A greedy look-ahead heuristic for the vehicle routing problem with time windows

Ioannou, George; Kritikos, Manolis N.; Prastacos, Gregory P.

doi:10.1057/palgrave.jors.2601113

Cited by 97 publications

(47 citation statements)

References 16 publications

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“…3 represents the extent to which the vehicle's k arrival time a u and the earliest time e u of customer u are close to one-another. This depends on the insertion position of customer u and provides a measure of the coverage of the selected customer's time window, which results from the insertion of u (Ioannou et al 2001).…”

Section: Construction Heuristicmentioning

confidence: 99%

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

et al. 2007

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This paper presents a solution methodology for the heterogeneous fleet vehicle routing problem with time windows. The objective is to minimize the total distribution costs, or similarly to determine the optimal fleet size and mix that minimizes both the total distance travelled by vehicles and the fixed vehicle costs, such that all problem's constraints are satisfied. The problem is solved using a two-phase solution framework based upon a hybridized Tabu Search, within a new Reactive Variable Neighborhood Search metaheuristic algorithm. Computational experiments on benchmark data sets yield high quality solutions, illustrating the effectiveness of the approach and its applicability to realistic routing problems.

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Section: Construction Heuristicmentioning

confidence: 99%

“…Similarly, C 1 ij,u indicates the compatibility of the time window of selected customer u with the specific insertion position into the current route (Ioannou et al 2001). More specifically, the insertion of u defines the push forward (time gap) between the latest service time l u of customer u and the arrival time a u .…”

Section: Construction Heuristicmentioning

confidence: 99%

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

et al. 2007

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“…Consequently, ADSI provides the same number of vehicles as IMPACT alone, but improves the total route cost by 7.3%; this is a substantial improvement utilizing just a single route of the assignment relaxation. Table 4 shows the results of ADSI with respect to the number of active vehicles, when applied to the standard data sets of Solomon (1987), as well as the results of IMPACT alone, when applied to the same data sets; note that the IMPACT results are obtained using a single setting of the various parameters of the algorithm (see Ioannou et al 2001). The last column of Table 4 reports the variance with respect to the total route cost between the two solutions, since the number of routes is identical for both methods.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…IMPACT, which is based on Atkinson's (1994) look-ahead framework, is very efficient and provides results comparable to meta-heuristics at a fraction of the computational effort. For a detailed description of IMPACT and the computational tests that support its effectiveness, the reader is referred to (Ioannou et al 2001). Figure 1 provides a schematic of the steps of the proposed method, which will be referred as ADSI.…”

Section: Algorithm Impactmentioning

confidence: 99%

An assignment-based heuristic for vehicle routing with time windows

2008

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In this paper, we consider the typical vehicle routing problem with time window constraints (VRPTW). The problem is approached via mathematical decomposition and solved using a three-stage method. First, we formulate the generalized assignment problem, which provides an approximation to the sequencing of customers that partially respects the time windows and apply the Hungarian method to obtain optimal solutions. Subsequently, we address the split of infeasible routes resulting from the assignment solution using a simple, time window-based decomposition heuristic. The best of these routes, in terms of traveling and vehicle waiting times, form part of the final solution, which is completed by the routes provided by a look-ahead heuristic applied to the remainder of the customers. The proposed method is applied to a standard literature data set, and provides very good results with respect to both the number of vehicles and the total travel time. Furthermore, the approach offers useful insights on the effect of employing optimal travel time solutions resulting from the assignment relaxation to derive partial route sets of VRPTW.

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“…Sequential route construction heuristics build routes one at a time whereas parallel construction heuristics create several routes simultaneously. For examples of construction heuristics, see insertion heuristics of Solomon [8], Potvin and Rousseau [9], Bramel and Simchi-Levi [10], Mester [11], Ioannou et al [12] and Dullaert and Br aysy [13].…”

Section: Introductionmentioning

confidence: 99%