A branch-and-cut algorithm for the generalized traveling salesman problem with time windows

Yuan, Yuan; Cattaruzza, Diego; Ogier, Maxime; Semet, Frédéric

doi:10.1016/j.ejor.2020.04.024

Cited by 36 publications

(27 citation statements)

References 26 publications

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“…Using the example introduced previously and depicted in Figure 1, it can be observed that Constraints (14) do not cut the solution proposed in Note that this result can be extended to routing problems with time windows (TW) as the generalized TSPTW (Yuan et al (2018)), the vehicle routing problem with TW (VRPTW) (Pecin et al…”

Section: Introductionmentioning

confidence: 90%

A note on the lifted Miller–Tucker–Zemlin subtour elimination constraints for routing problems with time windows

Yuan

Cattaruzza

Ogier

et al. 2020

Operations Research Letters

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We propose lifted versions of the Miller-Tucker-Zemlin subtour elimination constraints for routing problems with time windows (TW). The constraints are valid for problems such as the travelling salesman problem with TW, the vehicle routing problem with TW, the generalized travelling salesman problem with TW, and the general vehicle routing problem with TW. They are corrected versions of the constraints proposed by Desrochers and Laporte (1991).

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Section: Introductionmentioning

confidence: 90%

A note on the lifted Miller–Tucker–Zemlin subtour elimination constraints for routing problems with time windows

Yuan

Cattaruzza

Ogier

et al. 2020

Operations Research Letters

Self Cite

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“…We refer to Afsar et al [2014] for details on the GVRP, and to Moccia et al [2012] for details of the GVRPTW. Yuan et al [2018] develop a B&C for the GTSPTW (a single vehicle GVRPTW) that can solve instances with 30 clusters. Moccia et al [2012] propose a tabu search that is able to tackle instances with 120 clusters within few minutes.…”

Section: Delivery Optionsmentioning

confidence: 99%

A large neighborhood search approach to the vehicle routing problem with delivery options

Dumez

Lehuédé

Péton

2021

Transportation Research Part B: Methodological

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To reduce delivery failures in last mile delivery, several types of delivery options have been proposed in the past twenty years. Still, customer satisfaction is a challenge because a single location is chosen independently of the time at which customers will be delivered. In addition, delivery in shared locations such as lockers and shops also experience failure due to capacity or opening-time issues at the moment of delivery. To address this issue and foster consolidation in shared delivery points, we investigate the case where a customer can specify several delivery options together with preference levels and time windows. We define, in this article, the Vehicle Routing Problem with Delivery Options, which integrates several types of delivery locations. It consists of designing a set of routes for a fleet of vehicles that deliver to each customer at one of his/her options during the corresponding time window. These routes should respect capacities at shared locations such as lockers and minimum service level requirements, while minimizing the total routing costs. To solve this problem, we have designed a large neighborhood search in which a set partitioning problem is solved to regularly reassemble routes. Specific ruin and recreate operators are proposed and combined with numerous operators from the literature. A thorough experimental study was carried out to determine a subset of efficient and complementary operators. The proposed method outperforms existing algorithms from the literature on particular cases of the problem under consideration, such as the vehicle routing problem with roaming delivery locations and the vehicle routing problem with home and roaming delivery locations. New instances are generated and used both to serve as a benchmark and to propose some managerial insight into the vehicle routing problem with alternative delivery options.

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“…At that time, their order in the array that is sorted in ascending order represent the cities. For example the consisting 6 cities has the values of = [1.5, 6.3, 4.6, 5.7, 8.9, 4.0], the real tour of the is determined by _ = [1,6,3,4,2,5]. In which, the 1 st city is assigned for the first variable because it has the smallest value in the .…”

Section: Application Aeo For Finding the Shortest Tour Of The Tsp 31 Solution Descriptionmentioning

confidence: 99%

“…From the first time proposed in 1970 [3], the TSP problem has been solved by difference approaches consisting of the exacting methods such as branch-and-cut [4], branch-and-bound [5], Lagrangian [6] and the methods relied on the metaheuristic algorithms such as genetic algorithm (GA) [7]- [9], particle swarm optimization (PSO) [10]- [12], ant colony optimization (ACO) [13]- [16] cuckoo search (CS) [17], [18]. Generally, each group of approaches has certain advantages and limitations.…”

Section: Introductionmentioning

confidence: 99%

Finding the best tour for travelling salesman problem using artificial ecosystem optimization

Nguyen

Bui

2021

IJECE

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This paper presents a new method based on the artificial ecosystem optimization (AEO) algorithm for finding the shortest tour of the travelling salesman problem (TSP). Wherein, AEO is a newly developed algorithm based on the idea of the energy flow of living organisms in the ecosystem consisting of production, consumption and decomposition mechanisms. In order to improve the efficiency of the AEO for the TSP problem, the 2-opt movement technique is equipped to enhance the quality of the solutions created by the AEO. The effectiveness of AEO for the TSP problem has been verified on four TSP instances consisting of the 14, 30, 48 and 52 cities. Based on the calculated results and the compared results with the previous methods, the proposed AEO method is one of the effective approaches for solving the TSP problem.

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A branch-and-cut algorithm for the generalized traveling salesman problem with time windows

Cited by 36 publications

References 26 publications

A note on the lifted Miller–Tucker–Zemlin subtour elimination constraints for routing problems with time windows

A note on the lifted Miller–Tucker–Zemlin subtour elimination constraints for routing problems with time windows

A large neighborhood search approach to the vehicle routing problem with delivery options

Finding the best tour for travelling salesman problem using artificial ecosystem optimization

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