On tie breaking in competitive location under binary customer behavior

Pelegrı́n, Blas; Fernández, Pascual; Pérez, María Dolores García

doi:10.1016/j.omega.2014.10.010

Cited by 10 publications

(2 citation statements)

References 23 publications

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“…This would justify that there may be several facilities with the same maximum attraction for a customer, being able to be equally chosen to serve their demand. To have a better estimation of the market share captured by each new facility, we suppose that in case of ties in the maximum attraction, customer's demand is equally split among all these facilities (equity-based rule, see Pelegrín et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A Discrete Competitive Facility Location Model with Minimal Market Share Constraints and Equity-Based Ties Breaking Rule

Fernández¹,

Lančinskas²,

Pelegrı́n³

et al. 2020

Informatica

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We consider a geographical region with spatially separated customers, whose demand is currently served by some pre-existing facilities owned by different firms. An entering firm wants to compete for this market locating some new facilities. Trying to guarantee a future satisfactory captured demand for each new facility, the firm imposes a constraint over its possible locations (a finite set of candidates): a new facility will be opened only if a minimal market share is captured in the short-term. To check that, it is necessary to know the exact captured demand by each new facility. It is supposed that customers follow the partially binary choice rule to satisfy its demand. If there are several new facilities with maximal attraction for a customer, we consider that the proportion of demand captured by the entering firm will be equally distributed among such facilities (equitybased rule). This ties breaking rule involves that we will deal with a nonlinear constrained discrete competitive facility location problem. Moreover, minimal attraction conditions for customers and distances approximated by intervals have been incorporated to deal with a more realistic model. To solve this nonlinear model, we first linearize the model, which allows to solve small size problems because of its complexity, and then, for bigger size problems, a heuristic algorithm is proposed, which could also be used to solve other constrained problems.

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Section: Introductionmentioning

confidence: 99%

A Discrete Competitive Facility Location Model with Minimal Market Share Constraints and Equity-Based Ties Breaking Rule

Fernández¹,

Lančinskas²,

Pelegrı́n³

et al. 2020

Informatica

Self Cite

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“…In order to make the optimization problems computationally tractable in those problems and in other related modeling fields, customers are aggregated into geographic points (demand points), as shown in [4]. Several Integer linear Programming formulations have been proposed to cope with this type of problems for particular customer choice rules (see for instance [11,14,15]). However, solution of real-world CFLP usually is complex and computationally expensive due to reasons such as complex objective function(s), need of complex analysis of large amount of data, e.g.…”

Section: Introductionmentioning

confidence: 99%

Improving solution of discrete competitive facility location problems

et al. 2015

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We consider discrete competitive facility location problems in this paper. Such problems could be viewed as a search of nodes in a network, composed of candidate and customer demand nodes, which connections correspond to attractiveness between customers and facilities located at the candidate nodes. The number of customers is usually very large. For some models of customer behavior exact solution approaches could be used. However, for other models and/or when the size of problem is too high to solve exactly, heuristic algorithms may be used. The solution of discrete competitive facility location problems using genetic algorithms is considered in this paper. The new strategies for dynamic adjustment of some parameters of genetic algorithm, such as probabilities for the crossover and mutation operations are proposed and applied to improve the canonical genetic algorithm. The algorithm is also specially adopted to solve discrete competitive facility location problems by proposing a strategy for selection of the most promising values of the variables in the mutation procedure. The developed genetic algorithm is demonstrated by solving instances of competitive facility location problems for an entering firm.

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