Toward More Realistic Allocation in Location—Allocation Models: An Interaction Approach

1978 ◽  
Vol 10 (11) ◽  
pp. 1273-1285 ◽  
Author(s):  
M J Hodgson

Location—allocation models jointly specify the optimal locations of service facilities and the allocation of patrons to them. Almost without exception, the allocation rule employed in these models has assumed that patrons wish to use the facility at the least travel cost away from them. Spatial interaction theory suggests that a person's travel behaviour is influenced by many other factors, among them differential facility attractiveness and uncertainty about travel costs, and that the least-cost allocation rule is unrealistic. This paper presents a location—allocation model employing an entropy-maximizing interaction model to allocate patrons to facilities. A heuristic solution procedure is proposed and found to be effective and reasonably efficient for small problems. Insofar as travel behaviour in the system is suboptimal, the location—allocation model produces suboptimal solutions. In the interests of providing realistic solutions to real-world problems, however, it is essential that planners accommodate the behaviour of those they plan for, be it normative or not.

2010 ◽  
Vol 09 (03) ◽  
pp. 393-418 ◽  
Author(s):  
G. REZA NASIRI ◽  
HAMID DAVOUDPOUR ◽  
BEHROOZ KARIMI

In this paper a multi-product, multi-echelon location–allocation model for the optimization of a supply chain design is proposed. This model integrated inventory decisions into distribution network design with stochastic market demands. The goal is to select the optimum numbers, locations, and capacities of the opening warehouses so that all customer demands to be satisfied at minimum total costs of the distribution network. We develop a nonlinear mixed-integer model and propose an efficient heuristic solution procedure for the problem. The solution approach is based on Lagrangian relaxation, improved with efficient heuristic to solve complex sub-problems. Computational results indicate that the proposed method yields good solutions with high quality within a reasonable computational time for various real-size problems.


2021 ◽  
Vol 122 ◽  
pp. 102888
Author(s):  
Han Zou ◽  
Maged M. Dessouky ◽  
Shichun Hu

1989 ◽  
Vol 40 (3) ◽  
pp. 293-297 ◽  
Author(s):  
T D Fry ◽  
L Vicens ◽  
K Macleod ◽  
S Fernandez

2021 ◽  
Vol 11 (14) ◽  
pp. 6401
Author(s):  
Kateryna Czerniachowska ◽  
Karina Sachpazidu-Wójcicka ◽  
Piotr Sulikowski ◽  
Marcin Hernes ◽  
Artur Rot

This paper discusses the problem of retailers’ profit maximization regarding displaying products on the planogram shelves, which may have different dimensions in each store but allocate the same product sets. We develop a mathematical model and a genetic algorithm for solving the shelf space allocation problem with the criteria of retailers’ profit maximization. The implemented program executes in a reasonable time. The quality of the genetic algorithm has been evaluated using the CPLEX solver. We determine four groups of constraints for the products that should be allocated on a shelf: shelf constraints, shelf type constraints, product constraints, and virtual segment constraints. The validity of the developed genetic algorithm has been checked on 25 retailing test cases. Computational results prove that the proposed approach allows for obtaining efficient results in short running time, and the developed complex shelf space allocation model, which considers multiple attributes of a shelf, segment, and product, as well as product capping and nesting allocation rule, is of high practical relevance. The proposed approach allows retailers to receive higher store profits with regard to the actual merchandising rules.


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