Vehicle Routing Problem with Fuzzy Due-time Using Genetic Algorithms

Cheng, Runwei; Gen, Mitsuo; Tozawa, Tatsumi

doi:10.3156/jfuzzy.7.5_1050

Cited by 70 publications

(25 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To solve this bi-objective problem they consider a two-stage algorithm in which defuzzification of the values of each satisfaction level (corresponding to time windows) must be calculated separately. It is notable that originally the concept of fuzzy die-time in vehicle routing and scheduling context is defined by Cheng et al (1995). They believe the customers' preferences for services can be classified into two kinds: the tolerable and desirable interval of service time.…”

Section: Introductionmentioning

confidence: 99%

A green vehicle routing problem with customer satisfaction criteria

et al. 2016

View full text Add to dashboard Cite

This paper develops an MILP model, named Satisfactory-Green Vehicle Routing Problem. It consists of routing a heterogeneous fleet of vehicles in order to serve a set of customers within predefined time windows. In this model in addition to the traditional objective of the VRP, both the pollution and customers' satisfaction have been taken into account. Meanwhile, the introduced model prepares an effective dashboard for decision-makers that determines appropriate routes, the best mixed fleet, speed and idle time of vehicles. Additionally, some new factors evaluate the greening of each decision based on three criteria. This model applies piecewise linear functions (PLFs) to linearize a nonlinear fuzzy interval for incorporating customers' satisfaction into other linear objectives. We have presented a mixed integer linear programming formulation for the S-GVRP. This model enriches managerial insights by providing trade-offs between customers' satisfaction, total costs and emission levels. Finally, we have provided a numerical study for showing the applicability of the model.

show abstract

Section: Introductionmentioning

confidence: 99%

A green vehicle routing problem with customer satisfaction criteria

et al. 2016

View full text Add to dashboard Cite

show abstract

“…At present, there are mainly three types of routing algorithms: heuristics (Tijms, 1994;Christofides, 1985;Viswanathan and Mathur, 1997;Chao, 2002), mathematical programming (Popken, 1994;Wendy et al, 1999), and genetic algorithms (Gabbert et al, 1991;Chen and Gen, 1996;Chen et al, 1998). Ma and Davidrajuh (2005) propose genetic algorithms for routing, for ease of implementation.…”

Section: The Tactical Modulementioning

confidence: 99%

Structures for Stepwise Development of Adaptive Supply Chains

Davidrajuh¹

2006

Journal of Internet Commerce

View full text Add to dashboard Cite

ABSTRACT. Falling margins, globalization, and accelerating innova-tion cycles are forcing businesses to switch from traditional (linear and sequential) supply chains to adaptive supply chains that possess the flexibility needed to respond to changing market condition. Developing an adaptive supply chain is no easy task as it is large and complex. In this paper, first, a literature study is given on structures for developing adaptive supply chains; the purpose of this study is to identify the commonly used structures for design and development of some modern supply chain solutions. Second, a new iterative approach to developing an adaptive distribution supply chain is presented.

show abstract

“…Cheng et al [5] introduced the fuzzy vehicle routing and scheduling problems and proposed solution methods using genetic algorithms. They also introduced the idea of using fuzzy due-times to represent customers' preferences in VRPTW.…”

Section: Introductionmentioning

confidence: 99%

Cooperative coevolutionary algorithms for fuzzy vehicular routing problem: An analysis of efficiency vs. geographical distribution

Polat

Acan

Ünveren

2007

2007 IEEE Congress on Evolutionary Computation

View full text Add to dashboard Cite

In this article, a cooperative coevolutionary genetic algorithm for the solution of fuzzy vehicular routing problem (FVRP) is presented. FVRP is a variant of VRP with time windows; based on additional use of fuzzy due-times for customers' preferences. The objectives of FVRP are the minimization of total number of vehicles in service, total travel distance, and the total waiting time over all vehicles; while maximizing the average grade of customer satisfaction.Benchmark problem instances classified by geographical distribution of customers are used in experimental studies and results of cooperative coevolutionary GAs are compared with those of panmictic GAs. It has been observed that cooperative coevolutionary approach generates significantly improved solutions for multi-cluster tightly grouped problem instances, while it also performs equally well for other problem classes.

show abstract

Vehicle Routing Problem with Fuzzy Due-time Using Genetic Algorithms

Cited by 70 publications

References 3 publications

A green vehicle routing problem with customer satisfaction criteria

A green vehicle routing problem with customer satisfaction criteria

Structures for Stepwise Development of Adaptive Supply Chains

Cooperative coevolutionary algorithms for fuzzy vehicular routing problem: An analysis of efficiency vs. geographical distribution

Contact Info

Product

Resources

About