Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm

Ghoseiri, Keivan; Ghannadpour, Seyed Farid

doi:10.1016/j.asoc.2010.04.001

Cited by 224 publications

(120 citation statements)

References 39 publications

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“…In order to compare the efficiency of different algorithms to VRPTW, The state-of-the-art method of Goal Programming and Genetic Algorithm (GPGA) [28], Improved Large Neighborhood Search (I-LNS) [22] and Ant Colony Optimization Hybridized with Insertion Heuristics (ACO-IH) [25] are used to compare HPSO. The results of comparison experiments of VRPTW are shown in Tables 6-8.…”

Section: Comparison Results Of Hpso With Other State-of-the-art Methodsmentioning

confidence: 99%

“…Most implementations of ACO combined with other algorithms return feasibility to the solutions by applying a simple ad hoc post-insertion procedure, and when the post-insertion fails to route all the clients, the partial solution is discarded. (5) GA can also be combined with other algorithms to solve VRPTW [28,29]. GA mimics the evolution process in solving problems.…”

Section: Related Workmentioning

confidence: 99%

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A Hybrid Chaos-Particle Swarm Optimization Algorithm for the Vehicle Routing Problem with Time Window

Liang

Peng

et al. 2013

Entropy

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State-of-the-art heuristic algorithms to solve the vehicle routing problem with time windows (VRPTW) usually present slow speeds during the early iterations and easily fall into local optimal solutions. Focusing on solving the above problems, this paper analyzes the particle encoding and decoding strategy of the particle swarm optimization algorithm, the construction of the vehicle route and the judgment of the local optimal solution. Based on these, a hybrid chaos-particle swarm optimization algorithm (HPSO) is proposed to solve VRPTW. The chaos algorithm is employed to re-initialize the particle swarm. An efficient insertion heuristic algorithm is also proposed to build the valid vehicle route in the particle decoding process. A particle swarm premature convergence judgment mechanism is formulated and combined with the chaos algorithm and Gaussian mutation into HPSO when the particle swarm falls into the local convergence. Extensive experiments are carried out to test the parameter settings in the insertion heuristic algorithm and to evaluate that they are corresponding to the data's real-distribution in the concrete problem. It is also revealed that the HPSO achieves a better performance than the other state-of-the-art algorithms on solving VRPTW.

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Section: Comparison Results Of Hpso With Other State-of-the-art Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Hybrid Chaos-Particle Swarm Optimization Algorithm for the Vehicle Routing Problem with Time Window

Liang

Peng

et al. 2013

Entropy

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“…Constraint (11) defines the domain of the decision variable X k i j . Most researchers consider minimizing the number of vehicles as the primary objective (Bräysy, 2003), while others study it as a multi-objective problem (Ghoseiri and Ghannadpour, 2010). In the former case, a two-phase approach is often used, to minimize the vehicle number firstly and then minimize the distance with a fixed route number in the second phase.…”

Section: Problem Description and Related Workmentioning

confidence: 99%

“…In C1 and C2, customers are located in a number of clusters, while the objectives of (1) and (2) are positively related (Ghoseiri and Ghannadpour, 2010). Customers of R1 and R2 are randomly distributed geographically, while RC1 and RC2 are a mix of them.…”

Section: Problem Dataset and Parameter Settingmentioning

confidence: 99%

A Variable Neighbourhood Search Algorithm with Compound Neighbourhoods for VRPTW

Chen

Bai

et al. 2016

Proceedings of 5th the International Conference on Operations Research and Enterprise Systems

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Abstract:The Vehicle Routing Problem with Time Windows (VRPTW) consists of constructing least cost routes from a depot to a set of geographically scattered service points and back to the depot, satisfying service time interval and capacity constraints. A Variable Neighbourhood Search algorithm with Compound Neighbourhoods is proposed to solve VRPTW in this paper. A number of independent neighbourhood operators are composed into compound neighbourhood operators in a new way, to explore wider search area concerning two objectives (to minimize the number of vehicles and the total travel distance) simultaneously. Promising results are obtained on benchmark datasets.

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“…Therefore, various heuristic algorithms that do not guarantee obtaining the optimal solution but execute very fast have been introduced to solve the VRPTW in a short time, and became a main stream of development in this field. They encompass simulated annealing (Zhong and Pan 2007), tabu searches (Ho and Haugland 2004), ant colony systems (Gambardella et al 1999;Gomez et al 2014), swarm optimization algorithms (Hu et al 2013), evolutionary approaches (Repoussis et al 2009), genetic and memetic algorithms (GAs and MAs) (Ghoseiri and Ghannadpour 2010;Nagata et al 2010;Nalepa and Czech 2013;Vidal et al 2013;, and more (Bräysy and Gendreau 2005).…”

Section: Introductionmentioning

confidence: 99%

Adaptive memetic algorithm for minimizing distance in the vehicle routing problem with time windows

Nalepa

Błocho²

2015

Soft Comput

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This paper presents an adaptive memetic algorithm to solve the vehicle routing problem with time windows (VRPTW). It is a well-known NP-hard discrete optimization problem with two objectives-to minimize the number of vehicles serving a set of geographically dispersed customers, and to minimize the total distance traveled in the routing plan. Although memetic algorithms have been proven to be extremely efficient in solving the VRPTW, their main drawback is an unclear tuning of their numerous parameters. Here, we introduce the adaptive memetic algorithm (AMA-VRPTW) for minimizing the total travel distance. In AMA-VRPTW, a population of solutions evolves with time. The parameters of the algorithm, including the selection scheme, population size and the number of child solutions generated for each pair of parents, are adjusted dynamically during the search. We propose a new adaptive selection scheme to balance the exploration and exploitation of the solution space. Extensive experimental study performed on the well-known Solomon's and Gehring and Homberger's benchmark sets confirms the efficacy and convergence capabilities of the proposed AMA-VRPTW. We show that it is very competitive compared with other state-of-the-art techniques. Finally, the influence of the proposed adaptive schemes on the AMA-VRPTW behavior and performance is investigated in a thorough sensitivity analysis. This analysis is complemented with

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Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm

Cited by 224 publications

References 39 publications

A Hybrid Chaos-Particle Swarm Optimization Algorithm for the Vehicle Routing Problem with Time Window

A Hybrid Chaos-Particle Swarm Optimization Algorithm for the Vehicle Routing Problem with Time Window

A Variable Neighbourhood Search Algorithm with Compound Neighbourhoods for VRPTW

Adaptive memetic algorithm for minimizing distance in the vehicle routing problem with time windows

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