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A Proposal of Resilient Supply Chain Network Planning Method with Supplier Selection and Inventory Levels Determination Using Two-Stage stochastic programming

A Proposal of Resilient Supply Chain Network Planning Method...

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IFIP WG 5.7 International Conference of the Advances in Production Management Systems (APMS)

作者： Kobayashi, Hibiki Kaihara, Toshiya Kokuryo, Daisuke Tanaka, Rina Hara, Masashi Miyachi, Yuto Sariddichainunta, Puchit Kobe Univ Grad Sch Syst Informat 1-1 Rokkodai Cho Kobe Hyogo 6578501 Japan Lion Corp 1-3-28 Kuramae Taito Tokyo 1118644 Japan

ISBN: (纸本)9783031436901;9783031436888;9783031436871

The importance of risk management has been pointed out in supply chain management which stably supplies products with considering economic efficiency. Supply chain network plan usually consists of two-stage decision process in business environment. Two-stage stochastic programming is appropriate for decision making under uncertainly in business environment where two steps of decision processes are involved. Therefore, we propose a planning method of resilient supply chain networks using two-stage stochastic programming. To improve computational efficiency while taking uncertain future events into account, we also propose a risk optimization method to design a resilient supply chain with reducing the number of scenarios by scenario sampling. In this paper, we attempt to plan a supply chain network consisting of suppliers, manufacturers, and wholesalers by selecting material suppliers and determining appropriate inventory levels in consideration of risk. Its optimality and resilience are evaluated by computer experiments. Furthermore, we evaluate the effectiveness of the proposed method in terms of computational efficiency as well as the optimality of the solution with scenario sampling by computer experiments.

关键词： Supply Chain Network stochastic programming Resilience Inventory control Scenario reduction

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A multi-objective stochastic programming model for post-disaster management

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TRANSPORTMETRICA A-TRANSPORT SCIENCE 2022年第3期18卷 1103-1126页

作者： Gharib, Mehrdad Fatemi Ghomi, Seyyed Mohammad Taghi Jolai, Fariborz Amirkabir Univ Technol Dept Ind Engn 424 Hafez Ave Tehran *** Iran Univ Tehran Coll Engn Dept Ind Engn Tehran Iran

This paper develops a mathematical model for post-disaster planning with human casualties, which can be considered as operational guidance for the proper use of emergency resources. For this purpose, a stochastic mixed-integer programming model is provided to formulate the problem. The objective functions of the model are (1) maximizing the survival probability of patients, (2) minimizing the maximum of completion time of treatment of all patients, and (3) minimizing the total cost of operations. The model is solved with the epsilon-constraint method. Due to the NP-hardness of the problem which is a significant challenge in the literature, two innovative meta-heuristic algorithms are proposed, i.e. a non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective simulated annealing (MOSA). Finally, a comprehensive computational analysis is performed for evaluation purposes. Also, a case study is made on the earthquake in Iran, which illustrates the real-world application of the model.

关键词： Emergency medical services multi-objective stochastic programming epsilon-constraint method multi-objective simulated annealing non-dominated sorting genetic algorithm

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Problem-based optimal scenario generation and reduction in stochastic programming

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MATHEMATICAL programming 2022年第1期191卷 183-205页

作者： Henrion, R. Roemisch, W. Weierstrass Inst Appl Anal & Stochast Berlin Germany Humboldt Univ Inst Math Berlin Germany

Scenarios are indispensable ingredients for the numerical solution of stochastic programs. Earlier approaches to optimal scenario generation and reduction are based on stability arguments involving distances of probability measures. In this paper we review those ideas and suggest to make use of stability estimates based only on problem specific data. For linear two-stage stochastic programs we show that the problem-based approach to optimal scenario generation can be reformulated as best approximation problem for the expected recourse function which in turn can be rewritten as a generalized semi-infinite program. We show that the latter is convex if either right-hand sides or costs are random and can be transformed into a semi-infinite program in a number of cases. We also consider problem-based optimal scenario reduction for two-stage models and optimal scenario generation for chance constrained programs. Finally, we discuss problem-based scenario generation for the classical newsvendor problem.

关键词： stochastic programming

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A stochastic programming approach for the disassembly line balancing with hazardous task failures

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2022年第10期60卷 3237-3262页

作者： Kalaycilar, Eda Goksoy Batun, Sakine Azizoglu, Meral Middle East Tech Univ Dept Ind Engn TR-06800 Ankara Turkey

We consider a partial disassembly line balancing problem with hazardous tasks whose successful completions are uncertain. When any hazardous task fails, it causes damages of the tasks on the workstation that it is performed on and all remaining tasks to be performed in the succeeding workstations. We attribute probabilities for the successful completion and failure of the hazardous tasks and aim to maximise the total expected net revenue. We formulate the problem as a two-stage stochastic mixed-integer programme where the assignment of the tasks to the workstations is decided in the first-stage, before the resolution of the uncertainty. We give the formulation for one, two and three hazardous tasks, and then extend to the arbitrary number of hazardous tasks. Our numerical results reveal that proposed stochastic programming models return satisfactory performance and can solve instances with up to 73 tasks very quickly. We observe that the number of tasks, number of hazardous tasks and success probabilities are the most significant parameters that affect the performance. We quantify the value of capturing uncertainty using the expected objective values attained by the solution of the stochastic model and that of the expected value model, and obtain very satisfactory results.

关键词： Disassembly lines line balancing problem‌ hazardous tasks ‌ mathematical models stochastic programming

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Selective Maintenance Optimization under Uncertainty: stochastic programming Approach 4

Selective Maintenance Optimization under Uncertainty: Stocha...

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4th International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2024

作者： Ghorbani, Milad Nourelfath, Mustapha Interuniversity Research Centre On Enterprise Networks Logistics And Transportation Faculty Of Science And Engineering Department Of Mechanical Engineering QC Canada

ISBN: (纸本)9798350309508

This research delves into the idea of improving the likelihood of mission success in multi-component systems through selective maintenance during downtime. It considers uncertainties surrounding future mission conditions. stochastic programming is utilized to represent these uncertainties accurately. The study presents two distinct models: one focusing on uncertainty in operational conditions, while the other expands to include uncertainties in operating time. The efficacy of the proposed model underscores the importance of integrating uncertainty considerations into the broader model, enhancing our understanding of the system's performance robustness. © 2024 IEEE.

关键词： stochastic programming

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Risk-Averse stochastic programming vs. Adaptive Robust Optimization: A Virtual Power Plant Application

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INFORMS JOURNAL ON COMPUTING 2022年第3期34卷 1795-1818页

作者： Lima, Ricardo M. Conejo, Antonio J. Giraldi, Loic Le Maitre, Olivier Hoteit, Ibrahim Knio, Omar M. King Abdullah Univ Sci & Technol Comp Elect & Math Sci & Engn Div Thuwal 239556900 Saudi Arabia Ohio State Univ Dept Integrated Syst Engn Columbus OH 43210 USA Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA Res Inst Nucl Syst Low Carbon Energy Prod Fuel Dept Energy Div French Alternat Energies & Atom Energy Commiss F-13108 St Paul Les Durance France Ecole Polytech Ctr Natl Rech Sci Ctr Math Appl INRIA F-91128 Palaiseau France King Abdullah Univ Sci & Technol Phys Sci & Engn Div Thuwal 239556900 Saudi Arabia

This paper compares risk-averse optimization methods to address the selfscheduling and market involvement of a virtual power plant (VPP). The decision-making problem of the VPP involves uncertainty in the wind speed and electricity price forecast. We focus on two methods: risk-averse two-stage stochastic programming (SP) and two-stage adaptive robust optimization (ARO). We investigate both methods concerning formulations, uncertainty and risk, decomposition algorithms, and their computational performance. To quantify the risk in SP, we use the conditional value at risk (CVaR) because it can resemble a worst-case measure, which naturally links to ARO. We use two efficient implementations of the decomposition algorithms for SP and ARO;we assess (1) the operational results regarding first-stage decision variables, estimate of expected profit, and estimate of the CVaR of the profit and (2) their performance taking into consideration different sample sizes and risk management parameters. The results show that similar first-stage solutions are obtained depending on the risk parameterizations used in each formulation. Computationally, we identified three cases: (1) SP with a sample of 500 elements is competitive with ARO;(2) SP performance degrades comparing to the first case and ARO fails to converge in four out of five risk parameters;(3) SP fails to converge, whereas ARO converges in three out of five risk parameters. Overall, these performance cases depend on the combined effect of deterministic and uncertain data and risk parameters.

关键词： stochastic programming robust optimization risk management virtual power plant

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A Multi-Objective stochastic programming Model for Resilient and Sustainable Supply Chains Under Uncertainty

A Multi-Objective Stochastic Programming Model for Resilient...

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作者： Majdfaghihi, Mohammad H. Western Michigan University

学位级别：Ph.D., Doctor of Philosophy

Supply chain resilience (SCRES) and sustainable supply chain management (SSCM) have garnered significant attention in the field of Supply Chain Management (SCM) science. This heightened interest is largely attributable to the increasing frequency and severity of uncertainty and disruptions in global supply chains. Resilience is a beneficial approach to managing uncertainties and preparing for and mitigating the adverse consequences of Supply Chain (SC) disruptions. Sustainability is a key concept in SCM, driven by governmental regulations and heightened stakeholder and customer concerns for environmental and societal well-being. Studies on SCRES have traditionally focused on preparing for and reacting to disruptions and neglected sustainability factors, while SSCM research has incorporated environmental and social considerations into supply chain decision-making processes without addressing SC disruptions and uncertainty. However, due to the dynamic and complex business environment, a need has emerged to bridge the gap between these two areas of study and consider them simultaneously in SC decision-making processes. This dissertation contributes to the literature of supply chain management by developing a multi-objective, two-stage stochastic optimization modeling approach that considers SCRES and SSCM concurrently. Following SC modeling conventions, the model addresses economic, environmental, social, and resilience objectives while accounting for potential disruptions that are related to demand and supplier capacity fluctuations. It integrates resilience strategies, namely backup resources, along with sustainability measures including operations and transportation emissions, energy consumption, and job creation. The augmented e-constraint (AUGMECON) method is employed to derive optimal solutions for this multi-objective problem. Numerical examples are presented to validate the presented model and highlight its practical capabilities that will help decision-makers

关键词： Multi-objective optimization Resilience stochastic programming Supply Chain Network Design Supply Chains

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DISTRIBUTIONALLY ROBUST TWO-STAGE stochastic programming

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SIAM Journal on Computing 2022年第4期51卷 1499-1522页

作者： Duque, Daniel Mehrotra, Sanjay MORTON, DAVID P. Industrial Engineering and Management Sciences Northwestern University EvanstonIL60208 United States

Distributionally robust optimization is a popular modeling paradigm in which the underlying distribution of the random parameters in a stochastic optimization model is unknown. Therefore, hedging against a range of distributions, properly characterized in an ambiguity set, is of interest. We study two-stage stochastic programs with linear recourse in the context of distributional ambiguity, and formulate several distributionally robust models that vary in how the ambiguity set is built. We focus on the Wasserstein distance under a p-norm, and an extension, an optimal quadratic transport distance, as mechanisms to construct the set of probability distributions, allowing the support of the random variables to be a continuous space. We study both unbounded and bounded support sets, and provide guidance regarding which models are meaningful in the sense of yielding robust first-stage decisions. We develop cutting-plane algorithms to solve two classes of problems, and test them on a supply-allocation problem. Our numerical experiments provide further evidence as to what type of problems benefit the most from a distributionally robust solution. © 2022 Society for Industrial and Applied Mathematics.

关键词： stochastic programming

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A stochastic programming model for an energy planning problem: formulation, solution method and application

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ANNALS OF OPERATIONS RESEARCH 2022年第2期311卷 695-730页

作者： Irawan, Chandra Ade Hofman, Peter S. Chan, Hing Kai Paulraj, Antony Univ Nottingham Ningbo China Nottingham Univ Business Sch China 199 Taikang East Rd Ningbo 315100 Peoples R China

The paper investigates national/regional power generation expansion planning for medium/ long-term analysis in the presence of electricity demand uncertainty. A two-stage stochastic programming is designed to determine the optimal mix of energy supply sources with the aim to minimise the expected total cost of electricity generation considering the total carbon dioxide emissions produced by the power plants. Compared to models available in the extant literature, the proposed stochastic generation expansion model is constructed based on sets of feasible slots (schedules) of existing and potential power plants. To reduce the total emissions produced, two approaches are applied where the first one is performed by introducing emission costs to penalise the total emissions produced. The second approach transforms the stochastic model into a multi-objective problem using the epsilon-constraint method for producing the Pareto optimal solutions. As the proposed stochastic energy problem is challenging to solve, a technique that decomposes the problem into a set of smaller problems is designed to obtain good solutions within an acceptable computational time. The practical use of the proposed model has been assessed through application to the regional power system in Indonesia. The computational experiments showthat the proposed methodology runs well and the results of the model may also be used to provide directions/guidance for Indonesian government on which power plants/technologies are most feasible to be built in the future.

关键词： Energy planning stochastic programming Multi-objective optimization

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Mobile Network Slicing under Demand Uncertainty: A stochastic programming Approach 9

Mobile Network Slicing under Demand Uncertainty: A Stochasti...

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9th IEEE International Conference on Network Softwarization (IEEE NetSoft) - Boosting Future Networks through Advanced Softwarization

作者： Gholami, Anousheh Torkzaban, Nariman Baras, John S. Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA

ISBN: (纸本)9798350399806

Constant temporospatial variations in the user demand complicate the end-to-end (E2E) network slice (NS) resource provisioning beyond the limits of the existing best-effort schemes that are only effective under accurate demand forecasts for all NSs. This paper proposes a practical two-time-scale resource allocation framework for E2E network slicing under demand uncertainty. At each macro-scale instance, we assume that only the spatial probability distribution of the NS demands is available. We formulate the NSs resource allocation problem as a stochastic mixed integer program (SMIP) with the objective of minimizing the total CN and RAN resource costs. At each microscale instance, given the exact NSs demand profiles known at operation time, a linear program is solved to jointly minimize the unsupported traffic and RAN cost. We verify the effectiveness of our resource allocation scheme through numerical experiments.

关键词： Network slicing end-to-end resource provisioning demand uncertainty stochastic programming

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