Hybridized Shuffled Frog Leaping Algorithm for Solving Facility Location Problem for Maternal Healthcare

Abstract

In this paper, the shuffled frog leaping algorithm hybridized with a state-of-the-art solver is applied to solve the large-scale facility location-allocation problem for maternal healthcare. The problem considered is about to minimizing the total cost of establishing the various type of facilities and travelling costs incurred by mothers-to-be (MTBs) while providing them the required services within the maximum allowable distance using limited capacity of the facilities. The problem is formulated as a mixed-integer linear programming (MILP) mathematical model. The proposed model is NP-hard and combinatorial, and would thus require unmanageable computational effort in optimal planning of a real-world maternal healthcare network. To obtain good-quality solutions for large-sized problems in a reasonable amount of time, the shuffled frog leaping algorithm, and a population-based metaheuristic, is used in this paper. The binary version of this metaheuristics is used in this paper to determine the location of the facilities, and allocation is carried out using a state-of-the-art solver. To evaluate the efficiency and effectiveness of the proposed hybrid approach, extensive experiments are conducted on randomly generated problem instances. The computational results demonstrate that the proposed metaheuristic outperformed the solver for the large size problem instances. However, the objective function value for small size problem deviated by less than 10% from the optimal objective function value but required much lesser time. Since the real-world problems are large in size, the proposed hybridized approach is quite competitive both in terms of efficiency and efficacy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.