A Branch-and-Cut Algorithm Using a Strong Formulation and an A Priori Tour-Based Heuristic for an Inventory-Routing Problem

Solyalı, Oğuz; Süral, Haldun

doi:10.1287/trsc.1100.0354

Cited by 91 publications

(57 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…They addressed the single-vehicle case and both the variants with OU and ML replenishment policies. Then Solyalı and Süral [13] proposed a shortest-path network reformulation of the single-vehicle problem for the order-up replenishment policy. Avella et al [4] presented tight reformulations for the lot-sizing subproblems that arise for each client designed to strengthen the dual lower bounds, showing computational results for the single vehicle problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Single-Period Cutting Planes for Inventory Routing Problems

Avella¹,

Boccia²,

Wolsey

2018

Transportation Science

View full text Add to dashboard Cite

IRP involves the distribution of one or more products from a supplier to a set of clients over a discrete planning horizon. Each client has a known demand to be met in each period and can only hold a limited amount of stock. The product is shipped through a distribution network by one or more vehicles of limited capacity. The objective is to find replenishment decisions minimizing the sum of the storage and distribution costs. In this paper we present reformulations of IRP, under the Maximum Level replenishment policy, derived from a single-period substructure. We define a generic family of valid inequalities, and then introduce two specific subclasses for which the separation problem of generating violated inequalities can be solved effectively. A basic Branch-and-Cut algorithm has been implemented to demonstrate the strength of the single-period reformulations. Computational results are presented for the benchmark instances with 50 clients and three periods and 30 clients and six p... AbstractIRP involves the distribution of one or more products from a supplier to a set of clients over a discrete planning horizon. Each client has a known demand to be met in each period and can only hold a limited amount of stock. The product is shipped through a distribution network by one or more vehicles of limited capacity. The objective is to find replenishment decisions minimizing the sum of the storage and distribution costs. In this paper we present reformulations of IRP, under the Maximum Level replenishment policy, derived from a single-period substructure. We define a generic family of valid inequalities, and then introduce two specific subclasses for which the separation problem of generating violated inequalities can be solved effectively. A basic Branch-and-Cut algorithm has been implemented to demonstrate the strength of the single-period reformulations. Computational results are presented for the benchmark instances with 50 clients and three periods and 30 clients and six periods.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Single-Period Cutting Planes for Inventory Routing Problems

Avella¹,

Boccia²,

Wolsey

2018

Transportation Science

View full text Add to dashboard Cite

show abstract

“…• deliver-up-to-level (UL) replenishment policies; Bertazzi et al (2002), Archetti et al (2007), Solyali and Süral (2011);…”

Section: Inventory-routingmentioning

confidence: 99%

Stochastic Inventory Routing for Perishable Products

Crama

Rezaei

Savelsbergh

et al. 2018

Transportation Science

View full text Add to dashboard Cite

Different solution methods are developed to solve an inventory routing problem for a perishable product with stochastic demands. The solution methods are compared empirically in terms of average profit, service level, and actual freshness. The benefits of explicitly considering demand uncertainty are quantified. The computational study highlights that in certain situations a simple ordering policy can already reach a very good performance, but that statistically and economically significant improvements are achieved when using more advanced solution methods. Managerial insights concerning the impact of shelf life and store capacity on profit are also obtained.

show abstract

“…Some similar decisions are faced in lot-sizing and inventory/routing problems, which deal with variables to control activities between two consecutive periods (see Avella et al, 2015, Coelho and Laporte, 2013, Pochet and Wolsey, 2006, Solyal and Süral, 2011.…”

Section: Practical Applications and Related Literaturementioning

confidence: 99%

Scheduling policies for multi-period services

Nunez-del-Toro

Fernández

Kalcsics

et al. 2016

European Journal of Operational Research

View full text Add to dashboard Cite

This paper discusses a multi-period service scheduling problem. In this problem, a set of customers is given who periodically require service over a finite time horizon. To satisfy the service demands, a set of operators is given, each with a fixed capacity in terms of the number of customers an operator can serve per period. The task is to determine for each customer the periods in which he will be visited by an operator such that the periodic service requests of the customers are adhered to and the total number of operators used over the time horizon is minimal. Two alternative policies for scheduling customer visits are considered. In the first one, a customer is visited just on time, i.e., in the period where he or she has a demand for service. The second policy allows service visits ahead of time. The rationale behind this policy is that allowing irregular visits may reduce the overall number of operators needed throughout the time horizon. To solve the problem, integer linear programming formulations are proposed for both policies and numerical experiments are presented that show the reduction in the number of operators used when visits ahead of time are allowed. As only small instances can be solved optimally, a heuristic algorithm is introduced in order to obtain good quality solutions and shorter computing times.Peer ReviewedPostprint (author's final draft

show abstract

A Branch-and-Cut Algorithm Using a Strong Formulation and an A Priori Tour-Based Heuristic for an Inventory-Routing Problem

Cited by 91 publications

References 27 publications

Single-Period Cutting Planes for Inventory Routing Problems

Single-Period Cutting Planes for Inventory Routing Problems

Stochastic Inventory Routing for Perishable Products

Scheduling policies for multi-period services

Contact Info

Product

Resources

About