The patient path scheduling problem studied in this work is inspired by a real-world case from a large comprehensive hospital in China, where patients must visit multiple medical departments, requiring efficient scheduling of their examination paths. We introduce a new large-scale patient path scheduling problem that minimizes patient non-treatment time, reflecting the perspective of patient satisfaction. We consider the time patients spend walking between departments and waiting for treatment outside the departments, as well as environmental factors that may influence patients’ waiting experience. We first present a mixed integer programming model and solve it via the Gurobi optimization solver. To address large-scale benchmark instances with up to 1000 patients and 6 types of examinations, we propose an effective neighborhood reduction based local search algorithm. The algorithm distinguishes itself by employing an original neighborhood reduction strategy to avoid unpromising neighboring solutions and a dedicated rollback strategy to intensify the search. Computational results on 60 benchmark instances demonstrate that our algorithm is competitive compared to the Gurobi solver and three greedy algorithms used in real-world hospital settings. The key components of the algorithm are analyzed to understand its behavior. Moreover, we show how hospitals can leverage this study under different patient numbers, demonstrating that our approach is particularly beneficial in large-scale settings.
For surgery duration prediction, it has been shown that integrating human judgment into algorithmic prediction models results in a higher accuracy than machine learning based approaches can achieve alone. However, since humans are a scarce resource in healthcare, their involvement in surgery duration prediction has high opportunity costs as their time could be spent more effectively on patient care and other medical tasks. This raises the question of how these costs should influence human involvement in tasks where algorithms perform reasonably well at minimal cost. This paper introduces a framework for selectively integrating human judgment based on the costs of human involvement and prediction uncertainty, which is captured using algorithmic meta features. Our framework identifies instances in surgery duration prediction where human judgment should be integrated in algorithmic predictions and those where it should not. We evaluate our framework using a data set containing 70,610 surgeries from a large university hospital. We observe that while always integrating human judgment leads to an overall higher accuracy than never integrating human judgment, employing the framework can lead to an even higher accuracy with lower human workload. In our case study planning costs could be reduced by 27
Efficient resource leveling is a critical challenge in ship refit and maintenance planning, where fluctuating labor demand leads to costly subcontracting, overtime, and risks of project delay. To address this issue, this paper presents a continuous-time mixed-integer linear programming approach to Rough Cut Capacity Planning (RCCP) for tactical project planning, which integrates resource leveling by treating work package execution intensities as decision variables. The model allows greater flexibility in execution intensities and enforces a single-peaked resource usage profile, in which demand rises to a peak and then declines, thereby reducing repeated hiring and rehiring while assessing impacts on overall project duration. Computational experiments on specially generated instances show that allowing reduced coupling between resource-specific intensities improves workload leveling by up to 35.16 ε -constraint analysis quantifies the trade-off between project duration and resource stability for instances with extended horizons. The analysis considers three factors: the total project duration, the variation of workload between periods, and whether or not the workload profile is unimodal.
Sustainable energy technologies and efficient energy usage are important drivers for reducing greenhouse gas emissions. The manufacturing industry, with its high energy consumption, is particularly important. Integrated energy-oriented production planning becomes essential with the adoption of on-site renewable energy technologies such as photovoltaics, wind power, hydropower, and energy storage systems. However, energy-oriented planning approaches, particularly in terms of lot-sizing, are limited. Hence, we present a novel energy-oriented dynamic lot-sizing model for multiple products in a capacity-restricted production system. Decentralized renewable energy sources supply energy, which can be stored temporarily in an energy storage system. Connections to the national power grid and energy trading are included, assuming that power generation and energy prices are known. The model aims to minimize inventory, setup, and energy costs. Numerical results highlight the superiority of our model in terms of solvability over a model formulation based on the PLSP presented by Liao and Gicquel (2024). Furthermore, we explore strategies to improve the lower and upper bounds of the monolithic model. The proposed hybrid matheuristic, which combines a Fix-and-Optimize heuristic with local branching, achieves high solution quality. Comparisons with traditional (consecutive) planning approaches underscore the benefits of energy-oriented lot-sizing, including better utilization of renewable energy and cost reductions of up to 19
