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The continuous-space single- and multi-facility location problem has attracted much attention in previous studies. This study focuses on determining the globally optimal facility locations for two- and higher-dimensional continuous-space facility location problems when the Manhattan distance is considered. Before we propose the exact method, we sta...
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... However, it lacks a comprehensive exploration of the impact of market dynamics, such as market cannibalization, on facility location decisions. Additionally, the study does not delve into the utilization of MAS to address the real-time data flow challenges and the need for adaptive decision-making in facility location optimization, which are crucial aspects highlighted in the proposed MAS solution for this study [4]. ...
Effective facility location decisions are pivotal for enhancing a firm’s performance and competitive edge. Traditional methods often struggle to adapt to dynamic market conditions, leading to suboptimal outcomes. This research proposes a novel Multi-Agent System (MAS) application to address the Facility Location Problem (FLP). By leveraging distributed decision-making agents, the MAS platform aims to optimize facility locations in real time, integrating dynamic factors such as evolving consumer preferences and market trends. This study will design, implement, and evaluate an MAS platform where agents representing stakeholders—customers, suppliers, and facilities—interact to find optimal locations, considering cost minimization, customer satisfaction, and competitive advantage. The MAS framework also incorporates advanced decision-making algorithms and optimization techniques to enhance the efficiency and robustness of the solution. The system’s adaptability to market changes and real-time data integration capabilities will be thoroughly assessed through comprehensive evaluation metrics. The anticipated outcomes include improved decision-making efficiency, enhanced adaptability to market changes, and a robust solution capable of mitigating market cannibalization effects. Ultimately, this research aims to provide a practical and scalable approach to facility location optimization, fostering long-term organizational success in a competitive global environment.
... Also, the covering problem in the supply chain design field has several applications in the delivery center and warehouse location problems [24]. Gao et al. [25] proposed continuous-space single and multifacility location methods where the Manhattan distance is considered and gave the exact optimal global solution. Moreover, an efficient target barrier construction algorithm was proposed by Cheng and Wang [26] that could solve set covering problems with boundary constraints on the target barrier where the number of sensors was minimized. ...
This article analyzes a network of servers that cover mobile phone calls in a given convex service area. All servers work with the same reliability, and can cover all call requests within a circle of the same given radius. When working servers cover the whole area, the network system is working. System reliability is the probability that working servers can cover the whole area. Without loss of generality, we assume that each server's location, coverage radius, and reliability are known. Monte Carlo and Voronoi diagram methods are used to check the system state, and a binary search method is proposed to obtain the system reliability. Also, a simulation method is used to evaluate system reliability in special cases. Finally, numerical examples are studied to investigate the effects of the model parameters on system reliability.
... In recent years, modeling the continuously distributed demand coverage in MCLPs has attracted much attention (Wei and Murray 2015, Gao et al. 2021, Song et al. 2021. For example, Murray and Tong (2007) extended the circle intersection point set (CIPS) approach developed by Church (1984) to identify candidate locations for facilities in continuous space. ...
The reliable service coverage of many facilities or sensors used in smart city infrastructure is highly susceptible to obstructions in urban environments. Optimizing the line-of-sight (LOS) service coverage is essential to locating these facilities for smarter city services. Despite progression in the maximal coverage location problem (MCLP) model for locating facilities, maximizing the LOS service coverage in continuous demand space for facility location problems remains challenging. This study defined the LOS-constrained MCLPs (LOS-MCLPs) and proposed a service coverage optimization model to solve these LOS-MCLPs. We employed a computational geometry algorithm named the visibility polygon (VP) algorithm to simulate the LOS coverage in two-dimensional (2D) continuous demand space. We then coupled this algorithm with a robust heuristic algorithm to search for the optimal solutions to maximize effective LOS service coverage. An experiment applied the developed model to a Wi-Fi hotspot planning problem. The experimental results demonstrated that the proposed model can obtain optimal solutions for LOS-MCLPs according to the distribution of obstacles. Comparative results show that ignoring the LOS effect in the optimization of LOS-MCLPs might lead to large areas of service dead zones.
... Gupta et al. [38] used fuzzy c-means and particle swarm optimization to optimize the locations of public service centers. Researches mentioned above proved that the clustering-based algorithms are applicable in practice [39]. Nonetheless, few researchers have dedicated to improving the distance function in clustering. ...
Distribution centers are quite important for logistics. In order to save costs, reduce energy consumption and deal with increasingly uncertain demand, it is necessary for distribution centers to select the location strategically. In this paper, a two-stage model based on an improved clustering algorithm and the center-of-gravity method is proposed to deal with the multi-facility location problem arising from a real-world case. First, a distance function used in clustering is redefined to include both the spatial indicator and the socio-economic indicator. Then, an improved clustering algorithm is used to determine the optimal number of distribution centers needed and the coverage of each center. Third, the center-of-gravity method is used to determine the final location of each center. Finally, the improved method is compared with the traditional clustering method by testing data from 12 cities in Inner Mongolia Autonomous Region in China. The comparison result proves the proposed method’s effectiveness.
... Location problems could be studied in discrete space having a finite number of candidate locations, or in continuous space in which any point of the planning area could be a potential location for establishing the facilities [2]. The facilities could be equipped with mobile or immobile servers. ...
... Note that this study considers Q kFN P. The facility location problem, which determines the competitive location of a new facility, such as garbage incinerators, crematoriums, chemical plants, supermarkets, and police stations, is very important in real life when using the kFN join query applications. Particularly, determining the optimal facility location is still an open problem [15,16]. Facing such a research problem, efficiently evaluating the kFN join query is remarkably useful. ...
This paper considers k-farthest neighbor (kFN) join queries in spatial networks where the distance between two points is the length of the shortest path connecting them. Given a positive integer k, a set of query points Q, and a set of data points P, the kFN join query retrieves the k data points farthest from each query point in Q. There are many real-life applications using kFN join queries, including artificial intelligence, computational geometry, information retrieval, and pattern recognition. However, the solutions based on the Euclidean distance or nearest neighbor search are not suitable for our purpose due to the difference in the problem definition. Therefore, this paper proposes a cluster nested loop join (CNLJ) algorithm, which clusters query points (data points) into query clusters (data clusters) and reduces the number of kFN queries required to perform the kFN join. An empirical study was performed using real-life roadmaps to confirm the superiority and scalability of the CNLJ algorithm compared to the conventional solutions in various conditions.
