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Modeling Sustainability and Resilience of Supply Chains Under Uncertainty: An Analysis of Quantity-Based Strategies

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SSRN

SSRN 2024年

作者： Saidi, Dounia Hlyal, Mustapha El Alami, Jamila Bassou, Aziz Ait LASTIMI Laboratory High School of Technology in Sale Mohammed V University in Rabat Morocco Casablanca Morocco

In a globalized context, integrating resilience and sustainability into supply chains incurs significant costs. This study explores their impact on quantity-based strategies in supply chain networks. A conceptual framework focuses on a two-echelon network with a central supplier and multiple stores. A stochastic model addresses demand uncertainty, considering sustainability and resilience. Suppliers and stores negotiate competitively for profit maximization. Two store configurations, non-cooperative and cooperative, are explored. The supplier enhances resilience with security stocks and diversified sourcing. Sustainability efforts are considered by both. Findings show that demand following a uniform distribution benefits both store and supplier, with cooperation among stores proving more profitable. Sustainability efforts affect profits, with security stocks benefiting supplier profitability. The use of foreign products negatively impacts profits, highlighting the importance of government regulation. The paper concludes by discussing integrating sustainability and resilience in supply chains, with considerations for future research. © 2024, The Authors. All rights reserved.

关键词： stochastic programming

Decision-Based vs. Distribution-Driven Clustering for stochastic Energy System Design Optimization

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arXiv 2024年

作者： Jürgens, Boyung Seele, Hagen Schricker, Hendrik Reinert, Christiane von der Assen, Niklas Institute of Technical Thermodynamics RWTH Aachen University Schinkelstraße 8 Aachen52062 Germany

stochastic programming is widely used for energy system design optimization under uncertainty but can exponentially increase the computational complexity with the number of scenarios. Common scenario reduction techniques, like moments-matching or distribution-driven clustering, preselect representative scenarios based on input parameters. In contrast, decision-based clustering groups scenarios by similarity in resulting model decisions. Decision-based clustering has shown potential in network design and fleet planning. However, its potential in energy system design remains unexplored. In our work, we examine the effectiveness of decision-based clustering in energy system design using a four-step method: 1) Determine the optimal design for each scenario;2) Aggregate and normalize installed capacities as features reflecting optimal decisions;3) Use these features for k-medoids clustering to identify representative scenarios;4) Utilize these scenarios to optimize cost in stochastic programming. We apply our method to a real-world industrial energy system modeled as a mixed-integer linear program. We incorporate uncertainty by scaling time series with representative factors. We generate 500 single-year scenarios via Monte Carlo sampling, which we reduce using decision-based clustering. For benchmarking, we conduct distribution-driven k-medoids clustering based on the representative factors. In our case studies, both clustering methods yield designs with similar cost efficiency, although decision-based clustering requires substantially more computational resources. To our knowledge, this is the first application of decision-based clustering on energy system design optimization. Future research should investigate the conditions under which decision-based clustering yields more cost-efficient designs compared to distribution-driven clustering. © 2024, CC BY-NC-ND.

关键词： stochastic programming

Coordinated Deliverable Energy Flexibility from EV Aggregators in Distribution Networks

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Coordinated Deliverable Energy Flexibility from EV Aggregato...

2025 IEEE Green Technologies Conference, GreenTech 2025

作者： Baharvandi, Arash Nguyen, Duong Tung

ISBN: (纸本)9798331520182

This paper presents a coordinated framework to optimize electric vehicle (EV) charging considering grid constraints and system uncertainties. The proposed framework consists of two optimization models. In particular, the distribution system operator (DSO) solves the first model to optimize the amount of deliverable energy flexibility that can be obtained from EV ag-gregators. To address the uncertainties of loads and solar energy generation, a hybrid robust/stochastic approach is employed, enabling the transformation of uncertainty-related constraints into a set of equivalent deterministic constraints. Once the DSO has computed the optimal energy flexibility, each aggregator utilizes the second optimization model to optimize the charging schedule for its respective fleet of EVs. Numerical simulations are performed on a modified IEEE 33-bus distribution network to illustrate the efficiency of the proposed framework. © 2025 IEEE.

关键词： stochastic programming

Proactive Robust Hardening of Resilient Power Distribution Network: Decision-Dependent Uncertainty Modeling and Fast Solution Strategy

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arXiv 2025年

作者： Ma, Donglai Cao, Xiaoyu Zeng, Bo Jia, Qing-Shan Chen, Chen Zhai, Qiaozhu Guan, Xiaohong School of Automation Science and Engineering The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Department of Industrial Engineering The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15106 United States Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China School of Electrical Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China

To address the power system hardening problem, traditional approaches often adopt robust optimization (RO) that considers a fixed set of concerned contingencies, regardless of the fact that hardening some components actually renders relevant contingencies impractical. In this paper, we directly adopt a dynamic uncertainty set that explicitly incorporates the impact of hardening decisions on the worst-case contingencies, which leads to a decision-dependent uncertainty (DDU) set. Then, a DDU-based robust-stochastic optimization (DDU-RSO) model is proposed to support the hardening decisions on distribution lines and distributed generators (DGs). Also, the randomness of load variations and available storage levels is considered through stochastic programming (SP) in the innermost level problem. Various corrective measures (e.g., the joint scheduling of DGs and energy storage) are included, coupling with a finite support of stochastic scenarios, for resilience enhancement. To relieve the computation burden of this new hardening formulation, an enhanced customization of parametric column-and-constraint generation (P-C&CG) algorithm is developed. By leveraging the network structural information, the enhancement strategies based on resilience importance indices are designed to improve the convergence performance. Numerical results on 33-bus and 118-bus test distribution networks have demonstrated the effectiveness of DDU-RSO aided hardening scheme. Furthermore, in comparison to existing solution methods, the enhanced P-C&CG has achieved a superior performance by reducing the solution time by a few orders of magnitudes. Copyright © 2025, The Authors. All rights reserved.

关键词： stochastic programming

Programmable Electric Tweezers

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arXiv 2025年

作者： Chen, Yuang Tan, Haojing Zhuang, Jiahua Xu, Yang Zhang, Chen Feng, Jiandong Laboratory of Experimental Physical Biology Department of Chemistry Zhejiang University Hangzhou310027 China The First Affiliated Hospital School of Medicine Zhejiang University Hangzhou310003 China Institute of Fundamental and Transdisciplinary Research Zhejiang University Hangzhou310058 China

The interaction mechanism between a single microscopic object like a cell, a particle, a molecule, or an atom and its interacting electromagnetic field is fundamental in single-object manipulation such as optical trap and magnetic trap. Function-on-demand, single-object manipulation relies on a high degree of freedom control of electromagnetic field at localized scales, which remains challenging. Here we propose a manipulation concept: programmable single-object manipulation, based on programming the electromagnetic field in a multi-bit electrode system. This concept is materialized on a Programmable Electric Tweezer (PET) with four individually addressed electrodes, marking a transition from function-fixed single-object manipulation to function-programmable single-object manipulation. By programming the localized electric field, our PET can provide various manipulation functions for achieving precise trapping, movement and rotation of multiscale single microscopic objects, including single proteins, nucleic acids, microparticles and bacteria. Implementing these functions, we are able not only to manipulate the object of interest on demand but also quantitatively measure the charge to mass ratio of a single microparticle via the Paul trap and the electrical properties of an individual bacterial cell by the rotation analysis. Finally, with superposed single-particle trapping and rotation, we demonstrate the spontaneous relaxation of DNA supercoiling and observe an unexpected pause phenomenon in the relaxation process, highlighting the versatility and the potential of PET in uncovering stochastic biophysical phenomena at the single-molecule level. Copyright © 2025, The Authors. All rights reserved.

关键词： stochastic programming

A Two-Timescale Decision-Hazard-Decision Formulation for Storage Usage Values Calculation

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arXiv 2024年

作者： Parra, Camila Martínez De Lara, Michel Chancelier, Jean-Philippe Carpentier, Pierre Janin, Jean-Marc Ruiz, Manuel Réseau de Transport d’Électricité France CERMICS École nationale des ponts et chaussées IP Paris France UMA ENSTA Paris IP Paris France

The penetration of renewable energies requires additional storages to deal with intermittency. Accordingly, there is growing interest in evaluating the usage value (opportunity cost) associated with stored energy in large storages, a cost obtained by solving a multistage stochastic optimization problem. Today, to compute usage values under uncertainties, an adequacy resource problem is solved using stochastic dynamic programming assuming a hazard-decision information structure. This modelling assumes complete knowledge of the coming week uncertainties, which is not adapted to the system operation as they occur at smaller timescale (hour). This is why, in this paper, we propose to model the decision-making process with, on the one hand, two timescales (weekly time decomposition, but uncertainties that are realized hour by hour, and hourly energy balance constraints) and, on the other hand, a decision-hazard-decision information structure considering both planning and recourse decisions. This structure is used to decompose the multistage decision-making process into a nonanticipative planning step in which the binary on/off decisions for the thermal units are made, and a recourse step in which the power modulation decisions are made once the uncertainties have been disclosed. We perform time decomposition by stochastic dynamic programming, and not by SDDP, due to the presence of binary on-off decision variables. In a numerical case, we illustrate how usage values are sensitive to how the disclosure of information is modelled. Copyright © 2024, The Authors. All rights reserved.

关键词： stochastic programming

A Reliability Theory of Compromise Decisions for Large-Scale stochastic Programs

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arXiv 2024年

作者： Diao, Shuotao Sen, Suvrajeet Department of Industrial Engineering and Management Sciences Northwestern University EvanstonIL60208 United States Epstein Department of Industrial and Systems Engineering University of Southern California Los AngelesCA90089 United States

stochastic programming models can lead to very large-scale optimization problems for which it may be impossible to enumerate all possible scenarios. In such cases, one adopts a sampling-based solution methodology in which case the reliability of the resulting decisions may be suspect. For such instances, it is advisable to adopt methodologies that promote variance reduction. One such approach goes under a framework known as "compromise decision", which requires multiple replications of the solution procedure. This paper studies the reliability of stochastic programming solutions resulting from the "compromise decision" process. This process is characterized by minimizing an aggregation of objective function approximations across replications, presumably conducted in parallel. We refer to the post-parallel-processing problem as the problem of "compromise decision". We quantify the reliability of compromise decisions by estimating the expectation and variance of the "pessimistic distance" of sampled instances from the set of true optimal decisions. Such pessimistic distance is defined as an estimate of the largest possible distance of the solution of the sampled instance from the "true" optimal solution set. The Rademacher average of instances is used to bound the sample complexity of the compromise decision. © 2024, CC BY.

关键词： stochastic programming

A rolling horizon heuristic approach for a multi-stage stochastic waste collection problem

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arXiv 2024年

作者： Spinelli, Andrea Maggioni, Francesca Ramos, Tânia Rodrigues Pereira Barbosa-Póvoa, Ana Paula Vigo, Daniele Department of Management Information and Production Engineering University of Bergamo Viale G. Marconi 5 Dalmine24044 Italy Instituto Superior Técnico University of Lisbon Av. Rovisco Pais 1 Lisbon1049-001 Portugal Department of Electrical Electronic and Information Engineering "G. Marconi" University of Bologna Viale del Risorgimento 2 Bologna40136 Italy

In this paper we present a multi-stage stochastic optimization model to solve an inventory routing problem for recyclable waste collection. The objective is the maximization of the total expected profit of the waste collection company. The decisions are related to the selection of the bins to be visited and the corresponding routing plan in a predefined time horizon. stochasticity in waste accumulation is modeled through scenario trees generated via conditional density estimation and dynamic stochastic approximation techniques. The proposed formulation is solved through a rolling horizon approach, providing a worst-case analysis on its performance. Extensive computational experiments are carried out on small- and large-sized instances based on real data provided by a large Portuguese waste collection company. The impact of stochasticity on waste generation is examined through stochastic measures, and the performance of the rolling horizon approach is evaluated. Some managerial insights on different configurations of the instances are finally discussed. Copyright © 2024, The Authors. All rights reserved.

关键词： stochastic programming

Quality-Optimization Algorithm Based on stochastic Dynamic programming for MPEG DASH Video Streaming

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Quality-Optimization Algorithm Based on Stochastic Dynamic P...

IEEE International Conference on Consumer Electronics (ICCE)

作者： Garcia, Sergio Cabrera, Julian Garcia, Narciso Univ Politecn Madrid Grp Tratamiento Imagenes E-28040 Madrid Spain

ISBN: (纸本)9781479912919

In contrast to traditional push-based protocols, adaptive streaming techniques like Dynamic Adaptive Streaming over HTTP (DASH) fix attention on the client, who dynamically requests different-quality portions of the content to cope with a limited and variable bandwidth but aiming at maximizing the quality perceived by the user. Since DASH adaptation logic at the client is not covered by the standard, we propose a solution based on stochastic Dynamic programming (SDP) techniques to find the optimal request policies that guarantee the users' Quality of Experience (QoE). Our algorithm is evaluated in a simulated streaming session and is compared with other adaptation approaches. The results show that our proposal outperforms them in terms of QoE, requesting higher qualities on average.

关键词： protocols quality of experience stochastic programming video streaming DASH adaptation logic MPEG DASH video streaming QoE SDP technique dynamic adaptive streaming over HTTP optimal request policies push-based protocol quality of experience quality-optimization algorithm simulated streaming session stochastic dynamic programming Bandwidth Bit rate Consumer electronics Dynamic programming Heuristic algorithms Streaming media Throughput