobjective optimization over the efficient set of multiobjective integer programming problem. Journal of Industrial & Management Optimization, 2021, 17 (1) : 117-131. doi: 10.3934/jimo.2019102
[2]	Guo Zhou, Yongquan Zhou, Ruxin Zhao. Hybrid social spider optimization algorithm with differential mutation operator for the job-shop scheduling problem. Journal of Industrial & Management Optimization, 2021, 17 (2) : 533-548. doi: 10.3934/jimo.2019122
[3]	Wolfgang Riedl, Robert Baier, Matthias Gerdts. Optimization-based subdivision algorithm for reachable sets. Journal of Computational Dynamics, 2021, 8 (1) : 99-130. doi: 10.3934/jcd.2021005
[4]	M. S. Lee, H. G. Harno, B. S. Goh, K. H. Lim. On the bang-bang control approach via a component-wise line search strategy for unconstrained optimization. Numerical Algebra, Control & Optimization, 2021, 11 (1) : 45-61. doi: 10.3934/naco.2020014
[5]	Ömer Arslan, Selçuk Kürşat İşleyen. A model and two heuristic methods for The Multi-Product Inventory-Location-Routing Problem with heterogeneous fleet. Journal of Industrial & Management Optimization, 2020 doi: 10.3934/jimo.2021002
[6]	Mohammed Abdulrazaq Kahya, Suhaib Abduljabbar Altamir, Zakariya Yahya Algamal. Improving whale optimization algorithm for feature selection with a time-varying transfer function. Numerical Algebra, Control & Optimization, 2021, 11 (1) : 87-98. doi: 10.3934/naco.2020017
[7]	Zi Xu, Siwen Wang, Jinjin Huang. An efficient low complexity algorithm for box-constrained weighted maximin dispersion problem. Journal of Industrial & Management Optimization, 2021, 17 (2) : 971-979. doi: 10.3934/jimo.2020007
[8]	Alberto Bressan, Sondre Tesdal Galtung. A 2-dimensional shape optimization problem for tree branches. Networks & Heterogeneous Media, 2020 doi: 10.3934/nhm.2020031
[9]	Claudia Lederman, Noemi Wolanski. An optimization problem with volume constraint for an inhomogeneous operator with nonstandard growth. Discrete & Continuous Dynamical Systems - A, 2020 doi: 10.3934/dcds.2020391
[10]	Hui Gao, Jian Lv, Xiaoliang Wang, Liping Pang. An alternating linearization bundle method for a class of nonconvex optimization problem with inexact information. Journal of Industrial & Management Optimization, 2021, 17 (2) : 805-825. doi: 10.3934/jimo.2019135
[11]	Lekbir Afraites, Chorouk Masnaoui, Mourad Nachaoui. Shape optimization method for an inverse geometric source problem and stability at critical shape. Discrete & Continuous Dynamical Systems - S, 2021 doi: 10.3934/dcdss.2021006
[12]	Lan Luo, Zhe Zhang, Yong Yin. Simulated annealing and genetic algorithm based method for a bi-level seru loading problem with worker assignment in seru production systems. Journal of Industrial & Management Optimization, 2021, 17 (2) : 779-803. doi: 10.3934/jimo.2019134
[13]	Guangbin CAI, Yang Zhao, Wanzhen Quan, Xiusheng Zhang. Design of LPV fault-tolerant controller for hypersonic vehicle based on state observer. Journal of Industrial & Management Optimization, 2021, 17 (1) : 447-465. doi: 10.3934/jimo.2019120
[14]	Editorial Office. Retraction: Jinling Wei, Jinming Zhang, Meishuang Dong, Fan Zhang, Yunmo Chen, Sha Jin and Zhike Han, Applications of mathematics to maritime search. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 957-957. doi: 10.3934/dcdss.2019064
[15]	Min Xi, Wenyu Sun, Jun Chen. Survey of derivative-free optimization. Numerical Algebra, Control & Optimization, 2020, 10 (4) : 537-555. doi: 10.3934/naco.2020050
[16]	Predrag S. Stanimirović, Branislav Ivanov, Haifeng Ma, Dijana Mosić. A survey of gradient methods for solving nonlinear optimization. Electronic Research Archive, 2020, 28 (4) : 1573-1624. doi: 10.3934/era.2020115
[17]	Xinpeng Wang, Bingo Wing-Kuen Ling, Wei-Chao Kuang, Zhijing Yang. Orthogonal intrinsic mode functions via optimization approach. Journal of Industrial & Management Optimization, 2021, 17 (1) : 51-66. doi: 10.3934/jimo.2019098
[18]	Manxue You, Shengjie Li. Perturbation of Image and conjugate duality for vector optimization. Journal of Industrial & Management Optimization, 2020 doi: 10.3934/jimo.2020176
[19]	Marek Macák, Róbert Čunderlík, Karol Mikula, Zuzana Minarechová. Computational optimization in solving the geodetic boundary value problems. Discrete & Continuous Dynamical Systems - S, 2021, 14 (3) : 987-999. doi: 10.3934/dcdss.2020381
[20]	Cheng Peng, Zhaohui Tang, Weihua Gui, Qing Chen, Jing He. A bidirectional weighted boundary distance algorithm for time series similarity computation based on optimized sliding window size. Journal of Industrial & Management Optimization, 2021, 17 (1) : 205-220. doi: 10.3934/jimo.2019107

2019 Impact Factor: 1.366

Tools

Metrics

Tools

Article outline

Figures and Tables

2019 Impact Factor: 1.366

Tools

Figures and Tables

2019 Impact Factor: 1.366

American Institute of Mathematical Sciences

Multiobjective mathematical models and solution approaches for heterogeneous fixed fleet vehicle routing problems

References:

References:

Tools

Metrics

Other articles
by authors

Tools

Tools

Tools

Metrics

Other articles
by authors

American Institute of Mathematical Sciences

Multiobjective mathematical models and solution approaches for heterogeneous fixed fleet vehicle routing problems

References:

References:

Tools

Metrics

Other articlesby authors

Tools

Tools

Tools

Metrics

Other articlesby authors

Other articles
by authors

Other articles
by authors