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Multi-objective outpatient scheduling in health centers considering resource constraints and service quality: a robust optimization approach

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Abstract

Hospitals are among the largest and most sophisticated service organizations and the most critical service delivery units in the health system. Due to the high risk of hospital services, the services provided must be of acceptable quality. Also, patient scheduling and timely receiving of services in medical centers can lead to patient satisfaction. In this study, a patient admission scheduling is modeled in which it is assumed that the staff is not always available. To enhance the quality of healthcare services and increases patient satisfaction, in this research, mathematical modeling is presented, in which resource capacity and treatment sequence constraints are considered in the proposed model. Furthermore, the uncertainty in the service quality parameter is taken into account, which makes the model more realistic. In this regard, first, a robust optimization approach based on the Bertsimas and Sim model is applied. Then, due to its Np-hardness, the multi-objective particle swarm optimization algorithm is proposed. Finally, the quality of the proposed algorithm is examined by comparing its results with the GAMS solver and the NSGA-II algorithm in small and large-size instances, respectively. Results indicate the proper performance of the proposed algorithm.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran

    J. Behnamian & Z. Gharabaghli

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  1. J. Behnamian
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  2. Z. Gharabaghli
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Correspondence to J. Behnamian.

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Behnamian, J., Gharabaghli, Z. Multi-objective outpatient scheduling in health centers considering resource constraints and service quality: a robust optimization approach. J Comb Optim 45, 80 (2023). https://doi.org/10.1007/s10878-023-01000-1

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