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multi-period supplier selection under price uncertainty

JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2014年第11期65卷 1636-1648页

作者： Sen, Alper Yaman, Hande Guler, Kemal Korpeoglu, Evren Bilkent Univ TR-06800 Ankara Turkey Hewlett Packard Labs Palo Alto CA USA

We consider a problem faced by a procurement manager who needs to purchase a large volume of multiple items over multiple periods from multiple suppliers that provide base prices and discounts. Discounts are contingent on meeting various conditions on total volume or spend, and some are tied to future realizations of random events that can be mutually verified. We formulate a scenario-based multi-stage stochastic optimization model that allows us to consider random events such as a drop in price because of the most favoured customer clauses, a price change in the spot market or a new discount offer. We propose certainty-equivalent heuristics and evaluate the regret of using them. We use our model for three bidding events of a large manufacturing company. The results show that considering most favored customer clauses in supplier offers may create substantial savings that may surpass the savings from regular discount offers.

关键词： supplier selection quantity discounts price uncertainty most favoured customer multi-stage stochastic programming

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multi-stage stochastic Optimal Operation of Energy-efficient Building with Combined Heat and Power System

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2014年第3-4期42卷 327-338页

作者： Liu, Ping Fu, Yong Marvasti, Amin Kargarian Mississippi State Univ Dept Elect & Comp Engn Starkville MS 39762 USA

Due to the integration of volatile renewable energy and random energy consumption in the building grid, uncertainties have become a big concern for the operation of energy-efficient buildings. To minimize energy usage expenses under uncertainty, it is necessary to determine the optimal power production for a building from various energy sources, including the electric grid, battery, and combined heat and power with a boiler unit. This article presents a multi-stage mixed-integer stochastic programming model for optimal operation of energy-efficient building systems considering controllable electric and thermal loads. Taking into account the randomness of non-controllable electric and thermal loads, as well as solar power generations through the multi-stage scenario tree, the operation of energy-efficient buildings will be more robust against changes in uncertain variables. With information of uncertainties updated hourly, the rolling scheduling method is introduced to determine an adaptive power output of electric grid, charging/discharging status of the battery, and operation of combined heat and power with a boiler unit. The simulation results offer a set of adaptive decision solutions within the scheduling horizon.

关键词： combined heat and power system energy-efficient buildings multi-stage stochastic programming smart grid

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Integrated spare parts logistics and operations planning for maintenance service providers

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2014年 158卷 44-53页

作者： Zanjani, Masoumeh Kazemi Nourelfath, Mustapha Concordia Univ Dept Mech & Ind Engn Montreal PQ H3G 1M8 Canada Univ Laval Dept Mech Engn Quebec City PQ G1K 7P4 Canada Interuniv Res Ctr Enterprise Networks Logist & Tr Quebec City PQ G1V 0A6 Canada

This paper considers the problem of coordinated spare-part logistics and operations planning for third-party maintenance providers. Due to the multi-indenture structure of the equipment, different types of components might randomly fail to perform at different points of time. The spare part logistics literature has been focused on spare part inventory management in an in-house maintenance context. In this article, a mathematical programming model is first developed to formulate the problem in the context of a third-party maintenance provider who is faced with strict due dates for the delivery of repaired equipment. The model seeks the optimal number of maintenance jobs that can be completed to deliver at each period, as well as the order quantity of spare parts so as to minimize the procurement, inventory, and equipment late delivery costs, while taking into account the spare part supply lead-time. Next, we model the spare part demand uncertainty as a non-stationary stochastic process in each period in the planning horizon. The deterministic model is then reformulated as a multi-stage stochastic program with recourse. We also discuss the complexity of the stochastic model and propose a preprocessing approach to reduce its size for large instances. Numerical results demonstrate how the proposed model links the spare part logistics and equipment delivery decisions under spare part demand uncertainty. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Spare part logistics Third-party maintenance providers Uncertainty Non-stationary demand multi-stage stochastic programming

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Travel time resilience of roadway networks under disaster

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2014年 70卷 47-64页

作者： Faturechi, Reza Miller-Hooks, Elise Univ Maryland Dept Civil & Environm Engn College Pk MD 20742 USA

A bi-level, three-stage stochastic Mathematical Program with Equilibrium Constraints (SMPEC) is proposed for quantifying and optimizing travel time resilience in roadway networks under non-recurring natural or human-induced disaster events. At the upper-level, a sequence of optimal preparedness and response decisions is taken over pre-event mitigation and preparedness and post-event response stages of the disaster management life cycle. Assuming semi-adaptive user behavior exists shortly after the disaster and after the implementation of immediate response actions, the lower-level problem is formulated as a Partial User Equilibrium, where only affected users are likely to rethink their routing decisions. An exact Progressive Hedging Algorithm is presented for solution of a single-level equivalent, linear approximation of the SMPEC. A recently proposed technique from the literature that uses Schur's decomposition with SOS1 variables in creating a linear equivalent to complementarity constraints is employed. Similarly, recent advances in piecewise linearization are exploited in addressing nonseparable link travel time functions. The formulation and solution methodology are demonstrated on an illustrative example. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Disaster management lifecycle Mitigation multi-stage stochastic programming stochastic Mathematical Programs with Equilibrium Constraints (SMPEC) Partial User Equilibrium Piecewise linearization

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Optimization of power plant investments under uncertain renewable energy deployment paths: a multistage stochastic programming approach

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2014年第1期5卷 85-121页

作者： Fuersch, Michaela Nagl, Stephan Lindenberger, Dietmar Univ Cologne Inst Energy Econ D-50827 Cologne Germany

Electricity generation from renewable energy sources (RES-E) is planned to increase significantly within the coming decades. However, due to uncertainty about the progress of necessary infrastructure investments, public acceptance and cost developments of renewable energies, the achievement of political plans is uncertain. Implementation risks of renewable energy targets are challenging for investment planning, because different shares of RES-E fundamentally change the optimal mix of dispatchable power plants. Specifically, uncertain future RES-E deployment paths induce uncertainty about the level and the steepness of the residual load duration curve and the hourly residual load structure. In this paper, we show how uncertain future RES-E penetration levels impact the electricity system and try to quantify effects for the Central European power market. We use a multi-stage stochastic investment and dispatch model to analyze effects on investment choices, electricity generation and system costs. Our main findings include that uncertainty about the achievement of RES-E targets significantly affects optimal investment and dispatch decisions. In particular, plants with a medium capital/operating cost ratio have a higher value under uncertainty. We find that this technology choice is mainly driven by the uncertainty about the level rather than about the structure of the residual load. Furthermore, given larger investments in plants with medium capital/operating cost ratio under uncertainty, optimal investments in storage units are lower than under perfect foresight. In the case of the Central European power market, costs induced by the implementation risk of renewable energies are rather small compared to total system costs.

关键词： multi-stage stochastic programming Renewable energy Power plant optimization

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multi-stage scenario generation by the combined moment matching and scenario reduction method

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OPERATIONS RESEARCH LETTERS 2014年第5期42卷 374-377页

作者： Rubasheuski, Uladzimir Oppen, Johan Woodruff, David L. Molde Univ Coll N-6410 Molde Norway Univ Calif Davis Grad Sch Management Davis CA 95616 USA

We describe an opportunity to speed up multi-stage scenario generation and reduction using a combination of two well-known methods: the moment matching method (Hoyland and Wallace, 2001) and the method for scenario reduction to approximately minimize a metric (Heitsch and Romish, 2009). Our suggestion is to combine them rather than using them in serial by making use of a stage-wise approximation to the moment matching algorithm. Computational results show that combining the methods can bring significant benefits. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Scenario generation multi-stage stochastic programming Moment matching Scenario reduction

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On a multi-stage stochastic programming Model for Inventory Planning

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INFOR 2008年第3期46卷 155-163页

作者： Huang, Kai Ahmed, Shabbir Eindhoven Univ Technol Dept Technol Management NL-5600 MB Eindhoven Netherlands Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA

This paper considers a stochastic dynamic inventory problem involving a single item, linear cost structures, and finite distributions (but not necessarily independent) for the stochastic cost and demand parameters. We develop primal and dual algorithms for a multi-stage stochastic linear programming formulation for the problem. The complexity of the proposed algorithms is shown to be within O(N(2)), where N is the number of nodes in the scenario tree used to model the stochastic parameters.

关键词： multi-stage stochastic programming inventory control polynomial algorithm

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Financial planning for young households

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ANNALS OF OPERATIONS RESEARCH 2013年第1期205卷 55-76页

作者： Pedersen, Anne Marie B. Weissensteiner, Alex Poulsen, Rolf Nykredit Realkredit AS Copenhagen Denmark Free Univ Bozen Bolzano Bolzano Italy Univ Copenhagen Copenhagen Denmark

We analyze the financial planning problems of young households whose main decisions are how to finance the purchase of a house (liabilities) and how to allocate investments in pension savings schemes (assets). The problems are solved using a multi-stage stochastic programming model where the uncertainty is described by a scenario tree generated from a vector auto-regressive process for equity returns and interest rate evolution. We find strong evidence of the importance of taking into account the multi-stage nature of the problem, as well as the need to consider the asset and liability sides jointly.

关键词： Asset-liability management Mortgage Pension multi-stage stochastic programming Scenario tree Vector auto-regression

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Security-constrained transmission expansion planning: A stochastic multi-objective approach

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2012年第1期43卷 444-453页

作者： Akbari, Tohid Rahimi-Kian, Ashkan Bina, Mohammad Tavakoli KN Toosi Univ Technol Fac Elect Engn Tehran Iran Univ Tehran Coll Engn Sch ECE CIPCE Tehran 14174 Iran Univ Tehran Coll Engn Sch ECE Smart Networks Lab Tehran 14174 Iran

This article presents a stochastic multi-objective optimization framework for transmission expansion planning (TEP) with steady state voltage security management, using AC optimal power flow (AC-OPF). The objectives are to minimize the sum of transmission investment costs (ICs), minimize the Expected Operation Cost (EOC), minimize the Expected Load Shedding Cost (ELSC) and maximize the Expected Loading Factor (ELF). The system load uncertainty has been considered and the corresponding scenarios are generated employing the Monte Carlo (MC) simulations. A scenario reduction technique is applied to reduce the number of scenarios. A multi-objective mathematical programming (MMP) is formulated and the epsilon-constraint method is used to solve the formulated problem. The N - 1 contingency analysis is also considered for the proposed TEP problem. The proposed TEP model has been applied to the well-known IEEE 24-bus Reliability Test System. The detailed results of the case study are presented and thoroughly analyzed. The obtained TEP results show the efficiency of the proposed algorithm. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： AC-optimal power flow multi-objective mathematical programming multi-stage stochastic programming N-1 contingency analysis Security constrained transmission expansion planning (SC-TEP) Voltage stability margin

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Managing the financial risks of electricity producers using options

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ENERGY ECONOMICS 2012年第6期34卷 2216-2227页

作者： Pineda, S. Conejo, A. J. Univ Castilla La Mancha Dept Elect Engn E-13071 Ciudad Real Spain

Electricity producers participating in electricity markets face risks pertaining to both selling prices and the availability of the production units. Among electricity derivatives, options represent an adequate instrument to manage these risks. In this paper, we propose a multi-stage stochastic model to determine the optimal selling strategy of a risk-averse electricity producer including options, forward contracts, and pool trading. A detailed case study highlights the advantages of an option vs. a forward contract to hedge against the financial risks related to pool prices and unexpected unit failures. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Electricity market Option Forward contract Electricity producer Risk multi-stage stochastic programming

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