Multi-objective competitive location problem with distance-based attractiveness and its best non-dominated solution

Wang, Sheng-Chuan; Chen, Ta-Cheng

doi:10.1016/j.apm.2017.02.031

Cited by 19 publications

(9 citation statements)

References 54 publications

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“…Other than these customer behaviours, some research has also been conducted on the coverage-based model (Drezner et al ., 2012, 2015, 2016). Wang and Chen (2017) introduced three coverage concepts, full, partial and no coverage, in a multi-objective competitive location problem, in which attractiveness depends on distance.…”

Section: Findings and Discussionmentioning

confidence: 99%

Location of competitive facilities: a comprehensive review and future research agenda

Mishra

Singh

Gupta

2022

BIJ

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PurposeThe research in competitive facility location (CFL) is quite dynamic, both from a problem formulation and an algorithmic point of view. Research direction has changed immensely over the years to address various competitive challenges. This study aims to explore CFL literature to highlight these research trends, important issues and future research opportunities.Design/methodology/approachThis study utilises the Scopus database to search for related CFL models and adopts a five-step systematic approach for the review process. The five steps involve (1) Article Identification and keyword selection, (2) Selection criteria, (3) Literature review, (4) Literature analysis and (5) Research studies.FindingsThe paper presents a comprehensive review of CFL modelling efforts from 1981 to 2021 to provide a depth study of the research evolution in this area. The published articles are classified based on multiple characteristics, including the type of problem, type of competition, game-theoretical approaches, customer behaviour, decision space, type of demand, number of facilities, capacity and budget limitations. The review also highlights the popular problem areas and dedicated research in the respective domain. In addition, a second classification is also provided based on solution methods adopted to solve various CFL models and real-world case studies.Originality/valueThe paper covers 40 years of CFL literature from the perspective of the problem area, CFL characteristics and the solution approach. Additionally, it introduces characteristics such as capacity limit and budget constraint for the first time for classification purposes.

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Section: Findings and Discussionmentioning

confidence: 99%

Location of competitive facilities: a comprehensive review and future research agenda

Mishra

Singh

Gupta

2022

BIJ

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“…A new mathematical model for competitive location problem is proposed by Sadjadi et al [17] to determine the location of the new facilities and also the optimal product type for each of opened facility while the behavior of the customers is based on Huff rule. In addition to the above-mentioned studies, some researchers have studied multi objective models of competitive facility location (Bilir et al [18]; Wang & Chen [19]; Konak et al [20]).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraints (15)(16) act like constraint (5-6) but at the upper level. Constraints 17- (19) define the lower-level decision variables.…”

mentioning

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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“…The coverage criteria have been introduced several times in previous research on facility allocation problems based on different levels of coverage. The 1-coverage model is presented (S.-C. Wang & Chen, 2017a), where a single number of facilities always facilitates the demands. But when considering more realistic factors in facility allocation problems such as enormous demand and attractiveness of the facility, the uncertain possibility of the facility in 1-coverage failed to fulfil the ultimate solution to overcome such kind of problem.…”

Section: Introductionmentioning

confidence: 99%

Web Semantic-Based MOOP Algorithm for Facilitating Allocation Problems in the Supply Chain Domain

Lin,

Rahaman,

Moslehpour

et al. 2023

International Journal on Semantic Web and Information Systems

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The facility allocation of the supply chain is critical since it directly influences cost efficiency, customer service, supply chain responsiveness, risk reduction, network optimization, and overall competitiveness. When enterprises deploy their facilities wisely, they may achieve operational excellence, exceed customer expectations, and obtain a competitive advantage in today's volatile business climate. Due to this reason, a multi-objective facility allocation problem is introduced in this research with cooperative-based multi-level backup coverage considering distance-based facility attractiveness. The facility of the coverage is further described as two different layers of the coverage process, where demand can be covered as full, partial, and no coverage by their respective facilities. The main objectives of this facility allocation problem are to maximize the coverage of the facility to maximize overall facility coverage in the supply chain network and simultaneously minimize the overall cost.

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Multi-objective competitive location problem with distance-based attractiveness and its best non-dominated solution

Cited by 19 publications

References 54 publications

Location of competitive facilities: a comprehensive review and future research agenda

Location of competitive facilities: a comprehensive review and future research agenda

Influence of Two Different Producers in a Competitive Location Problem

Web Semantic-Based MOOP Algorithm for Facilitating Allocation Problems in the Supply Chain Domain

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