Discrete models for competitive location with foresight

Plastria, Frank; Vanhaverbeke, Lieselot

doi:10.1016/j.cor.2006.05.006

Cited by 103 publications

(56 citation statements)

References 7 publications

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“…The buying power of each demand point is randomly generated in the interval [1,10]. Quality values for all facilities are randomly generated in [1,5].…”

Section: Results and Computational Analysismentioning

confidence: 99%

“…Ashtiani et al provided the robust model for determining the optimal locations for the leader's new facilities considering that the number of the follower's new facilities is unknown for the leader [9]. Plastria and Vanhaverbeke solved the competitive location with foresight based on the maximal covering model by considering the competitor will locate a single new facility [10]. Alekseeva are concerned with the discrete (r|p)-centroid problem aiming at maximize their own profits, based on the deterministic customer behavior [11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Competitive Facility Location Problem with Foresight in Discrete Space based on Tabu Search

Qi¹,

Xia²,

Miao³

et al. 2015

Proceedings of the 2015 International Conference on Computer Science and Intelligent Communication

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Abstract-This paper presents the competitive facility location problem with foresight in the discrete space which considers probabilistic customer behavior. A practical bilevel nonlinear integer programming model is formulated to maximize the leader's market share while predicting the follower's response. For solving the model, a two-stage hybrid tabu search algorithm is designed. To illustrate the effectiveness of the proposed algorithm, a set of randomly generated instances are presented and analyzed statistically. The results indicate that the proposed algorithm provides an effective means to solve the problems.

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“…The buying power of each demand point is randomly generated in the interval [1,10]. Quality values for all facilities are randomly generated in [1,5].…”

Section: Results and Computational Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Competitive Facility Location Problem with Foresight in Discrete Space based on Tabu Search

Qi¹,

Xia²,

Miao³

et al. 2015

Proceedings of the 2015 International Conference on Computer Science and Intelligent Communication

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“…Also this is usually related to operational characteristics and leads to Nash game; most of the dynamic games in SC literature are unconstrained models that solved by differential systems (Xiao and Yang 2008;Zhang 2006;Dias 2010, 2013;Sinha and Sarmah 2010;Friesz et al 2011, Jain et al 2014Chen et al 2015;Nagurney et al 2015;Mousavi et al 2016;Santibanez-Gonzalez and Diabat 2016;Hjaila et al 2016a;Jahangoshai Rezaee et al 2017;Lipan et al 2017). (3) Competition with foresight: in this competition, the rivals show reactions to the entry of new comer in sequential manner and usually this is related to strategic characteristics; this competition leads to bilevel or multi-level models and stackelberg games (Drezner and Drezner 1998;Plastria and Vanhaverbeke 2008;Kucukaydın et al 2011, Zhang and Liu 2013Yue and You 2014;Zhu 2015;Drezner et al 2015;Taleizadeh and Charmchi 2015;Yang et al 2015, Esmaeilzadeh andTaleizadeh 2016;Hjaila et al 2016b;Aydin et al 2016;Ezimadu and Nwozo 2017;Genc andGiovanni 2017. Eiselt andLaporte 1997;Krass and Pesch 2012 have done a review of this kind of competition.…”

Section: Literature Reviewmentioning

confidence: 99%

Decentralized supply chain network design: monopoly, duopoly and oligopoly competitions under uncertainty

2017

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This paper presents the competitive supply chain network design problem in which n decentralized supply chains simultaneously enter the market with no existing rival chain, shape their networks and set wholesale and retail prices in competitive mode. The customer demand is elastic and price dependent, customer utility function is based on the Hoteling model and the chains produce identical or highly substitutable products. We construct a solution algorithm based on bi-level programming and possibility theory. In the proposed bi-level model, the inner part sets the prices based on simultaneous extra-and Stackleberg intra-chains competitions, and the outer part shapes the networks in cooperative competitions. Finally, we use a real-word study to discuss the effect of the different structures of the competitors on the equilibrium solution. Moreover, sensitivity analyses are conducted and managerial insights are offered.

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“…The problem is solved by a gradient search when the budget constraint is kept as equality. Plastria and Vanhaverbeke [77] combined the limited budget model with the leader-follower model.…”

Section: Location and Design With A Limited Budgetmentioning

confidence: 99%

“…In this section we survey two models for locating a new facility under conditions of uncertainty: the minimax regret objective (Drezner [25]) and the leader-follower, also called the Stackelberg equilibrium model (Stackelberg [82], Drezner and Drezner [32], Plastria and Vanhaverbeke [77]). …”

Section: Modeling Uncertaintymentioning

confidence: 99%

Modeling Competitive Facility Location Problems: New Approaches and Results

Berman¹,

Drezner²,

Drezner³

et al. 2009

Decision Technologies and Applications

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The gravity model as applied to competitive facility location is described in this tutorial. The gravity model is used mainly by marketers to estimate the market share attracted by competing retail facilities. A demand area consists of potential customers who spend their discretionary buying power at competing retail facilities. We discuss the gravity model and issues related to the determination of the parameters of the models. The basic model is to find the best locations for new facilities that would attract the maximum buying power from customers in the area. Extensions to this basic model include modeling uncertainty about future market conditions, minimizing the probability that the attracted market share falls short of a certain target, demand generated by intercepting a flow of customers, market expansion and lost demand, and allocation of a given budget among one's facilities to improve their attractiveness. We describe special tools that are utilized in the solution procedures. These are the generalized Weiszfeld procedure, the big triangle small triangle global optimization approach, and the tangent line approximation.

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Discrete models for competitive location with foresight

Cited by 103 publications

References 7 publications

Competitive Facility Location Problem with Foresight in Discrete Space based on Tabu Search

Competitive Facility Location Problem with Foresight in Discrete Space based on Tabu Search

Decentralized supply chain network design: monopoly, duopoly and oligopoly competitions under uncertainty

Modeling Competitive Facility Location Problems: New Approaches and Results

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