An improved branch & bound method for the Uncapacitated Competitive Location Problem

被引:36
作者
Benati, S [1 ]
机构
[1] Univ Trent, Dipartimento Informat & Studi Aziendali, I-38100 Trento, Italy
关键词
competitive location models; random utility theory; submodular functions; heuristic concentration; data-correcting method; MAXIMUM CAPTURE PROBLEM;
D O I
10.1023/A:1026182020346
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 [运筹学与控制论]; 120117 [社会管理工程];
摘要
In this paper, the problem of locating new facilities in a competitive environment is considered. The problem is formulated as the firm expected profit maximization and a set of nodes is selected in a graph representing the geographical zone. Profit depends on fixed and deterministic location costs and, since customers are independent decision-makers, on the expected market share. The problem is an instance of nonlinear integer programming, because the objective function is concave and submodular. Due to this complexity a branch & bound method is developed for solving small size problems ( that is, when the number of nodes is less than 50), while a heuristic is necessary for larger problems. The branch & bound is called data-correcting method, while the approximate solutions are obtained using the heuristic-concentration method.
引用
收藏
页码:43 / 58
页数:16
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