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作者： Munise Kübra Sahin Bilkent University

学位级别：硕士

The increase in the energy consumption puts pressure on natural resources and environment and results in a rise in the price of energy. This motivates residents to schedule their energy consumption through demand response mechanism. We propose a multi-stage stochastic programming model to schedule different kinds of electrical appliances under uncertain weather conditions and availability of renewable energy. We incorporate appliances with internal batteries to better utilize the renewable energy sources. Our aim is to minimize the electricity cost and the residents' dissatisfaction. We use a scenario groupwise decomposition approach to compute lower and upper bounds for instances with a large number of scenarios. The results of our computational experiments show that the approach is very effective in finding high quality solutions in small computation times. We provide insights about how optimization and renewable energy combined with batteries for storage result in peak demand reduction, savings in electricity cost and more pleasant schedules for residents with different levels of price sensitivity.

关键词： smart grid demand response multi-stage stochastic programming scenario groupwise decomposition

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A multi-stage stochastic programming approach for blood supply chain planning

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 122卷 1-14页

作者： Zahiri, B. Torabi, S. Ali Moharnmadi, M. Aghabegloo, M. SUNY Buffalo Dept Operat Management & Strategy Sch Management Buffalo NY USA Univ Tehran Coll Engn Sch Ind Engn Tehran Iran UBL IMT Atlantique Lab STICC F-29238 Brest France

Perishability of blood products and uncertainty in donation and demand sizes complicate the blood supply chain planning. This paper presents a novel bi-objective mixed-integer model for integrated collection, production/screening, distribution and routing planning of blood products, and seeks to simultaneously optimize the total cost and freshness of transported blood products to hospitals. To cope with inherent uncertainty of input data, a multi-stage stochastic programming approach with a combined scenario tree is presented. Due to the high complexity of the problem, a novel hybrid multi-objective self-adaptive differential evolution algorithm is developed, which benefits from the variable neighborhood search with fuzzy dominance sorting (hereafter it is briefly called MSDV). MSDV is validated through comparing its performance with two of the most common multi-objective evolutionary algorithms (i.e. MOICA and NSGA-II). Applicability of the proposed decision model is also tested through a real case study. Our results show that the solution efficiency of a network can be balanced with its effectiveness through customer satisfaction. Further, several sensitivity analyses are carried out to provide valuable managerial insights.

关键词： Blood supply chain planning Vehicle routing problem multi-stage stochastic programming Meta-heuristic algorithms

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A multi-stage stochastic programming for lot-sizing and scheduling under demand uncertainty

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 119卷 157-166页

作者： Hu, Zhengyang Hu, Guiping Iowa State Univ Dept Ind & Mfg Syst Engn Ames IA 50011 USA

A stochastic lot-sizing and scheduling problem with demand uncertainty is studied in this paper. Lot-sizing determines the batch size for each product and scheduling decides the sequence of production. A multi-stage stochastic programming model is developed to minimize overall system costs including production cost, setup cost, inventory cost and backlog cost. We aim to find the optimal production sequence and resource allocation decisions. Demand uncertainty is represented by scenario trees using moment matching technique. Scenario reduction is used to select scenarios with the best representation of original set. A case study based on a manufacturing company has been conducted to illustrate and verify the model. We compared the two-stage stochastic programming model to the multi-stage stochastic programming model. The major motivation to adopt multi-stage stochastic programming models is that it extends the two-stage stochastic programming models by allowing revised decision at each period based on the previous realizations of uncertainty as well as decisions. Stability test and weak out-of-sample test are applied to find an appropriate scenario sample size. By using the multi-stage stochastic programming model, we improved the quality of solution by 10-13%.

关键词： multi-stage stochastic programming Lot-sizing and scheduling Demand uncertainty Automotive industry

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A novel multi-stage fuzzy stochastic programming for electricity system structure optimization and planning with energy-water nexus - A case study of Tianjin, China

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ENERGY 2020年 190卷

作者： Ji, Ling Zhang, Beibei Huang, Guohe Wang, Peng Beijing Univ Technol Coll Econ & Management Res Base Beijing Modern Mfg Dev Beijing 100124 Peoples R China Univ Regina Fac Engn Environm Syst Engn Program Regina SK S4S 0A2 Canada Shaanxi Elect Power Design Inst Co Ltd China Energy Engn Grp Tech & Econ Ctr Xian 710054 Peoples R China

In this study, a novel multi-stage fuzzy stochastic programming (MSFSP) model was developed for regional energy system structure optimization and planning with energy-water nexus under multiple uncertainties. By cooperating the multistage stochastic programming and fuzzy theory, the developed model can deal with energy system planning problems under a mixture of probabilistic and possibilistic uncertainties. Fuzzy random scenarios are designed to express the uncertain future energy demand levels and vague decision maker's risk attitude. A MSFSP -based energy-water nexus system management model was applied to a practical energy system planning problem in an energy-intensive and water-stressed area, Tianjin, China. Results of optimal capacity expansion, power generation, and imported electricity strategies were obtained;meanwhile the water resource availability effects and decision makers' risk attitude were analyzed. It was found that water resource availability would be a significant factor in promoting local power structure and electricity generation in the future. More serious water resource deficiency would lead to reduce local coal-fired power capacity investment, while stimulate renewable energy development and increase imported electricity requirement. All above can facilitate decision supports for regional energy system planning from a comprehensive energy-water nexus perspective with more sustainable and risk-aversion manners. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Energy-water nexus Risk management Random fuzzy multi-stage stochastic programming

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Personalized goal-based investing via multi-stage stochastic goal programming

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QUANTITATIVE FINANCE 2020年第3期20卷 515-526页

作者： Kim, Woo Chang Kwon, Do-Gyun Lee, Yongjae Kim, Jang Ho Lin, Changle Korea Adv Inst Sci & Technol Dept Ind & Syst Engn Daejeon 34141 South Korea Ulsan Natl Inst Sci & Technol Sch Management Engn 50 UNIST Gil Ulsan 44919 South Korea Kyung Hee Univ Dept Ind & Management Syst Engn 1732 Deogyeong Daero Yongin 17104 Gyeonggi Do South Korea Tsinghua Univ Inst Interdisciplinary Informat Sci FinTech Ctr 30 Shuangqing Rd Beijing 100084 Peoples R China

In this paper, we propose a goal-based investment model that is suitable for personalized wealth management. The model only requires a few intuitive inputs such as size of wealth, investment amount, and consumption goals from individual investors. In particular, a priority level can be assigned to each consumption goal and the model provides a holistic solution based on a sequential approach starting with the highest priority. This allows strict prioritization by maximizing the probability of achieving higher priority goals that are not affected by goals with lower priorities. Furthermore, the proposed model is formulated as a linear program that efficiently finds the optimal financial plan. With its simplicity, flexibility, and computational efficiency, the proposed goal-based investment model provides a new framework for automated investment management services.

关键词： Goal-based Investing Goal programming multi-stage stochastic programming Automated Investment Management Robo-advisor Financial Technology (FinTech) Retirement planning Defined contribution pension plan

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Flexibility and real options analysis in emergency medical services systems using decision rules and multi-stage stochastic programming

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2017年 107卷 120-140页

作者： Zhang, Sizhe Cardin, Michel-Alexandre Natl Univ Singapore Dept Ind Syst Engn & Management Block E1A 06-251 Engn Dr 2 Singapore 117576 Singapore

A novel approach to EMS infrastructure systems design, planning, and operations under long-term uncertainty is introduced based on multi-stage stochastic programming and decision rules, accounting for strategic flexibility (also known as real options - RO). Different from standard RO analysis, the approach mimics real-world decision-making by exercising flexibility based on conditional-go decision rules. The objective is to minimize the expected total costs over the system's life cycle, and the outputs are the optimal initial configuration and decision rules. A flexible solution provides lower expected cost than stochastically optimal rigid solutions, especially valuable when required incident coverage rate is >95%. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Flexibility in engineering design Real options analysis Emergency medical services multi-stage stochastic programming Decision rules

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Bulk ship fleet renewal and deployment under uncertainty: A multi-stage stochastic programming approach

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2017年 97卷 69-96页

作者： Arslan, Ayse N. Papageorgiou, Dimitri J. Univ Florida Dept Ind & Syst Engn Gainesville FL 32611 USA ExxonMobil Res & Engn Co Corp Strateg Res 1545 Route 22 East Annandale NJ 08801 USA

Faced with simultaneous demand and charter cost uncertainty, an industrial shipping company must determine a suitable fleet size, mix, and deployment strategy to satisfy demand. It acquires vessels by time chartering and voyage chartering. Time chartered vessels are acquired for different durations, a decision made before stochastic parameters are known. Voyage charters are procured for a single voyage after uncertain parameters are realized. We introduce the first multi-stage stochastic programming model for the bulk ship fleet renewal problem and solve it in a rolling horizon fashion. Computational results indicate that our approach outperforms traditional methods relying on expected value forecasts. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Chartering Fleet planning Fleet renewal Maritime transportation Mixed-integer linear programming multi-stage stochastic programming

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Integrating multi-stage stochastic programming and machine learning for the evaluation of policies in the electricity portfolio problem

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IMA JOURNAL OF MANAGEMENT MATHEMATICS 2017年第1期28卷 109-130页

作者： Murgia, Gianluca Sbrilli, Simone Univ Siena Dept Informat Engn & Math Sci Siena Italy Univ Roma Tor Vergata Dept Enterprise Engn Rome Italy

multi-stage stochastic programming can support large consumers in developing electricity portfolios that balance the expected total cost and the risk level. Nevertheless, the adoption of multi-stage stochastic programming in real-world problems is often made difficult by the high computational burden required. In this paper, we present an innovative approach, called General Policy Function Approximation, that provides good solutions to the electricity portfolio problem in a limited computational time, owing to the integration of multi-stage stochastic programming and machine learning. Our approach improves the Policy Function Approximation (PFA) approach proposed by Defourny et al. [(2012). multi-stage stochastic programming: A scenario tree-based approach to planning under uncertainty. Decision Theory Models for Applications in Artificial Intelligence (L. E. Sucar, E. Morales, F. Eduardo & J. Hoey eds). vol. 6. Hershey: IGI Global, pp. 97-144], by developing a single policy function generated from a larger amount of data. Owing to a realistic computational campaign, we show that our approach outperforms PFA both in terms of quality of the policy obtained, and in terms of time required.

关键词： multi-stage stochastic programming machine learning electricity portfolio large consumer

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A multi-stage stochastic programming for Redesigning the Relief Logistics Network: A Real Case Study

A Multi-stage Stochastic Programming for Redesigning the Rel...

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作者： Hamidreza Rezaei Hasan Khademi Zare Mahdi Bashiri M.B.Fakhrzad Department of Industrial Engineering Yazd University Department of Industrial Engineering Shahed University

Disasters inevitably cause sweeping problems affecting all surroundings. Therefore, it seems that right decision making for agile and efficient management in disaster management is not negligible. Due to designed network in the past, current entities, facilities and network may not be optimal and to a great extent usable. In this paper a comprehensive model and solving approach have been proposed to redesign the relief network in dealing with the preparedness and response phases. In this regard, previous investigations on the preparedness phase have often been limited to the location of eligible facilities without considering other important factors such as current and working assets, entities and configuration. Thus, the present study proposes a reconfiguring and repositioning model in order to simultaneously assess whether existing distribution centers should remain, be consolidated or phased out as well as whether new facilities should be established and subsequently supply and demand requirements consideration. Moreover, in the proposed model, multi-stage stochastic programming has been implemented on a real data gathered by ArcGIS Software and demand scenarios derived from relevant references. The results based on a real case study in Tehran indicate definite advantages in the re-positioning or reconfiguring model compared with current configurations.

关键词： Disaster Management Relocation Redesign Preparedness Facility multi-stage stochastic programming GIS Data

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A novel multi-stage possibilistic stochastic programming approach (with an application in relief distribution planning)

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INFORMATION SCIENCES 2017年 385卷 225-249页

作者： Zahiri, B. Torabi, S. A. Tavakkoli-Moghaddam, R. SUNY Buffalo Jacobs Management Ctr 326 Sch Management Dept Operat Management & Strategy Buffalo NY 14260 USA Univ Tehran Sch Ind Engn Coll Engn Tehran Iran

In this paper, a novel multi-stage possibilistic stochastic programming (MSPSP) approach is developed. At the early stages, there is little information on how to pursue MSPSP the optimal decision, while in the course of time, new series of information become unfolded, which further necessitate the model to be updated. Compared to other competent uncertainty programming approaches (i.e. the classical two-stage stochastic programming (TSSP) and multi-stage stochastic programming (MSSP), and enhanced versions of TSSP and MSSP approaches), the proposed MSPSP method relaxes the subjectively setting the deterministic occurrence probabilities and allows possible perturbation in their values as fuzzy probabilities. It further enables these values to be updated so that the updated values do not conflict with each other and their summation does not exceed one. Also, given the existence of several sources of uncertainty and the deep level of uncertainty in the early stages, the scenario dependent variables at such stages are treated as random fuzzy variables. Finally, it takes into account the effect of unfolded information in an on-going fashion in the course of time. Applicability of the proposed approach is tested on a real case study for the post-disaster relief distribution planning in Tehran. Finally, useful managerial insights are provided through conducting several sensitivity analyses. (C) 2017 Elsevier Inc. All rights reserved.

关键词： multi-stage stochastic programming Fuzzy probabilities Random fuzzy variables Post-disaster relief distribution planning

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