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Investigating replenishment policies for centralised and decentralised supply chains using stochastic programming approach

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2015年第1期53卷 41-69页

作者： Fattahi, M. Mahootchi, M. Husseini, S. M. Moattar Keyvanshokooh, E. Alborzi, F. Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran Iowa State Univ Dept Ind & Mfg Syst Engn Ames IA USA N Carolina State Univ Edward P Fitts Dept Ind & Syst Engn Raleigh NC 27695 USA

In this paper, a multiple period replenishment problem based on (s, S) policy is investigated for a supply chain (SC) comprising one retailer and one manufacturer with uncertain demand. Novel mixed-integer linear programming (MILP) models are developed for centralised and decentralised decision-making modes using two-stage stochastic programming. To compare these decision-making modes, a Monte Carlo simulation is applied to the optimization models' policies. To deal with demand uncertainty, scenarios are generated using Latin Hypercube Sampling method and their number is reduced by a scenario reduction technique. In large test problems, where CPLEX solver is not able to reach an optimal solution in the centralised model, evolutionary strategies (ES) and imperialist competitive algorithm (ICA) are applied to find near optimal solutions. Sensitivity analysis is conducted to show the performance of the proposed mathematical models. Moreover, it is demonstrated that both ES and ICA provide acceptable solutions compared to the exact solutions of the MILP model. Finally, the main parameters affecting difference between profits of centralised and decentralised SCs are investigated using the simulation method.

关键词： centralised and decentralised supply chains inventory management simulation evolutionary strategies stochastic programming Imperialist competitive algorithm

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E-MRO Service Planning with Uncertain Constraints Based on stochastic programming

E-MRO Service Planning with Uncertain Constraints Based on S...

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IEEE International Conference on Computer Supported Cooperative Work in Design

作者： Ling Li Weiming Shen Min Liu Guoqing Cheng Feng Zhang School of Electronic and Information Engineering Tongji University School of Information Engineering Jingdezhen Ceramic Institute

ISBN: (纸本)9781509019168

E-business based maintenance, repair and overhaul (E-MRO) is a new MRO service mode. Although in real world there are a number of E-MRO prototype systems, few comprehensive studies have been conducted on this topic. Motivated by the challenges of making optimal E-MRO service planning, simultaneously considering the capacity constraints of MRO service providers and the maintenance constraints of equipment users, this paper proposes a stochastic programming model involving multi-choice parameters, where uncertain factors in E-MRO are quantified. To solve the model, the properties of expectation of a random variable, and the Lagrange interpolating polynomial approach are used to derive the deterministic model equivalent to the stochastic programming model. The objective of the model is to seek optimal service planning, including determining whether to configure the corresponding service from the corresponding provider to the corresponding user at the corresponding period, and determining the time of the corresponding service. The optimal service planning can be referred by practitioners for a more reasonable decision. A numerical example validated the feasibility of proposed model.

关键词： E-business based maintenance Repair and overhaul (E-MRO) Service planning stochastic programming Uncertainty

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Designing Seasonal Initial Attack Resource Deployment and Dispatch Rules Using a Two-Stage stochastic programming Procedure

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FOREST SCIENCE 2015年第6期61卷 1021-1032页

作者： Wei, Yu Bevers, Michael Belval, Erin J. Colorado State Univ Forest Rangeland & Watershed Stewardship Ft Collins CO 80523 USA Rocky Mt Res Stn Ft Collins CO USA Colorado State Univ Ft Collins CO 80523 USA

Initial attack dispatch rules can help shorten fire suppression response times by providing easy-to-follow recommendations based on fire weather, discovery time, location, and other factors that may influence fire behavior and the appropriate response. A new procedure is combined with a stochastic programming model and tested in this study for designing initial attack dispatch rules integrated with development of seasonal suppression resource deployment decisions to support initial attack. Historical fires are spatially simulated according to historical weather and actual fire locations to provide fire samples that capture the stochastic nature of this problem. Constraints are added to a two-stage stochastic programming resource deployment and dispatch model to enforce a requirement that the same combination of initial attack resources be dispatched to all fires in any given dispatch category. A postoptimization procedure is used to check the resulting solution, potentially leading to refinements of candidate solutions before a model solution is accepted. Our test results indicate that not accounting for the use of standard response dispatch rules where they are typically implemented could lead planners to underestimate initial attack resource requirements and costs.

关键词： suppression dispatch rule stochastic programming two-stage model

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Optimal Configuration of the CHP System Using stochastic programming

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IEEE TRANSACTIONS ON POWER DELIVERY 2015年第3期30卷 1048-1056页

作者： Benam, Masoud Rezaei Madani, Seyed Sohail Alavi, Seyed Mohammad Ehsan, Mehdi Sharif Univ Technol Ctr Excellence Power Syst Management & Control Dept Elect Engn Tehran Iran Imam Hossein Comprehens Univ Dept Elect Engn Tehran Iran Sharif Univ Technol Ctr Excellence Power Syst Management & Control Dept Elect Engn Tehran Iran

This study presents a reliability-constrained optimization approach to determine the number and size of combined heat and power (CHP) system components, including CHP units, auxiliary boilers, and heat-storage tanks. To this end, the loss-of-load expectation and the expected energy not supplied are considered since the reliability indices ensure the security of operation. The load forecasting inaccuracy and the random outages of CHP system components as well as the loss of mains are modeled as a scenario tree using the Monte Carlo sampling approach. The problem is formulated as two-stage stochastic mixed integer linear programming. A scenario reduction technique is also introduced to reduce the computational burden of the scenario-based planning problem. Finally, the proposed model is applied to a large residential complex in Tehran as a case study.

关键词： Cogeneration Monte Carlo method reliability indices stochastic programming

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An Application of stochastic programming in Solving Capacity Allocation and Migration Planning Problem under Uncertainty

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-16页

作者： Chen, Yin-Yann Fan, Hsiao-Yao Natl Formosa Univ Dept Ind Management Huwei Township 632 Yunlin Taiwan

The semiconductor packaging and testing industry, which utilizes high-technology manufacturing processes and a variety of machines, belongs to an uncertain make-to-order (MTO) production environment. Order release particularly originates from customer demand;hence, demand fluctuation directly affects capacity planning. Thus, managing capacity allocation is a difficult endeavor. This study aims to determine the best capacity allocation with limited resources to maximize the net profit. Three bottleneck stations in the semiconductor packaging and testing process are mainly investigated, namely, die bond (DB), wire bond (WB), and molding (MD) stations. Deviating from previous studies that consider the deterministic programming model, customer demand in the current study is regarded as an uncertain parameter in formulating a two-stage scenario-based stochastic programming (SP) model. The SP model seeks to respond to sharp demand fluctuations. Even if future demand is uncertain, migration decision for machines and tools will still obtain better robust results for various demand scenarios. A hybrid approach is proposed to solve the SP model. Moreover, two assessment indicators, namely, the expected value of perfect information (EVPI) and the value of the stochastic solution (VSS), are adopted to compare the solving results of the deterministic planning model and stochastic programming model. Sensitivity analysis is performed to evaluate the effects of different parameters on net profit.

关键词： capacity planning capacity allocation stochastic programming machine migration

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Comparison of two algorithms for solving a two-stage bilinear stochastic programming problem with quantile criterion

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APPLIED stochastic MODELS IN BUSINESS AND INDUSTRY 2015年第6期31卷 862-874页

作者： Kibzun, Andrey Moscow Inst Aviat Technol Sch Appl Math & Phys Dept Probabil Theory Moscow 125993 Russia

The paper is devoted to solving the two-stage problem of stochastic programming with quantile criterion. It is assumed that the loss function is bilinear in random parameters and strategies, and the random vector has a normal distribution. Two algorithms are suggested to solve the problem, and they are compared. The first algorithm is based on the reduction of the original stochastic problem to a mixed integer linear programming problem. The second algorithm is based on the reduction of the problem to a sequence of convex programming problems. Performance characteristics of both the algorithms are illustrated by an example. A modification of both the algorithms is suggested to reduce the computing time. The new algorithm uses the solution obtained by the second algorithm as a starting point for the first algorithm. Copyright (c) 2015 John Wiley & Sons, Ltd.

关键词： stochastic programming two-stage problem quantile function Value-at-Risk criterion confidence method mixed integer linear programming problem convex programming

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DESIGNING A SUSTAINABLE CLOSED-LOOP SUPPLY CHAIN USING ROBUST POSSIBILISTIC-STOCHAS TIC programming IN PENTAGONAL FUZZY NUMBERS

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TRANSPORT 2024年第4期39卷 323-349页

作者： Dehshiri, Seyyed Jalaladdin Hosseini Amiri, Maghsoud Hajiaghaei-keshteli, Mostafa Keshavarz-ghorabaee, Mehdi Zavadskas, Edmundas Kazimieras Antucheviciene, Jurgita Allameh Tabatabai Univ Dept Ind Management Tehran Iran Monterrey Inst Technol & Higher Educ Dept Ind Engn Puebla Mexico Gonbad Kavous Univ Dept Management Gonbad Kavous Iran Vilnius Gediminas Tech Univ Inst Sustainable Construct Vilnius Lithuania Vilnius Gediminas Tech Univ Dept Construct Management & Real Estate Vilnius Lithuania

The lack of information and hybrid uncertainties in Supply Chain (SC) parameters affect managerial decisions. It is inevitable to consider random uncertainty based on fuzzy scenarios and cognitive uncertainty to prehensiveness and accuracy than triangular and trapezoidal fuzzy numbers due to taking into account higher uncertainty, less lack of information, and taking into account maximum subjectivity Decision-Makers (DMs). There is a gap in the literature regarding the use of PFNs in SCLSC problems. This research presents a new model using PFNs to solve deficiencies in stochastic-possibilistic programming. Developing a Robust stochastic-Possibilistic (RSP) based on PFNs under fuzzy scenarios, presenting measures of necessity, possibility, and credibility for making decisions founded on different levels of DMs' risk, and proposing global solutions through providing linear programming models are the main innovations and contributions of the present research. An actual case study evaluates the presented approach to reduce the cost and carbon pollution in the stone paper SC. In the suggested method, trade-offs could be formed between the mean of objective functions and risk by modifying the robustness coefficients. According to the proposed approach, an optimal value of confidence is specified. Additionally, robustness deviations are controlled in the model, which results in more accurate and reliable results. Numerical simulations confirmed the efficacy of the robust approach proposed.

关键词： closed-loop supply chain possibilistic programming fuzzy scenarios robust approach sustainable design stochastic programming

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stochastic dual dynamic programming for optimal power flow problems under uncertainty

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第3期321卷 814-836页

作者： Kiszka, Adriana Wozabal, David Tech Univ Munich TUM Sch Management Arcisstr 21 D-80333 Munich Germany Vrije Univ Amsterdam Sch Business & Econ Boelelaan 1105 NL-1081 HV Amsterdam Netherlands

Planning in the power sector has to take into account the physical laws of alternating current (AC) power flows as well as uncertainty in the data of the problems, both of which greatly complicate optimal decision making. We propose a computationally tractable framework to solve multi-stage stochastic optimal power flow (OPF) problems in AC power systems. Our approach uses recent results on dual convex semi-definite programming (SDP) relaxations of OPF problems in order to adapt the stochastic dual dynamic programming (SDDP) algorithm for problems with a Markovian structure. We show that the usual SDDP lower bound remains valid and that the algorithm converges to a globally optimal policy of the stochastic AC-OPF problem as long as the SDP relaxations are tight. To test the practical viability of our approach, we set up a case study of a storage siting, sizing, and operations problem. We show that the convex SDP relaxation of the stochastic problem is usually tight and discuss ways to obtain near-optimal physically feasible solutions when this is not the case. The algorithm finds a physically feasible policy with an optimality gap of 3% and yields a significant added value of 27% over a rolling deterministic policy, which leads to overly optimistic policies and underinvestment in flexibility. This suggests that the common industry practice of assuming direct current and deterministic problems should be reevaluated by considering models that incorporate realistic AC flows and stochastic elements in the data as potentially more realistic alternatives.

关键词： stochastic programming Optimal power flow Semi-definite programming stochastic dual dynamic programming Electricity storage

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A hybrid approach to select the best sourcing policy using stochastic programming

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JOURNAL OF MANUFACTURING SYSTEMS 2015年 36卷 115-127页

作者： Keyvanloo, M. Kimiagari, A. M. Esfahanipour, A. Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran *** Iran

Supply contracts are known as the communication link among supply chain members. As sourcing of required goods is a challenging issue for supply chain members, different sourcing types for different market conditions have been presented in the literature. However, the uncertain price condition has not been much focused in the previous studies, and in the limited works on this issue the correlation between the periods has been ignored. In this paper, sourcing policies are analyzed in a multi-period system in which price and demand follow a Geometric Brownian Motion with drift. Wholesale contract, option contract, and purchase from the spot market are considered as the sourcing alternatives for the buyer. This paper applies the stochastic programming approach to model these three types of sourcing based upon price and demand uncertainties. Afterwards, a hybrid supply model of these sourcing types is developed. By a numerical example, the simulation results of the developed models reveal that each individual sourcing alternative can be selected as the best one in each price and demand behavior. The results also suggest that the proposed hybrid model dominates each of the individual sourcing types. Finally, the paper reports the effects of cost parameter alterations on the solution of the hybrid model through sensitivity analysis. (C) 2015 Published by Elsevier Ltd on behalf of The Society of Manufacturing Engineers.

关键词： Supply contract Wholesale contract Option contract Spot market sourcing Uncertain demand Uncertain price stochastic programming

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A stochastic programming Approach on Aircraft Recovery Problem

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷

作者： Zhu, Bo Zhu, Jin-fu Gao, Qiang Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 211100 Jiangsu Peoples R China

The unexpected aircraft failure is one of the main disruption factors that cause flight irregularity. The aircraft schedule recovery is a challenging problem in both industrial and academic fields, especially when aircraft restoration time is uncertain, which is often ignored in previous research. This paper established a two-stage stochastic recovery model to deal with the problem. The first stage model was a resource assignment model on aircraft schedule recovery, with the objective function of minimizing delay and cancellation cost. The second stage model used simple retiming strategy to adjust the aircraft routings obtained in the first stage, with the objective function of minimizing the expected cost on recourse decision. Based on different scenarios of restoration time, the second stage model can be degenerated as several linear models. A stochastic Greedy Simulated Annealing algorithm was designed to solve the model. The computational results indicate that the proposed stochastic model and algorithm can effectively improve the feasibility of the recovery solutions, and the analysis of value of stochastic solution shows that the stochastic model is worthy of implementation in real life.

关键词： stochastic programming

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