REVERSE LOGISTICS BASED ON GREEN OPEN VEHICLE ROUTING FOR WASTE COLLECTION IN THE URBAN CONTEXT

Rodriguez-Melquiades, Jose; Cerna, Lourdes Ramirez

doi:10.1590/0101-7438.2023.043.00269174

Jose Rodriguez-Melquiades ^**Corresponding author

Universidad Nacional de Trujillo, Departamento de Informática, Perú - E-mail: jrodriguez@unitru.edu.pe

https://orcid.org/0000-0002-4763-4663

Lourdes Ramirez Cerna

Universidad Nacional de Trujillo, Departamento de Informática, Perú - E-mail: lramirezc@unitru.edu.pe

https://orcid.org/0000-0002-7927-7875

*Corresponding author

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Figure 1
Green open vehicle routing scheme for solid waste collection.

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Figure 2
Co-occurrence of keywords.

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Figure 3
Open vehicle routing with three routes.

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Figure 4
Traversal of 31 collection points using the Genetic Algorithm.

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Figure 5
Traversal of 31 collection points using Local Search.

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Figure 6
Sectors of the District of Trujillo with 142 collection points and one deposit.

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Figure 7
Green open vehicle routing by Genetic Algorithm for the Sector 2.

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Figure 8
Green open vehicle routing by Local Search Algorithm for the Sector 2.

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Table 1
A comparative between the proposal paper and reviewed papers.

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Algorithm 1
Metaheuristics based on Genetic Algorithm

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Algorithm 2
Local Search Based Metaheuristics

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Table 2
Location of twelve collection points and their demands.

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Table 3
Results obtained with the optimization model.

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Table 4
Location of thirty-one collection points and their demands.

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Table 5
Results of the optimization model in six scenarios.

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Table 6
Experiments obtained in the initial configuration with 100 collection points.

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Table 7
Experiments with 31 collection points using the Genetic Algorithm.

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Table 8
Collection at 31 collection points using the Genetic Algorithm.

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Table 9
Collection at 31 collection points using Local Search.

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Table 10
Results with the proposed metaheuristics for 15 scenarios.

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Table 11
Collection points in four sectors of Trujillo.

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Table 12
Results obtained using the genetic algorithm.

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Table 13
Results obtained using the local search algorithm.

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	VRP	Distance	Fuel used	GHP	CO2	Model	Alg
Proposal paper	Green open	x	x		x	x	x
Abdoli et al. (2017ABDOLI B, MIRHASSANI S & HOOSHMAND F. 2017. Model and algorithm for bi-fuel vehicle routing problem to reduce GHG emissions. Environmental Science and Pollution Research, Springer, 24: 21610-21624.)	x	x	x	x	x	x	x
Afshar et al. (2016AFSHAR M, MEHRABI A, SAFARI H, MALEKI M & JOLAI F. 2016. A green vehicle routing problem with customer satisfaction criteria. Journal of Industrial Engineering International, Springer, 12: 529-544.)	Green	x	x		x	x
Amiri et al. (2022AMIRI A, AMIN SH & ZOLFAGHARINIA H. 2022. A bi-objective green vehicle routing problem with a mixed fleet of conventional and electric trucks: Considering charging power and density of stations. Expert Systems With Applications, Elsevier, 213(119228).)	Green	x		x		x	x
Defalque et al. (2021aDEFALQUE C, DA SILVA AF & SILVA MARINS FA. 2021a. Goal programming model applied to waste paper logistics processes. Applied Mathematical Modelling, Elsevier, 98: 185-206.)	x	x				x
Ferreira et al. (2021FERREIRA KM, ALVES DE QUEIROZ T & BRAGION F. 2021. An exact approach for the green vehicle routing problem with two-dimensional loading constraints and split delivery. Computers and Operations Research, Elsevier, 336(105452).)	Green	x	x		x	x	x
Feng et al. (2017FENG Y, ZHANG R & JIA G. 2017. Vehicle routing problems with fuel consumption and stochastic travel speeds. Mathematical problems in engineering, Hindawi, 2017(6329203): 16.)	x	x	x			x	x
Foroutan et al. (2020FOROUTAN RA, REZAEIAN J & MAHDAVI I. 2020. Green vehicle routing and scheduling problem with heterogeneous fleet including reverse logistics in the form of collecting returned goods. Applied Soft Computing Journal, Elsevier, 94(106462).)	Green	x			x	x	x
Hasheme (2021HASHEME SE. 2021. A fuzzy multi-objective optimization model for a sustainable reverse logistics network design of municipal waste-collecting considering the reduction of emissions. Journal of Cleaner Production, Elsevier, 318(128577).)	x		x			x	x
Moghdani et al. (2021MOGHDANI R, SALIMIFARD K, DEMIR E & BENYETTOU A. 2021. The green vehicle routing problem: A systematic literature review. Journal of Cleaner Production, Elsevier, 279(123691).)	Green
Mojtahedi et al. (2021MOJTAHEDI M, FATHOLLAHI-FARD AM & TAVAKKOLO-MOGHADDAM R. 2021. Sustainable vehicle routing problem for coordinated solid waste management. Journal of Industrial Information Integration, Elsevier, 23(100220).)	x					x	x
Niu et al. (2018NIU Y, YANG Z, CHEN P & XIAO J. 2018. Optimizing the green open vehicle routing problem with windows by minimizing comprehensive routing cost. Journal of Cleaner Production, Elsevier, 171: 962-971.)	Green open	x	x		x	x	x
Niu et al. (2022NIU Y, YANG Z, WEN R, XIAO J & ZHANG S. 2022. Solving the Green Open Vehicle Routing Problem Using a Membrane-Inspired Hybrid Algorithm. Sustainability, MDPI, 14(14): 8661.)	Green open	x	x		x	x	x
Normasari et al. (2019NORMASARI NME, YU VF, BACHTIYAR C & SUKOYO. 2019. A simulated annealing heuristic for the capacitated Green vehicle routing problem. Mathematical problems in engineering, Hindawi, 2019(2358258): 18.)	Green	x	x			x	x
Olgun et al. (2021OLGUN B, KOC C, & ALTIPARMAK F. 2021. A hyper heuristic for the green vehicle routing problem with simultaneous pickup and delivery. Computers & Industrial Engineering, Elsevier, 153(107010).)	Green	x	x			x	x
Peng et al. (2020PENG B, WU L, YI Y & CHEN X. 2020. Solving the multi-depot green vehicle routing problem by a hybrid evolutionary. Sustainability, MDPI, 12(5).)	Green				x	x	x
Rezaei et al. (2019REZAEI N, EBRAHIMNEJAD S, MOOSAVI A & NIKFARJAM A. 2019. A green vehicle routing problem with time windows considering the heterogeneous fleet of vehicles: two metaheuristic algorithms. European journal of industrial engineering, 13(4): 507-535.)	Green	x			x	x	x
Ruiz et al. (2019RUIZ E, SOTO-MENDOZA V, RUIZ BARBOSA AE & REYES R. 2019. Solving the open vehicle routing problem with capacity and distance constraints with a biased random key genetic algorithm. Computers & Industrial Engineering, Elsevier, 133(3): 207-219.)	Open	x					x
Wang et al. (2019WANG Y, ASSOGBA K, FAN J, XU M & LIU Y. 2019. Multi-depot green vehicle routing problem with shared transportation resource: Integration of time-dependent speed and piecewise penalty cost. Journal of Cleaner Production, Elsevier, 232: 12-29.)	Green	x			x	x	x
Wen et al. (2022WEN M, SUN W, YU Y, TANG J & LKOU K. 2022. An adaptive large neighborhood search for the larger-scale multi depot green vehicle routing problem with time windows. Journal of Cleaner Production, Elsevier, 374(133916).)	Green	x			x	x	x
Wu & Wu (2022WU D & WU C. 2022. Research on the Time-Dependent Split Delivery Green Vehicle Routing Problem for Fresh Agricultural Products with Multiple Time Windows. Agriculture, MDPI, 12(6).)	Green		x		x	x	x
Xu et al. (2019XU Z, ELOMRI A, POKHAREL S & MUTLU F. 2019. A model for capacitated green vehicle routing problem with the time-varying vehicle speed and soft time windows. Computers & Industrial Engineering, Elsevier, 137(106011).)	Green		x			x	x

Require: TamIndividuals, NumGenerations, ProbCrossing, FileTxT.
Ensure: Minimum number of vehicles and minimum distance traveled to collect solid waste from collection points.
1:	Initialize CollectPoints, depot and trucks when reading the FileTxt.
2:	Initialize TamIndividuals, NumGenerations, ProbCrossing, Individuals.
3:	t=time.time()
4:	Individuals:=GeneratePopulationInitialIndividuals()
5:	for Generation=1:Generation<NumGenerations:Generation++do
6:		kContestants=int(TamIndividuals*0.2)
7:		parents=selectionTournament(Individuals,kContestants)
8:		randon=random.uniform(0.0, 1.0)
9:		if randon≥ProbCrossing then
10:			Individuals=Croosover(parents, Individuals, trucks, CollectionPoints)
11:		else
12:			Individuals=Mutation(parents, Individuals, trucks, CollectionPoints)
13:		end if
14:	end for
15:	BestIndividual=BestSolution(Individuals) ▷ Find best solution based on minimum number of vehicles and minimum total distance traveled.
16:	elapsed=time.time()-t		▷ Total algorithm time.
17:	return Optimal Solution

Require: Collection points, vehicles.
Ensure: Minimum number of vehicles and minimum distance traveled to collect solid waste from collection points.
1:	Initialize CollectPoints, CollectPointsCoordinates, depot and trucks when reading the FileTxt.
2:	Initialize variables IndividualRoutes, truckId, TotalTank, QtyDistancy, QtyCO2, QtyLoad, routes, routeIndex, CollectionPointInitialPosition, CollectionPointListRoutes, Collection-PointsNumber
3:	t=time.time()
4:	for pointcollect=1: pointcollect<NumCollectPoints : pointcollect++do
5:		if QtyLoad+CollectionPoint[CollectionPointInitialPosition].getCantWastes() >=trucks[truckId].getCapacity() or TankTotal<=0 then
6:	▷ Creates a new route list with a new vehicle.
7:			truckId+=1
8:			TankTotal=trucks[truckId].getTank()
9:			pathIndex=0
10:			QtyLoad=0
11:	PointCollectInitialPosition=0			▷ Start with the deposit.
12:		else		▷ Continues to create arcs on the route of the current vehicle.
13:			route=Route()
14:			pathIndex+=1
15:		end if
16:		TankTotal, route, InitialCollectionPointPosition, routeIndex, QtyDistancy, QtyCO2, Qty-Load, ListCollectionPointRoutes=CreateMinimumDistancy(CollectionPointCoordinates, NumCollectionPoints, TotalTank, RouteCollectionPointList, CollectionPoints, routes, InitialPositionCollectionPoint, truckId, routeIndex, DistanceQty, QtyCO2, QtyLoad)
17:		routes.append(route)
18:	end for
19:	IndividualRoutes.append([routes, QtyDistancy, QtyCO2, ListCollectionPointRoutes])
20:	elapsed=time.time()-t			▷ Total algorithm time.
21:	return IndividualPaths

Collection points	Location	Demand to pick up
pc1	(3,25)	2
pc2	(20,61)	1
pc3	(31, 81)	4
pc4	(40, 90)	2
pc5	(80, 50)	2
pc6	(150, 30)	1
pc7	(40,25)	4
pc8	(45,35)	2
pc9	(20,5)	3
pc10	(19, 24)	3
pc11	(15, 40)	3
pc12	(35, 57)	5

Collection point	Collection point	Pickup	Distance	Fuel consumption	CO2
d1	pc1	0	12.2	30	0.23
pc1	pc6	2	13	29.76	0.24
pc6	pc11	3	10	29.52	0.18
pc11	pc2	6	21.58	29.33	0.4
pc2	pc12	7	15.52	28.92	0.29
pc12	pc3	12	24.33	28.63	0.45
pc3	pc4	16	12.72	28.17	0.24
d1	pc9	0	14.14	30	0.26
d1	pc10	0	12.72	30	0.24
pc10	pc7	3	21.02	29.75	0.39
pc7	pc8	7	11.18	29.36	0.21
pc8	pc5	9	38.07	29.15	0.71

Collection points	Location	Demand to pick up
pc1	(96,44)	19
pc2	(50,5)	21
pc3	(49,8)	6
pc4	(13,7)	19
pc5	(29,89)	7
pc6	(48,30)	12
pc7	(84,39)	16
pc8	(14,24)	6
pc9	(2,39)	16
pc10	(3,82)	8
pc11	(5,10)	14
pc12	(98,52)	21
pc13	(84,25)	16
pc14	(61,59)	3
pc15	(1,65)	22
pc16	(88,51)	18
pc17	(91,2)	19
pc18	(19,32)	1
pc19	(93,3)	24
pc20	(50,93)	8
pc21	(98,14)	12
pc22	(5,42)	4
pc23	(42,9)	8
pc24	(61,62)	24
pc25	(9,97)	24
pc26	(80,55)	2
pc27	(57,69)	20
pc28	(23,15)	15
pc29	(20,70)	2
pc30	(85,60)	14
pc31	(98,5)	9

Scenarios	Collection points	Time	Tour	Distance	CO2
1	5	0.1	1	134.33	1.26
2	10	3.8	2	249.96	2.36
3	15	108.5	3	310.08	2.93
4	18	927.9	3	361.9	3.41
5	20	4256.5	3	372.21	3.51
6	25	9912	-	-	-

Scenario	Number of Individuals	Number of Generations	Time of Processing	Distance total traveled	Emissions of CO2	Qty. from vehicles
100	100	100	7.17	2240.18	21.17	12
		200	13.46	1951.97	18.445	12
		300	20.41	2059.43	19.46	12
		400	24.18	2721.63	25.71	12
		500	31.56	2186.37	20.66	12
	200	100	7.16	2033.59	19.21	12
		200	11.79	2628.99	24.84	12
		300	19.13	1879.96	17.77	12
		400	22.96	1879.96	17.76	12
		500	34.01	1921.53	18.16	12
	300	100	5.36	2002.45	18.92	12
		200	15.27	1960.49	18.52	12
		300	21.48	1974.26	18.40	12
		400	25.43	1918.40	18.12	12
		500	31.66	1809.41	17.09	12
	400	100	6.01	2279.6	21.54	12
		200	12.35	2103.46	19.88	12
		300	18.75	2232.79	21.09	12
		400	22.99	2503.81	23.66	12
		500	32.13	2072.41	19.58	12
	500	100	6.1736	1954.78	18.47	12
		200	9.82	2237.45	21.14	12
		300	16.58	2550.79	24.10	12
		400	24.79	2846.43	26.89	11
		500	30.14	2680.10	23.14	11

Scenario	Quantity of Individuals	Number of Generations	Time of Processing	Total distance Tour	Number of vehicles
31	100	100	0.46	593.48	4
			0.28	583.60	4
			0.30	580.99	4
			0.45	580.99	4
			0.37	580.99	4
			0.35	580.99	4
			0.33	580.99	4
			0.34	573.26	4
			0.28	573.26	4
			0.40	573.26	4
Sample standard deviation			0.064	6.11	0
Media			0.36	580.18	4
Variance			0.004	37.28	0

Origin	Destination	Pickup	Distance	Fuel consumption	CO2
d0	pc30	0	16.28	29.85	0.15
pc30	pc1	14	19.42	29.66	0.18
pc1	pc16	33	10.63	29.56	0.10
pc16	pc14	51	28.16	29.30	0.27
pc14	pc24	54	3.0	29.27	0.03
pc24	pc27	78	8.06	29.19	0.08
pc27	pc20	98	25.0	28.96	0.24
d0	pc26	0	21.10	29.80	0.20
pc26	pc12	2	18.23	29.63	0.17
pc12	pc7	23	19.10	29.45	0.18
pc7	pc13	39	14.0	29.32	0.13
pc13	pc21	55	17.8	29.15	0.17
pc21	pc31	67	9.0	29.06	0.09
pc31	pc19	76	5.39	29.01	0.05
pc19	pc17	100	2.24	28.99	0.02
d0	pc6	0	51.88	29.51	0.49
pc6	pc2	12	26.25	29.26	0.25
pc2	pc3	33	3.16	29.23	0.03
pc3	pc23	39	7.07	29.16	0.07
pc23	pc28	47	19.92	28.98	0.19
pc28	pc4	62	12.81	28.86	0.12
pc4	pc11	81	8.54	28.77	0.08
pc11	pc8	95	16.64	28.62	0.16
pc8	pc18	101	9.43	28.53	0.09
d0	pc5	0	54.57	29.48	0.52
pc5	pc25	7	21.54	29.28	0.20
pc25	pc10	31	16.16	29.13	0.15
pc10	pc29	39	20.81	28.93	0.20
pc29	pc15	41	19.65	28.75	0.19
pc15	pc22	63	23.35	28.53	0.22
pc22	pc9	67	4.24	28.49	0.04

Origin	Destination	Pickup	Distance	Fuel consumption	CO2
d0	pc30	0	16.28	29.85	0.15
pc30	pc26	14	7.07	29.78	0.07
pc26	pc16	16	8.94	29.69	0.08
pc16	pc12	34	10.05	29.60	0.09
pc12	pc1	55	8.25	29.52	0.08
pc1	pc7	74	13.00	29.40	0.12
pc7	pc13	90	14.00	29.27	0.13
d0	pc24	0	25.24	29.76	0.24
pc24	pc14	24	3.00	29.73	0.03
pc14	pc27	27	10.77	29.63	0.10
pc27	pc20	47	25.00	29.40	0.24
pc20	pc5	55	21.38	29.19	0.20
pc5	pc29	62	21.02	28.99	0.20
pc29	pc15	64	19.65	28.81	0.19
pc15	pc10	86	17.12	28.65	0.16
pc10	pc25	94	16.16	28.49	0.15
d0	pc6	0	51.88	29.51	0.49
pc6	pc3	12	23.77	29.29	0.22
pc3	pc2	18	3.16	29.25	0.03
pc2	pc23	39	8.94	29.17	0.08
pc23	pc28	47	19.92	28.98	0.19
pc28	pc8	62	12.73	28.86	0.12
pc8	pc18	68	9.43	28.77	0.09
pc18	pc22	69	17.20	28.61	0.16
pc22	pc9	73	4.24	28.57	0.04
pc9	pc11	89	29.15	28.29	0.28
d0	pc21	0	64.03	29.39	0.60
pc21	pc31	12	9.0	29.31	0.09
pc31	pc19	21	5.39	29.26	0.05
pc19	pc17	45	2.24	29.24	0.02
pc17	pc4	64	78.16	28.50	0.74

Scenario	Collection points	Genetic Algorithm						Local Search
Scenario	Collection points	Individuals	Generations	Time (secs)	Trucks	Distance (km)	CO2 (kg/ton-km)	Time (secs)	Trucks	Distance (km)	CO2 (kg/ton-km)
1	4	100	100	0.06	1	134.35	1.27	0.0006	1	134.35	1.27
2	9	100	100	0.09	2	230.24	2.18	0.0008	2	230.24	2.18
3	14	100	100	0.46	2	326.7	3.09	0.0017	2	494.39	3.36
4	19	100	100	0.32	3	432.61	4.09	0.0020	3	456.34	4.67
5	24	100	100	1.36	4	600.93	5.68	0.0020	4	587.13	4.31
6	29	100	100	2.0	4	587.13	5.54	0.0030	4	576.18	5.54
7	31	300	300	1.99	4	533.45	5.04	0.0300	4	578.29	5.45
8	36	300	300	1.66	4	598.08	5.65	0.004	4	765.85	5.46
9	42	300	300	6.27	6	965.86	9.12	0.004	6	824.77	7.24
10	52	300	300	2.05	7	965.10	9.12	0.004	6	888.62	7.79
11	59	500	500	3.48	8	990.78	9.36	0.005	8	813.20	8.40
12	64	300	300	3.48	8	1082.90	10.23	0.007	8	916.38	7.68
13	68	300	300	3.33	8	1613.90	15.25	0.008	8	916.38	8.65
14	79	400	400	5.76	9	1586.60	14.99	0.013	9	1117.62	10.56
15	100	500	500	6.17	12	1954.78	18.47	0.016	12	1438.10	13.59

Información/Sectors	Sector 1	Sector 2	Sector 3	Sector 4
Collection point	42	50	50	142
Collector vehicles	5	5	5	5
Capacity collector vehicles (kgs)	13600	13600	13600	13600
Tank fuel (Gallon)	20	20	20	20

Results/Sector	Sector 1	Sector 2	Sector 3	Sector 4
Process time (secs)	1.40043	2.81397	2.08197	31.32175
Distance traveled (kms)	14.43415	13.84597	9.69341	56.24246
Emissions of CO2 (kg/ton-km)	0.00100	0.00096	0.00067	0.00390
Number of used vehicles	1	2	2	4

Results/Sector	Sector 1	Sector 2	Sector 3	Sector 4
Process time (secs)	0.00628	0.00475	0.00425	0.01678
Distance traveled(kms)	11.35138	11.01886	8.33513	32.10290
Emissions of CO2 (kg/ton-km)	0.00078	0.00076	0.00057	0.00223
Number of used vehicles	1	2	2	4

[1] *Corresponding author

Brasil

Brasil

REVERSE LOGISTICS BASED ON GREEN OPEN VEHICLE ROUTING FOR WASTE COLLECTION IN THE URBAN CONTEXT

ABSTRACT