An iterated local search algorithm for the single-vehicle cyclic inventory routing problem

Vansteenwegen, Pieter; Mateo, Manuel Otero

doi:10.1016/j.ejor.2014.02.020

Cited by 61 publications

(62 citation statements)

References 27 publications

(73 reference statements)

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“…Aghezzaf et al [28] formulated SV-CIRP as a mixed-integer program and proposed an exact algorithm for its solution after a thorough analysis of the structure of the problem and its features. Vansteenwegen and Mateo [29] presented an iterated local search meta-heuristic that exploits typical characteristics of the SV-CIRP and opportunities to reduce the computation time. To extend SV-CIRP, Raa and Aghezzaf [30] proposed the concept of distribution patterns, consisting of vehicles performing multiple tours with possibly different frequencies, and proposed a heuristic that is capable of solving a cyclical distribution problem.…”

Section: Literature Reviewmentioning

confidence: 99%

An Inventory Routing Policy Under Replenishment Lead Time

Wang

Chan

2017

IEEE Trans. Syst. Man Cybern, Syst.

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The inventory routing problem (IRP) arises in vendor-managed inventory systems, which is a combination of vehicle routing and inventory management. Differing from the traditional IRPs in which the time consumption in transportation is often ignored, in this paper, we take the time consumption into consideration, which brings the lead-time to replenishment. In this situation, the lead-time for replenishment depends on the routing decisions made by the vendor. Consequently, IRPs become more interesting because decisions on the replenishment quantity and routing depend more tightly on each other. The general case of the IRP with replenishment lead-time (i.e., one vendor and N retailers) is modeled mathematically. To solve the problem, first a simple case (i.e., one vendor and two retailers) is analyzed to obtain the theoretical optimal policy. By proving the K-convexity of the objective function, we confirm that the structure of the optimal replenishment and routing policy is of switching curve type. In this policy, the state space, which is composed of the inventory positions of the two retailers, is divided into several domains; and for inventory positions in each domain, there exists an optimal order-upto level. This structure reveals, for managerial insight, that when lead-time is considered, the current routing decision is not independent of the previous one. Second, since the optimal policy is difficult to realize in practice, a myopic policy that is easier to implement is proposed and numerical experiments are conducted to examine the near-optimal performance of the myopic policy. Finally, the myopic policy is extended to a realistic-size IRP with replenishment lead-time (i.e., the IRP for the case of one vendor and multiple retailers) and a numerical example is provided to indicate the feasibility of the policy.

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Section: Literature Reviewmentioning

confidence: 99%

An Inventory Routing Policy Under Replenishment Lead Time

Wang

Chan

2017

IEEE Trans. Syst. Man Cybern, Syst.

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“…Then, the routing of the central depot to multiple warehouses is planned by using a three‐stage heuristic based on clustering, allocation, and routing. An iterated local search algorithm for the cyclic IRP over an infinite planning horizon is discussed by Vansteenwegen and Mateo (). Other heuristics and metaheuristics for the cyclic IRP have also been presented by Chitsaz et al.…”

Section: Literature Reviewmentioning

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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“…Then, the routing of the central depot to multiple warehouses is planned by using a three‐stage heuristic based on clustering, allocation, and routing. An iterated local search algorithm for the cyclic IRP over an infinite planning horizon is discussed by Vansteenwegen and Mateo (). Other heuristic and metaheuristic techniques for the cyclic IRP have also been presented by Chitsaz et al.…”

Section: Literature Reviewmentioning

confidence: 99%

A variable neighborhood search simheuristic for the multiperiod inventory routing problem with stochastic demands

Gruler

Panadero

Armas

et al. 2018

Int Trans Operational Res

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The inventory routing problem (IRP) combines inventory management and delivery route‐planning decisions. This work presents a simheuristic approach that integrates Monte Carlo simulation within a variable neighborhood search (VNS) framework to solve the multiperiod IRP with stochastic customer demands. In this realistic variant of the problem, our goal is to establish the optimal refill policies for each customer–period combination, that is, those individual refill policies that minimize the total expected cost over the periods. This cost is the aggregation of both expected inventory and routing costs. Our simheuristic algorithm allows to consider the inventory changes between periods generated by the realization of the random demands in each period, which have an impact on the quantities to be delivered in the next period and, therefore, on the associated routing plans. A range of computational experiments are carried out in order to illustrate the potential of our simulation–optimization approach.

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An iterated local search algorithm for the single-vehicle cyclic inventory routing problem

Cited by 61 publications

References 27 publications

An Inventory Routing Policy Under Replenishment Lead Time

An Inventory Routing Policy Under Replenishment Lead Time

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

A variable neighborhood search simheuristic for the multiperiod inventory routing problem with stochastic demands

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