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

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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A resilient inventory management of pharmaceutical supply chains under demand disruption

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期180卷

作者： Pathy, Soumya Ranjan Rahimian, Hamed Clemson Univ Dept Ind Engn Clemson SC 29634 USA

Inspired by the global supply chain disruptions caused by the COVID-19 pandemic, we study optimal procurement and inventory decisions for a pharmaceutical supply chain over a finite planning horizon. To model disruption, we assume that the demand for medical drugs is uncertain and shows spatiotemporal variability. To address demand uncertainty, we propose a two-stage optimization framework, where in the first stage, the total cost of pre-positioning drugs at distribution centers and its associated risk is minimized, while the second stage minimizes the cost of recourse decisions (e.g., reallocation, inventory management). To allow for different risk preferences, we propose to capture the risk of demand uncertainty through the expectation and worst-case measures, leading to two different models, namely (risk-neutral) stochastic programming and (risk-averse) robust optimization. We consider a finite number of scenarios to represent the demand uncertainty, and to solve the resulting models efficiently, we propose L-shaped decomposition-based algorithms. Through extensive numerical experiments, we illustrate the impact of various parameters, such as travel time, product's shelf life, and waste due to transportation and storage, on the supply chain resiliency and cost, under optimal risk-neutral and risk-averse policies. These insights can assist decision makers in making informed choices.

关键词： Resilient pharmaceutical supply chain Risk aversion stochastic programming Robust optimization Benders' decomposition

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Multistage stochastic decision problems: Approximation by recursive structures and ambiguity modeling

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第3期306卷 1027-1039页

作者： Pflug, Georg Ch. Univ Vienna Fac Econ & Stat Wien Oskar Morgenstern Pl A-1090 Vienna Austria

stochastic multistage decision problems appear in many -if not all -application areas of Operations Re-search. While to define such problems is easy, to solve them is quite difficult, since they are of infinite dimension. Numerical solution can only be found by solving an approximate, easier problem. In this pa-per, we show good approximations can be found, where we emphasize the recursive structure of the involved algorithms and data structures. In a second part, the problem of coping with the model error of approximations is discussed. We present algorithms for finding distributionally robust solutions for the model error problem. We also review some application cases of such situations from the literature.(c) 2022 The Author. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： stochastic programming Scenario tree generation Recursive algorithms Model error Distributionally robust solutions

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Multi-Stage International Portfolio Selection with Factor-Based Scenario Tree Generation

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COMPUTATIONAL ECONOMICS 2024年 1-41页

作者： Chen, Zhiping Ji, Bingbing Liu, Jia Mei, Yu Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China Xian Int Acad Math & Math Technol Ctr Optimizat Tech & Quantitat Finance Xian 710049 Peoples R China

To comprehensively reflect the heteroscedasticity, nonlinear dependence and heavy-tailed distributions of stock returns while reducing the huge cost of parameter estimation, we use the Fama-French three-factor model to describe stock returns and then model the factor dynamics by using the ARMA-GARCH and Student-t copula models. A factor-based scenario tree generation algorithm is thus proposed, and the corresponding multi-stage international portfolio selection model is constructed and its reformulation is derived. Different from the current literature, our proposed models can capture the dynamic dependence among international markets and the dynamics of exchange rates, and what's more important, make it possible for the practical solution of large-scale multi-stage international portfolio selection problems. Considering three different objective functions and international investments in the USA, Japanese and European markets, we carry out a series of empirical studies to demonstrate the practicality and efficiency of the proposed factor-based scenario tree generation algorithm and multi-stage international portfolio selection models.

关键词： International portfolio selection stochastic programming Factor model Copula Scenario tree

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Improving aggregate production planning considering maximum inventory area and service level with demand uncertainty: a nearshoring context in Mexican companies

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2024年第5期41卷 442-455页

作者： Gomez-Rocha, Jose Emmanuel Hernandez-Gress, Eva Selene Santos-Borbolla, Cipriano Arturo Tecnol Monterrey Engn & Sch Sci Monterrey Mexico

Nearshoring is a phenomenon that has gained prominence in Mexico over the last 2 years, where companies have faced problems in meeting customer demand due to a service level and limited inventory space. Previous studies in aggregate planning did not consider the possibility of allowing rental of extra warehouse space if needed with a concurrent uncertainty in demand. Therefore, to address this gap in the research, an improved optimization model for multi-product and multi-period aggregate planning with uncertainty in demands given an area of inventory space is proposed. A set of cases based on real industry data were solved and compared to a model where no warehouse space is allowed to be rented. The results are improvements related to production costs and customer demand satisfaction. In addition, the model will help managers of manufacturing companies to determine the minimum inventory space required to effectively meet demand. [Graphical abstract]

关键词： Aggregate production planning stochastic programming inventory space area nearshoring

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Simple Inference on Functionals of Set-Identified Parameters Defined by Linear Moments

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JOURNAL OF BUSINESS & ECONOMIC STATISTICS 2024年第2期42卷 563-578页

作者： Cho, JoonHwan Russell, Thomas M. Univ Toronto Dept Econ Toronto ON Canada Carleton Univ Dept Econ Ottawa ON Canada Carleton Univ Dept Econ 1125 Colonel By Dr Ottawa ON K1S5B6 Canada

This article proposes a new approach to obtain uniformly valid inference for linear functionals or scalar subvectors of a partially identified parameter defined by linear moment inequalities. The procedure amounts to bootstrapping the value functions of randomly perturbed linear programming problems, and does not require the researcher to grid over the parameter space. The low-level conditions for uniform validity rely on genericity results for linear programs. The unconventional perturbation approach produces a confidence set with a coverage probability of 1 over the identified set, but obtains exact coverage on an outer set, is valid under weak assumptions, and is computationally simple to implement.

关键词： Linear programming Partial Identification stochastic programming Subvector inference

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The Value of Shared Information for allocation of drivers in ride-hailing: a proof-of-concept study

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COMPUTATIONAL MANAGEMENT SCIENCE 2024年第1期21卷 1-32页

作者： Liberona, Gianfranco Salas, David von Niederhaeusern, Leonard Univ OHiggins Alameda 611 Rancagua Chile Univ OHiggins Inst Ciencias Ingn Alameda 611 Rancagua Chile Univ Chile Ctr Modelamiento Matematico CNRS IRL 2807 Beauchef 851 Santiago Chile

For drivers in ride-hailing companies, allocation within the city is paramount to get matched with rides. This decision depends on many factors, where some of them (such as demand and allocation of others) are unknown for the drivers, but are available for the company. In this work, we investigate whether it is beneficial or not for the ride-hailing company to share this information with their drivers. To do so, we study the problem through the lens of Stackelberg games, and we propose a new indicator called the Expected Value of Shared Information. We present a simplified model to conduct a proof-of-concept study: we provide explicit single-level reformulations of the bilevel programming problems derived from the model, and perform several simulations with randomly generated data. Our preliminary results suggest that sharing information could be beneficial and deserves to be further studied.

关键词： stochastic programming Ride-hailing Stackelberg game Expected Value of Shared Information

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Distribution network design in FMCG sector by using stochastic programming 44

Distribution network design in FMCG sector by using stochast...

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Joint International Symposium on "The Social Impacts of Developments in Information, Manufacturing and Service Systems" 44th International Conference on Computers and Industrial Engineering, CIE 2014 and 9th International Symposium on Intelligent Manufacturing and Service Systems, IMSS 2014

作者： Tuzkaya, U.R. Kilic, Y.E. Yilmazer, K.B. Department of Industrial Engineering Yildiz Technical University Istanbul Turkey

Distribution network design is both strategic and tactical level problem that deals with the alternative locations selection, assignment of the customers to suppliers, and determining the product flow quantities among the echelons. There are various factors that affect decisions on this problem such as transportation mode availability, lead times, facility capacities, penalties, demand, unit costs, storing costs etc. In real life distribution networks, all the activities are realized in a dynamic environment and most of the above-mentioned factors include uncertainties. Obtaining an applicable solution for real life distribution network design requires a stochastic approach consideration. In this study, a two stage stochastic programming method is applied for modeling and solving the problem. In the first stage, the model tries to decide the location decision of the facilities of the network. In the second stage, the model aims to make a decision about the transported, stored and unmet demand quantities considering the demand and related handling cost. These demand and handling costs are obtained by combining the various scenarios. The proposed model is solved for a distribution network active in FMCG sector. The results are analyzed and compared with the deterministic solutions.

关键词： stochastic programming

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