A branch-and-cut algorithm for the Time Window Assignment Vehicle Routing Problem

Dalmeijer, Kevin; Spliet, Remy

doi:10.1016/j.cor.2017.08.015

Cited by 41 publications

(35 citation statements)

References 17 publications

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“…We first compare the performance of our algorithm with the results published in [10] for the case of the continuous TWAVRP. We do not compare with the algorithm of [33], since the authors of [10] have demonstrated that their algorithm is superior to the former. Table 1 The two methods are also compared in the performance profiles [12] of Figure 8a.…”

Section: Comparison With Existing Methodsmentioning

confidence: 99%

“…Our motivation for the new class of disjunctions comes from the path precedence inequalities proposed in [10] for the continuous TWAVRP and the inconsistent path elimination constraints To construct valid disjunctions based on the above observation, we first introduce some notation. Let π = (v 1 , .…”

Section: Path-based Disjunctionsmentioning

confidence: 99%

“…Algorithms to solve the aforementioned stochastic programming models have been proposed in [33,10] for the continuous version, and in [32] for the discrete version of the problem. The algorithms of [33,32] are based on branch-price-and-cut and can solve instances with 25 customers and 3 demand scenarios to optimality, while the algorithm of [10] is based on branch-and-cut and can address instances containing 40 customers and 3 scenarios. Several heuristics have also been proposed in [32] for the discrete setting that can address instances containing up to 60 customers.…”

Section: Introductionmentioning

confidence: 99%

“…Log-scaled performance profiles across all benchmark instances. The left graph compares the performance in the continuous setting, in which "DS18" shows the performance of the algorithm of[10], while the right graph compares the performance in the discrete setting, in which "SD15"shows the performance of the algorithm of[32]. In both graphs, "This work" shows the performance of our proposed algorithm.…”

mentioning

confidence: 99%

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A scenario decomposition algorithm for strategic time window assignment vehicle routing problems

Subramanyam

Wang

Gounaris

2018

Transportation Research Part B: Methodological

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We study the strategic decision-making problem of assigning time windows to customers in the context of vehicle routing applications that are affected by operational uncertainty. This problem, known as the Time Window Assignment Vehicle Routing Problem, can be viewed as a two-stage stochastic optimization problem, where time window assignments constitute first-stage decisions, vehicle routes adhering to the assigned time windows constitute second-stage decisions, and the objective is to minimize the expected routing costs. To that end, we develop in this paper a new scenario decomposition algorithm to solve the sampled deterministic equivalent of this stochastic model. From a modeling viewpoint, our approach can accommodate both continuous and discrete sets of feasible time window assignments as well as general scenario-based models of uncertainty for several routing-specific parameters, including customer demands and travel times, among others. From an algorithmic viewpoint, our approach can be easily parallelized, can utilize any available vehicle routing solver as a black box, and can be readily modified as a heuristic for large-scale instances. We perform a comprehensive computational study to demonstrate that our algorithm strongly outperforms all existing solution methods, as well as to quantify the trade-off between computational tractability and expected cost savings when considering a larger number of future scenarios during strategic time window assignment.

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Section: Comparison With Existing Methodsmentioning

confidence: 99%

Section: Path-based Disjunctionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A scenario decomposition algorithm for strategic time window assignment vehicle routing problems

Subramanyam

Wang

Gounaris

2018

Transportation Research Part B: Methodological

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“…The third model considered the basic pickup and delivery problem without the consideration of the green supply chain, and it was compared with the first two models. Dalmeijer and Spliet [30] studied the time window assignment VRP, in which time windows for delivery are assigned to clients before the demand volume of clients is known. To deal with demand uncertainty, the problem assumes that the demand scenarios and their probabilities of occurrence are known.…”

Section: Literature Reviewmentioning

confidence: 99%

An Enhanced Approach for the Multiple Vehicle Routing Problem with Heterogeneous Vehicles and a Soft Time Window

Kang

Lee

2018

Symmetry

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The vehicle routing problem (VRP) is a challenging combinatorial optimization problem. This research focuses on the problem under which a manufacturer needs to outsource materials from other suppliers and to ship the materials back to the company. Heterogeneous vehicles are available to ship the materials, and each vehicle has a limited loading capacity and a limited travelling distance. The purpose of this research is to study a multiple vehicle routing problem (MVRP) with soft time window and heterogeneous vehicles. Two models, using mixed integer programming (MIP) and genetic algorithm (GA), are developed to solve the problem. The MIP model is first constructed to minimize the total transportation cost, which includes the assignment cost, travelling cost, and the tardiness cost, for the manufacturer. The optimal solution can present multiple vehicle routing and the loading size of each vehicle in each period. The GA is next applied to solve the problem so that a near-optimal solution can be obtained when the problem is too difficult to be solved using the MIP. A case of a food manufacturing company is used to examine the practicality of the proposed MIP model and the GA model. The results show that the MIP model can obtain the optimal solution under a short computational time when the scale of the problem is small. When the problem becomes non-deterministic polynomial hard (NP-hard), the MIP model cannot find the optimal solution. On the other hand, the GA model can obtain a near-optimal solution within a reasonable amount of computational time. This paper is related to several important topics of the Symmetry journal in the areas of mathematics and computer science theory and methods. In the area of mathematics, the theories of linear and non-linear algebraic structures and information technology are adopted. In the area of computer science, theory and methods, and metaheuristics are applied.Unit of measure: min.Symmetry 2018, 10, 650 9 of 20 Table 6 lists the problem configurations for the three cases, including the number of periods, number of suppliers, number of vehicles, size of the transportation network, number of variables for the MIP, and number of constraints for the MIP. In the first case, there are five periods, the number of suppliers is five, the number of vehicles is three, the transportation network is 6 × 6, and the numbers of variables and constraints for the MIP model are 931 and 1171, respectively. In the second case, there are seven periods, the number of suppliers is nine, the number of vehicles is four, the transportation network is 10 × 10, and the numbers of variables and constraints for the MIP model are 3903 and 3977, respectively. In the third case, there are nine periods, the number of suppliers is 12, the number of vehicles is five, the transportation network is 13 × 13, and the numbers of variables and constraints for the MIP model are 9758 and 9919, respectively.

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Dynamic Assignment Vehicle Routing Problem with Time Windows

Los

Phillipson

Kempen

et al. 2020

Lecture Notes in Computer Science

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A branch-and-cut algorithm for the Time Window Assignment Vehicle Routing Problem

Cited by 41 publications

References 17 publications

A scenario decomposition algorithm for strategic time window assignment vehicle routing problems

A scenario decomposition algorithm for strategic time window assignment vehicle routing problems

An Enhanced Approach for the Multiple Vehicle Routing Problem with Heterogeneous Vehicles and a Soft Time Window

Dynamic Assignment Vehicle Routing Problem with Time Windows

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