An approximation algorithm for a competitive facility location problem with network effects

Kung, Ling-Chieh; Liao, Wei-Hung

doi:10.1016/j.ejor.2017.11.037

Cited by 26 publications

(10 citation statements)

References 19 publications

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“…For the sake of clarity, we represent facilities and demand points with squares and circles, respectively. Considering the Euclidean distance, the optimal solution value for the p-center problem is 1.44 units, and the facilities must be located at points (1, 1), (1,4), and (3,2). On the contrary, the value for the objective function of the p-dispersion problem is 2.24 units, when considering the same facilities.…”

Section: Problem Definitionmentioning

confidence: 99%

“…Facility location is a family of hard optimization problems that have been in the spotlight of the scientific community in the last few years, not only from a theoretical point of view, but also from practitioners in this field [1,2]. Given a set of candidate locations N representing demand points, the goal of this kind of problem is to select a subset of p locations, P ⊂ N, that will host a facility, with the aim of minimizing or maximizing a certain criterion.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Objective Parallel Iterated Greedy for Solving the p-Center and p-Dispersion Problem

et al. 2019

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This paper generalizes the iterated greedy algorithm to solve a multi-objective facility location problem known as the Bi-objective p-Center and p-Dispersion problem (BpCD). The new algorithm is coined as Multi-objective Parallel Iterated Greedy (MoPIG) and optimizes more than one objective at the same time. The BpCD seeks to locate p facilities to service or cover a set of n demand points, and the goal is to minimize the maximum distance between facilities and demand points and, at the same time, maximize the minimum distance between all pairs of selected facilities. Computational results demonstrate the effectiveness of the proposed algorithm over the evolutionary algorithms NSGA-II, MOEA/D, and the Strength Pareto Evolutionary Algorithm 2 (SPEA2), comparing them with the optimal solution found by the -constraint method.

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Section: Problem Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Parallel Iterated Greedy for Solving the p-Center and p-Dispersion Problem

et al. 2019

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“…Kung and Liao [14] have studied the competitive facility location problem by a discrete model to maximize the profit considering network effects. Two scenarios for demand function are assumed; in the first scenario, demand function is assumed to be linear and then the optimal solution find and when the demand function is nonlinear, they proved that the problem is NP-hard and then an approximation algorithm is developed to solve the problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Influence of Two Different Producers in a Competitive Location Problem

2019

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Facility location of two producers with preference of customers is discussed in this paper. Because of differences between two producers in terms of their influence on the market, the problem is formulated as a bi-level integer mathematical programming model with binary variables. It is considered that both leader and follower have some facilities at first and are going to open new facilities and this may lead to make changes in allocation of facilities and customers. To solve the problem, two metaheuristics algorithm based on genetic algorithm (GA) and hybrid of genetic algorithm and ant colony optimization (ACO) are proposed. In the first section of each algorithm, the location of facilities for two producers is determined and in the second section, each customer selects a facility. Upper bound of the competitive facility location problem is determined by solving the upper-level problem as an integer linear programming model without considering the follower's decision. To evaluate the efficiency of proposed algorithms, enumeration technique is used to find optimal solution. Computational results show that all of the developed algorithms are capable of achieving optimal solution for small size problems and high-quality solution in reasonable computational time for medium and large-scale problems.

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“…With the continual adjustment in the course of management, the risk cost C(x) is positively related to the proportion of drivers who take the strategy of illegal operation. Considering the network effects of TNCs, the demand degree of ridesourcing service is closely related to the scale of ridesourcing drivers (Pal, Scrimitore 2016;Kung, Liao 2018). It is easily understood that when the drivers of legal operation is rarely supplied, the TNCs will only develop the connection with a limited number of the passengers.…”

Section: The Establishment Of the Game Modelmentioning

confidence: 99%

Transportation Network Companies and Drivers Dilemma in China: An Evolutionary Game Theoretic Perspective

Lei

Gao

2019

Transport

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The ridesourcing services market in China has recently experienced significant changes, which stem from its legalization and management policy. These changes impact multiple stakeholders of this market (e.g., drivers, passengers, government, competing services) and present them with new opportunities and challenges. This paper develops an evolutionary game model to analyse the Evolutionary Stable Strategy (ESS) between the Transportation Network Companies (TNCs) and drivers. The new model is explored and analysed with simulation experiments to observe the dynamic route of multiple stakeholders. The theoretical research and simulation results indicate that under the authorities’ control over the TNCs, when the net income under strict management is higher than that of the loose management for the TNCs, the final ESS is “Legal Operation, Strict Management”. When the net income under strict management is less than that of the loose management for the THCs, the strategy of “Illegal Operation, Loose Management” may gain popularity and continue to grow; in this case, the ESS may also not exist. The model indicates the strength of the government’s control plays a significant role in leading the achievement of “Legal Operation, Strict Management”. As a consequence, to achieve the perfect evolution of “Legal Operation, Strict Management”, it is necessary for the government to impose a greater penalty on illegal drivers and ensure appropriate compensation measures. The results of the study provide a useful reference for the sustainable development of the ridesourcing services market.

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An approximation algorithm for a competitive facility location problem with network effects

Cited by 26 publications

References 19 publications

A Multi-Objective Parallel Iterated Greedy for Solving the p-Center and p-Dispersion Problem

A Multi-Objective Parallel Iterated Greedy for Solving the p-Center and p-Dispersion Problem

Influence of Two Different Producers in a Competitive Location Problem

Transportation Network Companies and Drivers Dilemma in China: An Evolutionary Game Theoretic Perspective

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