A reactive variable neighborhood tabu search for the heterogeneous fleet vehicle routing problem with time windows

Paraskevopoulos, Dimitris C.; Repoussis, Panagiotis P.; Tarantilis, Christos D.; Ioannou, George; Prastacos, Gregory P.

doi:10.1007/s10732-007-9045-z

Cited by 87 publications

(75 citation statements)

References 40 publications

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“…Further noteworthy research for the FSMFTW can be found in Dullaert et al (2002), Dell'Amico et al (2007), Paraskevopoulos et al (2008) and Bräysy et al (2008b).…”

Section: Related Workmentioning

confidence: 99%

“…Similar to the work of Paraskevopoulos et al (2008) and Repoussis and Tarantilis (2010), an iterative route construction heuristic is implemented. In each iteration, a single route for each vehicle type is created using only unassigned nodes until the capacity constraint is violated.…”

Section: Construction and Insertionmentioning

confidence: 99%

“…These routes are constructed independently, i.e., nodes can be used in multiple routes, and the route with the best lowest cost is added to the current partial solution. We use the Average Cost per Unit Transfered (ACUT k ) value defined in Paraskevopoulos et al (2008) to measure the cost of a tour. This value represents the relative costs of serving the demand of a route σ of vehicle type k (see Equation 4.14).…”

Section: Construction and Insertionmentioning

confidence: 99%

See 2 more Smart Citations

The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations

Hiermann

Puchinger

Røpke

et al. 2016

European Journal of Operational Research

395

222

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Due to new regulations and further technological progress in the field of electric vehicles, the research community faces the new challenge of incorporating the electric energy based restrictions into vehicle routing problems. One of these restrictions is the limited battery capacity which makes detours to recharging stations necessary, thus requiring efficient tour planning mechanisms in order to sustain the competitiveness of electric vehicles compared to conventional vehicles. We introduce the Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and recharging stations (E-FSMFTW) to model decisions to be made with regards to fleet composition and the actual vehicle routes including the choice of recharging times and locations. The available vehicle types differ in their transport capacity, battery size and acquisition cost. Furthermore, we consider time windows at customer locations, which is a common and important constraint in real-world routing and planning problems. We solve this problem by means of branch-and-price as well as proposing a hybrid heuristic, which combines an Adaptive Large Neighbourhood Search with an embedded local search and labelling procedure for intensification. By solving a newly created set of benchmark instances for the E-FSMFTW and the existing single vehicle type benchmark using an exact method as well, we show the effectiveness of the proposed approach.

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“…Further noteworthy research for the FSMFTW can be found in Dullaert et al (2002), Dell'Amico et al (2007), Paraskevopoulos et al (2008) and Bräysy et al (2008b).…”

Section: Related Workmentioning

confidence: 99%

Section: Construction and Insertionmentioning

confidence: 99%

Section: Construction and Insertionmentioning

confidence: 99%

See 1 more Smart Citation

The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations

Hiermann

Puchinger

Røpke

et al. 2016

European Journal of Operational Research

395

222

View full text Add to dashboard Cite

show abstract

“…The FSM and HF, combined with the two objectives above, give rise to four problem types: 1) the FSMTW with objective T, denoted by FSMTW(T), introduced by Liu and Shen (1999b); 2) the FSMTW with objective D, denoted by FSMTW(D), introduced by Bräysy et al (2008); 3) the HFTW with objective T, denoted by HFTW(T), introduced by Paraskevopoulos et al (2008); 4) the HFTW with objective D, denoted by HFTW(D), recently introduced by Koç et al (2015).…”

Section: Time Windowsmentioning

confidence: 99%

Thirty years of heterogeneous vehicle routing

Koç

Bektaş

Jabali

et al. 2016

European Journal of Operational Research

195

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It has been around thirty years since the heterogeneous vehicle routing problem was introduced, and significant progress has since been made on this problem and its variants. The aim of this survey paper is to classify and review the literature on heterogeneous vehicle routing problems. The paper also presents a comparative analysis of the metaheuristic algorithms that have been proposed for these problems.

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“…(1) A typical hybrid method is TS combined with other algorithms [15][16][17]. Tabu search keeps a list of forbidden transformations to transfer the solution step that deteriorates the objective function value; while a hybrid algorithm based on TS and variable neighborhood descent uses a sweep method to obtain an initial solution [18].…”

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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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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A reactive variable neighborhood tabu search for the heterogeneous fleet vehicle routing problem with time windows

Cited by 87 publications

References 40 publications

The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations

The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations

Thirty years of heterogeneous vehicle routing

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

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