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Economically viable reshoring of supply chains under ripple effect

OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2025年 131卷

作者： Sawik, Tadeusz Reykjavik Univ Dept Engn IS-101 Reykjavik Iceland Indian Inst Management Mumbai 400087 Maharashtra India

This paper presents stochastic mixed integer linear programming models for economically viable supply chain reshoring under the ripple effect, that is, for the integrated optimization of supply chain reshoring and supply chain viability. The problem objective is to select supply chain facilities for relocation and backup suppliers for recovery supplies to simultaneously minimize cost, maximize service and enhance viability. If the primary supplier is not selected for reshoring, the viability can alternatively be improved by recovery supplies from the backup supplier. The problem is formulated as a multi-portfolio stochastic optimization. The portfolio of suppliers/plants for reshoring is optimized along with an alternative recovery portfolio of backup supplies. This study indicates that reshoring decisions directly affect supply chain viability and have positive impact on both its average and worst-case performance. The more risk-aversive is viable reshoring (the confidence level greater than 0.90), the greater is also improvement of supply chain business-as-usual resilience. If increase of expected customer service is more important than cost reduction, supplier reshoring is preferable to backup sourcing. The findings also indicate that cost- and service-optimal reshoring decisions are getting closer when the government subsidy for reshoring significantly increases such that the reduced reshoring costs and domestic costs do not exceed the achieved reduction of lost sales and backup sourcing. Then both conflicting objectives can be simultaneously achieved, which is a highly desirable solution of the economically viable reshoring of supply chain. Numerical examples based on a real-world industrial supply chain prove practical usefulness of the proposed stochastic approach and its high computational efficiency.

关键词： Supply chain Disruption management Resilience Viability Ripple effect stochastic programming

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Topology optimization of random memristors for input-aware dynamic SNN

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SCIENCE ADVANCES 2025年第16期11卷 eads5340页

作者： Wang, Bo Zhang, Xinyuan Wang, Shaocong Lin, Ning Li, Yi Yu, Yifei Zhang, Yue Yang, Jichang Wu, Xiaoshan He, Yangu Wang, Songqi Wan, Tao Chen, Rui Li, Guoqi Deng, Yue Qi, Xiaojuan Wang, Zhongrui Shang, Dashan Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China ACCE SS AI Chip Ctr Emerging Smart Syst InnoHK Ctr Hong Kong Sci Pk Hong Kong Peoples R China Chinese Acad Sci Inst Microelect State Key Lab Fabricat Technol Integrated Circuits Beijing 100029 Peoples R China Chinese Acad Sci Inst Microelect Lab Microelect Devices & Integrated Technol Beijing 100029 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Key Lab Brain Cognit & Brain Inspired Intelligence Beijing 100190 Peoples R China Beihang Univ Sch Artificial Intelligence Beijing 100191 Peoples R China Beihang Univ Sch Astronaut Beijing 100191 Peoples R China

Machine learning has advanced unprecedentedly, exemplified by GPT-4 and SORA. However, they cannot parallel human brains in efficiency and adaptability due to differences in signal representation, optimization, runtime reconfigurability, and hardware architecture. To address these challenges, we introduce pruning optimization for input-aware dynamic memristive spiking neural network (PRIME). PRIME uses spiking neurons to emulate brain's spiking mechanisms and optimizes the topology of random memristive SNNs inspired by structural plasticity, effectively mitigating memristor programming stochasticity. It also uses the input-aware early-stop policy to reduce latency and leverages memristive in-memory computing to mitigate von Neumann bottleneck. Validated on a 40-nm, 256-K memristor-based macro, PRIME achieves comparable classification accuracy and inception score to software baselines, with energy efficiency improvements of 37.8x and 62.5x. In addition, it reduces computational loads by 77 and 12.5% with minimal performance degradation and demonstrates robustness to stochastic memristor noise. PRIME paves the way for brain-inspired neuromorphic computing.

关键词： stochastic programming

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A sample average approximation-based heuristic for the stochastic production routing problem

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CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2025年第1期33卷 121-144页

作者： Geiger, Andreas Univ Hamburg Inst Operat Res Moorweidenstr 18 D-20148 Hamburg Germany

The Production Routing Problem under demand uncertainty is an integrated problem containing production, inventory, and distribution decisions. At the planning level, the aim is to meet retailers demand, when only the demand distribution is known in advance, while minimizing the corresponding costs. In this study, a two-stage formulation is presented in which the routing can be adjusted at short notice. In the first stage, only production decisions are made, while delivery and inventory quantities and retailer visit schedules are determined in the second stage. To handle a large number of scenarios, two solution methods based on Sample Average Approximation are introduced. Furthermore, the impact of the routing quality is explored by applying a simple heuristic and an effective metaheuristic on the routing part. It is shown that, on average, the simple heuristic within an adjustable Sample Average Approximation approach provides better objective function values than the metaheuristic within a non-adjustable approach. Also all solution approaches outperform an expected value based approach in terms of runtime and objective function value.

关键词： Production routing problem Integrated planning stochastic programming Sample average approximation Demand uncertainty Heuristic

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An evaluation of common modeling choices for the vehicle routing problem with stochastic demands

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第1期321卷 107-122页

作者： Hoogendoorn, Y. N. Spliet, R. Erasmus Univ Burgermeester Oudlaan 50 NL-3062 PA Rotterdam Netherlands

We investigate three common modeling choices for the vehicle routing problem with stochastic demands: (i) the total expected demand of customers on a route may not exceed the capacity of the vehicle, (ii) the number of routes is fixed, and (iii) demand is distributed with a support that contains negative-valued realizations. We prove that modeling choices (i) and (ii) result in an arbitrarily large increase of the optimal objective value in the worst case. Additionally, we provide lower and upper bounds on the change of the optimal objective value following from (iii) in case the actual distribution of demand is censored, truncated or folded. We also evaluate the consequences of these choices numerically, by employing a state-of-the-art integer L-shaped method to solve the vehicle routing problem with stochastic demands to optimality, which we modify to deal with the alternative choices. We find that restricting the expected demand of a route to the vehicle's capacity has a limited effect on the optimal objective value for most, but not all, benchmark instances from the literature, while drastically reducing the computation times of the integer L-shaped method. When restricting the number of routes, a similar effect occurs when the total expected demand on a route is not restricted. Otherwise, the computation time decreases only slightly, and even increases for some benchmark instances. For instances from the literature, despite admitting negative realizations, the normal distributions used to model demand are an adequate approximation for censored, truncated and folded normal distributions that have nonnegative supports.

关键词： Routing stochastic demands stochastic programming

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On the convergence order of value function relaxations used in decomposition-based global optimization of nonconvex stochastic programs

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JOURNAL OF GLOBAL OPTIMIZATION 2025年第4期91卷 701-742页

作者： Robertson, Dillard Cheng, Pengfei Scott, Joseph K. Georgia Inst Technol Dept Chem & Biomol Engn 311 Ferst Dr NW Atlanta GA 30332 USA

This paper analyzes the convergence rate of decomposition algorithms for globally solving nonconvex stochastic programming problems. We focus on two recent algorithms, termed CZ (Cao and Zavala in J Glob Optim 75:393-416, 2019) and LG (Li and Grossmann in J Glob Optim 75:247-272, 2019), that guarantee global optimality while achieving a favorable decomposed scaling with respect to the number of scenarios. Both methods project the problem into the space of first-stage decisions and apply a spatial-B&B search in this reduced space. Consequently, we observe that they are subject to the results of prior studies on the efficiency of general spatial-B&B algorithms. Such studies have concluded that, to avoid very slow convergence due to the cluster problem, it is necessary (but not sufficient) for the B&B lower bounding problems to have a sufficiently high Hausdorff convergence order. We apply this concept to the CZ and LG decomposition algorithms by first arguing that their lower bounding procedures can be interpreted as defining relaxations in the reduced space of first-stage decisions, and then analyzing the Hausdorff convergence of these relaxations in detail. The results are found to depend strongly on the regularity of the recourse optimal value functions. The relaxations used by CZ are found to be first-order convergent or less, while second order is generally necessary to avoid clustering. In contrast, the relaxations used by LG achieve the highest order possible within the decomposition framework we consider, which is second order when the value functions are smooth, but first order or less otherwise. Unfortunately, these functions are only guaranteed to be lower semi-continuous under standard assumptions. This alludes to a larger limitation of the projection-based decomposition approach, which is discussed at length.

关键词： stochastic programming Decomposition Global optimization Nonconvex optimization Convergence rate

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stochastic unit commitment problem: A statistical approach

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 273卷

作者： Olivos, Carlos Valenzuela, Jorge Univ Catolica Norte Dept Ingn Ind Ave Angamos 0610 Antofagasta 1270709 Chile Auburn Univ Shelby Ctr 1301 Dept Ind & Syst Engn Auburn AL 36849 USA

The stochastic Unit Commitment Problem (SUCP) has been widely studied using scenario-based generation to include uncertainty in the mathematical model, transforming the stochastic problem into a large deterministic problem. However, the accuracy of the stochastic problem is highly dependent on the number of scenarios, leading to computational intractability when the number of scenarios is large. This paper proposes a novel paradigm that avoids scenario sampling. Instead, it derives a function that models the expected cost based on a merit order dispatch rule for the thermal units and incorporates the probability distribution of net demand. Thus, the expected cost is explicitly stated in a non-linear function. A piecewise linear approximation method is used to address the new model's nonlinearity, resulting in a mixed integer linear programming (MILP) model. The proposed model is compared to the traditional scenario-based SUCP in terms of computational effort, solution stability, and costs. Numerical experiments show that the new approach can reach optimality in more instances than the traditional scenario-based model. Moreover, it eliminates memory limitations and provides stable and cost-competitive solutions. Thus resulting in a scalable alternative for large-scale and realistic power systems. To the best of our knowledge, this is the first SUCP formulation that integrates uncertainty without relying on scenario-based methods.

关键词： stochastic unit commitment problem stochastic programming Power systems Renewable energy sources Uncertainty

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Impact of activating energy demand flexibility in the building stock: A case study of Norway as a highly electrified country in the European power market

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ENERGY 2025年 318卷

作者： Backe, Stian Delgado, Benjamin Manrique Askeland, Magnus Walnum, Harald Taxt Sorensen, ase Lekang Sartori, Igor SINTEF Energy Dept Energy Syst Trondheim Norway SINTEF Community Dept Architectural Engn Oslo Norway Norwegian Univ Sci & Technol Dept Ind Econ & Technol Management Trondheim Norway

On the transition towards an electrified European energy system, flexible electricity use is crucial. The building stock, including electric vehicles, can offer flexibility, but it is still unclear towards which objective the flexibility should be provided. Further, it is complicated for end-users to react to market prices. This paper investigates how buildings adapt their electricity use by following pre-defined tactics, known as heuristics, instead of market prices. Two heuristics are studied in two future scenarios compliant with European renovation policies. Based on the responses towards the heuristics, this study explores how flexibility from the Norwegian building stock impact electricity production and cross-border exchanges towards 2040. Results show that hourly electricity delivery to the building stock can be strongly modified, resulting in decreased peak electricity delivery by 10%-12% in 2040 when following the heuristics. Nevertheless, findings suggest limited impact on the strategic investments in the power market as flexible Norwegian hydropower adapts production patterns towards economic and operational stability in response to the demand-side load modifications. Further research should continue exploring local impacts of flexibility heuristics, including grid bottlenecks and on-site electricity production integration, and refine assumptions about hydropower flexibility in aggregated power system models.

关键词： Building stock modeling Flexible demand European energy system stochastic programming Hydropower flexibility

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Coordinating last-train timetabling with app-based ride-hailing service under uncertainty

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2024年 636卷

作者： Ning, Jia Xing, Xinjie Wang, Yadong Yao, Yu Kang, Liujiang Peng, Qiyuan Southwest Jiaotong Univ Sch Transportat & Logist Chengdu Sichuan Peoples R China Jingdong Zhenshi Informat Technol Co Ltd Dept Technol & Data Intelligence Beijing Peoples R China Univ Liverpool Management Sch Liverpool England Nanjing Univ Sci & Technol Sch Econ & Management Nanjing Jiangsu Peoples R China Hohai Univ Coll Civil & Transportat Engn Nanjing Jiangsu Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China

Since urban rail transit (URT) service is normally not running on 24 -hour operation in most cities, last -train timetabling is a prominent problem and challenges URT managers constantly. The rise of app -based ride -hailing (ARH) service opens up new opportunities and challenges for last -train operators to better serve late -night passengers. Specifically, when passengers cannot reach their destinations only through URT services during the last -train operation period, passengers could make good use of feasible train -to -train transfers to reach stations closer to their destinations, and transfer to flexible ARH services to reach their final destinations. However, uncertain road conditions and varying passenger travel preferences complicate the coordination of URT services with ARH services. By considering different passengers' traveling preferences, various travel path choices, and uncertain ARH travel times, we formulate a two -stage mixed -integer stochastic optimization model to achieve an optimal last -train timetable design for getting more passengers to their destinations in a cost-effective and efficient way. In addition, we propose a genetic algorithm -based solution strategy which outperforms commercial solvers with its computational performance and has its practicability assured. Through our numerical experiments, we reveal insights about how different customers' preferences and cost components affect the optimal results and provide operational suggestions accordingly for achieving better timetable performance.

关键词： Last train timetabling Ride -hailing services Passenger path choice Uncertainty Genetic algorithm stochastic programming

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EXACT CONVERGING BOUNDS FOR stochastic DUAL DYNAMIC programming VIA FENCHEL DUALITY

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SIAM JOURNAL ON OPTIMIZATION 2020年第2期30卷 1223-1250页

作者： Leclere, Vincent Carpentier, Pierre Chancelier, Jean-Philippe Lenoir, Arnaud Pacaud, Frakois Ecole Ponts CERMICS Marne La Vallee France ENSTA Paris Inst Polytech Paris UMA Paris France EDF F-91120 Palaiseau France Ecole Ponts CERMICS Efficac Marne La Vallee France

The stochastic dual dynamic programming (SDDP) algorithm has become one of the main tools used to address convex multistage stochastic optimal control problems. Recently a large amount of work has been devoted to improving the convergence speed of the algorithm through cut selection and regularization, and to extending the field of applications to nonlinear, integer, or risk-averse problems. However, one of the main downsides of the algorithm remains the difficulty in giving an upper bound of the optimal value, usually estimated through Monte Carlo methods and therefore difficult to use in the stopping criterion of the algorithm. In this paper we present a dual SDDP algorithm that yields a converging exact upper bound for the optimal value of the optimization problem. As an easy consequence of our approach, we show how to compute an alternative control policy based on an inner approximation of Bellman value functions instead of the outer approximation given by the standard SDDP algorithm. We illustrate the approach on an energy production problem involving zones of production and transportation links between the zones. The numerical experiments we carry out on this example show the effectiveness of the method.

关键词： stochastic programming dynamic programming SDDP Fenchel conjugacy

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Risk-averse stochastic multi-objective optimization for time-of-use demand response pricing in smart microgrids

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ENERGY 2025年 322卷

作者： Nikzad, Mehdi Islamic Azad Univ Dept Elect Engn Islamshahr Branch Tehran Iran

This paper presents a multi-objective optimization approach for the pricing of a time-of-use (TOU) demand response program in a smart microgrid (MG), addressing the perspectives of the MG operator, aggregators, and clients. The MG operator's objective is to maximize profit by balancing production costs and revenue, which varies with the implementation of TOU, while minimizing bonuses paid to aggregators. The aggregators aim to maximize the bonus received from the MG operator while minimizing both the compensation paid to clients for aligning their consumption with renewable generation and the discomfort experienced by clients. Clients seek to maximize their compensation while minimizing discomfort caused by adjusting their consumption patterns. The model includes uncertain parameters like solar radiation, wind speed, and load demand, characterized by probability density functions (PDFs) to generate scenarios, with backward scenario reduction technique used to manage computational complexity. The multi-objective optimization problem is modeled stochastically and solved using the Normal Boundary Intersection (NBI) method, with the Conditional Value at Risk (CVaR) index integrated to account for a risk-averse perspective. The proposed framework is validated on the IEEE 33-node test network from different perspectives. In all studied cases, TOU pricing incorporates higher tariffs during peak and mid-peak hours and lower tariffs during off-peak hours, resulting in load reduction during peak and mid-peak hours and load shifting to off-peak hours. When the microgrid operator's objective is prioritized, its average value is $945, with a CVaR of $903.5. The aggregator's average objective value is $150.1, while the clients' average objective value is $-44. This scenario achieves the highest operator profit and the lowest operating costs. When the aggregator's objective is prioritized, its average value increases to $158, with a CVaR of $141.8. The microgrid operator's average

关键词： Time-of-use Multi objective Scenario reduction method stochastic programming Normal boundary intersection optimization method

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