A Parallel Branch and Bound Algorithm for the Probabilistic TSP

Amar, Mohamed Abdellahi; Khaznaji, Walid; Bellalouna, Monia

doi:10.1007/978-3-030-05051-1_30

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…They extend previous deterministic TSP algorithms, leveraging the closed expected value evaluation expression of [14]. The authors additionally present in [21] another branch-and-bound algorithm exploiting parallelization techniques and solving instances for up to 30 customers.…”

Section: Routing With Customer Uncertaintymentioning

confidence: 99%

Deep Reinforcement Learning for Crowdshipping Last-Mile Delivery with Endogenous Uncertainty

Silva

Pedroso

2022

Mathematics

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In this work, we study a flexible compensation scheme for last-mile delivery where a company outsources part of the activity of delivering products to its customers to occasional drivers (ODs), under a scheme named crowdshipping. All deliveries are completed at the minimum total cost incurred with their vehicles and drivers plus the compensation paid to the ODs. The company decides on the best compensation scheme to offer to the ODs at the planning stage. We model our problem based on a stochastic and dynamic environment where delivery orders and ODs volunteering to make deliveries present themselves randomly within fixed time windows. The uncertainty is endogenous in the sense that the compensation paid to ODs influences their availability. We develop a deep reinforcement learning (DRL) algorithm that can deal with large instances while focusing on the quality of the solution: we combine the combinatorial structure of the action space with the neural network of the approximated value function, involving techniques from machine learning and integer optimization. The results show the effectiveness of the DRL approach by examining out-of-sample performance and that it is suitable to process large samples of uncertain data, which induces better solutions.

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Section: Routing With Customer Uncertaintymentioning

confidence: 99%

Deep Reinforcement Learning for Crowdshipping Last-Mile Delivery with Endogenous Uncertainty

Silva

Pedroso

2022

Mathematics

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“…Several approximate methods have been proposed to solve the PTSP. M. Abdellahi Amar et al have proposed an application and parallel tabu search algorithm for solving the PTSP [4], a parallel branch and bound algorithm for the probabilistic TSP [5], Balaprakash et al have presented a hybrid optimization approach using ant colonies [6], Bianchi et al have presented various ant colony optimization approaches [7], while Branke and Guntsch have proposed a hybrid optimization approach using ant colonies [8]. Gutjahr has proposed an SACO ant based approach [9], while, Marinakis and Marinaki have proposed a hybrid multi swarm optimization algorithm [10] and a hybrid bee mating optimization algorithm [11].…”

Section: Introductionmentioning

confidence: 99%

Investigation of ant colony optimization with Levy flight technique for a class of stochastic combinatorial optimization problem

El Asri,

Tajani,

Fakhouri

2023

Math. Model. Comput.

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The demand for efficient solutions to optimization problems with uncertain and stochastic data is increasing. Probabilistic traveling salesman problem (PTSP) is a class of Stochastic Combinatorial Optimization Problems (SCOPs) involving partially unknown information about problem data with a known probability distribution. It consists to minimize the expected length of the tour where each customer requires a visit only with a given probability, at which customers who do not need a tour are just ignored without further optimization. Since the PTSP is NP-hard, the usage of metaheuristic methods is necessary to solve the problem. In this paper, we present the Ant Colony Optimization (ACO) algorithm combined with the Levy Flight mechanism (LFACO), which is based on Levy distribution to balance searching space and speed global optimization. Experimental results on a large number of instances show that the proposed Levy ACO algorithm on the probabilistic traveling salesman problem allows to obtain better results compared with the classical ACO algorithm.

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IEDW: A BIM-based indoor electric distribution wiring algorithm using graph theory and capacity-limited multiple traveling salesman problem solver

Zhou

Sun

Wang

et al. 2023

Advanced Engineering Informatics

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A Parallel Branch and Bound Algorithm for the Probabilistic TSP

Cited by 5 publications

References 13 publications

Deep Reinforcement Learning for Crowdshipping Last-Mile Delivery with Endogenous Uncertainty

Deep Reinforcement Learning for Crowdshipping Last-Mile Delivery with Endogenous Uncertainty

Investigation of ant colony optimization with Levy flight technique for a class of stochastic combinatorial optimization problem

IEDW: A BIM-based indoor electric distribution wiring algorithm using graph theory and capacity-limited multiple traveling salesman problem solver

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