A chance constrained programming approach for HazMat capacitated vehicle routing problem in Type-2 fuzzy environment

This work focuses on a HazMat capacitated vehicle routing problem (H-CVRP) in type-2 fuzzy environment, which aims to determine a set of routes with the minimum transportation risk. Since uncertainty can lead to significant differences in transportation risk, we propose a H-CVRP model with the objec...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Journal of cleaner production Ročník 237; s. 117754
Hlavní autori: Men, Jinkun, Jiang, Peng, Xu, Huan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 10.11.2019
Predmet:
algorithms
Chance constrained programming
deterministic models
HazMat capacitated vehicle routing problem
Interval type-2 fuzzy variable
risk
Simulated annealing algorithm
transportation
uncertainty
Simulated annealing algorithm
HazMat capacitated vehicle routing problem
Chance constrained programming
Interval type-2 fuzzy variable
ISSN:0959-6526, 1879-1786
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:This work focuses on a HazMat capacitated vehicle routing problem (H-CVRP) in type-2 fuzzy environment, which aims to determine a set of routes with the minimum transportation risk. Since uncertainty can lead to significant differences in transportation risk, we propose a H-CVRP model with the objective function involving trapezoidal interval type-2 fuzzy variables (IT2-FVs). Based on the credibility measure, a chance constrained programming (CCP) approach is employed to transform the H-CVRP model into its equivalent deterministic form. A simulated annealing algorithm (SAA) is designed to solve the equivalent deterministic model. The proposed SAA is a global optimization algorithm, which converges to the optimal solution with probability and has high parallelism. To test the performance of the proposed algorithm, the optimal solutions obtained by SAA are compared with the counterparts obtained by genetic algorithm (GA) and tabu search (TS). Experimental results indicate that the proposed SAA is competitive in terms of stability and efficiency. At last, a sensitivity analysis is presented to demonstrate the applicability of the proposed method.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0959-6526
1879-1786
DOI:10.1016/j.jclepro.2019.117754