A Tabu Search algorithm for the Probabilistic Orienteering Problem

Chou, Xiaochen; Gambardella, Luca Maria; Montemanni, Roberto

doi:10.1016/j.cor.2020.105107

Cited by 42 publications

(13 citation statements)

References 32 publications

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“…In this paper, the actor network is exactly the stated DYPN model, and the critic is formed by two dense layers with rectified linear unit (ReLU) activation and one linear layer with a single output; the inputs are static and dynamic embeddings. The training loss is defined as (13), and its gradient is formed as (14):…”

Section: Training With the Reinforce Algorithmmentioning

confidence: 99%

“…Based on that, several local research options have been introduced to improve the ILS approach [9]. More recently, a large number of advanced heuristic algorithms, such as effective neighborhood search [10], large neighborhood search [11], variable neighborhood search, ant colony system [12], tabu search [13], and simulated annealing heuristics [14], [3], have been applied. Recently, to save on development costs and enhance model generalization, learning heuristics for combinatorial optimization problems have been the focus.…”

Section: Introductionmentioning

confidence: 99%

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Deep Reinforcement Learning for Orienteering Problems Based on Decomposition

Liu¹,

Zhang²,

Wang³

et al. 2022

Preprint

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This paper presents a new method for solving an orienteering problem (OP) by breaking it down into two parts: a knapsack problem (KP) and a traveling salesman problem (TSP). A KP solver is responsible for picking nodes, while a TSP solver is responsible for designing the proper path and assisting the KP solver in judging constraint violations. To address constraints, we propose a dual-population coevolutionary algorithm (DPCA) as the KP solver, which simultaneously maintains both feasible and infeasible populations. A dynamic pointer network (DYPN) is introduced as the TSP solver, which takes city locations as inputs and immediately outputs a permutation of nodes. The model, which is trained by reinforcement learning, can capture both the structural and dynamic patterns of the given problem. The model can generalize to other instances with different scales and distributions. Experimental results show that the proposed algorithm can outperform conventional approaches in terms of training, inference, and generalization ability.

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Section: Training With the Reinforce Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Orienteering Problems Based on Decomposition

Liu¹,

Zhang²,

Wang³

et al. 2022

Preprint

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“…The same problem was treated in [17], but in the case of a non-automated warehouse. Subsequently, in [18] the analysis is extended to the case of multiple pickers, and heuristic approaches (see [19]) are employed for large instances.…”

Section: Literature Reviewmentioning

confidence: 99%

AMR-Assisted Order Picking: Models for Picker-to-Parts Systems in a Two-Blocks Warehouse

et al. 2022

Self Cite

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Manual order picking, the process of retrieving stock keeping units from their storage location to fulfil customer orders, is one of the most labour-intensive and costly activity in modern supply chains. To improve the outcome of order picking systems, automated and robotized components are increasingly introduced creating hybrid order picking systems where humans and machines jointly work together. This study focuses on the application of a hybrid picker-to-parts order picking system, in which human operators collaborate with Automated Mobile Robots (AMRs). In this paper a warehouse with a two-blocks layout is investigated. The main contributions are new mathematical models for the optimization of picking operations and synchronizations. Two alternative implementations for an AMR system are considered. In the first one handover locations, where pickers load AMRs) are shared between pairs of opposite sub-aisles, while in the second they are not. It is shown that solving the mathematical models proposed by the meaning of black-box solvers provides a viable algorithmic optimization approach that can be used in practice to derive efficient operational plannings. The experimental study presented, based on a real warehouse and real orders, finally allows to evaluate and strategically compare the two alternative implementations considered for the AMR system.

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“…Fig 1 presents the chronology of the selected publications, indicating the adjustments to techniques to solve the OP. Meanwhile, more recent approaches to solving selected OP variants include the adaptation of the VNS [ 14 ] and a Biased Random-Key Genetic Algorithm [ 15 ] for the Set OP (a generalization of the OP), the proposal to apply the GSOA to the Close-Enough Traveling Salesman Problem [ 16 ], adaptation of the Tabu Search to the Probabilistic Orienteering Problem [ 17 ], solving OP with hotel selection by the Ant Colony System [ 18 ], and the use of Max–Min Ant Colony Optimization to solve the Thief OP instances [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

A novel approach to the Orienteering Problem based on the Harmony Search algorithm

Szwarc

Boryczka

2022

PLoS ONE

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This article presents a new approach to designing a Harmony Search (HS) algorithm, adapted to solve Orienteering Problem (OP) instances. OP is a significant N P-hard problem that has considerable practical application, requiring the development of an effective method for determining its solutions. The proposed HS has demonstrated its effectiveness through determined optimum results for each task from the six most popular benchmarks; a marginal number approximated the best results, with the average error below 0.01%. The article details the application of this described algorithm, comparing its results with those of state-of-the-art methods, indicating the significant efficiency of the proposed approach.

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A Tabu Search algorithm for the Probabilistic Orienteering Problem

Cited by 42 publications

References 32 publications

Deep Reinforcement Learning for Orienteering Problems Based on Decomposition

Deep Reinforcement Learning for Orienteering Problems Based on Decomposition

AMR-Assisted Order Picking: Models for Picker-to-Parts Systems in a Two-Blocks Warehouse

A novel approach to the Orienteering Problem based on the Harmony Search algorithm

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