A multi-agent based cooperative approach to scheduling and routing

Martin, Simon; Ouelhadj, Djamila; Beullens, Patrick; Özcan, Ender; Juan, Ángel A.; Burke, Edmund K.

doi:10.1016/j.ejor.2016.02.045

Cited by 75 publications

(38 citation statements)

References 28 publications

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“…Wang et al [24] propose a modified ACO algorithm integrated with other savings algorithms in order to solve the CVRP allowing ants to go in and out the depots more than once until they have visited all customers, aiming at simplifying the procedure of constructing feasible solutions. Martin et al [16] developed a multi-agent framework for scheduling and routing problems where agents use different metaheuristics and cooperate by sharing partial solutions during the search, giving rise to a reinforcement learning and pattern matching process. Hannan et al [11] address the routing and scheduling optimization problem in waste collection by using a modified particle swarm optimization algorithm in a CVRP model, with the objective of minimizing travel distance, collected waste and tightness.…”

Section: Literature Review and Research Contributionmentioning

confidence: 99%

Improving inbound logistic planning for large-scale real-world routing problems: a novel ant-colony simulation-based optimization

Calabrò

Torrisi

Inturri

et al. 2020

Eur. Transp. Res. Rev.

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This paper presents the first results of an agent-based model aimed at solving a Capacitated Vehicle Routing Problem (CVRP) for inbound logistics using a novel Ant Colony Optimization (ACO) algorithm, developed and implemented in the NetLogo multi-agent modelling environment. The proposed methodology has been applied to the case study of a freight transport and logistics company in South Italy in order to find an optimal set of routes able to transport palletized fruit and vegetables from different farms to the main depot, while minimizing the total distance travelled by trucks. Different scenarios have been analysed and compared with real data provided by the company, by using a set of key performance indicators including the load factor and the number of vehicles used. First results highlight the validity of the method to reduce cost and scheduling and provide useful suggestions for large-size operations of a freight transport service.

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Section: Literature Review and Research Contributionmentioning

confidence: 99%

Improving inbound logistic planning for large-scale real-world routing problems: a novel ant-colony simulation-based optimization

Calabrò

Torrisi

Inturri

et al. 2020

Eur. Transp. Res. Rev.

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“…The use of nonuniform random elements to better guide the searching process in vehicle routing problems was initially proposed in Faulin and Juan () and Faulin et al. () and then successfully used in solving different vehicle routing problems (Dominguez et al., ; Martin et al., ). Finally, it is extended into a simheuristic by integrating Monte Carlo simulation (MCS) in order to account for uncertainty in the demands (Grasas et al., ; Ferone et al., ).…”

Section: From a Static To A Reactive Simheuristic Approachmentioning

confidence: 99%

A reactive simheuristic using online data for a real‐life inventory routing problem with stochastic demands

Raba

Estrada‐Moreno

Panadero

et al. 2020

Int Trans Operational Res

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In the context of a supply chain for the animal‐feed industry, this paper focuses on optimizing replenishment strategies for silos in multiple farms. Assuming that a supply chain is essentially a value chain, our work aims at narrowing this chasm and putting analytics into practice by identifying and quantifying improvements on specific stages of an animal‐feed supply chain. Motivated by a real‐life case, the paper analyses a rich multi‐period inventory routing problem with homogeneous fleet, stochastic demands, and maximum route length. After describing the problem and reviewing the related literature, we introduce a reactive heuristic, which is then extended into a biased‐randomized simheuristic. Our reactive approach is validated and tested using a series of adapted instances to explore the gap between the solutions it provides and the ones generated by existing nonreactive approaches.

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“…A hyper-heuristic algorithm was used in [60] and was applied to the nurse training problem. A direct cooperation mechanism was used in [61] to solve the permutation Flow stores problem.…”

Section: Hybridizing Metaheuristics With Machine Learningmentioning

confidence: 99%

A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

et al. 2020

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This article proposes a hybrid algorithm that makes use of the db-scan unsupervised learning technique to obtain binary versions of continuous swarm intelligence algorithms. These binary versions are then applied to large instances of the well-known multidimensional knapsack problem. The contribution of the db-scan operator to the binarization process is systematically studied. For this, two random operators are built that serve as a baseline for comparison. Once the contribution is established, the db-scan operator is compared with two other binarization methods that have satisfactorily solved the multidimensional knapsack problem. The first method uses the unsupervised learning technique k-means as a binarization method. The second makes use of transfer functions as a mechanism to generate binary versions. The results show that the hybrid algorithm using db-scan produces more consistent results compared to transfer function (TF) and random operators.

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A multi-agent based cooperative approach to scheduling and routing

Cited by 75 publications

References 28 publications

Improving inbound logistic planning for large-scale real-world routing problems: a novel ant-colony simulation-based optimization

Improving inbound logistic planning for large-scale real-world routing problems: a novel ant-colony simulation-based optimization

A reactive simheuristic using online data for a real‐life inventory routing problem with stochastic demands

A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

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