TY - GEN
T1 - Tabu Search with Multiple Decision Levels for Solving Heterogeneous Fleet Pollution Routing Problem
AU - Salcedo-Moncada, Bryan F.
AU - Morillo-Torres, Daniel
AU - Gatica, Gustavo
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Organizations, in order to gain a competitive advantage, must improve their logistics performance along with the planning and distribution of their goods. Thus, they face significant challenges in managing their orders to be delivered on time. However, transportation is responsible for of the emissions of the total polluting gases in the atmosphere. Therefore, there is a growing interest to investigate methods to optimize logistics and to consider environmental aspects. However, the literature only considers realistic system characteristics such as: different vehicles and speeds, time windows and route inclination. For this reason, the focus is on the solution of an extension with a heterogeneous fleet and discrete speeds of the Vehicle Routing Pollution Problem (PRP), whose objective is the reduction of greenhouse gases (GHG). Based on the MEET model, the main polluting gases with the greatest impact on health are measured: carbon dioxide, nitrogen dioxide and carbon monoxide (CO). For its solution, a Tabu Search metaheuristic is proposed with different decision levels: node sequence, assigned speeds and vehicles used, from different neighborhood structures. Finally, the balance between exploration and exploitation is achieved by incorporating favorable attributes to the created solutions. The proposed metaheuristic achieves efficient results both in total logistic cost and in emissions released to the environment.
AB - Organizations, in order to gain a competitive advantage, must improve their logistics performance along with the planning and distribution of their goods. Thus, they face significant challenges in managing their orders to be delivered on time. However, transportation is responsible for of the emissions of the total polluting gases in the atmosphere. Therefore, there is a growing interest to investigate methods to optimize logistics and to consider environmental aspects. However, the literature only considers realistic system characteristics such as: different vehicles and speeds, time windows and route inclination. For this reason, the focus is on the solution of an extension with a heterogeneous fleet and discrete speeds of the Vehicle Routing Pollution Problem (PRP), whose objective is the reduction of greenhouse gases (GHG). Based on the MEET model, the main polluting gases with the greatest impact on health are measured: carbon dioxide, nitrogen dioxide and carbon monoxide (CO). For its solution, a Tabu Search metaheuristic is proposed with different decision levels: node sequence, assigned speeds and vehicles used, from different neighborhood structures. Finally, the balance between exploration and exploitation is achieved by incorporating favorable attributes to the created solutions. The proposed metaheuristic achieves efficient results both in total logistic cost and in emissions released to the environment.
KW - Heterogeneous fleet
KW - Pollution Routing Problem
KW - Tabu search
UR - http://www.scopus.com/inward/record.url?scp=85149683417&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-26504-4_5
DO - 10.1007/978-3-031-26504-4_5
M3 - Conference contribution
AN - SCOPUS:85149683417
SN - 9783031265037
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 61
EP - 75
BT - Metaheuristics - 14th International Conference, MIC 2022, Proceedings
A2 - Di Gaspero, Luca
A2 - Festa, Paola
A2 - Nakib, Amir
A2 - Pavone, Mario
PB - Springer Science and Business Media Deutschland GmbH
T2 - 14th Metaheuristics International Conference, MIC 2022
Y2 - 11 July 2022 through 14 July 2022
ER -