Processing, Please wait...

  • Publisher Home
  • Home
  • 🔙 Back
  • 📚 Journals
    • ⚙️ IJIEC - Industrial Engineering Computations
    • 🌐 IJDNS - Data and Network Science
    • 🧪 CCL - Current Chemistry Letters
    • 💹 AC - Accounting
    • 🎯 DSL - Decision Science Letters
    • 🚛 USCM - Uncertain Supply Chain Management
    • 🏗️ JPM - Journal of Project Management
    • 🏥 HE - Healthcare Engineering
    • 📈 SCI - Scientometrica
    • 🔩 ESM - Engineering Solid Mechanics
    • 🌿 JFS - Journal of Future Sustainability
    • 💼 MSL - Management Science Letters
  • 📝 Submit Article
  • 📊 Statistics
  • 📋 About
    • 📄 About Us
    • 📰 Blog
    • 📢 News
    • 📧 Contact
  • 📺 Tutorial
  • Search:
  • Advanced Search

Growing Science » Uncertain Supply Chain Management » A multi-depot location routing problem to reduce the differences between the vehicles’ traveled distances; a comparative study of heuristics

⭐ Highly Cited Articles

  • Jaya Algorithm
  • Rao Algorithm
  • TLBO Algorithm
  • ChatGPT and Blended Learning

Journals

  • IJIEC (840)
  • IJDS (1032)
  • DSL (759)
  • ESM (434)
  • CCL (563)
  • JPM (350)
  • AC (572)
  • JFS (106)
  • MSL (2658)
  • USCM (1104)
  • HE (54)
  • SCI (53)

USCM Volumes

    • ▼ Volume 15 (5)
      • Issue 1 (5)
    • ▼ Volume 14 (22)
      • Issue 1 (5)
      • Issue 2 (5)
      • Issue 3 (5)
      • Issue 4 (7)
    • ▼ Volume 13 (62)
      • Issue 1 (15)
      • Issue 2 (15)
      • Issue 3 (15)
      • Issue 4 (17)
    • ▼ Volume 12 (244)
      • Issue 1 (55)
      • Issue 2 (59)
      • Issue 3 (63)
      • Issue 4 (67)
    • ▼ Volume 11 (190)
      • Issue 1 (42)
      • Issue 2 (45)
      • Issue 3 (50)
      • Issue 4 (53)
    • ▼ Volume 10 (150)
      • Issue 1 (28)
      • Issue 2 (32)
      • Issue 3 (44)
      • Issue 4 (46)
    • ▼ Volume 9 (117)
      • Issue 1 (25)
      • Issue 2 (26)
      • Issue 3 (32)
      • Issue 4 (34)
    • ▼ Volume 8 (82)
      • Issue 1 (20)
      • Issue 2 (15)
      • Issue 3 (17)
      • Issue 4 (30)
    • ▼ Volume 7 (57)
      • Issue 1 (8)
      • Issue 2 (19)
      • Issue 3 (14)
      • Issue 4 (16)
    • ▼ Volume 6 (25)
      • Issue 1 (7)
      • Issue 2 (6)
      • Issue 3 (6)
      • Issue 4 (6)
    • ▼ Volume 5 (26)
      • Issue 1 (6)
      • Issue 2 (6)
      • Issue 3 (8)
      • Issue 4 (6)
    • ▼ Volume 4 (31)
      • Issue 1 (10)
      • Issue 2 (6)
      • Issue 3 (6)
      • Issue 4 (9)
    • ▼ Volume 3 (39)
      • Issue 1 (9)
      • Issue 2 (13)
      • Issue 3 (10)
      • Issue 4 (7)
    • ▼ Volume 2 (32)
      • Issue 1 (7)
      • Issue 2 (5)
      • Issue 3 (10)
      • Issue 4 (10)
    • ▼ Volume 1 (22)
      • Issue 1 (4)
      • Issue 2 (6)
      • Issue 3 (6)
      • Issue 4 (6)

🔑 Keywords

Jordan(175)
Supply chain management(172)
Vietnam(154)
Customer satisfaction(124)
Performance(117)
Supply chain(115)
Artificial intelligence(108)
Service quality(101)
Competitive advantage(100)
SMEs(95)
Sustainability(94)
Tehran Stock Exchange(94)
optimization(88)
Financial performance(86)
TOPSIS(85)
Trust(85)
Genetic Algorithm(81)
Organizational performance(81)
Job satisfaction(81)
Social media(80)


» Show all keywords

✍️ Authors

Naser Azad(82)
Zeplin Jiwa Husada Tarigan(70)
Mohammad Reza Iravani(65)
Endri Endri(45)
Hotlan Siagian(43)
Muhammad Alshurideh(42)
Dmaithan Almajali(39)
Jumadil Saputra(37)
Muhammad Turki Alshurideh(35)
Ahmad Makui(33)
Sautma Ronni Basana(33)
Barween Al Kurdi(32)
Basrowi Basrowi(31)
Mohammad Khodaei Valahzaghard(30)
Haitham M. Alzoubi(30)
Ni Nyoman Kerti Yasa(30)
Hassan Ghodrati(30)
Shankar Chakraborty(29)
Sulieman Ibraheem Shelash Al-Hawary(28)
Mahmoud Allahham(28)


» Show all authors

🌍 Countries

1. Algeria (52)
2. Angola (2)
3. Argentina (22)
4. Armenia (2)
5. Australia (52)
6. Austria (2)
7. Bahrain (26)
8. Bangladesh (58)
9. Belarus (4)
10. Belgium (3)
11. Benin (2)
12. Benin Republic (1)
13. Bhutan (1)
14. Bosnia and Herzegovina (1)
15. Botswana (9)
16. Brazil (40)
17. Brunei (1)
18. Bulgaria (1)
19. Burkina Faso (1)
20. Cameroon (1)
Total: 121 countries

Show all countries
Uncertain Supply Chain Management
ISSN 2291-6830 (Online) - ISSN 2291-6822 (Print)
Quarterly Publication
Volume 7 Issue 1 pp. 17-32, 2019

A multi-depot location routing problem to reduce the differences between the vehicles’ traveled distances; a comparative study of heuristics Pages 17-32 PDF Download PDF

Authors: Hengameh Hadian, Amir-Mohammad Golmohammadi, Akbar Hemmati, Omolbanin Mashkani

📋 Author Affiliations:
H. Hadian ORCID 1, A.-M. Golmohammadi ORCID 2, A. Hemmati3, O. Mashkani4
1 Department of Industrial Engineering, University of Nahavand, Nahavand, Iran
2 Department of Industrial Engineering, Yazd University, Yazd, Iran
3 Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
4 University of Technology Sydney, Sydney, Australia
doi 10.5267/j.uscm.2018.6.001
Crossref 9 Source: CrossRef

🔑 Keywords: Location routing problem (LRP), Vehicle routing; Facility location, Imperialist competitive algorithm (ICA), NSGA-II

Abstract: This paper presents a model to solve the multi-objective location-routing problem with capacitated vehicles. The main purposes of the model are to find the optimal number and location of depots, the optimal number of vehicles, and the best allocation of customers to distribution centers and to the vehicles. In addition, the model seeks to optimize vehicle routes and sequence to serve the customers. The proposed model considers vehicles’ traveled distances, service time and waiting time while guaranteeing that the sum of these parameters is lower than a predetermined value. Two objective functions are investigated. First objective function minimizes the total cost of the system and the second one minimizes the gap between the vehicles’ traveled distances. To solve the problem, a Multi-Objective Imperialist Competitive Algorithm (MOICA) is developed. The efficiency of the MOICA is demonstrated via comparing with a famous meta-heuristics, named Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). Based on response surface methodology, for each algorithm, several crossover and mutation strategies are adjusted. The results, in terms of two well-known comparison metrics, indicate that the proposed MOICA outperforms NSGA-II especially in large sized problems.

How to cite this paper
APA: Hadian, H., Golmohammadi, A., Hemmati, A & Mashkani, O. (2019). A multi-depot location routing problem to reduce the differences between the vehicles’ traveled distances; a comparative study of heuristics. Uncertain Supply Chain Management, 7(1), 17-32.
Chicago/Turabian: Hadian, H., Golmohammadi, A., Hemmati, A & Mashkani, O. 2019. "A multi-depot location routing problem to reduce the differences between the vehicles’ traveled distances; a comparative study of heuristics." Uncertain Supply Chain Management 7, no. 1 (2019): 17-32.
AMA: Hadian, H., Golmohammadi, A., Hemmati, A & Mashkani, O. A multi-depot location routing problem to reduce the differences between the vehicles’ traveled distances; a comparative study of heuristics. Uncertain Supply Chain Management. 2019;7(1):17-32.

References
References

Ahn, J., de Weck, O., Geng, Y., & Klabjan, D. (2012). Column generation based heuristics for a generalized location routing problem with profits arising in space exploration. European Journal of Operational Research, 223(1), 47-59.
Atashpaz-Gargari, E., & Lucas, C. (2007). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. Paper presented at the Evolutionary computation, 2007. CEC 2007. IEEE Congress on.
Balakrishnan, A., Ward, J. E., & Wong, R. T. (1987). Integrated facility location and vehicle routing models: Recent work and future prospects. American Journal of Mathematical and Management Sciences, 7(1-2), 35-61.
Boventer, E. (1961). The relationship between transportation costs and location rent in transportation problems. Journal of Regional Science, 3(2), 27-40.
C Montgomery, D. (1997). Montgomery Design and Analysis of Experiments.
Caballero, R., González, M., Guerrero, F. M., Molina, J., & Paralera, C. (2007). Solving a multiobjective location routing problem with a metaheuristic based on tabu search. Application to a real case in Andalusia. European Journal of Operational Research, 177(3), 1751-1763.
Çetiner, S., Sepil, C., & Süral, H. (2010). Hubbing and routing in postal delivery systems. Annals of Operations Research, 181(1), 109-124.
Christofides, N., & Eilon, S. (1969). An algorithm for the vehicle-dispatching problem. Or, 309-318.
Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Lecture notes in computer science, 1917, 849-858.
Deb, K., & Pratap, A. (2002). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II.
Diabat, A. (2014). Hybrid algorithm for a vendor managed inventory system in a two-echelon supply chain. European Journal of Operational Research, 238(1), 114-121.
Drexl, M., & Schneider, M. (2015). A survey of variants and extensions of the location-routing problem. European Journal of Operational Research, 241(2), 283-308. doi: 10.1016/j.ejor.2014.08.030
Fakhrzada, M., & Esfahanib, A. S. (2013). Modeling the time windows vehicle routing problem in cross-docking strategy using two meta-heuristic algorithms. International Journal of Engineering-Transactions A: Basics, 27(7), 1113.
Goścień, R., Walkowiak, K., & Klinkowski, M. (2015). Tabu search algorithm for routing, modulation and spectrum allocation in elastic optical network with anycast and unicast traffic. Computer Networks, 79, 148-165. doi: 10.1016/j.comnet.2014.12.004
Govindan, K., Jafarian, A., Khodaverdi, R., & Devika, K. (2014). Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food. International Journal of Production Economics, 152, 9-28. doi: 10.1016/j.ijpe.2013.12.028
Gu, J., Goetschalckx, M., & McGinnis, L. F. (2007). Research on warehouse operation: A comprehensive review. European Journal of Operational Research, 177(1), 1-21. doi: 10.1016/j.ejor.2006.02.025
Jula, A., Othman, Z., & Sundararajan, E. (2015). Imperialist competitive algorithm with PROCLUS classifier for service time optimization in cloud computing service composition. Expert Systems with Applications, 42(1), 135-145. doi: 10.1016/j.eswa.2014.07.043
Karakatič, S., & Podgorelec, V. (2015). A survey of genetic algorithms for solving multi depot vehicle routing problem. Applied Soft Computing, 27, 519-532. doi: 10.1016/j.asoc.2014.11.005
Koç, Ç., Bektaş, T., Jabali, O., & Laporte, G. (2015). A hybrid evolutionary algorithm for heterogeneous fleet vehicle routing problems with time windows. Computers & Operations Research, 64, 11-27. doi: 10.1016/j.cor.2015.05.004
Kopfer, H., Schwardt, M., & Dethloff, J. (2005). Solving a continuous location‐routing problem by use of a self‐organizing map. International Journal of Physical Distribution & Logistics Management, 35(6), 390-408. doi: 10.1108/09600030510611639
Maranzana, F. (1964). On the location of supply points to minimize transport costs. OR, 261-270.
Marinakis, Y., Iordanidou, G.-R., & Marinaki, M. (2013). Particle Swarm Optimization for the Vehicle Routing Problem with Stochastic Demands. Applied Soft Computing, 13(4), 1693-1704. doi: 10.1016/j.asoc.2013.01.007
Martínez-Salazar, I. A., Molina, J., Ángel-Bello, F., Gómez, T., & Caballero, R. (2014). Solving a bi-objective Transportation Location Routing Problem by metaheuristic algorithms. European Journal of Operational Research, 234(1), 25-36. doi: 10.1016/j.ejor.2013.09.008
Megiddo, N., & Supowit, K. J. (1984). On the complexity of some common geometric location problems. SIAM journal on computing, 13(1), 182-196.
Min, H., Jayaraman, V., & Srivastava, R. (1998). Combined location-routing problems: A synthesis and future research directions. European journal of operational research, 108(1), 1-15.
Mohammadi-Ivatloo, B., Rabiee, A., Soroudi, A., & Ehsan, M. (2012). Imperialist competitive algorithm for solving non-convex dynamic economic power dispatch. Energy, 44(1), 228-240.
Mohammadkhanloo, M., & Bashiri, M. (2013). A clustering based location-allocation problem considering transportation costs and statistical properties (research note). International Journal of Engineering-Transactions C: Aspects, 26(6), 597.
Moradi, H., Zandieh, M., & Mahdavi, I. (2011). Non-dominated ranked genetic algorithm for a multi-objective mixed-model assembly line sequencing problem. International Journal of Production Research, 49(12), 3479-3499.
Nagy, G., & Salhi, S. (2007). Location-routing: Issues, models and methods. European Journal of Operational Research, 177(2), 649-672. doi: 10.1016/j.ejor.2006.04.004
Nazari-Shirkouhi, S., Eivazy, H., Ghodsi, R., Rezaie, K., & Atashpaz-Gargari, E. (2010). Solving the integrated product mix-outsourcing problem using the Imperialist Competitive Algorithm. Expert Systems with Applications, 37(12), 7615-7626. doi: 10.1016/j.eswa.2010.04.081
Norouzi, N., Sadegh-Amalnick, M., & Alinaghiyan, M. (2015). Evaluating of the particle swarm optimization in a periodic vehicle routing problem. Measurement, 62, 162-169. doi: 10.1016/j.measurement.2014.10.024
Or, I., & Pierskalla, W. P. (1979). A transportation location-allocation model for regional blood banking. AIIE transactions, 11(2), 86-95.
Prodhon, C. (2011). A hybrid evolutionary algorithm for the periodic location-routing problem. European Journal of Operational Research, 210(2), 204-212. doi: 10.1016/j.ejor.2010.09.021
Prodhon, C., & Prins, C. (2014). A survey of recent research on location-routing problems. European Journal of Operational Research, 238(1), 1-17. doi: 10.1016/j.ejor.2014.01.005
Roozbeh Nia, A., Hemmati Far, M., & Niaki, S. T. A. (2015). A hybrid genetic and imperialist competitive algorithm for green vendor managed inventory of multi-item multi-constraint EOQ model under shortage. Applied Soft Computing, 30, 353-364. doi: 10.1016/j.asoc.2015.02.004
Salhi, S., & Nagy, G. (1999). Consistency and robustness in location-routing. Studies in Locational Analysis(13), 3-19.
Salhi, S., & Rand, G. K. (1989). The effect of ignoring routes when locating depots. European journal of operational research, 39(2), 150-156.
Schwardt, M., & Fischer, K. (2008). Combined location-routing problems—a neural network approach. Annals of Operations Research, 167(1), 253-269. doi: 10.1007/s10479-008-0377-3
Setak, M., Jalili Bolhassani, S., & Karimi, H. (2014). A node-based mathematical model towards the location routing problem with intermediate replenishment facilities under capacity constraint. International Journal of Engineering, 27(6), 911-920.
Shiripour, S., Mahdavi, I., Amiri-Aref, M., Mohammadnia-Otaghsara, M., & Mahdavi-Amiri, N. (2012). Multi-facility location problems in the presence of a probabilistic line barrier: a mixed integer quadratic programming model. International Journal of Production Research, 50(15), 3988-4008. doi: 10.1080/00207543.2011.579639
Sim, K. M., & Sun, W. H. (2003). Ant colony optimization for routing and load-balancing: survey and new directions. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 33(5), 560-572.
Srinivas, N., & Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary computation, 2(3), 221-248.
Tavakkoli-Moghaddam, R., Makui, A., & Mazloomi, Z. (2010). A new integrated mathematical model for a bi-objective multi-depot location-routing problem solved by a multi-objective scatter search algorithm. Journal of Manufacturing Systems, 29(2-3), 111-119. doi: 10.1016/j.jmsy.2010.11.005
Ting, C.-J., & Chen, C.-H. (2013). A multiple ant colony optimization algorithm for the capacitated location routing problem. International Journal of Production Economics, 141(1), 34-44.
Wang, H., Du, L., & Ma, S. (2014). Multi-objective open location-routing model with split delivery for optimized relief distribution in post-earthquake. Transportation Research Part E: Logistics and Transportation Review, 69, 160-179. doi: http://dx.doi.org/10.1016/j.tre.2014.06.006
Wasner, M., & Zäpfel, G. (2004). An integrated multi-depot hub-location vehicle routing model for network planning of parcel service. International Journal of Production Economics, 90(3), 403-419.
Watson-Gandy, C., & Dohrn, P. (1973). Depot location with van salesmen—a practical approach. Omega, 1(3), 321-329.
Webb, M. (1968). Cost functions in the location of depots for multiple-delivery journeys. OR, 311-320.
Yu, V. F., & Lin, S.-Y. (2015). A simulated annealing heuristic for the open location-routing problem. Computers & Operations Research, 62, 184-196. doi: 10.1016/j.cor.2014.10.009
Zandieh, M., Dorri, B., & Khamseh, A. (2009). Robust metaheuristics for group scheduling with sequence-dependent setup times in hybrid flexible flow shops. The International Journal of Advanced Manufacturing Technology, 43(7-8), 767.
Zarandi, M. H. F., Hemmati, A., & Davari, S. (2011). The multi-depot capacitated location-routing problem with fuzzy travel times. Expert Systems with Applications, 38(8), 10075-10084.
  • 0
  • 1
  • 2
  • 3
  • 4
  • 5

📚 Journal: Uncertain Supply Chain Management | 📅 Year: 2019 | 📖 Volume: 7 | 📄 Issue: 1 | 👁️ Views: 3570 | 📊 Crossref: 9

Related Articles:
  • A sustainable transportation-location-routing problem with soft time windows for distribution systems
  • A multi-objective location routing problem using imperialist competitive algorithm
  • A multi-objective Pareto ant colony algorithm for the Multi-Depot Vehicle Routing problem with Backhauls
  • A variable neighborhood descent based heuristic to solve the capacitated location-routing problem
  • Modeling a four-layer location-routing problem

📝 Ready to share your research?

Uncertain Supply Chain Management is accepting new submissions for upcoming issues. Join our community of authors and publish your work with us.

✓ Open access
✓ Rigorous peer review
✓ Fast publication
📤 Submit Your Manuscript →

📖 Author Guidelines

® 2010-2026 GrowingScience.Com