In this article we propose, for any ε > 0, a 2(1 + ε)-approximation algorithm for a facility location problem with stochastic demands. At open facilities, inventory is kept such that arriving requests find a zero inventory with (at most) some pre-specified probability. The incurred costs are the expected transportation costs from the demand points to the facilities, the operating costs of the facilities and the investment in inventory

Bumb, Adriana F.

Ommeren, Jan-Kees C.W. van

In this article we propose, for any ε &gt; 0, a 2(1 + ε)-approximation algorithm for a facility location problem with stochastic demands. At open facilities, inventory is kept such that arriving requests find a zero inventory with (at most) some pre-specified probability. The incurred costs are the expected transportation costs from the demand points to the facilities, the operating costs of the facilities and the investment in inventory. AMS Classification: 68W25, 90B06, 60K30

Bumb, A.F.

Ommeren, van, J.C.W.

Pure OAI Repository

An approximation algorithm for a facility location problem with inventories and stochastic demands

In this article we propose, for any $\epsilon>0$, a $2(1+\epsilon)$-approximation algorithm for a facility location problem with stochastic demands. This problem can be described as follows. There are a number of locations, where facilities may be opened and a number of demand points, where requests for items arise at random. The requests are sent to open facilities. At the open facilities, inventory is kept such that arriving requests find a zero inventory with (at most) some pre-specified probability. After constant times, the inventory is replenished to a fixed order up to level. The time interval between consecutive replenishments is called a reorder period. The problem is where to locate the facilities and how to assign the demand points to facilities at minimal cost per reorder period such that the above mentioned quality of service is insured. The incurred costs are the expected transportation costs from the demand points to the facilities, the operating costs (opening costs) of the facilities and the investment in inventory (inventory costs)

van Ommeren, Jan C.W.

NARCIS 

An approximation algorithm for a facility location problem with stochastic demands

In this article we propose, for any $\epsilon&gt;0$, a $2(1+\epsilon)$-approximation algorithm for a facility location problem with stochastic demands. This problem can be described as follows. There are a number of locations, where facilities may be opened and a number of demand points, where requests for items arise at random. The requests are sent to open facilities. At the open facilities, inventory is kept such that arriving requests find a zero inventory with (at most) some pre-specified probability. After constant times, the inventory is replenished to a fixed order up to level. The time interval between consecutive replenishments is called a reorder period. The problem is where to locate the facilities and how to assign the demand points to facilities at minimal cost per reorder period such that the above mentioned quality of service is insured. The incurred costs are the expected transportation costs from the demand points to the facilities, the operating costs (opening costs) of the facilities and the investment in inventory (inventory costs)

University of Twente Research Information

English

Adriana F. Bumb

Jan-Kees C. W. van Ommeren

Crossref

An Approximation Algorithm for a Facility Location Problem with Inventories and Stochastic Demands

In this article we propose, for any $\varepsilon$ &gt; 0, a 2(1 + $\varepsilon$)-approximation algorithm for a facility location problem with stochastic demands. At open facilities, inventory is kept such that arriving requests find a zero inventory with (at most) some pre-specified probability. The incurred costs are the expected transportation costs from the demand points to the facilities, the operating costs of the facilities and the investment in inventory

van Ommeren, Jan-Kees C.W.

A 1.52 approximation algorithm for the uncapacitated facility location problem, in:

A 2-Approximation Algorithm for the SoftCapacitated Facility Location Problem,

A constant factor approximation algorithm for the fault-tolerant facility location problem.

A Multi-exchange Local Search Algorithm for the Capacitated Facility Location Problem,

A queuing location model with expected service time dependent queuing disciplines,

Algorithms for a facility location problem with stochastic customer demand and immobile servers.

Analysis of a Local Search Heuristic for Facility Location Problems.

Approximation algorithms for facility location problems, in:

Approximation algorithms for metric facility location and k-median problems using the primal-dual schema and Lagrangian relaxation,

Facility Location and the Analysis of Algorithms through FactorRevealing Programs,

Greedy facility location algorithms analyzed using dual ﬁtting with factor-revealing LP.

Greedy strikes back: Improved facility location algorithms,

Hedging Uncertainty: Approximation Algorithms for Stochastic Optimization Problems,

Improved Approximation Algorithms for the Uncapacitated Facility Location Problem,

Local search heuristics for k-median and facility location problems, in:

Location-allocation of multiple-server service centers with constrained queues or waiting times,

Location-allocation on congested networks,

Optimal control of service for facilities holding inventory,

Optimal server location on a network operating as a M/G/1 queue,

Personal communication. Cited in S. Guha, Approximation algorithms for facility location problems, PhD thesis,

Probabilistic, maximal covering location-allocation models for congested systems,

An approximation algorithm for a facility location problem with inventories and stochastic demands

Abstract

Similar works

Full text

Available Versions

Pure OAI Repository

NARCIS

University of Twente Research Information

Pure OAI Repository

Crossref

University of Twente Research Information