ALNS: a Python implementation of the adaptive large
neighbourhood search metaheuristic

Wouda, Niels A.; Leon, Lan,

doi:10.21105/joss.05028

Cited by 5 publications

(2 citation statements)

References 16 publications

(12 reference statements)

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“…Consequently, the VRPDO can be considered the same way as the classic VRP. This novel approach has enabled the solution of problem instances encompassing up to 1000 customers, a notable advancement beyond what is found in the literature, in which, problem instances featur- This research paper benefits from successfully implementing the alns package, originally developed by N. Wouda [28], using Python. This package was originally developed to provide solutions to simple vehicle routing and cutting stock problems.…”

Section: Solution Using Adaptive Large Neighbourhood Search Approachmentioning

confidence: 99%

Adaptive Large Neighborhood Search Metaheuristic for the Capacitated Vehicle Routing Problem with Parcel Lockers

Saker,

Eltawil,

Ali

2023

Logistics

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With the expansion of online shopping, urban logistics must handle the increasing customer demand, making the last-mile delivery process more challenging. Furthermore, this expansion significantly contributes to companies’ distribution costs. To overcome some of the last-mile delivery costs, parcel lockers-as a delivery option-can be an alternative solution. Parcel lockers extend delivery options beyond home delivery, offering cost-saving benefits. However, incorporating multiple delivery options introduces additional complexity to the delivery management system. Existing studies on the Capacitated Vehicle Routing Problem (CVRP) have primarily focused on home delivery. This study provides a mathematical model and a solution algorithm for the Capacitated Vehicle Routing Problem with Delivery Options (CVRPDO), which includes locker delivery as one of the delivery options. The problem involves creating a set of routes for vehicles that visit each customer at their delivery location or deliver their parcels to one of the lockers. These routes should respect the capacity of each vehicle and the capacities at shared delivery locations while minimizing the total routing costs and the number of utilized vehicles. This problem is solved with An Adaptive Large Neighborhood Search (ALNS). The solution suggests some specific destroy and repair operators and integrates them with various selection schemes. The proposed method results are compared with the exact solution of the MIP model of the problem for validation. Also, it was found that the ALNS algorithm outperformed the solver solution, especially with large-size instances. The results of objective function values were improved when solving the problem with an ALNS with a 120 s runtime compared with the MIP model (with a runtime of 3 h) by 25%, 30%, 7%, 5%, and 6% for 1000, 800, 600, 400, and 200 customers, respectively.

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Section: Solution Using Adaptive Large Neighbourhood Search Approachmentioning

confidence: 99%

Adaptive Large Neighborhood Search Metaheuristic for the Capacitated Vehicle Routing Problem with Parcel Lockers

Saker,

Eltawil,

Ali

2023

Logistics

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“…The DRL application method is denoted as DRL. In the comparison of the DRL-LNS, GA [41], and ALNS [42] algorithms, the number of iteration times was set to 1500. Agile-satellite mission planning problem is NP-hard.…”

Section: Comparison Of Algorithms Testmentioning

confidence: 99%

DRL-Based Dynamic Destroy Approaches for Agile-Satellite Mission Planning

Huang,

Li,

et al. 2023

Remote Sensing

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Agile-satellite mission planning is a crucial issue in the construction of satellite constellations. The large scale of remote sensing missions and the high complexity of constraints in agile-satellite mission planning pose challenges in the search for an optimal solution. To tackle the issue, a dynamic destroy deep-reinforcement learning (D3RL) model is designed to facilitate subsequent optimization operations via adaptive destruction to the existing solutions. Specifically, we first perform a clustering and embedding operation to reconstruct tasks into a clustering graph, thereby improving data utilization. Secondly, the D3RL model is established based on graph attention networks (GATs) to enhance the search efficiency for optimal solutions. Moreover, we present two applications of the D3RL model for intensive scenes: the deep-reinforcement learning (DRL) method and the D3RL-based large-neighborhood search method (DRL-LNS). Experimental simulation results illustrate that the D3RL-based approaches outperform the competition in terms of solutions’ quality and computational efficiency, particularly in more challenging large-scale scenarios. DRL-LNS outperforms ALNS with an average scheduling rate improvement of approximately 11% in Area instances. In contrast, the DRL approach performs better in World scenarios, with an average scheduling rate that is around 8% higher than that of ALNS.

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