A comparison of acceptance criteria for the adaptive large neighbourhood search metaheuristic

Santini, Alberto; Røpke, Stefan; Hvattum, Lars Magnus

doi:10.1007/s10732-018-9377-x

Cited by 54 publications

(28 citation statements)

References 39 publications

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“…The resulting parameter configurations were: AlnsBestMult = 1.9307, AlnsAcceptMult = 1.3667, AlnsRejectMult = 0.8836, AlnsInitProb = 0.8975, β = 15 000. Notice that ALNS+LS parameters appeared to diverge from common values in the literature (see, e.g., Santini et al [29]). In particular, we would have expected the values of AlnsBestMult, AlnsAcceptMult, and AlnsRejectMult to be closer to 1.0, and that of AlnsInitProb to be much smaller.…”

Section: Metaheuristic Algorithm Parameter Tuningmentioning

confidence: 78%

“…For these reason, an acceptance criterion that accepts a solution based on its objective value does not seem particularly suited for the PCP. We, therefore, decided to use the "Worse Accept" criterion, proposed by Santini et al [29], which accepts a new solution x if either it uses strictly fewer colors than the current one, or otherwise with a certain probability p, which starts at a high value, and decreases linearly to reach 0 at the end of the solution process. Notice that p does not depend on the value f (x ) of the new solution.…”

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confidence: 99%

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An exact algorithm for the Partition Coloring Problem

Furini

Malaguti

Santini

2018

Computers & Operations Research

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We study the Partition Coloring Problem (PCP), a generalization of the Vertex Coloring Problem where the vertex set is partitioned. The PCP asks to select one vertex for each subset of the partition in such a way that the chromatic number of the induced graph is minimum. We propose a new Integer Linear Programming formulation with an exponential number of variables. To solve this formulation to optimality, we design an effective Branch-and-Price algorithm. Good quality initial solutions are computed via a new metaheuristic algorithm based on adaptive large neighbourhood search. Extensive computational experiments on a benchmark test of instances from the literature show that our Branch-and-Price algorithm, combined with the new metaheuristic algorithm, is able to solve for the first time to proven optimality several open instances, and compares favourably with the current state-of-the-art exact algorithm.

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Section: Metaheuristic Algorithm Parameter Tuningmentioning

confidence: 78%

mentioning

confidence: 99%

An exact algorithm for the Partition Coloring Problem

Furini

Malaguti

Santini

2018

Computers & Operations Research

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“…In this way, the focus is shifted from the horse race to gaining knowledge, but the literature is sparse with respect to such studies. One example is [108], which investigates the acceptance criteria for adaptive large neighborhood search, by comparing a range of alternatives. However, the comparisons are carried out on set of instances from only three problems (capacitated vehicle routing problem, capacitated minimum spanning tree problem, quadratic assignment problem), and the authors themselves acknowledge that the conclusions drawn from the experiments will not necessarily apply to all other implementations, or other unrelated problems; they also acknowledge that the relative merit of the acceptance criteria may also change based on the number of iterations that can be run.…”

Section: Motivation For Meta-analysismentioning

confidence: 99%

“…A few contacted authors suggested including results also for additional articles (not identi ed with Google Scholar, or with no ALNS in the title: [59,107,108,110,123]), which we decided to consider in order to have as many potential studies as possible. These studies therefore did not need to satisfy our eligibility criteria, since the researchers were already clear on precisely what type of information was needed.…”

Section: Identification Selection and Main Properties Of Studiesmentioning

confidence: 99%

Meta-analysis of metaheuristics: Quantifying the effect of adaptiveness in adaptive large neighborhood search

Turkeš

Sörensen

Hvattum

2021

European Journal of Operational Research

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Research on metaheuristics has focused almost exclusively on (novel) algorithmic development and on competitive testing, both of which have been frequently argued to yield very little generalizable knowledge. The main goal of this paper is to promote meta-analysis-a systematic statistical examination that combines the results of several independent studies-as a more suitable way to obtain problem-and implementation-independent insights on metaheuristics. Meta-analysis is widely used in several scienti c domains, most notably the medical sciences (e.g., to establish the e cacy of a certain treatment). To the best of our knowledge this is the rst meta-analysis in the eld of metaheuristics.

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“…Azi et al 2014;Ghilas et al 2016;Kovacs et al 2014;Li et al 2016;Masson et al 2013)-use a Simulated Annealing approach as the acceptance criterion. In their recent evaluation of acceptance criteria for the ALNS, however, Santini et al (2017) conclude that Simulated Annealing does not necessarily provide better results and that linear threshold acceptance excels both in simplicity and in the quality of solutions. Infeasible solutions in terms of distance and assignment constraints are rejected until a feasible solution is found.…”

Section: Acceptance and Stopping Criterionmentioning

confidence: 99%

Assignment constraints in shared transportation services

2020

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Competitive markets, increased fuel costs, and underutilized vehicle fleets are characteristics that currently define the logistics sector. Given an increasing pressure to act in a manner that is economically and ecologically efficient, mechanisms that help to benefit from idle capacities are on the rise. In the Sharing Economy, collaborative usage is typically organized through platforms that facilitate the exchange of goods or services. Our study examines a collaborative pickup and delivery problem where carriers can exchange customer requests. The aim is to quantify the potential of horizontal collaborations under a centralized framework. An Adaptive Large Neighborhood Search is developed to solve yet unsolved test instances. A computational study confirms the results of past studies which have reported cost savings between 20 and 30%. In addition, the numerical results indicate an even greater potential for settings with a high degree of regional customer overlap. Unfortunately, these high collaborative gains typically come at the cost of an uneven customer distribution, which is known to be one of the main barriers that prevent companies from entering into horizontal collaborations. To generate acceptable solutions for all participants, several constraints are included in the model. The introduction of these constraints to single-vehicle instances, decreases the potential collaborative gain considerably. Surprisingly, this does not happen in more realistic settings of carriers operating multiple vehicles. Overall, the computational study shows that centralized collaborative frameworks have the potential to generate considerable cost savings, while at the same time limiting customer or profit share losses and enabling carriers to keep some of their most valued customers. Keywords Logistics • Collaborations • Transportation • ALNS • Central planning 1 Introduction Competitive markets, increased fuel costs, underutilized vehicle fleets and stricter customer demands are characteristics that currently define the logistics sector. Due to the increasing B Margaretha Gansterer

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A comparison of acceptance criteria for the adaptive large neighbourhood search metaheuristic

Cited by 54 publications

References 39 publications

An exact algorithm for the Partition Coloring Problem

An exact algorithm for the Partition Coloring Problem

Meta-analysis of metaheuristics: Quantifying the effect of adaptiveness in adaptive large neighborhood search

Assignment constraints in shared transportation services

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