A genetic algorithm with exact dynamic programming for the green vehicle routing & scheduling problem

Yiyong Xiao, Abdullah Konak

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

Traffic congestion significantly increases CO2 (a well-known greenhouse gas) emissions of vehicles in road transportation and causes other environmental costs as well. A road-based delivery company can reduce its CO2 emissions through operational decisions such as efficient vehicle routes and delivery schedules by considering time-varying traffic congestion in its service area. In this paper, we study the time-dependent vehicle routing & scheduling problem with CO2 emissions optimization (TD-VRSP-CO2) and develop an exact dynamic programming algorithm to determine the optimal vehicle schedules for given vehicle routes. A hybrid solution approach that combines a genetic algorithm with the exact dynamic programming procedure (GA-DP) is proposed as an efficient solution approach for the TD-VRSP-CO2. Computational experiments on 30 small-sized instances and 14 large-sized instances are used to study the efficiency and effectiveness of the proposed hybrid optimization approach with promising results. Contributions of this study can help road-based delivery companies be ready for a low-carbon economy and also help individual vehicle drivers make better vehicle scheduling plans with lower CO2 emissions and fuel consumption.

Original languageEnglish (US)
Pages (from-to)1450-1463
Number of pages14
JournalJournal of Cleaner Production
Volume167
DOIs
StatePublished - Nov 20 2017

Fingerprint

Vehicle routing
routing
Dynamic programming
genetic algorithm
Genetic algorithms
Scheduling
Traffic congestion
traffic congestion
road
Gas emissions
Greenhouse gases
Fuel consumption
fuel consumption
vehicle
Roads
CO2 emissions
Co2
Genetic algorithm
Industry
greenhouse gas

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Environmental Science(all)
  • Strategy and Management
  • Industrial and Manufacturing Engineering

Cite this

@article{ead0d5e9c3aa40658918d89c4a7b9054,
title = "A genetic algorithm with exact dynamic programming for the green vehicle routing & scheduling problem",
abstract = "Traffic congestion significantly increases CO2 (a well-known greenhouse gas) emissions of vehicles in road transportation and causes other environmental costs as well. A road-based delivery company can reduce its CO2 emissions through operational decisions such as efficient vehicle routes and delivery schedules by considering time-varying traffic congestion in its service area. In this paper, we study the time-dependent vehicle routing & scheduling problem with CO2 emissions optimization (TD-VRSP-CO2) and develop an exact dynamic programming algorithm to determine the optimal vehicle schedules for given vehicle routes. A hybrid solution approach that combines a genetic algorithm with the exact dynamic programming procedure (GA-DP) is proposed as an efficient solution approach for the TD-VRSP-CO2. Computational experiments on 30 small-sized instances and 14 large-sized instances are used to study the efficiency and effectiveness of the proposed hybrid optimization approach with promising results. Contributions of this study can help road-based delivery companies be ready for a low-carbon economy and also help individual vehicle drivers make better vehicle scheduling plans with lower CO2 emissions and fuel consumption.",
author = "Yiyong Xiao and Abdullah Konak",
year = "2017",
month = "11",
day = "20",
doi = "10.1016/j.jclepro.2016.11.115",
language = "English (US)",
volume = "167",
pages = "1450--1463",
journal = "Journal of Cleaner Production",
issn = "0959-6526",
publisher = "Elsevier Limited",

}

A genetic algorithm with exact dynamic programming for the green vehicle routing & scheduling problem. / Xiao, Yiyong; Konak, Abdullah.

In: Journal of Cleaner Production, Vol. 167, 20.11.2017, p. 1450-1463.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A genetic algorithm with exact dynamic programming for the green vehicle routing & scheduling problem

AU - Xiao, Yiyong

AU - Konak, Abdullah

PY - 2017/11/20

Y1 - 2017/11/20

N2 - Traffic congestion significantly increases CO2 (a well-known greenhouse gas) emissions of vehicles in road transportation and causes other environmental costs as well. A road-based delivery company can reduce its CO2 emissions through operational decisions such as efficient vehicle routes and delivery schedules by considering time-varying traffic congestion in its service area. In this paper, we study the time-dependent vehicle routing & scheduling problem with CO2 emissions optimization (TD-VRSP-CO2) and develop an exact dynamic programming algorithm to determine the optimal vehicle schedules for given vehicle routes. A hybrid solution approach that combines a genetic algorithm with the exact dynamic programming procedure (GA-DP) is proposed as an efficient solution approach for the TD-VRSP-CO2. Computational experiments on 30 small-sized instances and 14 large-sized instances are used to study the efficiency and effectiveness of the proposed hybrid optimization approach with promising results. Contributions of this study can help road-based delivery companies be ready for a low-carbon economy and also help individual vehicle drivers make better vehicle scheduling plans with lower CO2 emissions and fuel consumption.

AB - Traffic congestion significantly increases CO2 (a well-known greenhouse gas) emissions of vehicles in road transportation and causes other environmental costs as well. A road-based delivery company can reduce its CO2 emissions through operational decisions such as efficient vehicle routes and delivery schedules by considering time-varying traffic congestion in its service area. In this paper, we study the time-dependent vehicle routing & scheduling problem with CO2 emissions optimization (TD-VRSP-CO2) and develop an exact dynamic programming algorithm to determine the optimal vehicle schedules for given vehicle routes. A hybrid solution approach that combines a genetic algorithm with the exact dynamic programming procedure (GA-DP) is proposed as an efficient solution approach for the TD-VRSP-CO2. Computational experiments on 30 small-sized instances and 14 large-sized instances are used to study the efficiency and effectiveness of the proposed hybrid optimization approach with promising results. Contributions of this study can help road-based delivery companies be ready for a low-carbon economy and also help individual vehicle drivers make better vehicle scheduling plans with lower CO2 emissions and fuel consumption.

UR - http://www.scopus.com/inward/record.url?scp=85007454331&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85007454331&partnerID=8YFLogxK

U2 - 10.1016/j.jclepro.2016.11.115

DO - 10.1016/j.jclepro.2016.11.115

M3 - Article

AN - SCOPUS:85007454331

VL - 167

SP - 1450

EP - 1463

JO - Journal of Cleaner Production

JF - Journal of Cleaner Production

SN - 0959-6526

ER -