Results 1 
6 of
6
A priori optimization
 Operations Research
, 1990
"... Algorithm for cardinalityconstrained quadratic ..."
Facility location models for distribution system design
, 2004
"... The design of the distribution system is a strategic issue for almost every company. The problem of locating facilities and allocating customers covers the core topics of distribution system design. Model formulations and solution algorithms which address the issue vary widely in terms of fundamenta ..."
Abstract

Cited by 33 (0 self)
 Add to MetaCart
The design of the distribution system is a strategic issue for almost every company. The problem of locating facilities and allocating customers covers the core topics of distribution system design. Model formulations and solution algorithms which address the issue vary widely in terms of fundamental assumptions, mathematical complexity and computational performance. This paper reviews some of the contributions to the current stateoftheart. In particular, continuous location models, network location models, mixedinteger programming models, and applications are summarized.
Locationrouting: Issues, models and methods
, 2006
"... This paper is a survey of locationrouting: a relatively new branch of locational analysis that takes into account vehicle routing aspects. We propose a classification scheme and look at a number of problem variants. Both exact and heuristic algorithms are investigated. Finally, some suggestions for ..."
Abstract

Cited by 7 (0 self)
 Add to MetaCart
This paper is a survey of locationrouting: a relatively new branch of locational analysis that takes into account vehicle routing aspects. We propose a classification scheme and look at a number of problem variants. Both exact and heuristic algorithms are investigated. Finally, some suggestions for future research are presented.
Ecole Nationale des Ponis et Chaussees. Noisy Le Grand
, 1988
"... Consider a complete graph G = (K, E) in which each node is present with probability p,. We are interested in solving combinatorial optimization problems on subsets of nodes which are present with a certain probability. We introduce the idea of a priori optimization as a strategy competitive to the s ..."
Abstract
 Add to MetaCart
Consider a complete graph G = (K, E) in which each node is present with probability p,. We are interested in solving combinatorial optimization problems on subsets of nodes which are present with a certain probability. We introduce the idea of a priori optimization as a strategy competitive to the strategy of reoptimization, under which the combinatorial optimization problem is solved optimally for every instance. We consider four problems: the traveling salesman problem (TSP), the minimum spanning tree, vehicle routing, and traveling salesman facility location. We discuss the applicability of a priori optimization strategies in several areas and show that if the nodes are randomly distributed in the plane the a priori and reoptimization strategies are very close in terms of performance. We characterize the complexity of a priori optimization and address the question of approximating the optimal a priori solutions with polynomial time heuristics with provable worstcase guarantees. Finally, we use the TSP as an example to find practical solutions based on ideas of local optimality. This paper is concerned with a specific family of combinatorial optitnization problems whose common characteristic is the explicit inclusion of probabilistic elements in the problem defmitions, as we will explain. For this reason, we shall refer to them
Two Traveling Salesman Facility Location Problems
"... We consider two generic facility location problems, the traveling salesman facility location problem (TSFLP) and the the probabilistic traveling salesman facility location problem (PTSFLP), both of which have been a subject of intensive investigation recently. Concerning the TSFLP, we first prove th ..."
Abstract
 Add to MetaCart
We consider two generic facility location problems, the traveling salesman facility location problem (TSFLP) and the the probabilistic traveling salesman facility location problem (PTSFLP), both of which have been a subject of intensive investigation recently. Concerning the TSFLP, we first prove that it is optimal under the triangle inequality to locate the facility at a node, which always requires a visit, and we improve the worstcase bound of the heuristic proposed in [5]. Concerning the PTSFLP, we reduce it to the solution of n probabilistic traveling salesman problems and moreover, we propose an O(n2) heuristic which is a factor of O(log n) from both the optimal TSFLP and the PTSFLP. We conjecture that the worstcase guarantee can be improved to 0(1). A byproduct of our analysis is an O(n 2) algorithm which solves the PTSFLP in a general network given an a priori probabilistic traveling salesman type of tour, thus improving the algorithm proposed in [4] by a factor of n. If customer locations are random in the Euclidean plane, we prove that the heuristic proposed in [5] is asymptotically optimal, the PTSFLP is asymptotically equivalent to TSFLP and the heuristic we are proposing is within 25% of the optimal TSFLP. Key words: Probabilistic combinatorial optimization problems, facility location, heuristics, probabilistic analysis. 2 11
Accessed: 06/06/2011 09:04
, 1988
"... Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at. ..."
Abstract
 Add to MetaCart
Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at.