<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This study describes a pickup and delivery vehicle routing problem, considering time windows in reality. The problem of tractor truck routes is formulated by a mixed integer programming model. Besides this, three algorithms - a guided local search, a tabu search, and simulated annealing - are proposed as solutions. The aims of our study are to optimize the number of internal tractor trucks used, and create optimal routes in order to minimize total logistics costs, including the fixed and variable costs of an internal vehicle group and the renting cost of external vehicles. Besides, our study also evaluates both the quality of solutions and the time to find optimal solutions to select the best suitable algorithm for the real problem mentioned above. A novel mathematical model is formulated by OR tools for Python. Compared to the current solution, our results reduced total costs by 18%, increased the proportion of orders completed by internal vehicles (84%), and the proportion of orders delivered on time (100%). Our study provides a mathematical model with time constraints and large job volumes for a complex distribution network in reality. The proposed mathematical model provides effective solutions for making decisions at logistics companies. Furthermore, our study emphasizes that simulated annealing is a more suitable algorithm than the two others for this vehicle routing problem.</p></abstract>

This study describes a pickup and delivery vehicle routing problem, considering time windows in reality. The problem of tractor truck routes is formulated by a mixed integer programming model. Besides this, three algorithms - a guided local search, a tabu search, and simulated annealing - are proposed as solutions. The aims of our study are to optimize the number of internal tractor trucks used, and create optimal routes in order to minimize total logistics costs, including the fixed and variable costs of an internal vehicle group and the renting cost of external vehicles. Besides, our study also evaluates both the quality of solutions and the time to find optimal solutions to select the best suitable algorithm for the real problem mentioned above. A novel mathematical model is formulated by OR tools for Python. Compared to the current solution, our results reduced total costs by 18%, increased the proportion of orders completed by internal vehicles (84%), and the proportion of orders delivered on time (100%). Our study provides a mathematical model with time constraints and large job volumes for a complex distribution network in reality. The proposed mathematical model provides effective solutions for making decisions at logistics companies. Furthermore, our study emphasizes that simulated annealing is a more suitable algorithm than the two others for this vehicle routing problem.

Optimal vehicle route schedules in picking up and delivering cargo containers considering time windows in logistics distribution networks: A case study

Ho Chi Minh City University of Technology-VNU

TRADE Research Entity, North-West University

Production Engineering Archives

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

This study describes a pickup and delivery vehicle routing problem, considering time windows in reality. The problem of tractor truck routes is formulated by a...

Optimal vehicle route schedules in picking up and delivering cargo containers considering time windows in logistics distribution networks: A case study

Published Online: Dec 31, 2020

Page range: 174 - 184

Received: Aug 26, 2020

Accepted: Oct 12, 2020

DOI: https://doi.org/10.30657/pea.2020.26.31

Keywords
Vehicle routing problem, Logistics distribution network, Time window, Guided local search, Tabu search, Simulated annealing

© 2020 Thi Diem Chau Le et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Optimal vehicle route schedules in picking up and delivering cargo containers considering time windows in logistics distribution networks: A case study

Published Online: Dec 31, 2020

Page range: 174 - 184

Received: Aug 26, 2020

Accepted: Oct 12, 2020

DOI: https://doi.org/10.30657/pea.2020.26.31

KeywordsVehicle routing problem, Logistics distribution network, Time window, Guided local search, Tabu search, Simulated annealing

© 2020 Thi Diem Chau Le et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Keywords
Vehicle routing problem, Logistics distribution network, Time window, Guided local search, Tabu search, Simulated annealing