An adaptive large neighborhood search algorithm for a selective and periodic inventory routing problem

Aksen, Deniz; Kaya, Onur; Salman, Serdar; Tüncel, Özge

doi:10.1016/j.ejor.2014.05.043

Cited by 77 publications

(30 citation statements)

References 37 publications

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“…For example, Berman and Larson [6] viewed a delivery tour as a sequential decision process. Aksen et al [7] studied a selective and periodic IRP and developed an adaptive large neighborhood search algorithm for its solution. Li et al [8] considered an IRP with the objective of minimizing the maximum route travel time and presented a Tabu search algorithm to tackle the 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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“…This ALNS aims to improve an initial feasible solution progressively by means of multiple removal (destroy) and insertion (repair) mechanisms competing in an adaptive environment to diversify and intensify the search. ALNS has been successfully implemented for solving many VRP variants including pickup and delivery problems with transshipment (Qu and Bard, 2012) and with vehicle transfers (Masson et al, 2013), cumulative capacitated VRP (Ribeiro and Laporte, 2012), pollution-routing problem (Demir et al, 2012), two-echelon VRP (Hemmelmayr et al, 2012), VRP with multiple routes (Azi et al, 2014), periodic inventory routing problem (Aksen et al, 2014), and production routing problem (Adulyasak et al, 2014). Due to its flexibility, the ALNS framework has been also applied to a wide range of problems such as the resource-constrained project scheduling problem (Muller, 2009), technician task scheduling in telecommunications industry (Cordeau et al, 2010), lot-sizing with setup times (Muller et al, 2012), consultation timetabling problem at Danish high schools (Kristiansen, 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

An Adaptive Large Neighborhood Search for an E-grocery Delivery Routing Problem

Emeç

Çatay

Bozkaya

2016

Computers & Operations Research

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“…Different neighborhood structures are also used in the adaptive large neighborhood search (ALNS) metaheuristic developed by Aksen et al. (). The authors study a selective and periodic IRP in the collection of vegetable oil from different origin nodes.…”

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 adaptive large neighborhood search algorithm for a selective and periodic inventory routing problem

Cited by 77 publications

References 37 publications

An Inventory Routing Policy Under Replenishment Lead Time

An Inventory Routing Policy Under Replenishment Lead Time

An Adaptive Large Neighborhood Search for an E-grocery Delivery Routing Problem

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

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