Two-stage Stochastic Problem Research Articles

Network Risk Limiting Dispatch: Optimal Control and Price of Uncertainty

Increased uncertainty due to high penetration of renewables imposes significant costs to the system operators. The added costs depend on several factors including market design, performance of renewable generation forecasting and the specific dispatch procedure. Quantifying these costs has been limited to small sample Monte Carlo approaches applied specific dispatch algorithms. The computational complexity and accuracy of these approaches has limited the understanding of tradeoffs between different factors. {In this work we consider a two-stage stochastic economic dispatch problem. Our goal is to provide an analytical quantification and an intuitive understanding of the effects of uncertainties and network congestion on the dispatch procedure and the optimal cost.} We first consider an uncongested network and calculate the risk limiting dispatch. In addition, we derive the price of uncertainty, a number that characterizes the intrinsic impact of uncertainty on the integration cost of renewables. Then we extend the results to a network where one link can become congested. Under mild conditions, we calculate price of uncertainty even in this case. We show that risk limiting dispatch is given by a set of deterministic equilibrium equations. The dispatch solution yields an important insight: congested links do not create isolated nodes, even in a two-node network. In fact, the network can support backflows in congested links, that are useful to reduce the uncertainty by averaging supply across the network. We demonstrate the performance of our approach in standard IEEE benchmark networks.

A two-stage stochastic transportation problem with fixed handling costs and a priori selection of the distribution channels

In this paper, a transportation problem comprising stochastic demands, fixed handling costs at the origins, and fixed costs associated with the links is addressed. It is assumed that uncertainty is adequately captured via a finite set of scenarios. The problem is formulated as a two-stage stochastic program. The goal is to minimize the total cost associated with the selected links plus the expected transportation and fixed handling costs. A prototype problem is initially presented which is then progressively extended to accommodate capacities at the origins and multiple commodities. The results of an extensive set of computational tests are reported and discussed.

Quantitative Stability Analysis of Stochastic Generalized Equations

We consider the solution of a system of stochastic generalized equations (SGE) where the underlying functions are mathematical expectation of random set-valued mappings. SGE has many applications such as characterizing optimality conditions of a nonsmooth stochastic optimization problem or equilibrium conditions of a stochastic equilibrium problem. We derive quantitative continuity of expected value of the set-valued mapping with respect to the variation of the underlying probability measure in a metric space. This leads to the subsequent qualitative and quantitative stability analysis of solution set mappings of the SGE. Under some metric regularity conditions, we derive Aubin's property of the solution set mapping with respect to the change of probability measure. The established results are applied to stability analysis of stochastic variational inequality, stationary points of classical one-stage and two-stage stochastic minimization problems, two-stage stochastic mathematical programs with equilibrium constraints, and stochastic programs with second order dominance constraints.

Metaheuristic procedure for a bi-objective supply chain design problem with uncertainty

We consider the design of a two echelon production distribution network with multiple manufacturing plants, distribution centers (DC’s) and a set of candidate warehouses. One of the main contributions of the study is to extend the existing literature by incorporating the demand uncertainty of DC’s within the warehouse location and transportation mode allocation decisions, as well as providing a network design satisfying the both economical and service quality objectives of the decision maker within two echelon supply network setting. In order to take into account the effects of the uncertainty we apply an scenario-based approach and a two-stage stochastic problem is formulated in order to minimize total cost and total service time, simultaneously. Another important contribution is the development of a solution procedure for this bi-objective stochastic problem by applying tabu search within the framework of Multi-objective Adaptive Memory Programming. Results are compared with the optimal Pareto fronts obtained for small instances using the ∊-constraint method and standard branch and bound techniques. Numerical results demonstrate the computational effectiveness of the algorithm proposed. Finally, we include some results that confirm the convenience of including the randomness in the demand parameter.

Medium-term trading strategy of a dominant electricity producer

This paper presents a multi-objective two-stage bilevel stochastic programming framework for a dominant electricity producer to determine an optimal trading strategy in a deregulated electricity spot market in a medium-term time horizon: at the first stage and upper level, the dominant producer aims at maximizing its expected market share and profit, while taking into account the trade-off between the two objectives, and at the second stage and lower level, the independent system operator determines the dispatches and power flows on an hourly basis after realization of uncertainty in market demand, by solving an optimization problem which aims at maximizing the total social welfare. Through Karush–Kuhn–Tucker conditions, the lower level problem is formulated as a complementarity problem and subsequently the dominant producer’s optimal decision making problem as a two-stage stochastic mathematical problem with equilibrium constraints (SMPEC). To solve the SMPEC, it is proposed to reformulate the SMPEC as a mixed integer linear program by representing the complementarity constraints as a system of mixed integer linear inequalities with binary variables. Numerical tests results are reported through a medium size case study based on Italian electricity market.

Two-stage stochastic equilibrium problems with equilibrium constraints: modeling and numerical schemes

This article presents a two-stage stochastic equilibrium problem with equilibrium constraints (SEPEC) model. Some source problems which motivate the model are discussed. Monte Carlo sampling method is applied to solve the SEPEC. Convergence analysis on the statistical estimators of Nash equilibria and Nash stationary points are presented.

A per-scenario bound for the two-stage stochastic facility location problem with linear penalty

In this paper, we consider the two-stage stochastic facility location problem with linear penalty. We develop an LP rounding algorithm with the first per-scenario constant approximation bound for this problem.

An Approximation Algorithm for the Risk-Adjusted Two-Stage Stochastic Facility Location Problem with Penalties

In this paper, we consider the risk-adjusted two-stage stochastic facility location problem with penalties (RSFLPP). Using the monotonicity and positive homogeneity of the risk measure function, we present an LP-rounding-based 6-approximation algorithm.

A scenario decomposition algorithm for 0–1 stochastic programs

We propose a scenario decomposition algorithm for stochastic 0–1 programs. The algorithm recovers an optimal solution by iteratively exploring and cutting-off candidate solutions obtained from solving scenario subproblems. The scheme is applicable to quite general problem structures and can be implemented in a distributed framework. Illustrative computational results on standard two-stage stochastic integer programming and nonlinear stochastic integer programming test problems are presented.

Price-Based Unit Commitment With Wind Power Utilization Constraints

This paper proposes an optimal bidding strategy for independent power producers (IPPs) in the deregulated electricity market. The IPPs are assumed to be price takers, whose objectives are to maximize their profits considering price and wind power output uncertainties, while ensuring high wind power utilization. The problem is formulated as a two-stage stochastic price-based unit commitment problem with chance constraints to ensure wind power utilization. In our model, the first stage decision includes unit commitment and quantity of electricity submitted to the day-ahead market. The second stage decision includes generation dispatch, actual usage of wind power, and amount of energy imbalance between the day-ahead and real-time markets. The chance constraint is applied to ensure a certain percentage of wind power utilization so as to comply with renewable energy utilization regulations. Finally, a sample average approximation (SAA) approach is applied to solve the problem, and the computational results are reported for the proposed SAA algorithm showing the sensitivity of the total profit as the requirement of wind power utilization changes.

Approximability of the two-stage stochastic knapsack problem with discretely distributed weights

In this paper the two-stage knapsack problem with random weights is studied under the aspect of approximability. We assume finite probability distributions for the weights and show that, unless P=NP, the so obtained problem cannot be approximated in polynomial time within a better ratio than K−1/2 (where K is the number of second-stage scenarios). We further study the special cases where in the second stage items can only be added or only be removed, but not both. Positive approximation results are given for three particular cases, namely linearly dependent first- and second-stage rewards, the polynomial scenario model and the case where the number of scenarios is assumed to be a constant. To the best of our knowledge, this is the first study of a two-stage knapsack problem under the aspect of approximability and the first time a non-approximability result has been proven for a stochastic knapsack problem of any kind.

Two-stage stochastic lot-sizing problem under cost uncertainty

For production planning problems, cost parameters can be uncertain due to marketing activities and interest rate fluctuation. In this paper, we consider a single-item two-stage stochastic lot-sizing problem under cost parameter uncertainty. Assuming cost parameters will increase or decrease after time period p each with certain probability, we minimize the total expected cost for a finite horizon problem. We develop an extended linear programming formulation in a higher dimensional space that can provide integral solutions by showing that its constraint matrix is totally unimodular. We also project this extended formulation to a lower dimensional space and obtain a corresponding extended formulation in the lower dimensional space. Final computational experiments demonstrate that the extended formulation is more efficient and performs more stable than the two-stage stochastic mixed-integer programming formulation.

Optimal policies in hybrid manufacturing/remanufacturing systems with random price-sensitive product returns

We study a production planning problem in which a manufacturer aims to meet a random market demand by manufacturing new product and remanufacturing returned product. The product returns are random and price-sensitive. To maximise his profit, the manufacturer needs to optimally decide on the acquisition price for the returned product as well as the quantities of the new product to be manufactured and the returned product to be remanufactured. We investigate two cases based on the relative lengths of the manufacturing and remanufacturing lead times. (1) In the case with a shorter manufacturing lead time, the manufacturer first decides his acquisition price for the returned product before the production starts, and then decides the quantities of manufacturing/remanufacturing after the product returns are realised. (2) In the case with a shorter remanufacturing lead time, the manufacturer first decides the manufacturing quantity and the acquisition price simultaneously, and then the remanufacturing quantity based on the quantity of manufactured and returned products. Each case is formulated as a two-stage stochastic optimisation problem, and the corresponding optimal polices of both are characterised and derived.

On greedy approximation algorithms for a class of two-stage stochastic assignment problems

In this paper we consider the two-stage stochastic linear assignment (2SSLA) problem, which is a stochastic extension of the classical deterministic linear assignment problem. For each agent and job, the decision maker has to decide whether to make assignments now or to wait for the second stage. Assignments of agents and jobs, for which decisions are delayed to the second stage, are then completed based on the scenario realized. We discuss two greedy approximation algorithms from the literature and derive a simple necessary optimality condition that generalizes the key ideas behind both of these approaches. Subsequently, based on this result we design a new greedy approximation method. Theoretical observations and the results of computational experiments are also presented.

Dynamic Slope Scaling Procedure for Stochastic Integer Programming Problem

Mathematical programming has been applied to various problems. For many actual problems, the assumption that the parameters involved are deterministic known data is often unjustified. In such cases, these data contain uncertainty and are thus represented as random variables, since they represent information about the future. Decision-making under uncertainty involves potential risk. Stochastic programming is a commonly used method for optimization under uncertainty. A stochastic programming problem with recourse is referred to as a two-stage stochastic problem. In this study, we consider a stochastic programming problem with simple integer recourse in which the value of the recourse variable is restricted to a multiple of a nonnegative integer. The algorithm of a dynamic slope scaling procedure for solving this problem is developed by using a property of the expected recourse function. Numerical experiments demonstrate that the proposed algorithm is quite efficient. The stochastic programming model defined in this paper is quite useful for a variety of design and operational problems. Keywords—stochastic programming problem with recourse, simple integer recourse, dynamic slope scaling procedure

A decomposition approach to the two-stage stochastic unit commitment problem

The unit commitment problem has been a very important problem in the power system operations, because it is aimed at reducing the power production cost by optimally scheduling the commitments of generation units. Meanwhile, it is a challenging problem because it involves a large amount of integer variables. With the increasing penetration of renewable energy sources in power systems, power system operations and control have been more affected by uncertainties than before. This paper discusses a stochastic unit commitment model which takes into account various uncertainties affecting thermal energy demand and two types of power generators, i.e., quick-start and non-quick-start generators. This problem is a stochastic mixed integer program with discrete decision variables in both first and second stages. In order to solve this difficult problem, a method based on Benders decomposition is applied. Numerical experiments show that the proposed algorithm can solve the stochastic unit commitment problem efficiently, especially those with large numbers of scenarios.

A so-called Cluster Benders Decomposition approach for solving two-stage stochastic linear problems

The optimization of stochastic linear problems, via scenario analysis, based on Benders decomposition requires appending feasibility and/or optimality cuts to the master problem until the iterative procedure reaches the optimal solution. The cuts are identified by solving the auxiliary submodels attached to the scenarios. In this work, we propose the algorithm named scenario Cluster Benders Decomposition (CBD) for dealing with the feasibility cut identification in the Benders method for solving large-scale two-stage stochastic linear problems. The scenario tree is decomposed into a set of scenario clusters and tighter feasibility cuts are obtained by solving the auxiliary submodel for each cluster instead of each individual scenario. Then, the scenario cluster based scheme allows to identify tighter feasibility cuts that yield feasible second stage decisions in reasonable computing time. Some computational experience is reported by using CPLEX as the solver of choice for the auxiliary LP submodels at each iteration of the algorithm CBD. The results that are reported show the favorable performance of the new approach over the traditional single scenario based Benders decomposition; it also outperforms the plain use of CPLEX for medium-large and large size instances.

Multi-service multi-facility network design under uncertainty

The problem of designing high speed networks using different modules of link capacities, in the same model, in order to meet uncertain demands obtained from different probability distribution functions (PDF) is a very hard and challenging real network design problem. The novelty of the new model, compared to previous ones, is to allow installing more than one module per link having equal or different capacities. Moreover, the scenarios of traffic can be generated, according to practical observations, from the main classes of uncertain demands (multi-service) simulated from different PDFs, including heavy tailed ones. These classes of traffic are considered simultaneously for the scenario generation, different from related works in the literature that use only one probability distribution function to simulate the scenarios of traffic. In this work we present the problem formulation and report computational results using branch-and-bound and L-shaped decomposition solution approaches. We consider in the same model up to three different types of modular capacities (multi-facility), since it seems that using more than this can lead to an intractable model. The objective is to minimize penalty (in case of unmet demands) and investment costs. We obtain confidence intervals (with 95% of covering rate) on the expected optimal solution value for the resulting two-stage stochastic integer-modular problem and discuss when they are meaningful. Numerical experiments show that our model can handle up to medium real size instances.

On service degrade at a discount: Capacity, demand pooling, and optimal discounting

In this paper we study the capacity investment decisions and operational strategies of a firm providing two-class services facing uncertain demands. The capacity decisions of the resources are made before demands are observed. Each service can be implemented by its corresponding resource. Should a mismatch between the capacity and the actual demand for the services arise, the low-class resource can be used as a substitute for the high-class service. We introduce an operational strategy called degrade-at-a-discount, where a price discount is offered to motivate customers to accept a lower class service when their original choice is out of capacity. By formulating the problem as a one-period, two-stage stochastic problem, we analyze how to set up the optimal capacity and the optimal discount. We also conduct a comprehensive numerical study to show the benefits of the degrade-at-a-discount strategy.

Convergence of Stationary Points of Sample Average Two-Stage Stochastic Programs: A Generalized Equation Approach

This paper presents an asymptotic analysis of a Monte Carlo method, variously known as sample average approximation (SAA) or sample path optimization (SPO), for a general two-stage stochastic minimization problem. We study the case when the second-stage problem may have multiple local optima or stationary points that are not global solutions and SAA is implemented using a general nonlinear programming solver that is only guaranteed to find stationary points. New optimality conditions are developed for both the true problem and its SAA problem to accommodate Karush-Kuhn-Tucker points. Because the optimality conditions are essentially stochastic generalized equations, the asymptotic analysis is carried out for the generalized equations first and then applied to optimality conditions. For this purpose, we analyze piecewise continuous (PC0) stochastic mappings to understand when their expectations are piecewise continuous and thereby derive exponential convergence of SAA. It is shown under moderate conditions that, with probability one, an accumulation point of the SAA stationary points satisfies a relaxed stationary condition for the true problem and further that, with probability approaching one exponentially fast with increasing sample size, a stationary point of SAA converges to the set of relaxed stationary points. These results strengthen or complement existing results where the second-stage problem is often assumed to have a unique solution and the exponential convergence is focused on how fast a solution of the true problem becomes an approximate solution of an SAA problem rather than the other way around.