Formulations and algorithms for the Pickup and Delivery Traveling Salesman Problem with Multiple Stacks

Pereira, Armando Honorio; Urrutia, Sebastián

doi:10.1016/j.cor.2018.01.005

Cited by 15 publications

(7 citation statements)

References 21 publications

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“…Another variation arises when considering a pickup and delivery traveling salesman problem with multiple stacks, where the nodes to visit are not necessarily split into two separate regions. A large neighborhood search was proposed for this generalization by Côté et al [18], whereas both Pereira and Urrutia [19] and Sampaio and Urrutia [20] presented branch-and-cut algorithms.…”

Section: Literaturementioning

confidence: 99%

The Double Traveling Salesman Problem with Multiple Stacks and a Choice of Container Types

2020

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The double traveling salesman problem with multiple stacks involves the transportation of goods between two regions. In one region, a vehicle carrying a container visits customers, where pallets of goods are loaded into the container. The container is then shipped to a different region, where another vehicle visits another set of customers where the pallets are unloaded. Pallets are loaded in several rows inside the container, where each row follows the last-in-first-out principle. The standard test instances for the double traveling salesman problem with multiple stacks implies the use of a 45-foot pallet wide container to carry EUR-1 pallets. This paper investigates the effect on transportation costs if an open side container could be used when transporting the pallets. Computational experiments show savings in transportation costs of up to 20%. Moreover, by using a container loaded from the side, rather than from the rear, the defining attributes of the double traveling salesman problem seem to be lost.

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Section: Literaturementioning

confidence: 99%

The Double Traveling Salesman Problem with Multiple Stacks and a Choice of Container Types

2020

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“…In this way, there is an increase in the flexibility of loading/unloading operations, which can lead to a reduction in the operating costs of transport. The PDTSPMS was mainly treated by exact algorithms, which employ different and efficient branch-and-cut algorithms (Côté et al, 2009;Sampaio and Urrutia, 2016;Pereira and Urrutia, 2018). Petersen and Madsen (2009) introduced the DTSPMS from a real-world case that a software company encountered with one of its customers.…”

Section: Introductionmentioning

confidence: 99%

“…In this way, there is an increase in the flexibility of loading/unloading operations, which can lead to a reduction in transportation costs. The PDTSPMS was mainly treated by exact algorithms that employ different branch‐and‐cut schemes (Côté et al., 2009; Sampaio and Urrutia, 2017; Pereira and Urrutia, 2018).…”

Section: Introductionmentioning

confidence: 99%

The double traveling salesman problem with partial last‐in‐first‐out loading constraints

Chagas

Toffolo

Souza

et al. 2020

Int Trans Operational Res

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In this paper, we introduce the Double Traveling Salesman Problem with Partial Last-In-First-Out Loading Constraints (DTSPPL), a pickup-and-delivery single-vehicle routing problem where all pickup operations must be performed before any delivery one because the pickup and delivery areas are geographically separated. The vehicle collects items in the pickup area and loads them into its container, a horizontal stack. After performing all pickup operations, the vehicle begins delivering the items in the delivery area. Loading and unloading operations must obey a partial Last-In-First-Out (LIFO) policy, i.e., a version of the LIFO policy that may be violated within a given reloading depth. The objective of the DTSPPL is to minimize the total cost, which involves the total distance traveled by the vehicle and the number of reloaded items due to violations of the standard LIFO policy. We formally describe the DTSPPL by means of two Integer Linear Programming (ILP) formulations, and propose a heuristic algorithm based on the Biased Random-Key Genetic Algorithm (BRKGA) to find high-quality solutions. The performance of the proposed solution approaches is assessed over a broad set of instances. Computational results have shown that both ILP formulations were able to solve only the smaller instances, whereas the BRKGA obtained better solutions for almost all instances, requiring shorter computational time.

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“…Their computational results are compatible to those obtained by . Pereira and Urrutia (2018) have proposed new valid inequalities and two formulations with ad hoc branch-and-cut algorithms for the SVPDP with multiple stacks. Compared to the methods of and Sampaio and Urrutia (2017), their algorithm is faster and solved some instances that were previously unsolved.…”

Section: The Pickup and Delivery Problem With Multiple Stacksmentioning

confidence: 99%

“…LIFO loading or first-in-first-out (FIFO) loading (see, e.g.,Carrabs, Cerulli, and Cordeau 2007;Carrabs, Cordeau, and Laporte 2007;Erdogan, Cordeau, and Laporte 2009;Cordeau, Dell'Amico, and Iori 2010;Li et al 2011), and the pickup and delivery problem with time windows and LIFO loading (PDPTWL) (see, e.g.,Cherkesly, Desaulniers, and Laporte 2015;Alyasiry, Forbes, and Bulmer 2019), for the single-stack case.For the multiple stacks case there are papers on the single-vehicle pickup and delivery problem with multiple stacks (see, e.g.,Sampaio and Urrutia 2017;Pereira and Urrutia 2018), the double traveling salesman problem with multiple stacks (DTSPMS) (see, e.g.,Petersen and Madsen 2009;Felipe, Ortuño, and Tirado 2009;Petersen, Archetti, and Speranza 2010;Lusby et al 2010;Carrabs, Cerulli, and Speranza 2013;Alba Martínez et al 2013), and the double vehicle routing problem with multiple stacks (DVRPMS)(Iori and Riera-Ledesma 2015).…”

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confidence: 99%

Rich variants of the vehicle routing problem

Al-Yasiry¹

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Set of all minimal-matching fragments to f , M( f ) ⊆ Φ, and |M( f )| ≥ 1 14 15 The following publication has been incorporated as Chapter 3. Alyasiry A, Forbes M, and Bulmer M (2019) An exact algorithm for the pickup and delivery problem with time windows and last-in-first-out loading. Transportation Science 53(6):1695-1705. Contribution to the authorship: Ali Alyasiry and Michael Forbes conceived the main idea. Ali Alyasiry with support from Michael Forbes developed the theoretical framework. Ali Alyasiry drafted the manuscript, processed the experimental data, analyzed and interpreted the data, performed the calculations, and designed the figures. Ali Alyasiry and Michael Forbes carried out the proofreading of the manuscript. Michael Forbes and Michael Bulmer supervised the project. Two anonymous reviewers provided feedback that helped to improve the presentation of the manuscript.

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Formulations and algorithms for the Pickup and Delivery Traveling Salesman Problem with Multiple Stacks

Cited by 15 publications

References 21 publications

The Double Traveling Salesman Problem with Multiple Stacks and a Choice of Container Types

The Double Traveling Salesman Problem with Multiple Stacks and a Choice of Container Types

The double traveling salesman problem with partial last‐in‐first‐out loading constraints

Rich variants of the vehicle routing problem

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Formulations and algorithms for the Pickup and Delivery Traveling Salesman Problem with Multiple Stacks

Cited by 15 publications

References 21 publications

The Double Traveling Salesman Problem with Multiple Stacks and a Choice of Container Types

The Double Traveling Salesman Problem with Multiple Stacks and a Choice of Container Types

The double traveling salesman problem with partial last‐in‐first‐out loading constraints

Rich variants of the vehicle routing problem​​​​​​​

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Rich variants of the vehicle routing problem