Combining variable neighborhood search with simulation for the inventory routing problem with stochastic demands and stock-outs

Gruler, Aljoscha; Panadero, Javier; Armas, Jésica de; Moreno-Pérez, José A.

doi:10.1016/j.cie.2018.06.036

Cited by 70 publications

(35 citation statements)

References 54 publications

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“…Moreover, the possibility of product stock‐outs at the end of each period is considered as proposed in Gruler et al. (). This differs from other works such as the ones by Bertazzi et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…An enhanced algorithm for the single‐period IPR with stochastic demands is discussed in Gruler et al. (). Finally, Gruler et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…() and Gruler et al. () present simheuristic algorithms for solving inventory routing problems with stochastic demands; De Armas et al. () propose a simheuristic algorithm for the uncapacitated facility location problem with stochastic costs; in a similar way, Onggo et al.…”

Section: Literature Reviewmentioning

confidence: 99%

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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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Section: Literature Reviewmentioning

confidence: 99%

“…An enhanced algorithm for the single‐period IPR with stochastic demands is discussed in Gruler et al. (). Finally, Gruler et al.…”

Section: Literature Reviewmentioning

confidence: 99%

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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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“…Notice that simheuristics might employ any type of simulation, e.g., discrete-event, agent-based, or MCS. These algorithms have also been used recently in multiple sectors, including: flow-shop scheduling (Gonzalez-Neira et al 2017;Hatami et al 2018), waste collection management , arc routing (Gonzalez-Martin et al 2018), Internet computing (Cabrera et al 2014), finance (Panadero et al 2020), e-commerce (Pages-Bernaus et al 2019), and inventory routing (Gruler et al 2018(Gruler et al 2020. All in all, simheuristics illustrate the extraordinary potential that simulation has when combined with heuristics and metaheuristics, specially when solving stochastic versions of NP-hard optimization problems.…”

Section: Potential Use Of Simheuristics and Learnheuristicsmentioning

confidence: 99%

Modern Optimization and Simulation Methods in Managerial and Business Economics: A Review

Calvet

Torre

Goyal

et al. 2020

Administrative Sciences

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Managerial and Business Economics (ME/BE) aims at using quantitative and computational methods to make an efficient (ideally optimal) assignment of the scarce resources owned by firms and organizations. In the current global market, characterized by a fierce competition, an optimal use of the available resources is more important than ever for guaranteeing the economical sustainability of organizations and enterprises of any size. Heuristic optimization algorithms and simulation methods have been successfully employed to analyze and enhance complex systems and processes in a myriad of ME/BE-related fields. This paper reviews recent works on the use of these methodologies in competitive markets, as well as in imperfect markets considering externalities. The paper also discusses open challenges and how state-of-the art methods combining optimization, simulation, and machine learning can contribute to properly address them.

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“…Latorre-Biel et al ( 2021) considered correlated stochastic demands within a capacitated VRP, combining a simheuristic with a Petri net predictor to update the mean demands. Gruler et al (2018) studied an Inventory Routing Problem (IRP) with stochastic demands. They applied a simheuristic in a VNS approach, using biased randomization with a Clarke & Wright Savings heuristic to build initial solutions.…”

Section: Simheuristicsmentioning

confidence: 99%

Solving the Deterministic and Stochastic Pipe-Laying Support Vessel Scheduling Problem

CUNHA¹

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Combining variable neighborhood search with simulation for the inventory routing problem with stochastic demands and stock-outs

Cited by 70 publications

References 54 publications

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

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

Modern Optimization and Simulation Methods in Managerial and Business Economics: A Review

Solving the Deterministic and Stochastic Pipe-Laying Support Vessel Scheduling Problem

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