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Nursing care flexibility in chemotherapy appointment scheduling

FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2024年第3期36卷 918-945页

作者： Gul, Serhat Univ Massachusetts Dept Operat & Informat Management Isenberg Sch Management Amherst MA 01003 USA TED Univ Dept Ind Engn TR-06420 Ankara Turkiye

The flexibility level allowed in nursing care delivery and the uncertainty in infusion durations are important factors for chemotherapy scheduling. The nursing care delivery scheme employed in an outpatient chemotherapy clinic (OCC) determines the strictness of the patient-to-nurse assignment policies, while the estimation of infusion durations affects the trade-off between patient waiting time and nurse overtime. We study the problem of daily scheduling of patients, assignment of patients to nurses and chairs in the presence of uncertainty in infusion durations for an OCC that functions according to any of the commonly used nursing care delivery system representing fully, partially, and inflexible care systems. We develop a two-stage stochastic mixed-integer programming model minimizing expected weighted cost of patient waiting time and nurse overtime. We propose multiple variants of a scenario grouping-based decomposition algorithm to solve the model using data from a major university oncology hospital. We compare input-based, solution-based, and random scenario grouping methods within the decomposition algorithm. We obtain near-optimal schedules that are also significantly better than the schedules generated based on the policy used in the clinic. We analyze the impact of nursing care flexibility in order to determine whether a partial or fully flexible delivery system is necessary to adequately improve waiting time and overtime.

关键词： Chemotherapy scheduling Nursing care delivery Flexibility stochastic programming Scenario grouping-based decomposition

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A dry port hub-and-spoke network design: An optimization model, solution method, and application

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COMPUTERS & OPERATIONS RESEARCH 2024年 167卷

作者： Irawan, Chandra Ade Salhi, Said Jones, Dylan Dai, Jing Liu, Martin J. Univ Nottingham Nottingham Univ Business Sch Ningbo Peoples R China Nottingham Ningbo China Beacons Excellence Res & I Ningbo Peoples R China Khalifa Univ Coll Engn & Phys Sci Abu Dhabi U Arab Emirates Univ Kent Kent Business Sch Canterbury England Univ Portsmouth Ctr Operat Res & Logist Sch Math & Phys Portsmouth England

This paper studies an innovative bi-objective optimization model for the dry port hub -and -spoke network that considers both the minimization of the total costs and the total amount of carbon emissions. The model is formulated as a two -stage stochastic program due to the uncertain nature of demand. Here, the decision variables include the optimal locations of dry ports, their respective numbers, and their connections, together with the flows of containers. Two types of dry ports are taken into account, where there may be container flows from a relatively small dry port (feeder dry port) to a large dry port (hub dry port). As the problem is NP -hard and practically too complex to solve by an exact method, an efficient hybrid Genetic Algorithm (GA) with interesting ingredients is developed to obtain a promising set of non -dominated solutions. The performance of the proposed methodology is evaluated on a case study of Tianjin Port, China. The computational experiments reveal that the proposed method is promising while providing a useful practical optimization tool that can provide insightful directions for governmental and industrial stakeholders as well as logistic companies on which dry ports can make a suitable addition to their portfolio.

关键词： Combinatorial optimization Dry port network Discrete location stochastic programming Genetic algorithm

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Security-constrained stochastic optimal power flow analysis using optimally reduced scenarios for wind generation

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ELECTRICAL ENGINEERING 2025年 1-23页

作者： Das, Sourav Das, Barsa Barnali Sengupta, Ananyo Natl Inst Technol Rourkela Dept Elect Engn Rourkela 769008 Orissa India

Electrical power scheduling typically occurs in two stages: day-ahead (DA) planning and real-time (RT) balancing. In DA scheduling, generation and reserve capacities are set for the next day, with reserves used to balance power in RT under variable conditions. Due to the emerging need for electricity, power systems often operate in marginal states, increasing the probability of equipment failure (contingencies). These uncertainties make potential changes in network topology;this situation becomes even more challenging in the presence of renewable energy sources (RESs) due to their inherent uncertain nature. If this combined effect of uncertainty is not considered explicitly during scheduling, the network may collapse. This paper presents a security-constrained stochastic optimal power flow (SC-SOPF) method to schedule generation and reserves in systems with high wind penetration. The stochastic nature of wind power is modeled through scenario-generation and scenario-reduction techniques by formulating it as a non-convex optimization problem. The proposed scenario-reduction technique ensures that the moments of the reduced scenario set remain the same as the original one while maximizing the distinct features in each scenario. The aforesaid problem was solved using particle swarm optimization (PSO), genetic algorithm, differential evolution, ant colony optimization, and whale optimization techniques. It is found that the value of the objective function obtained by the PSO is at least 21.08% lower than other evolutionary algorithms. The reduced scenarios are used in a two-stage stochastic SC-SOPF model, solved using the benders decomposition technique with group cuts. The effectiveness of the proposed SC-SOPF is evaluated with respect to the expected load not served (ELNS) during real-time operations. Tests on modified IEEE 9-bus and 39-bus systems show that although the DA cost increases on average by 8.07% for the modified 9-bus system and only by 0.18% for the modi

关键词： Benders decomposition Day-ahead scheduling Non-convex optimization Power system security Real-time scheduling Scenario reduction stochastic programming

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Real-Option-Based Planning of Utility-Owned Distributed Energy Resources for Islanded Distribution Networks in Southeast Asia

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IEEE TRANSACTIONS ON SMART GRID 2025年第2期16卷 1314-1327页

作者： Leng, Ruoxuan Xu, Yan Nanyang Technol Univ Ctr Power Engn Sch Elect & Elect Engn Singapore 639798 Singapore

This paper proposes a new approach for planning distributed energy resource (DER) units in islanded distribution networks (DNs) of Southeast Asia, a region characterized by abundant solar irradiance, high temperature, and volatile weather conditions. First, a comprehensive thermal model for stationary energy storage systems (ESSs) is introduced with a detailed assessment of ESS internal cooling power consumption. Then, to address voltage fluctuation caused by intermittent photovoltaic (PV) power output due to the region's highly variable weather conditions, the PV inverter oversizing is modeled to provide reactive power support, considering the degradation of the inverter lifespan. Last, supply chain risks are considered a growing concern in the power industry. Real option-based criteria are then applied to provide deferral options for flexible investment. The whole problem is formulated as a two-stage stochastic programming model and a solution method based on candidate bus selection and model linearization and relaxation is developed. Simulation results on an IEEE-34 bus three-phase DN illustrate the necessity and efficiency of the proposed approach.

关键词： Batteries Planning Heating systems Costs Meteorology Inverters Asia Reactive power Investment Distributed power generation Distribution network (DN) stochastic programming distributed energy resources (DERs) planning

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Optimizing the loading of double stack trains under uncertain container availability

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 196卷

作者： Ng, Manwo Lee, Yu-Chi Lin, Dung-Ying Old Dominion Univ Strome Coll Business Dept Informat Technol & Decis Sci 2154 Constant Hall Norfolk VA 23529 USA Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu Taiwan

This paper contributes to the literature on the operations management of double stack trains by introducing a new, real-world research problem that arises when loading trains at marine container terminals with on-dock rail service. Specifically, in this research we model the reality that containers that need to be loaded on railcars can be unavailable at the time of loading, while optimizing the assignment of railcars to hubs and trains, and containers to railcars. To this end, we propose a two stage stochastic program that aims to minimize the number of well cars used when container availability is uncertain (first stage) while also maximizing their space utilization when taking corrective actions (second stage). For its solution, a tailored integer L-shaped solution method is presented. Algorithmic performance and managerial insights are highlighted in a series of numerical experiments. Findings include: 1) The proposed L-shaped method is superior compared to a state-of-the-art commercial solver (up to 5 times faster in our experiments). 2) It is beneficial for the rail manager to prioritize making available 40-foot containers versus 20-foot containers. 3) The higher the probability of container availability in the second stage, the more well cars should be made available in the first stage.

关键词： stochastic programming Integer L-shaped method Container Maritime Double stack train

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Simultaneous planning of distribution automation and battery energy storage systems for improving resilience of distribution network

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2025年 42卷

作者： Sadeghian-Jahromi, Mohammad Fotuhi-Firuzabad, Mahmud Fattaheian-Dehkordi, Sajjad Wang, Fei Lehtonen, Matti Aalto Univ Dept Elect Engn & Automat Espoo Finland Sharif Univ Technol Dept Elect Engn Tehran Iran North China Elect Power Univ Dept Elect Engn Baoding Peoples R China

Recent widespread blackouts around the world have highlighted the fact that power grids must not only ensure reliability against high-probability, low-impact events (HPLI), but also withstand against low-probability, highimpact events (HILP) which could endanger the reliable operation of the system. Therefore, with the aim of improving the resilience of distribution networks, this paper proposes a model for the simultaneous planning of distribution automation and energy storage systems to address the operational challenges in case of hurricane occurrences in the system. For this, first, a hurricane model is presented to predict the impact of future hurricanes on the network. Respectively, the planning optimization is formulated as a mixed integer linear programming (MILP) to optimally determine the location and number of the remote-control switches (RCSs) as well as the capacity, number and location of the battery energy storage systems (BESSs). This study enables the cooptimization of distribution automation and energy storages investments to effectively improve the resilience of the system. Finally, this model is implemented on the Roy Billinton Test System (RBTS) to investigate the effectiveness of the proposed method in improving the resilience of system in an economic manner.

关键词： Reliability Resilience Distribution network Simultaneous planning Distribution automation Battery energy storage systems Remote control switches Automation control stochastic programming Distribution system optimization

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Joint relocation and pricing in electric car-sharing systems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第2期315卷 553-566页

作者： Eilertsen, Ulrik Falck-Pedersen, Olav M. Henriksen, Jone, V Fagerholt, Kjetil Pantuso, Giovanni Norwegian Univ Sci & Technol Dept Ind Econ & Technol Management Alfred Getz Veg 3 NO-7491 Trondheim Norway Univ Copenhagen Dept Math Sci DK-2100 Copenhagen Denmark

In this paper we study the integrated planning problem of determining car-sharing prices between zones of the operating area and routing employees (operators) to relocate cars in preparation for future uncertain demand. We present a novel two-stage integer stochastic programming model for this problem together with a heuristic algorithm, based on Adaptive Large Neighborhood Search (ALNS), to obtain solutions to realistically sized instances. We test the ALNS heuristic on a set of instances generated based on data from a real car-sharing organization and show that it outperforms a commercial solver.

关键词： Transportation Car-sharing Pricing stochastic programming Adaptive Large Neighborhood Search

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Accelerating Benders decomposition approach for shared parking spaces allocation considering parking unpunctuality and no-shows

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 240卷

作者： Hu, Jinyan Jiang, Yanping Northeastern Univ Sch Business Adm Shenyang 110167 Peoples R China

By allocating idle private parking spaces to demanders, shared parking reuses idle resources and effectively alleviates parking problems. In practice, there may be parking unpunctuality behavior of demanders, as well as the potential no-show behavior of owners and demanders. These stochastic factors affect the allocation of shared parking spaces. In this paper, we study the shared parking spaces allocation problem considering parking unpunctuality and no-shows. First, the allocation problem considering parking unpunctuality is formulated as a stochastic programming model, with the objective to maximize the profit of the shared parking platform. Sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm is exploited to solve the model effectively, where initial optimality cuts are explored to speed up the convergence of the algorithm. Then the proposed approach is extended to incorporate no-shows. Numerical experiments show great performance of the proposed approach. The experiments also show that the fluctuations of the unpunctual time and the no-show probability have a significant impact on the shared parking system, and balanced supply and demand is helpful to increase the profit of the platform and the satisfaction of demanders.

关键词： Shared parking Parking spaces allocation stochastic programming Benders decomposition

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Is it beneficial to invest in flexible capacity for hybrid remanufacturing?

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第21期62卷 7836-7859页

作者： Francas, David Oener-Koezen, Miray Minner, Stefan Hsch Worms Fac Business & Econ Worms Germany Tech Univ Munich Sch Management Munich Germany Hsch Worms Fac Business & Econ Erenburgerstr 19 D-67549 Worms Germany

Firms that aim to close the loop via remanufacturing returned products face uncertainties on both the demand and supply side. Inspired by industrial circular business models, we study the capacity investment problem of a manufacturer. The manufacturer can invest in a flexible (shared) resource to share capacity across processes and/or less costly dedicated manufacturing and remanufacturing resources. We model the capacity investment problem as a two-stage stochastic programme and provide structural results. Our analysis shows how the optimal resource selection depends on margin (price) and cost differentials and highlights the focal role of capacity coefficients. We identify conditions under which an investment in flexibility is beneficial even if remanufacturing is a lower-margin process. Moreover, an investment in flexible capacity can be optimal if demand and returns are perfectly positively correlated, and thus, return risk is eliminated. Contrary to intuition, optimal profits may decrease in demand-return correlation if the optimal investment includes a flexible resource. The analysis of investment thresholds shows two benefits of resource flexibility: (a) mitigation of demand and return mismatches and (b) an ex-post revenue maximisation option to allocate capacity to the more profitable process.

关键词： stochastic programming hybrid remanufacturing systems capacity investment flexibility circular economy SDG 12: Responsible consumption and production

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Decision-focused neural adaptive search and diving for optimizing mining complexes

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第3期320卷 699-719页

作者： Yaakoubi, Yassine Dimitrakopoulos, Roussos McGill Univ Stochast Mine Planning Lab COSMO 3450 Univ St Montreal PQ H3A 2A7 Canada Univ Montreal Campus GERAD Res Grp Decis Anal Montreal PQ H3T 1J4 Canada

Optimizing industrial mining complexes, from extraction to end-product delivery, presents a significant challenge due to non-linear aspects and uncertainties inherent in mining operations. The two-stage stochastic integer program for optimizing mining complexes under joint supply and demand uncertainties leads to a formulation with tens of millions of variables and non-linear constraints, thereby challenging the computational limits of state-of-the-art solvers. To address this complexity, a novel solution methodology is proposed, integrating context-aware machine learning and optimization for decision-making under uncertainty. This methodology comprises three components: (i) a hyper-heuristic that optimizes the dynamics of mining complexes, modeled as a graph structure, (ii) a neural diving policy that efficiently performs dives into the primal heuristic selection tree, and (iii) a neural adaptive search policy that learns a block sampling function to guide low-level heuristics and restrict the search space. The proposed neural adaptive search policy introduces the first soft (heuristic) branching strategy in mining literature, adapting the learning-to- branch framework to an industrial context. Deployed in an online fashion, the proposed hybrid methodology is shown to optimize some of the most complex case studies, accounting for varying degrees of uncertainty modeling complexity. Theoretical analyses and computational experiments validate the components' efficacy, adaptability, and robustness, showing substantial reductions in primal suboptimality and decreased execution times, with improved and more robust solutions that yield higher net present values of up to 40%. While primarily grounded in mining, the methodology shows potential for enabling smart, robust decision-making under uncertainty.

关键词： Large scale optimization stochastic programming Heuristics Machine learning OR in natural resources

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