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A two-stage stochastic programming approach for identifying optimal postponement strategies in supply chains with uncertain demand

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2019年 83卷 123-138页

作者： Weskamp, Christoph Koberstein, Achim Schwartz, Frank Suhl, Leena Voss, Stefan Univ Paderborn Decis Support & Operat Res Lab Warburger Str 100 D-33098 Paderborn Germany European Univ Viadrina Frankfurt Oder Chair Business Informat Grosse Scharrnstr 59 D-15230 Frankfurt Oder Germany Univ Hamburg Inst Informat Syst Von Melle Pk 5 D-20146 Hamburg Germany

In the course of globalization, applying mass-customization strategies has led to a high diversity of variants in many economic sectors. Thus, customer demands are often less predictable, and handling increasing inventory stocks as well as avoiding shortfalls have become particularly important. All these complexity drivers result in higher supply chain risks. Postponement strategies have been proposed as a suitable approach to address these problems. Although the concept of postponement and its impact on the supply chain are theoretically well discussed, optimally configuring the entire production and distribution activities is still challenging. We present a two-stage stochastic mixed-integer linear programming model, which comprises an integrated production and distribution planning approach, and considers postponement concepts. In comparison to earlier approaches that examine postponement strategies, our model supports the decision maker under demand uncertainty and considers lead times, penalty costs for shortfalls, as well as inventory-keeping decisions over a tactical planning horizon. This allows an integrated investigation of both form and logistics postponement concepts. Moreover, we consider the decision maker's risk attitude identifying non-dominated profitable and risk-averse strategies. We illustrate the benefits of the model by using a case study from the apparel industry, and present the results of a sensitivity analysis with respect to varying demand uncertainty and demand correlations as well as different preferences regarding risk aversion. Furthermore, we carry out performance and quality benchmarks and compare the results of a standard mixed-integer linear programming solver, a parallel nested Benders approach and a sample average approximation technique. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Decision making two-stage stochastic programming Supply chain Risk Optimization

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Scenario-based two-stage stochastic programming for a Hybrid Manufacturing-Remanufacturing System with the uncertainty of returns, quality and demand

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2021年第1期46卷 1-9页

作者： Reddy, K. Nageswara Kumar, Akhilesh Velaga, Nagendra R. Indian Inst Technol Kharagpur Dept Ind & Syst Engn Kharagpur 721302 W Bengal India Indian Inst Technol Civil Engn Dept Mumbai Maharashtra India

This paper addresses the optimization of make-to-order hybrid manufacturing - remanufacturing system to make capacity and inventory decisions jointly along with production decisions. The proposed system considers a common production facility and the same assembly/disassembly line to perform manufacturing and remanufacturing operations simultaneously. The current study takes into account an environment where new and remanufactured (reman) products competing to each other that is, the common demand stream for both products but different selling prices. Furthermore, the relative capacity consumed by remanufacturing over the manufacturing is explained in two ways, namely less capacity intensive case and more capacity intensive case. Differently, from previous studies, we consider a scenario with a discounted selling price for reman products, shortage penalty costs, lost sales, disposal and uncertainty in demand, amount and yield of returns. Hence, to handle those uncertainties, a scenario-based stochastic programming model in a two-stage setting is presented. In the first stage, the raw material inventory and production capacity levels are planned and in the second stage, the production, inventory and disposal decisions are determined by balancing overage and underage costs. The results indicate that net values associated with new and reman products can be decisive in choosing either manufacturing or remanufacturing.

关键词： Capacity investment inventory planning remanufacturing two-stage stochastic programming scenario-based approach

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A two-stage stochastic programming approach for value-based closed-loop supply chain network design

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2017年第Sep.期105卷 1-17页

作者： Badri, Hossein Ghomi, S. M. T. Fatemi Hejazi, Taha-Hossein Amirkabir Univ Technol Dept Ind Engn Tehran Iran Amirkabir Univ Technol Dept Ind Engn Garmsar Campus Garmsar Iran

The ultimate goal for almost all companies in the current business world is to increase their value;therefore, managers concentrate their efforts on the control of value drivers. This research develops a two-stage stochastic programming model for the value-based supply chain network design, where all parts of a supply chain are configured and controlled in such a way that the total value of the company increases. The proposed model is a three-echelon, multi-commodity, and multi-period model for the tactical and strategic decision making. Also, a comprehensive computational analysis is conducted to evaluate the performance of the proposed approach. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Closed-loop supply chain network design Value-based management two-stage stochastic programming

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two-stage stochastic programming model for optimal scheduling of the wind-thermal-hydropower-pumped storage system considering the flexibility assessment

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ENERGY 2020年第0期193卷 969-981页

作者： Daneshvar, Mohammadreza Mohammadi-Ivatloo, Behnam Zare, Kazem Asadi, Somayeh Univ Tabriz Fac Elect & Comp Engn Tabriz Iran Penn State Univ Dept Architectural Engn University Pk PA 16802 USA Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam

This paper presents a two-stage stochastic programming model for optimal scheduling of the wind-thermal-hydropower-pumped storage system considering the competitive interactions between the electrical generation units. The first stage is focused on the day-ahead scheduling of the thermal power plants while the balancing market dispatch is considered in the second stage using the stochastic producers and quick dispatch units. To capture the uncertainties associated with electricity demand and wind speed, Latin hyperbolic sampling and fast forward selection methods are applied for scenario generation and reduction processes, respectively. The flexibility of the studied system is analyzed considering the variations of the key parameters of thermal units and the transmission line's capacity. For this work, the modified IEEE 14-bus standard test system integrated with the components of the studied system is selected as the case study. After solving the problem, the maximum potential of clean energy production units is used in comparison with fossil fuel-based units through the optimal scheduling of the wind-thermal-hydropower-pumped storage system. Given the numerical results, reducing the flexibility of the system by reducing the ramp up/down parameters, increasing the minimum up/down parameters, and reducing the transmission line capacity has been led to increase of 6.47%, 7.3%, and 9.77% in the total energy cost, respectively. (C) 2019 Published by Elsevier Ltd.

关键词： two-stage stochastic programming Wind-thermal-hydropower-pumped storage system Uncertainty modeling Clean energy production Flexibility assessment

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two-stage stochastic programming formulation for optimal design and operation of multi-microgrid system using data-based modeling of renewable energy sources

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APPLIED ENERGY 2021年 291卷 116830-116830页

作者： Han, Dongho Lee, Jay H. Korea Adv Inst Sci & Technol Dept Chem & Biomol Engn 291 Daehak Ro Daejeon 34141 South Korea

Microgrid (MG) is intended to accommodate distributed renewable energy sources and manage the generated electricity using a dispatch and storage system to satisfy the demand of a local region over time. Main challenges associated with operating such a system stems primarily from the intermittent and uncertain nature of renewable energy sources causing the energy generation to be highly irregular and less predictable. Multi-microgrid (MMG) systems with flexibility to trade energy between individual MGs can alleviate the demand-supply mismatch problem, but its design and operation become more complicated. Previous studies on MMGs addressed only the case where all the MGs are fully connected to share electricity. However, the installation of electric cable connections over the full network can be costly and is often unjustified. To address the question of how to best achieve a tradeoff, this work proposes the use of a two-stage stochastic decision-making approach for designing and operating an MMG considering both the capital cost for installing the electric cables and operating cost. First, real weather data are used to develop stochastic models that describe the uncertain, intermittent nature of energy production through renewable sources over a day. Then, in the first stage, design-level decisions regarding the installation of the electric cable between individual pairs of the MGs are to be determined to achieve the best tradeoff for the modeled system and weather pattern. In the second stage, the optimal energy dispatch planning for each individual MG including the energy trading between the MGs is carried out based on a particular realized scenario of the renewable energy production.

关键词： Multi-microgrid Uncertainty modeling two-stage stochastic programming Monte Carlo simulation Optimal topology Optimal operation

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Optimizing Water Allocation under Uncertain System Conditions in Alfeios River Basin (Greece), Part A: two-stage stochastic programming Model with Deterministic Boundary Intervals

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WATER 2015年第10期7卷 5305-5344页

作者： Bekri, Eleni Disse, Markus Yannopoulos, Panayotis Tech Univ Munich Chair Hydrol & River Basin Management D-80333 Munich Germany Univ Patras Dept Civil Engn Environm Engn Lab Patras 26504 Greece

The enactment of the Water Framework Directive, constituting the basis of the European water policy, introduced various challenges and complexities for water resources management. River basins are exposed to a plethora of environmental stresses, resulting in degradation of their quantitative and qualitative status. This led to the reduction of clean available water, increasing competition among water users and imposing the need for optimal water allocation for each river unit. In most countries (including those in the Mediterranean), water resources management is characterized by lack of effective operational strategies combined with the absence of permanent measuring systems and low financial means, hampering the implementation of efficient river monitoring. Therefore, water resources management is indicated by high uncertainty and by imprecise and limited data, which may be easily approximated through estimates of intervals. In the present work, optimal water allocation under uncertain system conditions is undertaken for the Alfeios River Basin (Greece) based on an inexact two-stage stochastic programming methodology developed by Huang and Loucks (2000). It combines ordinary two-stage stochastic programming with uncertainties expressed as deterministic boundary intervals. Stable intervals for optimized water allocation targets and probabilistic water allocation and shortages are estimated under a baseline scenario and four water and agricultural policy future scenarios.

关键词： optimal water allocation two-stage stochastic programming intervals uncertainty stochastic hydrologic scenarios future scenarios

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A two-stage stochastic programming approach for a multi-objective course timetabling problem with courses cancelation risk

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 130卷 650-660页

作者： Yasari, Peyman Ranjbar, Mohammad Jamili, Negin Shaelaie, Mohammad-Hesam Ferdowsi Univ Mashhad Fac Engn Dept Ind Engn Mashhad Razavi Khorasan Iran

We study a university course timetabling problem, where the registration is implemented in two steps: pre-registration and drop/add phases. Since the ultimate timetable is finalized based on the students' decisions made on the drop/add phase, there may be some courses need to be canceled because of not reaching to the threshold value in terms of total registered students. As a result of cancelations, some undesirable changes may be imposed on the final timetable for the students and the professors as well. In this paper, we ideally arrange course timetable, while considering the courses cancelations risk and the possible changes which may happen after the drop/add period;ultimately the undesirability is minimized in the final timetable. In order to achieve that, we optimize an objective function which consists of seven different objectives combined as a weighted sum function. As regards the solution method, we develop a two-stage stochastic programming model, a heuristic approach and a two-stage separated model where the latter one model the traditional method. The performance of all developed algorithms is then analyzed using randomly generated test instances.

关键词： University course timetabling problem two-stage stochastic programming Risk

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Material and energy coupling systems optimization for large-scale industrial refinery with sustainable energy penetration under multiple uncertainties using two-stage stochastic programming

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APPLIED ENERGY 2024年 371卷

作者： Xu, Tiantian Long, Jian Zhao, Liang Du, Wenli East China Univ Sci & Technol Minist Educ Key Lab Smart Mfg Energy Chem Proc Shanghai 200237 Peoples R China Qingyuan Innovat Lab Quanzhou 362801 Peoples R China

The coupling of multimedia materials and energy with sustainable energy penetration in large-scale industrial refineries significantly lowers energy consumption and greenhouse gas (GHG) emissions. This paper presents a sustainable retrofitting framework for coupled production materials and steam systems (CPMSS) utilising twostage stochastic programming (TSSP). A novel sustainable energy-integrated CPMSS (SEICPMSS) model that includes wind, solar, and hydrogen energy and considers multiple operating modes and hydrogen consumption of the process units was established. First-principles models of wind turbines, solar thermal collectors, and proton exchange membrane electrolysers were adopted to sustainably retrofit the CPMSS. Latin hypercube sampling and Gaussian mixture model methods were applied to classify high volumes of uncertain wind speed and solar radiation data. A SEICPMSS optimisation model formulated as a mixed-integer nonlinear programming problem was developed by considering the investment costs, carbon taxes, and operational costs. Finally, a series of case studies from the industrial refinery's CPMSS were conducted to illustrate the effectiveness of the proposed method. The optimisation results indicate that the TSSP method can reduce operating costs by 5.3355 x 108 CNY/year and decrease GHG emissions by 7.4104 x 106 t/year compared to the traditional CPMSS without the integration of sustainable energy systems.

关键词： Refinery Integrated sustainable energy system Mixed-integer nonlinear programming two-stage stochastic programming Uncertainty Greenhouse gas emission

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Inexact rough-interval two-stage stochastic programming for conjunctive water allocation problems

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2009年第1期91卷 261-269页

作者： Lu, Hongwei Huang, Guohe He, Li Univ Regina EVSE Fac Engn & Appl Sci Regina SK S4S 0A2 Canada Peking Univ Coll Urban Environm Sci Beijing 100871 Peoples R China Ryerson Univ Dept Civil Engn Toronto ON M5B 2K3 Canada

An inexact rough-interval two-stage stochastic programming (IRTSP) method is developed for conjunctive water allocation problems. Rough intervals (RIs), as a particular case of rough sets, are introduced into the modeling framework to tackle dual-layer information provided by decision makers. Through embeding upper and lower approximation intervals, rough intervals are capable of reflecting complex parameters with the most reliable and possible variation ranges being identified. An interactive solution method is also derived. A conjunctive water-allocation system is then structured for characterizing the proposed model. Solutions indicate a detailed optimal allocation scheme with a rough-interval form;a total of [[1048.83, 2078.29]:[1482.26, 2020.60]] would be obtained under the pre-regulated inputs. Comparisons of the proposed model to a conventional and an interval two-stage stochastic programming model are also conducted. The results indicate that the optimal objective function values of TSP and ITSP always fall into the range of f(opt)(+/-(UAI)), while they are sometimes out of the range of f(opt)(+/-(LAI));the optimal solutions of decision variables also present this feature. This implies the reliability of IRTSP in handling conjunctive water allocation problems. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Rough intervals Conjunctive use Water resources management Uncertainty Inexact Agricultural irrigation two-stage stochastic programming

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A robust decision-making approach for p-hub median location problems based on two-stage stochastic programming and mean-variance theory: a real case study

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2015年第9-12期77卷 1943-1953页

作者： Ahmadi, Taher Karimi, Hadi Davoudpour, Hamid Hosseinijou, Seyed Abbas Amirkabir Univ Technol Dept Ind Engn Tehran Iran Pars Khodro Automobile Manufacturer Co Tehran Iran

The stochastic location-allocation p-hub median problems are related to long-term decisions made in risky situations. Due to the importance of this type of problems in real-world applications, the authors were motivated to propose an approach to obtain more reliable policies in stochastic environments considering the decision makers' preferences. Therefore, a systematic approach to make robust decisions for the single location-allocation p-hub median problem based on mean-variance theory and two-stage stochastic programming was developed. The approach involves three main phases, namely location modeling, risk modeling, and decision making, each including several steps. In the first phase, the pertinent location-allocation model of the problem is developed in the form of a two-stage stochastic model based on its deterministic version. A risk measure, based on total cost function and mean-variance theory, is derived in the second phase. Furthermore, two heterogeneous terms of the risk measure have been normalized and an innovative procedure has been proposed to significantly improve the calculation efficiency. In the third phase, the Pareto solution is obtained, the frontier curve is depicted to determine the decision maker's risk aversion coefficient, and a robust policy is obtained through optimization based on decision makers' preferences. Finally, a case study of an automobile part distribution system with stochastic demand is described to further illustrate our risk management and analysis approach.

关键词： p-Hub location-allocation median problem two-stage stochastic programming Risk analysis and management Robust decision making Risk aversion coefficient

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