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Designing a sustainable-resilient-responsive supply chain network considering uncertainty in the COVID-19 era

EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期227卷 120334页

作者： Moadab, Amirhossein Kordi, Ghazale Paydar, Mohammad Mahdi Divsalar, Ali Hajiaghaei-Keshteli, Mostafa Washington State Univ Carson Coll Business Dept Finance & Management Sci Pullman WA USA Univ Helsinki Dept Econ & Management Helsinki Finland Babol Noshirvani Univ Technol Dept Ind Engn Babol Iran Tecnol Monterrey Sch Engn & Sci Dept Ind Engn Puebla Mexico

Effective supply chain management is crucial for economic growth, and sustainability is becoming a key consideration for large companies. COVID-19 has presented significant challenges to supply chains, making PCR testing a vital product during the pandemic. It detects the presence of the virus if you are infected at the time and detects fragments of the virus even after you are no longer infected. This paper proposes a multi-objective mathematical linear model to optimize a sustainable, resilient, and responsive supply chain for PCR diagnostic tests. The model aims to minimize costs, negative societal impact caused by shortages, and environmental impact, using a scenario-based approach with stochastic programming. The model is validated by investigating a real-life case study in one of Iran's high-risk supply chain areas. The proposed model is solved using the revised multi-choice goal programming method. Lastly, sensitivity analyses based on effective parameters are conducted to analyze the behavior of the developed Mixed-Integer Linear programming. According to the results, not only is the model capable of balancing three objective functions, but it is also capable of providing resilient and responsive networks. To enhance the design of the supply chain network, this paper has considered various COVID-19 variants and their infectious rates, in contrast to prior studies that did not consider the variations in demand and societal impact exhibited by different virus variants.

关键词： Supply chain design Polymerase chain reaction (PCR) COVID-19 Mathematical modelling stochastic programming Goal programming

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Sensitivity Analysis of stochastic Constraint and Variational Systems via Generalized Differentiation

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SET-VALUED AND VARIATIONAL ANALYSIS 2023年第1期31卷 4-4页

作者： Mordukhovich, Boris S. Perez-Aros, Pedro Wayne State Univ Dept Math Detroit MI 48202 USA Univ OHiggins Inst Ciencias Ingn Rancagua Chile

This paper conducts sensitivity analysis of random constraint and variational systems related to stochastic optimization and variational inequalities. We establish efficient conditions for well-posedness, in the sense of robust Lipschitzian stability and/or metric regularity, of such systems by employing and developing coderivative characterizations of well-posedness properties for random multifunctions and efficiently evaluating coderivatives of special classes of random integral set-valued mappings that naturally emerge in stochastic programming and stochastic variational inequalities.

关键词： Variational analysis Set-valued analysis Lipschitzian stability Generalized differentiation Coderivatives stochastic programming stochastic variational inequalities Primary Secondary

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A decentralized stochastic bidding strategy for aggregators of prosumers in electricity reserve markets

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JOURNAL OF CLEANER PRODUCTION 2023年第1期389卷

作者： Manna, Carlo Sanjab, Anibal Flemish Inst Technol Res VITO Boeretang 200 B-2400 Mol Belgium EnergyVille Thor Pk 8310 B-3600 Genk Belgium

Recent years have seen an increasing interest in demand response as a means to provide flexibility and support the penetration of renewable resources in the power system. Aggregators play a key role, in this regard, by enabling the participation of small-scale distributed energy resources and loads connected at the distribution network into local and central *** paper proposes an optimal strategy to support aggregators of a large number of small prosumers to participate in the tertiary reserve procurement market. In particular, a two-stage decentralized approach is proposed in which, first, prosumers individually optimize their flexible appliances schedule according to their own requirements and preferences, without sharing any information with the aggregator. At the aggregator level, a trading strategy is proposed for participation in the reserve market aiming at maximizing the aggregator's profit under the pay-as-bid pricing scheme, while only requiring a linear programming formulation ensuring the efficiency of the trading problem. In the developed approach, a scenario-based stochastic programming method is introduced to capture the uncertainties of market prices, weather conditions, loads, and user *** demonstrate the applicability of the proposed method, using real data, simulations on 2500 small prosumers in a realistic setting are carried out. The proposed linear and decentralized method can be executed within a few minutes, enabling its applicability for aggregators with a large number of customers.

关键词： Aggregators Electricity markets Flexibility Reserve procurement stochastic programming

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A decomposition-based crash-start for stochastic programming

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2013年第2期55卷 311-340页

作者： Colombo, Marco Grothey, Andreas Univ Edinburgh Sch Math Edinburgh Midlothian Scotland Univ Edinburgh Maxwell Inst Edinburgh Midlothian Scotland

In this paper we propose a crash-start technique for interior point methods applicable to multi-stage stochastic programming problems. The main idea is to generate an initial point for the interior point solver by decomposing the barrier problem associated with the deterministic equivalent at the second stage and using a concatenation of the solutions of the subproblems as a warm-starting point for the complete instance. We analyse this scheme and produce theoretical conditions under which the warm-start iterate is successful. We describe the implementation within the OOPS solver and the results of the numerical tests we performed.

关键词： stochastic programming Interior point methods Warm-starting

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Wind Power Scenario Generation and Reduction in stochastic programming Framework

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2013年第3期41卷 271-285页

作者： Sharma, Kailash Chand Jain, Prerna Bhakar, Rohit Malaviya Natl Inst Technol Jaipur Dept Elect Engn Jaipur 302017 Rajasthan India

Wind power trading in pool-based electricity markets is a decision-making problem and is generally modeled using a multi-stage stochastic programming approach because of the implicit uncertainty of wind input. In any stochastic programming approach, representation of random input process is a major issue. Due to uncertainty in wind availability, generated power by wind turbines is stochastic and is represented by possible values with corresponding probability of occurrence or scenarios. Accurate representation of uncertainty generally requires the consideration of large number of scenarios, thus necessitating the need for scenario-reduction techniques. This article presents simplified algorithms for wind power scenario generation and reduction. A time series based auto regressive moving average model is used for scenario generation, and probability distance based backward reduction is used for scenario reduction. The algorithms have been implemented for next-day scenario generation of wind farm located at Barnstable, Massachusetts, USA. The results prove the ability of the proposed algorithms in wind uncertainty modeling. These algorithms can successfully be utilized to generate optimal wind power bids for trading in electricity markets.

关键词： wind power uncertainty time series scenario generation scenario reduction stochastic programming

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A decision-dependent randomness stochastic program for asset-liability management model with a pricing decision

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ANNALS OF OPERATIONS RESEARCH 2021年第1-2期299卷 241-271页

作者： Kopa, Milos Rusy, Tomas Charles Univ Prague Fac Math & Phys Dept Probabil & Math Stat Sokolovska 83 Prague 18675 Czech Republic

In this study, we present a stochastic programming asset-liability management model which deals with decision-dependent randomness. The model focuses on a pricing problem and the subsequent asset-liability management problem describing the typical life of a consumer loan. Such problems are frequently tackled by many companies, including multinationals. When doing so, they must consider numerous factors. These factors include the possibility of their customer rejecting the loan, the possibility of the customer defaulting on the loan and the possibility of prepayment. The randomness associated with these factors have a clear relationship with the offered interest rate of the loan which is the company's decision and thus, induces decision-dependent randomness. Another important factor, which plays a major role for liabilities, is the price of money in the market. This is determined by the market interest rates. We captured their evolution in the form of a scenario tree. In summary, we formulated a non-linear, multi-stage stochastic program with decision-dependent randomness, which spanned the lifetime of a typical consumer loan. Its solution showed us the optimal decisions that the company should make. In addition, we performed a sensitivity analysis demonstrating the results of the model for various parameter settings that described different types of customers. Finally, we discuss the losses caused if companies do not act in the optimal way.

关键词： stochastic programming Decision-dependent randomness Asset-liability management

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Modeling and solution algorithm for project scheduling problem considering man-hour under uncertainty

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Journal of Japan Industrial Management Association 2021年第1期72卷 37-45页

作者： Kitamura, Takumi Shiina, Takayuki Department of Industrial and Management Systems Engineering School of Creative Science and Engineering Waseda University Japan

Projects are often executed under uncertain circumstances and require prior decisions that take uncertainty into account. Among them, the schedule of the initial plan and the plan for additional decisions corresponding to the uncertainties become important. In this study, we developed a mathematical model of a two-stage stochastic programming problem considering time and cost tradeoffs and crushing, which are important in project scheduling. An effective solution using a stochastic integer linear model and a moment matching method was presented for DTCTP-C with random fluctuations. In project management, the duration of a job is determined by the man-hour and the amount of resources required for the job. The duration can be shortened by increasing the amount of additional resources utilized. The cost increases according to the the amount of resources used. This problem is referred to as the Time/Cost Trade-off Problem (TCTP). In this study, the relation between time and cost is represented by an inverse proportional curve. We present a solution to the stochastic Discrete TCTP-Curve (SDTCTP-C) in which the duration of the job is defined as a random variable. It may also be necessary to study mathematical models that require multiple resources. Future prospects include expanding the model to take into account the amount of resources available at each time period. Furthermore, by reducing the difference between the maximum number of resources used and the minimum number of resources used to the furthest possible extent, it can be said that more realistic scheduling can be performed. © 2021 Japan Industrial Management Association. All rights reserved.

关键词： stochastic programming

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Multi-Objective optimization of the Hydrocarbon supply chain under price and demand uncertainty

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INTERNATIONAL JOURNAL OF SUSTAINABLE ENGINEERING 2021年第6期14卷 1525-1537页

作者： Attia, Ahmed M. Ghaithan, Ahmed M. Duffuaa, Salih O. King Fahd Univ Petr & Minerals Dhahran Dept Syst Engn Dhahran Saudi Arabia King Fahd Univ Petr & Minerals Dept Construct Engn & Management Dhahran Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Smart Mobil & Logist Dhahran Saudi Arabia

The energy sector is known for its enormous investments, despite the erratic behaviour of the oil market (e.g. changes in crude oil prices). Therefore, strategic and tactical planning of the Hydrocarbon supply chain (HCSC), considering market uncertainty, is a significant area of research. HCSC construction involves the integration of crude oil and natural gas supply chains (SCs). In this study, a stochastic multi-objective optimization model is developed for the tactical planning of HCSC. The model considers price and demand uncertainty by formulating a two-stage stochastic programming model. Financial objectives are considered in terms of cost minimization and revenue maximization, while a non-financial objective is considered in terms of depletion rate minimization (i.e. reserves sustainability maximization). The model assists the decision-maker in quantifying the amount of production required to meet demand under different scenarios. Furthermore, the proposed model assists in evaluating the trade-offs among alternatives. A real-world HCSC is used to elucidate the practicability of the model, and some managerial insights are derived by conducting a sensitivity analysis. For instance, production can be reduced during high demand periods to maintain enough reserves, and the excess demand can be satisfied from the outside market based on medium-term contracts.

关键词： Intermediate-term planning multi-objective optimization petroleum supply chain management scenario based optimization stochastic programming

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Simultaneous stochastic optimization of an open-pit mining complex with preconcentration using reinforcement learning

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APPLIED SOFT COMPUTING 2023年 138卷

作者： Levinson, Zachary Dimitrakopoulos, Roussos Keutchayan, Julien McGill Univ Dept Min & Mat Engn COSMO Stochast Mine Planning Lab FDA Bldg3450 Univ St Montreal PQ H3A 0E8 Canada

A preconcentration facility is a major operational component that is critical for managing capacities and improving process plant efficiency in a mining complex. These facilities have not been considered in previous short-term production scheduling frameworks for mining complexes. Short-term production scheduling is a vital part of planning that helps ensure long-term production targets are meet without compromising value. In this work, a new stochastic mathematical programming formulation for simultaneously optimizing the short-term production schedule with preconcentration considerations is proposed. The optimization formulation considers optimizing the extraction sequence, destination policy, stockpiling and preconcentration decisions jointly to capture potential synergies. In addition, this work investigates a new approach for short-term production scheduling that combines reinforcement learning with stochastic mathematical programming. An actor-critic reinforcement learning agent learns to optimize the short-term production schedule and provides a more flexible framework for adapting heuristics to the scheduling problem. The optimization approach and stochastic formulation are tested in a copper mining complex with multiple mining areas, several material properties, stockpiles, preconcentration facilities, leach pads, process plants and waste dumps. The case study shows the practical aspects of the proposed optimization and the direct benefit of integrating preconcentration decisions in the short-term production schedule;this led to a $140M improvement in annual cashflow. Additionally, the actor-critic reinforcement learning algorithm learns a stable policy that provides operational extraction sequences.& COPY;2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Mining complex Preconcentration Reinforcement learning stochastic programming

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Optimal risk-driven operation of renewable-penetrated distribution network during natural-disasters: A resiliency-oriented analysis

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SUSTAINABLE CITIES AND SOCIETY 2023年 99卷

作者： Wen, Mingming Lv, Zhaoheng Mamonov, Konstantin Guangdong Ocean Univ Sch Management Zhanjiang 524088 Peoples R China Guangdong Coastal Econ Belt Dev Res Inst Zhanjiang 524088 Peoples R China OM Beketov Natl Univ Urban Econ Kharkiv 17 Marshal Bazhanov St UA-1002 Kharkiv Ukraine

The increasing penetration of renewable energy sources in distribution networks has brought new challenges in ensuring reliable and resilient operation during natural disasters. The proposed approach considers the uncertainties associated with renewable energy sources, natural disasters, and demand using scenario-based stochastic programming to minimize the expected operational cost of the distribution network. Due to the huge uncertainties related to renewable resources, an efficient risk analysis is applied to obtain a sense of the worst realizations of uncertainties using the downside risk constraints method. Besides, two types of demand response schemes (DRSs) are considered to prevent widespread blackouts in the distribution network. This study provides insights into the integration of renewable energy sources in distribution networks and highlights the importance of considering resiliency optimization of this networks. The effectiveness of the proposed approach is demonstrated through simulations on a 33-bus test distribution system, and the results show successful ride-through of the islanding hours with 100% risk reduction by only 2.91% increase in operation cost over the considered scenarios, while risks may still be present in other set of scenarios. Overall, this research contributes to the investigate the uncertainty-driven operational risks of renewable energy integration of resilient distribution network.

关键词： Renewable resources natural disasters resiliency-oriented operation stochastic programming downside risk constraints distribution network

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