A novel multi-objective green vehicle routing and scheduling model with stochastic demand, supply, and variable travel times

[Display omitted] •We propose an optimization approach to stochastic routing and scheduling problems.•Our transport network considers stochastic demand, supply and variable travel times.•We limit continuous driving time and define soft service time windows at the nodes.•We simultaneously minimize co...

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Vydané v:Computers & operations research Ročník 141; s. 105698
Hlavní autori: Zarouk, Yaser, Mahdavi, Iraj, Rezaeian, Javad, Santos-Arteaga, Francisco J.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Elsevier Ltd 01.05.2022
Pergamon Press Inc
Predmet:
Customer satisfaction
Customer services
Driver fatigue
Energy consumption
Exact solutions
Genetic algorithms
GVRSP
Heterogeneous vehicle routing
Heuristic methods
Hybrid optimization models
Integer programming
Mixed integer
Multi-objective stochastic programming
Nodes
Operations research
Optimization
Parameter modification
Route planning
Scheduling
Simulated annealing
Supply chains
Time window
Transportation networks
Travel time
Vehicle routing
Windows (intervals)
GVRSP
Driver fatigue
Heterogeneous vehicle routing
Multi-objective stochastic programming
Time window
Hybrid optimization models
ISSN:0305-0548, 1873-765X, 0305-0548
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Popis
Shrnutí:[Display omitted] •We propose an optimization approach to stochastic routing and scheduling problems.•Our transport network considers stochastic demand, supply and variable travel times.•We limit continuous driving time and define soft service time windows at the nodes.•We simultaneously minimize cost of energy consumed and maximize customer satisfaction.•Hybrid approaches are used to solve the multi-objective mixed integer programming model. We propose an optimization approach to the routing and scheduling problem for a heterogeneous transportation network that considers (i) stochastic values of demand and supply at the nodes, (ii) variable travel times between nodes conditioned by the fatigue of drivers, (iii) maximum allowed continuous driving time, and (iv) soft service time windows per customer at the nodes. The problem minimizes energy consumption and maximizes customer satisfaction. The subsequent stochastic multi-objective mixed integer programming model is solved using a hybrid approach based on chance- and epsilon-constraint methods. Given the NP-hard quality of the model, we introduce a hybrid meta-heuristic method based on genetic algorithm (GA) and simulated annealing (SA). This novel technique, named MOGASA, combines the global and local search capacities of both meta-heuristic algorithms, providing an intuitive solution approach that allows to solve problem instances considering large distribution networks with multiple types of vehicles in reasonable CPU time. We illustrate how MOGASA improves upon the hybrid chance- and epsilon-constraint exact solution method, particularly when dealing with large problem instances that cannot be solved by the latter. A medium instance scenario is used to analyze the reaction of the objective functions and the subsequent Pareto frontiers to modifications in the main structural parameters defining the transportation network. Potential applications of our stochastic framework to different types of logistic structures and retail supply chains are highlighted.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2022.105698