An Improved Tabu Search Approach to Vehicle Routing Problem

Jia, Hongmei; Yang, Li; Dong, Bo; Ya, Hongying

doi:10.1016/j.sbspro.2013.08.138

Cited by 33 publications

(30 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…If the start-of-service time to the i-th customer is after , then the profit previously earned will only be equal to the amount of independent demand. Constraints (12) to (15) relate to maximizing profits. Constraint (16) indicates the difference between the profits earned in the scenarios and expected value of earning profits for all scenarios.…”

Section: Mathematical Modelmentioning

confidence: 99%

See 1 more Smart Citation

A Green Competitive Vehicle Routing Problem under Uncertainty Solved by an Improved Differential Evolution Algorithm

Fallah

Tavakkoli–Moghaddam

Salamatbakhsh-Varjovi

et al. 2019

IJE

View full text Add to dashboard Cite

A B S T R A C TRegarding the development of distribution systems in the recent decades, fuel consumption of trucks has increased noticeably, which has a huge impact on greenhouse gas emissions. For this reason, the reduction of fuel consumption has been one of the most important research areas in the last decades. The aim of this paper is to propose a robust mathematical model for a variant of a vehicle routing problem (VRP) to optimize sales of distributers, in which the time of distributor service to customers is uncertain. T o solve the model precisely, the improved differential evolution (IDE) algorithm is used and obtained results were compared with the result of a particle swarm optimization (PSO) algorithm. The results indicate that the IDE algorithm is able to obtain better solutions in solving large-sized problems; however, the computational time is worse than PSO.

show abstract

Section: Mathematical Modelmentioning

confidence: 99%

“…A VRP is classified as NP-Hard problem [14]. In recent years, different metaheuristic methods were developed in order to solve a VRP, like tabu search [15], particle swarm optimizatio n algorithm (PSO) and simulated annealing (SA) algorithm [16,17].…”

Section: Introductionmentioning

confidence: 99%

A Green Competitive Vehicle Routing Problem under Uncertainty Solved by an Improved Differential Evolution Algorithm

Fallah

Tavakkoli–Moghaddam

Salamatbakhsh-Varjovi

et al. 2019

IJE

View full text Add to dashboard Cite

show abstract

“…Some of the most commonly used are: Tabu search (TS), developed from heuristic from local search while additionally including a solution evaluation, local searching tactics, termination criteria and elements such as tabus in a list and the tabu length (Jia et al, 2013); Ant Colony Optimization (ACO), inspired in the feeding process of the ants and is a collective intelligence algorithm used to solve complex combinatorial optimization problems as shortest path problems (Ding et al, 2012); and Genetic Algorithm (GA), defined as adaptive search heuristic that operates over a population of solutions, based on the evolution principle that improves the solution using crossover and mutation process (Pereira & Tavares, 2009). Another metaheuristic used to solve VRPs using neighborhoods is the work of RinconGarcia et al (2017) that uses Large Neighborhood Search approaches and Variable Neighborhood Search techniques to guide the search.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

A hybrid expert system, clustering and ant colony optimization approach for scheduling and routing problem in courier services

López-Santana¹,

Rodríguez-Vásquez²,

Méndez-Giraldo³

2018

10.5267/j.ijiec

View full text Add to dashboard Cite

This paper focuses on the problem of scheduling and routing workers in a courier service to deliver packages for a set of geographically distributed customers and, on a specific date and time window. The crew of workers has a limited capacity and a time window that represents their labor length. The problem deals with a combination of multiples variants of the vehicle routing problem as capacity, multiple periods, time windows, due dates and distance as constraints. Since in the courier services the demands could be of hundreds or thousands of packages to be delivered, the problem is computationally unmanageable. We present a threephase solution approach. In the first phase, a scheduling model determines the visit date for each customer in the planning horizon by considering the release date, due date to visit and travel times. We use an expert system based on the know-how of the courier service, which uses an inference engine that works as a rule interpreter. In the second phase, a clustering model assigns, for each period, customers to workers according to the travel times, maximum load capacity and customer's time windows. We use a centroid based and sweep algorithms to solve the resulted problem. Finally, in the third phase, a routing model finds the order in which each worker will visit all customers taking into account their time windows and worker's available time. To solve the routing problem we use an Ant Colony Optimization metaheuristic. We present some numerical results using a case study, in which the proposed method of this paper finds better results in comparison with the current method used in the case study.

show abstract

“…Heuristic algorithms based on Tabu Search (TS) for the HFVRP have been proposed in the literature [12][13][14]. Brandão [12] introduced an algorithm based on TS that included strategic oscillation, perturbation procedure, and memory based on frequencies.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Previously, the same author proposed a TS deterministic that used different heuristic procedures to generate the initial solution [13]. Finally, an algorithm based on TS that included mutations and different procedures of local search was proposed by [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative analysis of granular neighborhoods in a Tabu Search for the vehicle routing problem with heterogeneous fleet and variable costs (HFVRP)

2017

View full text Add to dashboard Cite

In the vehicle routing problem with heterogeneous fleet and variable costs (HFVRP), the group of routes to be developed to satisfy the demand of the customer must be determined, considering the minimization of the total costs of the travelled distance. Heuristic algorithms based on local searches use simple movements (neighborhoods) to generate feasible solutions to problems related to route design. In this article, we conduct a comparative analysis of granular neighborhoods in a Tabu Search for the HFVRP, in terms of the quality of the obtained solution. The computational experiments, performed on instances of benchmarking for the HFVRP, showed the efficiency and effectiveness of implementing some neighborhoods in metaheuristic algorithms of path, such as the Tabu Search.

show abstract

An Improved Tabu Search Approach to Vehicle Routing Problem

Cited by 33 publications

References 11 publications

A Green Competitive Vehicle Routing Problem under Uncertainty Solved by an Improved Differential Evolution Algorithm

A Green Competitive Vehicle Routing Problem under Uncertainty Solved by an Improved Differential Evolution Algorithm

A hybrid expert system, clustering and ant colony optimization approach for scheduling and routing problem in courier services

Comparative analysis of granular neighborhoods in a Tabu Search for the vehicle routing problem with heterogeneous fleet and variable costs (HFVRP)

Contact Info

Product

Resources

About