In this paper a portfolio optimization problem with bounded parameters is proposed taking into consideration the minimax risk measure, in which liquidity of the stocks is allied with selection of the portfolio. Interv...
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In this paper a portfolio optimization problem with bounded parameters is proposed taking into consideration the minimax risk measure, in which liquidity of the stocks is allied with selection of the portfolio. Interval uncertainty of the model is dealt with through a fusion between interval and random variable. As a result of this, the interval inequalities are converted to chance constraints. A solution methodology is developed using this concept to obtain an efficient portfolio. The theoretical developments are illustrated on a large data set taken from National Stock Exchange, India.
Practical scheduling problems typically require decisions without full information about the outcomes of those decisions. Yields, resource availability performance, demand, costs, and revenues may all vary. Incorporat...
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Practical scheduling problems typically require decisions without full information about the outcomes of those decisions. Yields, resource availability performance, demand, costs, and revenues may all vary. Incorporating these quantities into stochastic scheduling models often produces difficulties in analysis that may be addressed in a variety of ways. In this paper, we present results based on stochastic programming approaches to the hierarchy of decisions in typical stochastic scheduling situations. Our unifying framework allows us to treat all aspects of a decision in a similar framework. We show how views from different levels enable approximations that can overcome nonconvexities and duality gaps that appear in deterministic formulations. In particular, eve show that the stochastic program structure leads to a vanishing Lagrangian duality gap in stochastic integer programs as the number of scenarios increases.
When ocean transportation is used, possible disruptions both at sea and on land should be taken into account in the planning process of the affected supply chain. In this paper, a framework to enable flexible global s...
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When ocean transportation is used, possible disruptions both at sea and on land should be taken into account in the planning process of the affected supply chain. In this paper, a framework to enable flexible global supply chain operational planning in stochastic environments is presented. In order to cope with unexpected events like natural or man-made disasters, flexible international long-distance transportation modes and postponement strategies are taken into account in our supply chain model. In order to balance supply chain costs and the flexibility of supply chains, a two-stage multi-scenario stochastic programming model is developed where the stochastic events are represented by corresponding scenarios. High quality solutions of all our problem instances are generated by using a Python based stochastic programming framework to solve the model. Finally, managerial insights related to flexible supply chain planning in stochastic environments are derived from our computational results.
This paper addresses the optimal design and strategic planning of the integrated biofuel and petroleum supply chain system in the presence of pricing and quantity uncertainties. The drop-in properties of advanced hydr...
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This paper addresses the optimal design and strategic planning of the integrated biofuel and petroleum supply chain system in the presence of pricing and quantity uncertainties. The drop-in properties of advanced hydrocarbon biofuels pose considerable potential for biofuel supply chains to leverage the existing production and distribution infrastructures of petroleum supply chains, which may lead to significant capital savings. To achieve a higher modeling resolution and improve the overall economic performance, we explicitly model equipment units and material streams in the retrofitted petroleum processes and, propose a multi-period planning model to coordinate the various activities in the petroleum refineries. Furthermore, in order to develop an integrated supply chain that is reliable in the dynamic marketplace, we employ a stochastic programming approach to optimize the expectation under a number of scenarios associated with biomass availability, fuel demand, crude oil prices, and technology evolution. The integrated model is formulated as a stochastic mixed-integer linear program, which is illustrated by a case study involving 21 harvesting sites, 7 potential preconversion facilities, 6 potential integrated biorefineries, 2 petroleum refineries, and 39 demand zones. Results show the market share of biofuels increases gradually due to the increasing crude oil price and biomass availability.
From the point of view of a price-taking hydropower producer participating in the day-ahead power market, market prices are highly uncertain. The present paper provides a model for determining optimal bidding strategi...
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From the point of view of a price-taking hydropower producer participating in the day-ahead power market, market prices are highly uncertain. The present paper provides a model for determining optimal bidding strategies taking this uncertainty into account. In particular, market price scenarios are generated and a stochastic mixed-integer linear programming model that involves both hydropower production and physical trading aspects is developed. The idea is to explore the effects of including uncertainty explicitly into optimization by comparing the stochastic approach to a deterministic approach. The model is illustrated with data from a Norwegian hydropower producer and the Nordic power market at Nord Pool. (c) 2006 Elsevier B.V. All rights reserved.
This paper studies selective maintenance for multi-component systems that undergo consecutive missions with scheduled breaks after each mission. To increase the likelihood of mission success, maintenance activities ar...
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This paper studies selective maintenance for multi-component systems that undergo consecutive missions with scheduled breaks after each mission. To increase the likelihood of mission success, maintenance activities are performed on system components during the breaks. This study considers uncertainties in mission time and operating conditions. A two-stage stochastic programming approach is applied to model the uncertainties in the operating conditions of the next mission. Uncertainties in the operating conditions of the next mission affect the likelihood of successfully completing the mission, which may require reducing the mission time in worst-case scenarios. In the proposed two-stage model, the first stage involves making decisions on the maintenance actions required on selected components during the break. In the second stage, decisions are made regarding the completion or termination of the mission, and a penalty is assigned based on the probability of system failure during the next mission. The Sample Average Approximation algorithm, Wait-and-See, and Expected Value approaches are employed to demonstrate the efficiency of the optimal solution obtained from stochastic programming and to conduct large-scale analyses of the problem under various scenarios. Moreover, the effectiveness of the proposed model underscores the importance of incorporating uncertainty into the model.
In large cities, signalized intersections often become oversaturated in rush hours due to growing traffic demand. If not controlled properly, they may collectively result in serious congestion. How to schedule traffic...
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In large cities, signalized intersections often become oversaturated in rush hours due to growing traffic demand. If not controlled properly, they may collectively result in serious congestion. How to schedule traffic signals for oversaturated intersections has thus received increasing interests in recent years. Among various factors that may influence control performance, uncertainty in traffic demand remains as an important one that needs to be further studied. In some recent works, e.g., Yin (2008) and Li (2011), robust optimization models have been utilized to address uncertainty in traffic demand and to design fixed-timed signal control for oversaturated intersections. In this paper, we propose a stochastic programming (SP) model to schedule adaptive signal timing plans that minimize the expected vehicle delay. Our numerical experiments show that the proposed SP model better describes the fluctuations of traffic flows and outperforms the deterministic linear programming (LP) model in total vehicle delay, total throughput, and vehicle queue lengths. Moreover, we compare the proposed SP model with the adaptive signal control model proposed in Lin et al. (2011) to provide insights on such improvements from green time utilization and queue balancing perspectives. Furthermore, we study the feasibility of the proposed SP model in practice, with an emphasis on the required sample sizes. (C) 2015 Elsevier Ltd. All rights reserved.
MARKAL-Geneva is a system analysis model of energy and environment technology assessment for the Canton de Geneve in Switzerland. This model innovates by taking into account the uncertainties characterizing the scenar...
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MARKAL-Geneva is a system analysis model of energy and environment technology assessment for the Canton de Geneve in Switzerland. This model innovates by taking into account the uncertainties characterizing the scenarios through stochastic programming techniques. In stochastic programming, the whole set of scenarios is combined into an event tree, which describes the unfolding of uncertainties over the period of energy planning. The scenarios presented in this paper focus on the evaluation of efficient CO2 abatement policies and of the potential for demand-side management.
Forward contracts for electricity are valuable to consumers (suppliers) that wish to obtain (sell) power at prices that are more stable than those typically seen in electricity markets. Only a limited variety of forwa...
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Forward contracts for electricity are valuable to consumers (suppliers) that wish to obtain (sell) power at prices that are more stable than those typically seen in electricity markets. Only a limited variety of forward contracts are available on the market so the need is for a "custom" contract that meets a specific profile of electricity requirements (usually uncertain) over time. This paper develops stochastic programming models that can be used by the supplier of a custom contract to design a procurement strategy that minimizes its expected costs of supply in meeting contract obligations. The procurement strategy will consist of a mix of forwards available in the market, and, in each period, blending its own generation with spot purchases of power. The model also integrates spot selling of power. We consider that expected spot prices and forward prices may disagree since electricity is not storable, creating apparent arbitrage opportunities. We bound the transaction amounts to limit effects of apparent arbitrage and for consistency with the assumption of constant variable generation costs and market prices. For sample cases we compute the optimal procurement strategy, demonstrate the magnitude of the saving, and illustrate the sensitivity of this saving to the magnitude of the upper bounds on the allowed forward positions (a proxy for risk). (C) 2006 Wiley Periodicals, Inc.
In this work we present an optimization framework for shale gas well development and refracturing planning. This problem is concerned with if and when a new shale gas well should be drilled at a prospective location, ...
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In this work we present an optimization framework for shale gas well development and refracturing planning. This problem is concerned with if and when a new shale gas well should be drilled at a prospective location, and whether or not it should be refractured over its lifespan. We account for exogenous gas price uncertainty and endogenous well performance uncertainty. We propose a mixed-integer linear, two-stage stochastic programming model embedded in a moving horizon strategy to dynamically solve the planning problem. A generalized production estimate function is described that predicts the gas production over time depending on how often a well has been refractured, and when exactly it was restimulated last. From a detailed case study, we conclude that early in the life of an active shale well, refracturing makes economic sense even in low-price environments, whereas additional restimulations only appear to be justified if prices are high. (c) 2017 American Institute of Chemical Engineers
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