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Multi-configuration modular capacity wheat supply chain network design under uncertainty: robust optimisation-based approach

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS 2025年第1期12卷

作者： Ostovari, Alireza Bozorgi Amiri, Ali Yousefi-Babadi, Abolghasem Benyoucef, Lyes Aix Marseille Univ CNRS LIS Marseille France Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Yazd Univ Dept Ind Engn Yazd Iran

The Food Supply Chain typically refers to the processes involved in producing and distributing food, taking it from farms to consumers' homes. This research presents a robust modular capacity model for designing a wheat supply chain network, incorporating multi-level, multi-product, and multi-period considerations. The model incorporates key factors such as sleep periods, wheat quality, inter-regional transport, and flour extraction rates. Unlike previous studies that primarily focussed on national-level modelling, this study emphasises regional data for network design. This approach enables more precise and practical decision-making, tailored to specific regional needs and conditions. Regarding demand and supply fluctuations, the system optimises storage capacity at varying levels throughout the planning horizon. This approach minimises surplus infrastructure costs and avoids expenses associated with unused capacity. A modular capacity management strategy is employed to determine silo capacities effectively. The robust optimisation approach is applied to handle uncertainties in demand and transportation costs. The study evaluates three different approaches for conservatism levels in the optimisation process. Also through the real data, the model demonstrates its applicability, and the model addresses challenges such as facility underutilisation, and fluctuating demand. Sensitivity analyses reveal the trade-offs between cost efficiency and robustness. The results highlight the importance of modular infrastructure, dynamic inventory management, and resilience to uncertainties.

关键词： Wheat supply chain network modular capacity agri-food supply chain robust optimisation stochastic programming

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Optimal Placement and Sizing of Distributed Generation in Electrical DC Distribution Networks Using a stochastic Mixed-Integer LP Model

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年第8期50卷 5835-5851页

作者： Valencia-Diaz, Alejandro Hincapie, Ricardo A. Gallego, Ramon A. Univ Tecnol Pereira Dept Elect Engn Pereira 660003 Risaralda Colombia

This paper presents a stochastic mixed-integer linear mathematical model for finding the optimal placement and sizing of distributed generation in a DC distribution network, considering the uncertainty of electrical demand and distributed renewable sources. The proposed model accurately represents the original mixed-integer nonlinear model, obtaining a globally optimal solution in less computational time with low errors. The mathematical model allows for considering constraints related to the maximum limits for the penetration of distributed generation, such as those specified by Resolution CREG 174 of 2021. Furthermore, the uncertainties of the electrical demand, wind energy-based distributed generation (DG), and solar energy-based DG are considered in the mathematical models using a two-stage stochastic programming approach. The accuracy and efficiency of the proposed model were tested and validated on a 21-node DC test system from the specialized literature, and the effectiveness and robustness were assessed on a 69-node DC test system. The obtained results show that the proposed stochastic mixed-integer linear mathematical model performs well.

关键词： Distributed generation DC distribution systems Mixed-integer linear programming model stochastic programming

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Maintaining fairness in stochastic chemotherapy scheduling

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2025年 137卷

作者： Celik, Batuhan Gul, Serhat Karsu, Ozlem Bilkent Univ Dept Ind Engn TR-06800 Ankara Turkiye Rensselaer Polytech Inst Dept Ind & Syst Engn Troy NY USA TED Univ Dept Ind Engn Ankara Turkiye

Chemotherapy scheduling is hard to manage under uncertainty in infusion durations, and focusing on expected performance measure values may lead to unfavorable outcomes for some patients. In this study, we aim to design daily patient appointment schedules considering a fair environment regarding patient waiting times. We propose using a metric that encourages fairness and efficiency in waiting time allocations. To optimize this metric, we formulate a two-stage stochastic mixed-integer nonlinear programming model. We employ a binary search algorithm to identify the optimal schedule, and then propose a modified binary search algorithm (MBSA) to enhance computational capability. Moreover, to address stochastic feasibility problems at each MBSA iteration, we introduce a novel reduce-and-augment algorithm that utilizes scenario set reduction and augmentation methods. We use real data from a major oncology hospital to show the efficacy of MBSA. We compare the schedules identified by MBSA with both the baseline schedules from the oncology hospital and those generated by commonly employed scheduling heuristics. Finally, we highlight the significance of considering uncertainty in infusion durations to maintain fairness while creating appointment schedules.

关键词： Healthcare operations Chemotherapy Scheduling Fairness stochastic programming

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Optimising rainwater harvesting systems under uncertainty: A multi-objective stochastic approach with risk considerations

RESOURCES CONSERVATION & RECYCLING ADVANCES

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RESOURCES CONSERVATION & RECYCLING ADVANCES 2025年 26卷

作者： Shefaei, Alireza Maleki, Arash van der Hoek, Jan Peter van de Giesen, Nick Abraham, Edo Delft Univ Technol Fac Civil Engn & Geosci Dept Water Management Stevinweg 1 NL-2628 CN Delft Netherlands Waternet Res & Innovat Korte Ouderkerkerdijk 7 NL-1096 AC Amsterdam Netherlands

Optimising rainwater harvesting (RWH) systems' design involves sizing the storage and catchment areas enhance cost-effectiveness, self-sufficiency, and water quality indicators. This paper considers the design RWH systems under long-term uncertainty in precipitation and demands. In this work, we formulate and solve a multi-objective stochastic optimisation problem that allows explicit trade-offs under uncertainty, maximising system efficiency and minimising deployment cost. We use the yield after spillage (YAS) approach to incorporate the physical and operational constraints and the big-M method to reformulate the nonlinear min\max rules of this approach as a mixed-integer linear programming (MILP) problem. By posing a risk averseness measure on efficiency as a conditional value at risk (CVaR) formulation, we guarantee the designer against the highest demand and driest weather conditions. We then exploit the lexicographic method effectively solve the multi-objective stochastic problem as a sequence of equivalent single-objective problems. detailed case study of a botanical garden in Amsterdam demonstrates the framework's practical application;we show significant improvements in system efficiency of up to 15.5% and 28.9% in the driest scenarios under risk neutral and risk-averse conditions, respectively, compared to deterministic approaches. The findings highlight the importance of taking into account multiple objectives and uncertainties when designing RWH systems, allowing designers to optimise efficiency and costs based on their specific requirements without extensive parameterisation.

关键词： Rainwater harvesting system stochastic programming Multi-objective optimisation Risk assessment

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Dual dominance: how Harry Markowitz and William Ziemba impacted portfolio management

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ANNALS OF OPERATIONS RESEARCH 2024年第1期346卷 181-216页

作者： Lleo, Sebastien Maclean, Leonard C. NEOMA Business Sch Finance Dept 59 Rue Pierre Taittinger F-51100 Reims France Dalhousie Univ Fac Management 6100 Univ Ave Halifax NS B3H 4R2 Canada

Models for determining a portfolio of investment decisions in risky assets have been at the forefront of financial research for almost a century. Among the celebrated researchers are Harry Markowitz and William Ziemba. These titans devoted their working years to developing quantitative models and adapting the models to changes in financial markets and investor attitudes. This paper presents a general lens through which the Markowitz mean-variance model and the Ziemba capital growth model can be viewed. This lens is risk-sensitive stochastic control. The optimal control approach places the expected utility, mean-variance, and capital growth models in a common setting to elucidate their connection. In particular, benchmarking and risk factors, two standard refinements to control risk, are seamlessly incorporated into the stochastic control model. The solution to the risk-sensitive control problem isolates the effect of benchmarks and factors to provide insights into model-based portfolios.

关键词： Portfolio selection Mean-variance optimization Harry Markowitz Kelly criterion stochastic programming William Ziemba Risk-sensitive control

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Flexible Ramping Product Procurement in Day-Ahead Markets

IEEE TRANSACTIONS ON ENERGY MARKETS POLICY AND REGULATION

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IEEE TRANSACTIONS ON ENERGY MARKETS POLICY AND REGULATION 2025年第1期3卷 13-31页

作者： Yurdakul, Ogun Ela, Erik Billimoria, Farhad Argonne Natl Lab Energy Syst & Infrastruct Anal Div Lemont IL 60439 USA Elect Power Res Inst Energy Delivery & Customer Solut Dept Palo Alto CA 94304 USA Univ Oxford Dept Engn Sci Energy & Power Grp Oxford OX1 3PJ England

Flexible ramping products (FRPs) emerge as a promising instrument for addressing steep and uncertain ramping needs through market mechanisms. Initial implementations of FRPs in North American electricity markets, however, revealed several shortcomings in existing FRP designs. In many instances, FRP prices failed to signal the true value of ramping capacity, most notably evident in zero FRP prices observed in a myriad of periods during which the system was in acute need for rampable capacity. These periods were marked by scheduled but undeliverable FRPs, often calling for operator out-of-market actions. On top of that, the methods used for procuring FRPs have been primarily rule-based, lacking explicit economic underpinnings. In this paper, we put forth an alternative framework for FRP procurement, which seeks to set FRP requirements and schedule FRP awards such that the expected system operation cost is minimized. Using real-world data from U.S. ISOs, we showcase the relative merits of the framework in (i) reducing the total system operation cost, (ii) improving price formation, (iii) enhancing the the deliverability of FRP awards, and (iv) reducing the need for out-of-market actions.

关键词： Costs Procurement Uncertainty MISO communication Dams Regulation Schedules Day-ahead market flexible ramping product stochastic programming unit commitment

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Integration of text-mining and telemedicine appointment optimization

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ANNALS OF OPERATIONS RESEARCH 2024年第1期341卷 621-645页

作者： Ji, Menglei Mosaffa, Mohammad Ardestani-Jaafari, Amir Li, Jinlin Peng, Chun Beijing Inst Technol Sch Management & Econ Beijing 100081 Peoples R China Univ British Columbia Fac Management 1137 Alumni Ave Kelowna BC V1V 1V7 Canada Beijing Jiaotong Univ Sch Econ & Management Beijing 100044 Peoples R China

Nowadays, many countries view profitable telemedicine as a viable strategy for meeting healthcare needs, especially during the pandemic. Existing appointment models are based on patients' structured data. We study the value of incorporating textual patient data into telemedicine appointment optimization. Our research contributes to the healthcare operations management literature by developing a new framework showing (1) the value of the text in the telemedicine appointment problem, (2) the value of incorporating the textual and structured data in the problem. In particular, in the first phase of the framework, a text-driven classification model is developed to classify patients into normal and prolonged service time classes. In the second phase, we integrate the classification model into two existing decision-making policies. We analyze the performance of our proposed policy in the presence of existing methods on a data set from the National Telemedicine Center of China (NTCC). We first show that our classifier can achieve 90.4% AUC in a binary task based on textual data. We next show that our method outperforms the stochastic model available in the literature. In particular, with a slight change of actual distribution from historical data to a normal distribution, we observe that our policy improves the average profit of the policy obtained from the stochastic model by 42% and obtains lower relative regret (18%) from full information than the stochastic model (148%). Furthermore, our policy provides a promising trade-off between the cancellation and postponement rates of patients, resulting in a higher profit and a better schedule strategy for the telemedicine center.

关键词： Telemedicine appointment scheduling Machine learning Text mining stochastic programming

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A generic stochastic network-based formulation for production optimization of district heating systems

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2024年 1-38页

作者： Guericke, Daniela Schledorn, Amos Madsen, Henrik Univ Twente Dept High Tech Business & Entrepreneurship Drienerlolaan 5 NL-7522 NB Enschede Netherlands Tech Univ Denmark Dept Appl Math & Comp Sci Richard Petersens Plads DK-2800 Lyngby Denmark

District heating (DH) systems are an important component in the EU strategy to reach the emission goals, since they allow an efficient supply of heat while using the advantages of sector coupling between different energy carriers such as power, heat, gas and biomass. Most DH systems use several different types of units to produce heat for hundreds or thousands of households (e.g. natural gas-fired boilers, electric boilers, biomass-fired units, waste heat from industry, solar thermal units). Furthermore, combined heat and power units units are often included to use the synergy effects of excess heat from electricity production. To address the challenge of providing optimization tools for a vast variety of different system configurations, we propose a generic mixed-integer linear programming formulation for the operational production optimization in DH systems. The model is based on a network structure that can represent different system setups while the underlying model formulation stays the same. Therefore, the model can be used for most DH systems although they might use different combinations of technologies in different system layouts. The mathematical formulation is based on stochastic programming to account for the uncertainty of energy prices and production from non-dispatchable units such as waste heat and solar heat. Furthermore, the model is easily adaptable to different application cases in DH systems such as operational planning, bidding to electricity markets and long-term evaluation. We present results from three real cases in Denmark with different requirements.

关键词： District heating Production optimization Network formulation stochastic programming Bidding

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Comparing an expected value with a multistage stochastic optimization approach for the case of wine grape harvesting operations with quality degradation

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2023年第4期30卷 1843-1873页

作者： Avanzini, Elbio L. Mac Cawley, Alejandro F. Vera, Jorge R. Maturana, Sergio Pontificia Univ Catolica Chile Dept Ind & Syst Engn Avda Vicuna Mackenna 4860 Santiago 750000 Chile

Operations planning is an important step in any activity as it aligns resources to achieve economic production value. In agriculture operations where uncertainty is present, planners must deal with biological and environmental factors, among others, which add variability and complexity to the production planning process. In this work, we consider operations planning to harvest grapes for wine production where uncertainty in weather conditions will affect the quality of grapes and, consequently, the economic value of the product. In this setting, planners make decisions on labor allocation and harvesting schedules, considering uncertainty of future rain. Weather uncertainty is modeled following a Markov Chain approach, in which rain affects the quality of grapes and labor productivity. We compare an expected value with a multi-stage stochastic optimization approach using standard metrics such as Value of stochastic Solution and Expected Value of Perfect Information. We analyze the impact of grape quality over time, if they are not harvested on the optimal ripeness day, and also consider differences in ability between workers, which accounts for the impact of rain in their productivity. Results are presented for a small grape harvest instance and we compare the performance of both models under different scenarios of uncertainty, manpower ability, and product qualities. Results indicate that the multi-stage approach produces better results than the expected value approach, especially under high uncertainty and high grape quality scenarios. Worker ability is also a mechanism for dealing with uncertainty, and both models take advantage of this variable.

关键词： stochastic programming expected value optimization quality management uncertainty modeling optimization harvest planning OR in agriculture

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A two-stage, four-layer robust optimisation model for distributed cooperation in multi-microgrids

IET ENERGY SYSTEMS INTEGRATION

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IET ENERGY SYSTEMS INTEGRATION 2024年第4期6卷 406-420页

作者： Rong, Haobo Wang, Jianhui Kuang, Honghai Nanjing Polytech Inst Dept Elect & Control Engn Nanjing Peoples R China Lanzhou Univ Technol Dept Elect & Informat Engn Lanzhou Peoples R China Hunan Univ Technol Dept Elect & Informat Engn Zhuzhou Peoples R China Lanzhou Univ Technol Lanzhou 730050 Peoples R China

As the integration of microgrids (MG) and energy storage continues to grow, the need for efficient distributed cooperation between MGs and common energy storage (CES) becomes paramount. A robust optimisation model for the distributed cooperation of MG-CES is presented, taking into account distributed generation under uncertainty. The proposed model follows a two-stage, four-layer 'min-min-max-min' structure. In the first stage, the initial layer 'min' addresses the distributed cooperation problem between MG and CES, while the second stage employs 'min-max-min' to optimise the scheduling of MG. To enhance the solution process and expedite convergence, the authors introduce a column-constrained generation algorithm with alternating iterations of U and D variables (CCG-UD) specifically designed for the three-layer structure in the second stage. This algorithm effectively decouples subproblems, contributing to accelerated solutions. To tackle the convergence challenges posed by the non-convex MG-CES model, the authors integrate the Bregman alternating direction method with multipliers (BADMM) with CCG-UD in the final solution step. Real case tests are conducted using three zone-level MGs to validate the efficacy of the proposed model and methodology. The results demonstrate the practical utility and efficiency of the developed approach in addressing distributed cooperation challenges in microgrid systems with energy storage.

关键词： cooling distributed power generation distribution networks energy management systems game theory matlab optimisation renewable energy sources stochastic programming

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