
-
Previous Article
Network data envelopment analysis with fuzzy non-discretionary factors
- JIMO Home
- This Issue
-
Next Article
Genetic algorithm for obstacle location-allocation problems with customer priorities
Adaptive large neighborhood search Algorithm for route planning of freight buses with pickup and delivery
1. | Research Institute of Highway Ministry of Transport, Beijing 100070, China |
2. | University of Technology of Troyes, TROYES 10004, France |
3. | Hunan University of Technology and Business, ChangSha 410205, Hunan, China |
Freight bus is a new public transportation means for city logistics, and each freight bus can deliver and pick up goods at each customer/supplier location it passes. In this paper, we study the route planning problem of freight buses in an urban distribution system. Since each freight bus makes a tour visiting a set of pickup/delivery locations once at every given time interval in each day following a fixed route, the route planning problem can be considered a new variant of periodic vehicle routing problem with pickup and delivery. In order to solve the problem, a Mixed-Integer Linear Programming (MILP) model is formulated and an Adaptive Large Neighborhood Search (ALNS) algorithm is developed. The development of our algorithm takes into consideration specific characteristics of this problem, such as fixed route for each freight bus, possibly serving a demand in a later period but with a late service penalty, etc. The relevance of the mathematical model and the effectiveness of the proposed ALNS algorithm are proved by numerical experiments.
References:
[1] |
D. Aksen, O. Kaya, F. S. Salman and Ö. Tuncel,
An adaptive large neighbor- hood search algorithm for a selective and periodic inventory routing problem, European Journal of Operational Research, 239 (2014), 413-426.
doi: 10.1016/j.ejor.2014.05.043. |
[2] |
E. J. Beltrami and L. D. Bodin,
Networks and vehicle routing for municipal waste collection, Networks, 4 (1974), 65-94.
doi: 10.1002/net.3230040106. |
[3] |
R. Bent and P. V. Hentenryck,
A two-stage hybrid algorithm for pickup and delivery vehicle routing problems with time windows, Computers & Operations Research, 33 (2006), 875-893.
|
[4] |
N. Christofides and J. E. Beasley,
The period routing problem, Networks, 14 (1984), 237-256.
|
[5] |
A. M. Campbell and H. W. Jill,
Forty years of periodic vehicle routing, Networks, 63 (2014), 2-15.
doi: 10.1002/net.21527. |
[6] |
I.-M. Chao, B. L. Golden and E. Wasil,
An improved heuristic for the period vehicle-routing problem, Networks, 26 (1995), 25-44.
doi: 10.1002/net.3230260104. |
[7] |
Z. Chang and H. Chen, Freight buses in three-tiered distribution systems for city logistics: Modeling and evaluation, 7th IESM Conference, (2017). |
[8] |
J. F. Cordeau, M. Gendreau and G. Laporte,
A guide to vehicle routing heuristics, Journal of the Operational Research Society, 53 (2002), 512-522.
|
[9] |
G. Clarke and J. W. Wright,
Scheduling of vehicles from a central depot to a number of delivery points, Operations Research, 12 (1964), 568-581.
doi: 10.1007/978-3-642-27922-5_18. |
[10] |
B. Dai and H. X. Chen,
Proportional egalitarian core solution for profit allocation games with an application to collaborative transportation planning, European Journal of Industrial Engineering, 9 (2015), 53-76.
doi: 10.1504/EJIE.2015.067456. |
[11] |
L. M. A. Drummond, L. S. Ochi and D. S. Vianna,
An asynchronous parallel metaheuristic for the period vehicle routing problem, Future Generation Computer System, 17 (2001), 379-386.
doi: 10.1016/S0167-739X(99)00118-1. |
[12] |
E. Demir, T. Bektas and G. Laporte,
An adaptive large neighborhood search heuristic for the Pollution-Routing Problem, European Journal of Operational Research, 223 (2012), 346-359.
doi: 10.1016/j.ejor.2012.06.044. |
[13] |
B. Dai, H. X. Chen and G. K. Yang,
Price-setting based combinatorial auction approach for carrier collaboration with pickup and delivery requests, Operational Research, 14 (2014), 361-386.
doi: 10.1007/s12351-014-0141-1. |
[14] |
P. Francis, K. Smilowitz and M. Tzur,
The period vehicle routing problem with service choice, Transportation Science, 40 (2006), 439-454.
doi: 10.1287/trsc.1050.0140. |
[15] |
L. E. Gill and R. P. Allerheiligen,
Co-operation in channels of distribution: Physical distribution leads the way, International Journal of Physical Distribution & Logistics Management, 26 (1996), 49-63.
doi: 10.1108/eb014521. |
[16] |
M. Gaudioso and G. Paletta,
A heuristic for the periodic vehicle routing problem, Transport Science, 26 (1992), 86-92.
doi: 10.1287/trsc.26.2.86. |
[17] |
D. Goeke and M. Schneider,
Routing a mixed fleet of electric and conventional vehicles, European Journal of Operational Research, 245 (2015), 81-99.
doi: 10.1016/j.ejor.2015.01.049. |
[18] |
Y. Hao and Y. Su, The research on joint distribution mode of the chain retail enterprises, International Conference on Mechatronics, Electronic, Industrial and Control Engineering, MEIC, (2014), 1694–1697. |
[19] |
M. Y. Lai, H. M. Yang, S. P. Yang, J. H. Zhao and Y. Xu,
Cyber-physical logistics system-based vehicle routing optimization, Journal of Industrial and Management Optimization, 10 (2014), 701-715.
doi: 10.3934/jimo.2014.10.701. |
[20] |
L. L. Lv, Z. Zhang, L. Zhang and W. S. Wang,
An iterative algorithm for periodic Sylvester matrix equations, Journal of Industrial and Management Optimization, 14 (2018), 413-425.
doi: 10.3934/jimo.2017053. |
[21] |
N. Labadie, R. Mansini, J. Melechovský and R. Wolfler-Calvo,
The team orienteering problem with time windows: An LP-based granular variable neighborhood search, European Journal of Operational Research, 220 (2012), 15-27.
doi: 10.1016/j.ejor.2012.01.030. |
[22] |
Y. Li, H. X. Chen and C. Prins,
Adaptive large neighborhood search for the pickup and delivery problem with time windows, profits, and reserved requests, European Journal of Operational Research, 252 (2016), 27-38.
doi: 10.1016/j.ejor.2015.12.032. |
[23] |
S. Majidi, S. M. Hosseini-Motlagh and J. Ignatius,
Adaptive large neighborhood search heuristic for pollution-routing problem with simultaneous pickup and delivery, Soft Computing, 22 (2018), 2851-2865.
doi: 10.1007/s00500-017-2535-5. |
[24] |
A. C. Matos and R. C. Oliverira,
An experimental study of the ant colony system for the period vehicle routing problem, Lecture Notes in Computer Science, 3172 (2004), 286-293.
doi: 10.1007/978-3-540-28646-2_26. |
[25] |
A. R. Pourghaderi, R. Tavakkoli-Moghaddam, M. Alinaghian and B. Beheshti-Pour, A simple and effective heuristic for periodic vehicle routing problem, Proceedings of the 2008 IEEE International Conference on Industrial Engineering Management, (2008), 133–137.
doi: 10.1109/IEEM.2008.4737846. |
[26] |
D. Pisinger and S. Ropker,
A general heuristic for vehicle routing problems, Computers and Operations Research, 34 (2007), 2403-2435.
doi: 10.1016/j.cor.2005.09.012. |
[27] |
R. Russell and W. Igo,
An assignment routing problem, Networks, 9 (1979), 1-17.
doi: 10.1002/net.3230090102. |
[28] |
S. Roker and D. Pisinger,
An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows, Transportation Science, 40 (2006), 455-472.
|
[29] |
I. Roozbeh, M. Ozlen and J. W. Hearne,
An Adaptive Large Neighborhood Search for asset protection during escaped wildfires, Computers and Operations Research, 97 (2018), 125-134.
doi: 10.1016/j.cor.2018.05.002. |
[30] |
X. Shize,
Introductions of joint distribution, Circulation and economic study, 8 (1973), 87-94.
|
[31] |
P. Shaw, Using constraint programming and local search methods to solve vehicle routingproblems, Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming Springer New York, (1998), 417–431. |
[32] |
A. Trentini, A. Campi, N. Malhene and F. Boscacci,
Shared passengers & goods urban transport solutions: New ideas for milan through en international comparison, Territorio, 59 (2011), 38-44.
|
[33] |
L. Verdonck, A. Caris, K. Ramaekers and G. K. Janssens,
Collaborative logistics from the perspective of road transportation companies, Transport Reviews, 33 (2013), 700-719.
doi: 10.1080/01441647.2013.853706. |
[34] |
L. Xu and D. Yang,
Research on joint distribution cost allocation model, Boletin Tecnico/Technical Bulletin, 55 (2017), 291-297.
|
[35] |
N. Yalaoui, L. Amodeo, F. Yalaoui and H. Mahdi,
Efficient methods to schedule reentrant flow shop system, Journal of Intelligent and Fuzzy Systems, 26 (2014), 1113-1121.
doi: 10.3233/IFS-130771. |
[36] |
S. Zhou,
Logistics bottleneck of online retail industry in China, Journal of Supply Chain and Operations Management, 11 (2013), 1-11.
|
show all references
References:
[1] |
D. Aksen, O. Kaya, F. S. Salman and Ö. Tuncel,
An adaptive large neighbor- hood search algorithm for a selective and periodic inventory routing problem, European Journal of Operational Research, 239 (2014), 413-426.
doi: 10.1016/j.ejor.2014.05.043. |
[2] |
E. J. Beltrami and L. D. Bodin,
Networks and vehicle routing for municipal waste collection, Networks, 4 (1974), 65-94.
doi: 10.1002/net.3230040106. |
[3] |
R. Bent and P. V. Hentenryck,
A two-stage hybrid algorithm for pickup and delivery vehicle routing problems with time windows, Computers & Operations Research, 33 (2006), 875-893.
|
[4] |
N. Christofides and J. E. Beasley,
The period routing problem, Networks, 14 (1984), 237-256.
|
[5] |
A. M. Campbell and H. W. Jill,
Forty years of periodic vehicle routing, Networks, 63 (2014), 2-15.
doi: 10.1002/net.21527. |
[6] |
I.-M. Chao, B. L. Golden and E. Wasil,
An improved heuristic for the period vehicle-routing problem, Networks, 26 (1995), 25-44.
doi: 10.1002/net.3230260104. |
[7] |
Z. Chang and H. Chen, Freight buses in three-tiered distribution systems for city logistics: Modeling and evaluation, 7th IESM Conference, (2017). |
[8] |
J. F. Cordeau, M. Gendreau and G. Laporte,
A guide to vehicle routing heuristics, Journal of the Operational Research Society, 53 (2002), 512-522.
|
[9] |
G. Clarke and J. W. Wright,
Scheduling of vehicles from a central depot to a number of delivery points, Operations Research, 12 (1964), 568-581.
doi: 10.1007/978-3-642-27922-5_18. |
[10] |
B. Dai and H. X. Chen,
Proportional egalitarian core solution for profit allocation games with an application to collaborative transportation planning, European Journal of Industrial Engineering, 9 (2015), 53-76.
doi: 10.1504/EJIE.2015.067456. |
[11] |
L. M. A. Drummond, L. S. Ochi and D. S. Vianna,
An asynchronous parallel metaheuristic for the period vehicle routing problem, Future Generation Computer System, 17 (2001), 379-386.
doi: 10.1016/S0167-739X(99)00118-1. |
[12] |
E. Demir, T. Bektas and G. Laporte,
An adaptive large neighborhood search heuristic for the Pollution-Routing Problem, European Journal of Operational Research, 223 (2012), 346-359.
doi: 10.1016/j.ejor.2012.06.044. |
[13] |
B. Dai, H. X. Chen and G. K. Yang,
Price-setting based combinatorial auction approach for carrier collaboration with pickup and delivery requests, Operational Research, 14 (2014), 361-386.
doi: 10.1007/s12351-014-0141-1. |
[14] |
P. Francis, K. Smilowitz and M. Tzur,
The period vehicle routing problem with service choice, Transportation Science, 40 (2006), 439-454.
doi: 10.1287/trsc.1050.0140. |
[15] |
L. E. Gill and R. P. Allerheiligen,
Co-operation in channels of distribution: Physical distribution leads the way, International Journal of Physical Distribution & Logistics Management, 26 (1996), 49-63.
doi: 10.1108/eb014521. |
[16] |
M. Gaudioso and G. Paletta,
A heuristic for the periodic vehicle routing problem, Transport Science, 26 (1992), 86-92.
doi: 10.1287/trsc.26.2.86. |
[17] |
D. Goeke and M. Schneider,
Routing a mixed fleet of electric and conventional vehicles, European Journal of Operational Research, 245 (2015), 81-99.
doi: 10.1016/j.ejor.2015.01.049. |
[18] |
Y. Hao and Y. Su, The research on joint distribution mode of the chain retail enterprises, International Conference on Mechatronics, Electronic, Industrial and Control Engineering, MEIC, (2014), 1694–1697. |
[19] |
M. Y. Lai, H. M. Yang, S. P. Yang, J. H. Zhao and Y. Xu,
Cyber-physical logistics system-based vehicle routing optimization, Journal of Industrial and Management Optimization, 10 (2014), 701-715.
doi: 10.3934/jimo.2014.10.701. |
[20] |
L. L. Lv, Z. Zhang, L. Zhang and W. S. Wang,
An iterative algorithm for periodic Sylvester matrix equations, Journal of Industrial and Management Optimization, 14 (2018), 413-425.
doi: 10.3934/jimo.2017053. |
[21] |
N. Labadie, R. Mansini, J. Melechovský and R. Wolfler-Calvo,
The team orienteering problem with time windows: An LP-based granular variable neighborhood search, European Journal of Operational Research, 220 (2012), 15-27.
doi: 10.1016/j.ejor.2012.01.030. |
[22] |
Y. Li, H. X. Chen and C. Prins,
Adaptive large neighborhood search for the pickup and delivery problem with time windows, profits, and reserved requests, European Journal of Operational Research, 252 (2016), 27-38.
doi: 10.1016/j.ejor.2015.12.032. |
[23] |
S. Majidi, S. M. Hosseini-Motlagh and J. Ignatius,
Adaptive large neighborhood search heuristic for pollution-routing problem with simultaneous pickup and delivery, Soft Computing, 22 (2018), 2851-2865.
doi: 10.1007/s00500-017-2535-5. |
[24] |
A. C. Matos and R. C. Oliverira,
An experimental study of the ant colony system for the period vehicle routing problem, Lecture Notes in Computer Science, 3172 (2004), 286-293.
doi: 10.1007/978-3-540-28646-2_26. |
[25] |
A. R. Pourghaderi, R. Tavakkoli-Moghaddam, M. Alinaghian and B. Beheshti-Pour, A simple and effective heuristic for periodic vehicle routing problem, Proceedings of the 2008 IEEE International Conference on Industrial Engineering Management, (2008), 133–137.
doi: 10.1109/IEEM.2008.4737846. |
[26] |
D. Pisinger and S. Ropker,
A general heuristic for vehicle routing problems, Computers and Operations Research, 34 (2007), 2403-2435.
doi: 10.1016/j.cor.2005.09.012. |
[27] |
R. Russell and W. Igo,
An assignment routing problem, Networks, 9 (1979), 1-17.
doi: 10.1002/net.3230090102. |
[28] |
S. Roker and D. Pisinger,
An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows, Transportation Science, 40 (2006), 455-472.
|
[29] |
I. Roozbeh, M. Ozlen and J. W. Hearne,
An Adaptive Large Neighborhood Search for asset protection during escaped wildfires, Computers and Operations Research, 97 (2018), 125-134.
doi: 10.1016/j.cor.2018.05.002. |
[30] |
X. Shize,
Introductions of joint distribution, Circulation and economic study, 8 (1973), 87-94.
|
[31] |
P. Shaw, Using constraint programming and local search methods to solve vehicle routingproblems, Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming Springer New York, (1998), 417–431. |
[32] |
A. Trentini, A. Campi, N. Malhene and F. Boscacci,
Shared passengers & goods urban transport solutions: New ideas for milan through en international comparison, Territorio, 59 (2011), 38-44.
|
[33] |
L. Verdonck, A. Caris, K. Ramaekers and G. K. Janssens,
Collaborative logistics from the perspective of road transportation companies, Transport Reviews, 33 (2013), 700-719.
doi: 10.1080/01441647.2013.853706. |
[34] |
L. Xu and D. Yang,
Research on joint distribution cost allocation model, Boletin Tecnico/Technical Bulletin, 55 (2017), 291-297.
|
[35] |
N. Yalaoui, L. Amodeo, F. Yalaoui and H. Mahdi,
Efficient methods to schedule reentrant flow shop system, Journal of Intelligent and Fuzzy Systems, 26 (2014), 1113-1121.
doi: 10.3233/IFS-130771. |
[36] |
S. Zhou,
Logistics bottleneck of online retail industry in China, Journal of Supply Chain and Operations Management, 11 (2013), 1-11.
|






Instances | |
LB | |
|
|
|
|
|
7-3a | 1067.1 | 1067.1 | 1067.1 | 0.00 | 0.00 | 0.00 | 86.3 | 7.9 |
7-3b | 986.6 | 986.6 | 986.6 | 0.00 | 0.00 | 0.00 | 89.6 | 7.6 |
7-3c | 1076.9 | 1076.9 | 1076.9 | 0.00 | 0.00 | 0.00 | 87.9 | 8.2 |
7-3d | 962.0 | 962.0 | 962.0 | 0.00 | 0.00 | 0.00 | 87.6 | 8.7 |
7-3e | 1005.8 | 1005.8 | 1005.8 | 0.00 | 0.00 | 0.00 | 87.5 | 7.9 |
7-5a | 1984.1 | 1984.1 | 1984.1 | 0.00 | 0.00 | 0.00 | 257.3 | 19.7 |
7-5b | 1945.1 | 1945.1 | 1945.1 | 0.00 | 0.00 | 0.00 | 262.1 | 20.3 |
7-5c | 1887.0 | 1887.0 | 1887.0 | 0.00 | 0.00 | 0.00 | 258.2 | 21.9 |
7-5d | 1940.4 | 1940.4 | 1940.4 | 0.00 | 0.00 | 0.00 | 259.3 | 19.8 |
7-5e | 1896.6 | 1896.6 | 1896.6 | 0.00 | 0.00 | 0.00 | 260.3 | 20.3 |
13-3a | 1459.9 | 1074.8 | 1197.0 | 35.83 | 11.37 | 18.01 | 3600 | 25.8 |
13-3b | 1519.1 | 1089.3 | 1207.8 | 39.46 | 10.88 | 20.49 | 3600 | 25.6 |
13-3c | 1800.1 | 1342.1 | 1530.6 | 34.13 | 14.05 | 14.97 | 3600 | 26.9 |
13-3d | 1397.4 | 1007.7 | 1123.6 | 38.67 | 11.50 | 19.59 | 3600 | 26.3 |
13-3e | 2878.7 | 1848.4 | 2086.5 | 55.74 | 12.88 | 27.52 | 3600 | 27.1 |
13-5a | 3761.2 | 2527.9 | 2847.0 | 48.79 | 12.62 | 24.30 | 3600 | 40.3 |
13-5b | 3232.8 | 2264.2 | 2673.5 | 42.78 | 18.08 | 17.30 | 3600 | 39.7 |
13-5c | 3329.6 | 2220.5 | 2607.1 | 49.95 | 17.41 | 21.70 | 3600 | 42.3 |
13-5d | 3402.7 | 2238.7 | 2581.8 | 51.99 | 15.33 | 24.13 | 3600 | 41.2 |
13-5e | 3405.6 | 2427.8 | 2856.6 | 40.28 | 17.66 | 16.12 | 3600 | 40.3 |
Instances | |
LB | |
|
|
|
|
|
7-3a | 1067.1 | 1067.1 | 1067.1 | 0.00 | 0.00 | 0.00 | 86.3 | 7.9 |
7-3b | 986.6 | 986.6 | 986.6 | 0.00 | 0.00 | 0.00 | 89.6 | 7.6 |
7-3c | 1076.9 | 1076.9 | 1076.9 | 0.00 | 0.00 | 0.00 | 87.9 | 8.2 |
7-3d | 962.0 | 962.0 | 962.0 | 0.00 | 0.00 | 0.00 | 87.6 | 8.7 |
7-3e | 1005.8 | 1005.8 | 1005.8 | 0.00 | 0.00 | 0.00 | 87.5 | 7.9 |
7-5a | 1984.1 | 1984.1 | 1984.1 | 0.00 | 0.00 | 0.00 | 257.3 | 19.7 |
7-5b | 1945.1 | 1945.1 | 1945.1 | 0.00 | 0.00 | 0.00 | 262.1 | 20.3 |
7-5c | 1887.0 | 1887.0 | 1887.0 | 0.00 | 0.00 | 0.00 | 258.2 | 21.9 |
7-5d | 1940.4 | 1940.4 | 1940.4 | 0.00 | 0.00 | 0.00 | 259.3 | 19.8 |
7-5e | 1896.6 | 1896.6 | 1896.6 | 0.00 | 0.00 | 0.00 | 260.3 | 20.3 |
13-3a | 1459.9 | 1074.8 | 1197.0 | 35.83 | 11.37 | 18.01 | 3600 | 25.8 |
13-3b | 1519.1 | 1089.3 | 1207.8 | 39.46 | 10.88 | 20.49 | 3600 | 25.6 |
13-3c | 1800.1 | 1342.1 | 1530.6 | 34.13 | 14.05 | 14.97 | 3600 | 26.9 |
13-3d | 1397.4 | 1007.7 | 1123.6 | 38.67 | 11.50 | 19.59 | 3600 | 26.3 |
13-3e | 2878.7 | 1848.4 | 2086.5 | 55.74 | 12.88 | 27.52 | 3600 | 27.1 |
13-5a | 3761.2 | 2527.9 | 2847.0 | 48.79 | 12.62 | 24.30 | 3600 | 40.3 |
13-5b | 3232.8 | 2264.2 | 2673.5 | 42.78 | 18.08 | 17.30 | 3600 | 39.7 |
13-5c | 3329.6 | 2220.5 | 2607.1 | 49.95 | 17.41 | 21.70 | 3600 | 42.3 |
13-5d | 3402.7 | 2238.7 | 2581.8 | 51.99 | 15.33 | 24.13 | 3600 | 41.2 |
13-5e | 3405.6 | 2427.8 | 2856.6 | 40.28 | 17.66 | 16.12 | 3600 | 40.3 |
Parameter | Small instances | Medium instances | Large instances |
Maximum iterations number of ALNS |
25000 | 30000 | 35000 |
The roulette parameter |
0.1 | 0.1 | 0.1 |
The score increment of generating a new best solution (Q1) | 5 | 5 | 5 |
The score increment of generating a better solution (Q2) | 3 | 3 | 3 |
The score increment of generating a worse solution (Q3) | 1 | 1 | 1 |
Initial temperature parameter |
100 | 120 | 120 |
Cooling rate |
0.992 | 0.994 | 0.996 |
Removal fraction |
0.2 | 0.2 | 0.3 |
Noise parameter |
0.1 | 0.1 | 0.1 |
Parameter | Small instances | Medium instances | Large instances |
Maximum iterations number of ALNS |
25000 | 30000 | 35000 |
The roulette parameter |
0.1 | 0.1 | 0.1 |
The score increment of generating a new best solution (Q1) | 5 | 5 | 5 |
The score increment of generating a better solution (Q2) | 3 | 3 | 3 |
The score increment of generating a worse solution (Q3) | 1 | 1 | 1 |
Initial temperature parameter |
100 | 120 | 120 |
Cooling rate |
0.992 | 0.994 | 0.996 |
Removal fraction |
0.2 | 0.2 | 0.3 |
Noise parameter |
0.1 | 0.1 | 0.1 |
Abbreviation | Definition |
The best feasible objective value found by CPLEX in a preset running time | |
LB | The lower bound produced by CPLEX in a preset running time |
The best feasible objective value obtained by ALNS after a preset number of iterations | |
The gap between |
|
The gap between |
|
The improvement (reduction) of |
|
The CPU time (second) of CPLEX | |
The CPU time (second) of ALNS |
Abbreviation | Definition |
The best feasible objective value found by CPLEX in a preset running time | |
LB | The lower bound produced by CPLEX in a preset running time |
The best feasible objective value obtained by ALNS after a preset number of iterations | |
The gap between |
|
The gap between |
|
The improvement (reduction) of |
|
The CPU time (second) of CPLEX | |
The CPU time (second) of ALNS |
Instances | |
LB | |
|
|
|
|
|
20-3a | 2884.5 | 1966.6 | 2161.4 | 46.67 | 9.91 | 25.07 | 5400 | 135.7 |
20-3b | 3014.2 | 2101.2 | 2251.3 | 43.45 | 7.14 | 25.31 | 5400 | 140.7 |
20-3c | 3058.2 | 2069.5 | 2268.2 | 47.77 | 9.60 | 25.83 | 5400 | 133.2 |
20-3d | 2649.0 | 1807.7 | 2013.2 | 46.54 | 11.37 | 24.00 | 5400 | 135.7 |
20-3e | 2904.7 | 1949.3 | 2141.2 | 49.01 | 9.84 | 26.29 | 5400 | 134.2 |
20-5a | 5161.4 | 2951.8 | 3365.4 | 74.86 | 14.01 | 34.80 | 5400 | 216.4 |
20-5b | 4527.3 | 2627.2 | 2992.6 | 72.32 | 13.91 | 33.90 | 5400 | 203.5 |
20-5c | 4758.4 | 2698.5 | 3065.1 | 76.34 | 13.59 | 35.59 | 5400 | 218.8 |
20-5d | 4474.2 | 2556.1 | 2852.5 | 75.04 | 11.60 | 36.25 | 5400 | 199.5 |
20-5e | 4522.8 | 2573.0 | 2927.5 | 75.78 | 13.78 | 35.27 | 5400 | 220.3 |
30-3a | 4964.2 | 2664.8 | 3003.9 | 86.29 | 12.73 | 39.49 | 7200 | 289.3 |
30-3b | 5401.6 | 2922.8 | 3321.7 | 84.81 | 13.65 | 38.50 | 7200 | 276.8 |
30-3c | 4688.7 | 2520.6 | 2860.1 | 86.02 | 13.47 | 39.00 | 7200 | 296.3 |
30-3d | 4540.4 | 2381.9 | 2728.1 | 90.62 | 14.53 | 39.92 | 7200 | 295.3 |
30-3e | 4654.2 | 2502.6 | 2844.2 | 85.97 | 13.65 | 38.89 | 7200 | 287.6 |
30-5a | - | 4325.6 | 5167.3 | - | 19.46 | - | 7200 | 356.7 |
30-5b | 8140.4 | 4084.0 | 4859.6 | 99.32 | 18.99 | 40.30 | 7200 | 320.7 |
30-5c | - | 4390.0 | 5249.4 | - | 19.58 | - | 7200 | 364.3 |
30-5d | - | 3973.0 | 4733.7 | - | 19.15 | - | 7200 | 378.9 |
30-5e | - | 4154.9 | 4938.6 | - | 18.86 | - | 7200 | 352.1 |
40-3a | 6214.3 | 2835.6 | 3371.3 | 119.15 | 18.89 | 45.75 | 10800 | 478.7 |
40-3b | 6389.9 | 2832.7 | 3372.6 | 125.58 | 19.06 | 47.22 | 10800 | 480.3 |
40-3c | - | 2917.5 | 3469.4 | - | 18.92 | - | 10800 | 437.9 |
40-3d | 6072.1 | 2832.0 | 3381.9 | 114.41 | 19.42 | 44.30 | 10800 | 509.3 |
40-3e | 6112.3 | 3008.5 | 3590.5 | 103.17 | 19.35 | 41.26 | 10800 | 469.5 |
40-5a | - | 4824.4 | 6020.8 | - | 24.80 | - | 10800 | 597.3 |
40-5b | - | 5065.7 | 6325.8 | - | 24.88 | - | 10800 | 600.1 |
40-5c | - | 4944.1 | 6088.1 | - | 23.14 | - | 10800 | 623.5 |
40-5d | - | 4823.4 | 5940.9 | - | 23.17 | - | 10800 | 591.2 |
40-5e | - | 4664.0 | 5764.5 | - | 23.60 | - | 10800 | 589.7 |
Instances | |
LB | |
|
|
|
|
|
20-3a | 2884.5 | 1966.6 | 2161.4 | 46.67 | 9.91 | 25.07 | 5400 | 135.7 |
20-3b | 3014.2 | 2101.2 | 2251.3 | 43.45 | 7.14 | 25.31 | 5400 | 140.7 |
20-3c | 3058.2 | 2069.5 | 2268.2 | 47.77 | 9.60 | 25.83 | 5400 | 133.2 |
20-3d | 2649.0 | 1807.7 | 2013.2 | 46.54 | 11.37 | 24.00 | 5400 | 135.7 |
20-3e | 2904.7 | 1949.3 | 2141.2 | 49.01 | 9.84 | 26.29 | 5400 | 134.2 |
20-5a | 5161.4 | 2951.8 | 3365.4 | 74.86 | 14.01 | 34.80 | 5400 | 216.4 |
20-5b | 4527.3 | 2627.2 | 2992.6 | 72.32 | 13.91 | 33.90 | 5400 | 203.5 |
20-5c | 4758.4 | 2698.5 | 3065.1 | 76.34 | 13.59 | 35.59 | 5400 | 218.8 |
20-5d | 4474.2 | 2556.1 | 2852.5 | 75.04 | 11.60 | 36.25 | 5400 | 199.5 |
20-5e | 4522.8 | 2573.0 | 2927.5 | 75.78 | 13.78 | 35.27 | 5400 | 220.3 |
30-3a | 4964.2 | 2664.8 | 3003.9 | 86.29 | 12.73 | 39.49 | 7200 | 289.3 |
30-3b | 5401.6 | 2922.8 | 3321.7 | 84.81 | 13.65 | 38.50 | 7200 | 276.8 |
30-3c | 4688.7 | 2520.6 | 2860.1 | 86.02 | 13.47 | 39.00 | 7200 | 296.3 |
30-3d | 4540.4 | 2381.9 | 2728.1 | 90.62 | 14.53 | 39.92 | 7200 | 295.3 |
30-3e | 4654.2 | 2502.6 | 2844.2 | 85.97 | 13.65 | 38.89 | 7200 | 287.6 |
30-5a | - | 4325.6 | 5167.3 | - | 19.46 | - | 7200 | 356.7 |
30-5b | 8140.4 | 4084.0 | 4859.6 | 99.32 | 18.99 | 40.30 | 7200 | 320.7 |
30-5c | - | 4390.0 | 5249.4 | - | 19.58 | - | 7200 | 364.3 |
30-5d | - | 3973.0 | 4733.7 | - | 19.15 | - | 7200 | 378.9 |
30-5e | - | 4154.9 | 4938.6 | - | 18.86 | - | 7200 | 352.1 |
40-3a | 6214.3 | 2835.6 | 3371.3 | 119.15 | 18.89 | 45.75 | 10800 | 478.7 |
40-3b | 6389.9 | 2832.7 | 3372.6 | 125.58 | 19.06 | 47.22 | 10800 | 480.3 |
40-3c | - | 2917.5 | 3469.4 | - | 18.92 | - | 10800 | 437.9 |
40-3d | 6072.1 | 2832.0 | 3381.9 | 114.41 | 19.42 | 44.30 | 10800 | 509.3 |
40-3e | 6112.3 | 3008.5 | 3590.5 | 103.17 | 19.35 | 41.26 | 10800 | 469.5 |
40-5a | - | 4824.4 | 6020.8 | - | 24.80 | - | 10800 | 597.3 |
40-5b | - | 5065.7 | 6325.8 | - | 24.88 | - | 10800 | 600.1 |
40-5c | - | 4944.1 | 6088.1 | - | 23.14 | - | 10800 | 623.5 |
40-5d | - | 4823.4 | 5940.9 | - | 23.17 | - | 10800 | 591.2 |
40-5e | - | 4664.0 | 5764.5 | - | 23.60 | - | 10800 | 589.7 |
Instances | |
LB | |
|
|
|
|
|
60-3a | 11510.5 | 4550.1 | 5313.7 | 152.97 | 16.78 | 53.84 | 14400 | 979.3 |
60-3b | - | 5140.4 | 5971.6 | - | 16.17 | - | 14400 | 1000.1 |
60-3c | 11918.4 | 4742.3 | 5507.3 | 151.32 | 16.13 | 53.79 | 14400 | 1005.3 |
60-3d | - | 4340.2 | 5106.7 | - | 17.66 | - | 14400 | 989.3 |
60-3e | 11509.7 | 4694.8 | 5494.9 | 145.16 | 17.04 | 52.26 | 14400 | 996.1 |
60-5a | - | 8123.9 | 9972.9 | - | 22.76 | - | 14400 | 1421.6 |
60-5b | - | 7669.4 | 9493.1 | - | 23.78 | - | 14400 | 1432.3 |
60-5c | - | 8041.2 | 9809.9 | - | 22.00 | - | 14400 | 1410.5 |
60-5d | - | 8318.1 | 10172.5 | - | 22.29 | - | 14400 | 1396.3 |
60-5e | - | 8372.7 | 10135.2 | - | 21.05 | - | 14400 | 1389.3 |
80-3a | - | 6288.6 | 7419.3 | - | 17.98 | - | 18000 | 1523.4 |
80-3b | - | 5992.0 | 7135.8 | - | 19.09 | - | 18000 | 1612.3 |
80-3c | - | 6431.5 | 7683.1 | - | 19.46 | - | 18000 | 1496.8 |
80-3d | - | 5785.2 | 6836.7 | - | 18.18 | - | 18000 | 1527.4 |
80-3e | - | 6104.0 | 7264.1 | - | 19.01 | - | 18000 | 1559.3 |
80-5a | - | 8670.0 | 10889.2 | - | 25.60 | - | 18000 | 1963.2 |
80-5b | - | 8260.9 | 10207.5 | - | 23.56 | - | 18000 | 2001.3 |
80-5c | - | 8855.8 | 11163.3 | - | 26.06 | - | 18000 | 1989.7 |
80-5d | - | 8611.5 | 10627.6 | - | 23.41 | - | 18000 | 1967.3 |
80-5e | - | 8596.7 | 10703.1 | - | 24.50 | - | 18000 | 1995.3 |
Instances | |
LB | |
|
|
|
|
|
60-3a | 11510.5 | 4550.1 | 5313.7 | 152.97 | 16.78 | 53.84 | 14400 | 979.3 |
60-3b | - | 5140.4 | 5971.6 | - | 16.17 | - | 14400 | 1000.1 |
60-3c | 11918.4 | 4742.3 | 5507.3 | 151.32 | 16.13 | 53.79 | 14400 | 1005.3 |
60-3d | - | 4340.2 | 5106.7 | - | 17.66 | - | 14400 | 989.3 |
60-3e | 11509.7 | 4694.8 | 5494.9 | 145.16 | 17.04 | 52.26 | 14400 | 996.1 |
60-5a | - | 8123.9 | 9972.9 | - | 22.76 | - | 14400 | 1421.6 |
60-5b | - | 7669.4 | 9493.1 | - | 23.78 | - | 14400 | 1432.3 |
60-5c | - | 8041.2 | 9809.9 | - | 22.00 | - | 14400 | 1410.5 |
60-5d | - | 8318.1 | 10172.5 | - | 22.29 | - | 14400 | 1396.3 |
60-5e | - | 8372.7 | 10135.2 | - | 21.05 | - | 14400 | 1389.3 |
80-3a | - | 6288.6 | 7419.3 | - | 17.98 | - | 18000 | 1523.4 |
80-3b | - | 5992.0 | 7135.8 | - | 19.09 | - | 18000 | 1612.3 |
80-3c | - | 6431.5 | 7683.1 | - | 19.46 | - | 18000 | 1496.8 |
80-3d | - | 5785.2 | 6836.7 | - | 18.18 | - | 18000 | 1527.4 |
80-3e | - | 6104.0 | 7264.1 | - | 19.01 | - | 18000 | 1559.3 |
80-5a | - | 8670.0 | 10889.2 | - | 25.60 | - | 18000 | 1963.2 |
80-5b | - | 8260.9 | 10207.5 | - | 23.56 | - | 18000 | 2001.3 |
80-5c | - | 8855.8 | 11163.3 | - | 26.06 | - | 18000 | 1989.7 |
80-5d | - | 8611.5 | 10627.6 | - | 23.41 | - | 18000 | 1967.3 |
80-5e | - | 8596.7 | 10703.1 | - | 24.50 | - | 18000 | 1995.3 |
Instances | |||||
7-3 | 0 | 0 | 0 | 87.78 | 8.06 |
7-5 | 0 | 0 | 0 | 259.44 | 20.4 |
13-3 | 40.76 | 12.14 | 20.12 | 3600 | 26.3 |
13-5 | 46.76 | 16.22 | 20.71 | 3600 | 40.76 |
20-3 | 46.69 | 9.57 | 25.30 | 5400 | 135.9 |
20-5 | 74.87 | 13.38 | 35.16 | 5400 | 211.7 |
30-3 | 86.74 | 13.61 | 39.16 | 7200 | 289.1 |
30-5 | 99.32 | 19.21 | 40.30 | 7200 | 354.5 |
40-3 | 115.58 | 19.13 | 44.63 | 10800 | 475.1 |
40-5 | - | 23.92 | - | 10800 | 600.4 |
60-3 | 149.82 | 16.76 | 53.30 | 14400 | 994.0 |
60-5 | - | 22.38 | - | 14400 | 1410.0 |
80-3 | - | 18.74 | - | 18000 | 1543.8 |
80-5 | - | 24.63 | - | 18000 | 1983.4 |
Instances | |||||
7-3 | 0 | 0 | 0 | 87.78 | 8.06 |
7-5 | 0 | 0 | 0 | 259.44 | 20.4 |
13-3 | 40.76 | 12.14 | 20.12 | 3600 | 26.3 |
13-5 | 46.76 | 16.22 | 20.71 | 3600 | 40.76 |
20-3 | 46.69 | 9.57 | 25.30 | 5400 | 135.9 |
20-5 | 74.87 | 13.38 | 35.16 | 5400 | 211.7 |
30-3 | 86.74 | 13.61 | 39.16 | 7200 | 289.1 |
30-5 | 99.32 | 19.21 | 40.30 | 7200 | 354.5 |
40-3 | 115.58 | 19.13 | 44.63 | 10800 | 475.1 |
40-5 | - | 23.92 | - | 10800 | 600.4 |
60-3 | 149.82 | 16.76 | 53.30 | 14400 | 994.0 |
60-5 | - | 22.38 | - | 14400 | 1410.0 |
80-3 | - | 18.74 | - | 18000 | 1543.8 |
80-5 | - | 24.63 | - | 18000 | 1983.4 |
Instances | System without Freight bus | System with Freight bus | Cost Saving in percentage | |
Small size instances | 7-3 | 1237.50 | 1019.7 | 17.6 |
7-5 | 2363.04 | 1930.6 | 18.3 | |
13-3 | 1779.70 | 1429.1 | 19.7 | |
13-5 | 3400.00 | 2713.2 | 20.2 | |
Medium size instances | 20-3 | 2818.08 | 2167.1 | 23.1 |
20-5 | 3938.60 | 3040.6 | 22.8 | |
30-3 | 3909.40 | 2951.6 | 24.5 | |
30-5 | 6635.24 | 4989.7 | 24.8 | |
40-3 | 4625.98 | 3437.1 | 25.7 | |
40-5 | 8168.02 | 6028 | 26.2 | |
Large size instances | 60-3 | 7749.36 | 5478.8 | 29.3 |
60-5 | 14186.98 | 9916.7 | 30.1 | |
80-3 | 10945.48 | 7267.8 | 33.6 | |
80-5 | 16565.84 | 10718.1 | 35.3 |
Instances | System without Freight bus | System with Freight bus | Cost Saving in percentage | |
Small size instances | 7-3 | 1237.50 | 1019.7 | 17.6 |
7-5 | 2363.04 | 1930.6 | 18.3 | |
13-3 | 1779.70 | 1429.1 | 19.7 | |
13-5 | 3400.00 | 2713.2 | 20.2 | |
Medium size instances | 20-3 | 2818.08 | 2167.1 | 23.1 |
20-5 | 3938.60 | 3040.6 | 22.8 | |
30-3 | 3909.40 | 2951.6 | 24.5 | |
30-5 | 6635.24 | 4989.7 | 24.8 | |
40-3 | 4625.98 | 3437.1 | 25.7 | |
40-5 | 8168.02 | 6028 | 26.2 | |
Large size instances | 60-3 | 7749.36 | 5478.8 | 29.3 |
60-5 | 14186.98 | 9916.7 | 30.1 | |
80-3 | 10945.48 | 7267.8 | 33.6 | |
80-5 | 16565.84 | 10718.1 | 35.3 |
[1] |
Bin Feng, Lixin Wei, Ziyu Hu. An adaptive large neighborhood search algorithm for Vehicle Routing Problem with Multiple Time Windows constraints. Journal of Industrial and Management Optimization, 2021 doi: 10.3934/jimo.2021197 |
[2] |
Shengyang Jia, Lei Deng, Quanwu Zhao, Yunkai Chen. An adaptive large neighborhood search heuristic for multi-commodity two-echelon vehicle routing problem with satellite synchronization. Journal of Industrial and Management Optimization, 2022 doi: 10.3934/jimo.2021225 |
[3] |
Rodolfo Mendoza-Gómez, Roger Z. Ríos-Mercado, Karla B. Valenzuela-Ocaña. An iterated greedy algorithm with variable neighborhood descent for the planning of specialized diagnostic services in a segmented healthcare system. Journal of Industrial and Management Optimization, 2020, 16 (2) : 857-885. doi: 10.3934/jimo.2018182 |
[4] |
Jiuping Xu, Pei Wei. Production-distribution planning of construction supply chain management under fuzzy random environment for large-scale construction projects. Journal of Industrial and Management Optimization, 2013, 9 (1) : 31-56. doi: 10.3934/jimo.2013.9.31 |
[5] |
Zhou Sheng, Gonglin Yuan, Zengru Cui, Xiabin Duan, Xiaoliang Wang. An adaptive trust region algorithm for large-residual nonsmooth least squares problems. Journal of Industrial and Management Optimization, 2018, 14 (2) : 707-718. doi: 10.3934/jimo.2017070 |
[6] |
Tao Zhang, Yue-Jie Zhang, Qipeng P. Zheng, P. M. Pardalos. A hybrid particle swarm optimization and tabu search algorithm for order planning problems of steel factories based on the Make-To-Stock and Make-To-Order management architecture. Journal of Industrial and Management Optimization, 2011, 7 (1) : 31-51. doi: 10.3934/jimo.2011.7.31 |
[7] |
Ming-Jong Yao, Yu-Chun Wang. Theoretical analysis and a search procedure for the joint replenishment problem with deteriorating products. Journal of Industrial and Management Optimization, 2005, 1 (3) : 359-375. doi: 10.3934/jimo.2005.1.359 |
[8] |
Lou Caccetta, Elham Mardaneh. Joint pricing and production planning for fixed priced multiple products with backorders. Journal of Industrial and Management Optimization, 2010, 6 (1) : 123-147. doi: 10.3934/jimo.2010.6.123 |
[9] |
Peng Guo, Wenming Cheng, Yi Wang. A general variable neighborhood search for single-machine total tardiness scheduling problem with step-deteriorating jobs. Journal of Industrial and Management Optimization, 2014, 10 (4) : 1071-1090. doi: 10.3934/jimo.2014.10.1071 |
[10] |
Adel Dabah, Ahcene Bendjoudi, Abdelhakim AitZai. An efficient Tabu Search neighborhood based on reconstruction strategy to solve the blocking job shop scheduling problem. Journal of Industrial and Management Optimization, 2017, 13 (4) : 2015-2031. doi: 10.3934/jimo.2017029 |
[11] |
Jianjun Liu, Min Zeng, Yifan Ge, Changzhi Wu, Xiangyu Wang. Improved Cuckoo Search algorithm for numerical function optimization. Journal of Industrial and Management Optimization, 2020, 16 (1) : 103-115. doi: 10.3934/jimo.2018142 |
[12] |
Qin Sheng, David A. Voss, Q. M. Khaliq. An adaptive splitting algorithm for the sine-Gordon equation. Conference Publications, 2005, 2005 (Special) : 792-797. doi: 10.3934/proc.2005.2005.792 |
[13] |
Kegui Chen, Xinyu Wang, Min Huang, Wai-Ki Ching. Salesforce contract design, joint pricing and production planning with asymmetric overconfidence sales agent. Journal of Industrial and Management Optimization, 2017, 13 (2) : 873-899. doi: 10.3934/jimo.2016051 |
[14] |
Leong-Kwan Li, Sally Shao. Convergence analysis of the weighted state space search algorithm for recurrent neural networks. Numerical Algebra, Control and Optimization, 2014, 4 (3) : 193-207. doi: 10.3934/naco.2014.4.193 |
[15] |
Behrouz Kheirfam, Morteza Moslemi. On the extension of an arc-search interior-point algorithm for semidefinite optimization. Numerical Algebra, Control and Optimization, 2018, 8 (2) : 261-275. doi: 10.3934/naco.2018015 |
[16] |
Kien Ming Ng, Trung Hieu Tran. A parallel water flow algorithm with local search for solving the quadratic assignment problem. Journal of Industrial and Management Optimization, 2019, 15 (1) : 235-259. doi: 10.3934/jimo.2018041 |
[17] |
Mohamed A. Tawhid, Ahmed F. Ali. An effective hybrid firefly algorithm with the cuckoo search for engineering optimization problems. Mathematical Foundations of Computing, 2018, 1 (4) : 349-368. doi: 10.3934/mfc.2018017 |
[18] |
Yi Jing, Wenchuan Li. Integrated recycling-integrated production - distribution planning for decentralized closed-loop supply chain. Journal of Industrial and Management Optimization, 2018, 14 (2) : 511-539. doi: 10.3934/jimo.2017058 |
[19] |
Xuefeng Zhang, Hui Yan. Image enhancement algorithm using adaptive fractional differential mask technique. Mathematical Foundations of Computing, 2019, 2 (4) : 347-359. doi: 10.3934/mfc.2019022 |
[20] |
Zheng-Ru Zhang, Tao Tang. An adaptive mesh redistribution algorithm for convection-dominated problems. Communications on Pure and Applied Analysis, 2002, 1 (3) : 341-357. doi: 10.3934/cpaa.2002.1.341 |
2021 Impact Factor: 1.411
Tools
Metrics
Other articles
by authors
[Back to Top]