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Yunfeng Kong

Publications and source records attributed to Yunfeng Kong.

2 recordsLinked to original sources

Extended p-median problems for balancing service efficiency and equality

This article deals with the location problem for balancing the service efficiency and equality. In public service systems, some individuals may experience envy if they have to travel longer distances to access services compared to others. This envy can be simplified by comparing an individual's travel distance to a service facility against a threshold distance. Four extended p-median problems are proposed, utilizing the total travel distance and total envy to balance service efficiency and spatial equality. The new objective function is designed to be inequity-averse and exhibits several analytical properties that pertain to both service efficiency and equality. The extended problems were extensively tested on two sets of benchmark instances and one set of geographical instances. The experimentation shows that the equality measures, such as the standard deviation, mean absolute deviation, and Gini coefficient between travel distances, can be substantially improved by slightly increasing the travel distance. Additionally, the advantages of the proposed problems were validated through Pareto optimality analysis and comparisons with other location problems.

cs.CY

A matheuristic algorithm for the single-source capacitated facility location problem and its variants

This article presents a matheuristic algorithm for the single-source capacitated facility location problem (SSCFLP) and its variants: SSCFLP with K facilities (SSCKFLP), SSCFLP with contiguous service areas (CFLSAP), and SSCFLP with K facilities and contiguous service areas (CKFLSAP). The algorithm starts from an initial solution, and iteratively improves the solution by exactly solving large neighborhood-based sub-problems. The performance of the algorithm is tested on 5 sets of SSCFLP benchmark instances. Among the 272 instances, 191 optimal solutions are found, and 35 best-known solutions are updated. For the largest set of instances with 300-1000 facilities and 300-1500 customers (Avella and Boccia 2009), the proposed algorithm outperforms existing methods in terms of the solution quality and the computational time. Furthermore, based on two geographic areas, two sets of instances are generated to test the algorithm for solving SSCFLP and its variants. The solutions found by the proposed algorithm approximate optimal solutions or the lower bounds with average gaps of 0.07% for SSCFLP, 0.22% for CFLSAP, 0.04% for SSCKFLP, and 0.13% for CKFLSAP.

cs.DM