We examine a uniform parallel machine scheduling problem with dedicated machines, job splitting, and limited setup resources for makespan minimization. In this problem, machines have different processing speeds, and each job can only be processed at several designated machines. A job can be split into multiple sections and those sections can be processed on multiple machines simultaneously. Sequence-independent setup times are assumed, and setup operations between jobs require setup operators that are limited. For the problem, we first develop a mathematical optimization model and for large-sized problems a constructive heuristic algorithm is proposed. Finally, we show that the algorithm developed is efficient and provides good solutions by experiments with various scenarios.

Background: Estimating realistic access to health services is essential for designing support policies for healthcare delivery systems. Many studies have proposed a metric to calculate accessibility. However, patients’ realistic willingness to use a hospital was not explicitly considered. This study aims to derive a new type of potential accessibility that incorporates a patient’s realistic preference in selecting a hospital. Methods: This study proposes a floating catchment area (FCA)-type metric combined with a discrete choice model. Specifically, a new FCA-type metric (clmFCA) was proposed using a conditional logit model. Such a model estimates patients’ realistic willingness to use health services. The proposed metric was then applied to calculate the accessibility of obstetric care services in Korea. Results: The clmFCA takes advantage of patients’ realistic preferences. Specifically, it can represent each patient’s heterogeneous characteristics regarding hospital choice. Such characteristics include bypassing behavior, which could not be considered using prior FCA metrics. Empirical analysis reveals that the clmFCA avoids the misestimation of accessibility to health services to an extent. Conclusions: The clmFCA offers a new framework that more realistically estimates patients’ accessibility to health services. This is achieved by accurately estimating the potential demand for a service. The proposed method’s effectiveness was verified through a case study using nationwide data.

Increasing investment and interest in research and development (R&D) requires an efficient management system for achieving better research project outputs. In tandem with this trend, there is a growing need to develop a method for predicting research project outputs. Motivated by this, using information gathered in the early stage of projects, this study addresses the problem of predicting research projects’ output, which is binary coded as either successful or not. To build the prediction model, we apply six machine learning algorithms: five are well-known supervised learning algorithms and the other is autoML, characterized by its ability to produce a learning model appropriate to the data characteristics on its own, with minimal user intervention. Our empirical analysis with real R&D data provided by the South Korean government over 5 years (2014–8) confirms that the autoML-based model performs better than models based on other machine learning algorithms for this task. We also find that project duration and research funding are important factors in predicting R&D project outputs. Based on the results, our study provides insightful implications leading to a paradigm shift for data-based R&D project management.