This paper presents a heuristic rule-based genetic algorithm (GA) for large-size single-stage multi-product scheduling problems (SMSP) in batch plants with parallel units. SMSP have been widely studied by the research...
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This paper presents a heuristic rule-based genetic algorithm (GA) for large-size single-stage multi-product scheduling problems (SMSP) in batch plants with parallel units. SMSP have been widely studied by the researchers. Most of them used mixed-integer linear programming (MILP) formulation to solve the problems. With the problem size increasing, the computational effort of MILP increases greatly. Therefore, it is very difficult for MILP to obtain acceptable solutions to large-size problems within reasonable time. To solve large-size problems, the preferred method in industry is the use of scheduling rules. However, due to the constraints in SMSP, the simple rule-based method may not guarantee the feasibility and quality of the solution. In this study, a random search based on heuristic rules was proposed first. Through exploring a set of random solutions, better feasible solutions can be achieved. To improve the quality of the random solutions, a genetic algorithm-based on heuristic rules has been proposed. The heuristic rules play a very important role in cutting down the solution space and reducing the search time. Through comparative study, the proposed method demonstrates promising performance in solving large-size SMSP. (C) 2008 Elsevier Ltd. All rights reserved.
In an electricity market cleared by a merit-order economic dispatch we make use of the mixed-integer linear programming (MILP) scheme derived in Part I to find the market outcomes supported by a pure strategy Nash equ...
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In an electricity market cleared by a merit-order economic dispatch we make use of the mixed-integer linear programming (MILP) scheme derived in Part I to find the market outcomes supported by a pure strategy Nash equilibria (NE). From these NE, we identify offer strategies in terms of gaming or not gaming that best meet the risk/benefit expectations of the participating Gencos. To do this, a number of measures of potential profit gain and loss are developed that quantify the notion of risk/benefit under the possible multiple NE. The NE identification scheme is tested on several systems of up to 30 generating units, each with four incremental cost blocks, also showing how market power is influenced by the number and size of the competing Gencos as well as by the imposed price cap.
The paper deals with a unit commitment problem of a generation company whose aim is to find the optimal scheduling of a multiunit pump-storage hydro power station, for a short term period in which the electricity pric...
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The paper deals with a unit commitment problem of a generation company whose aim is to find the optimal scheduling of a multiunit pump-storage hydro power station, for a short term period in which the electricity prices are forecasted. The problem has a mixed-integer nonlinear structure, which makes very hard to handle the corresponding mathematical models. However, modern mixed-integer linear programming (MILP) software tools have reached a high efficiency, both in terms of solution accuracy and computing time. Hence we introduce MILP models of increasing complexity, which allow to accurately represent most of the hydroelectric system characteristics, and turn out to be computationally solvable. In particular we present a model that takes into account the head effects on power production through an enhanced linearization technique, and turns out to be more general and efficient than those available in the literature. The practical behavior of the models is analyzed through computational experiments on real-world data.
This paper proposes a new inventory control system called the inventory/distribution plan (IDP) control system for a one-warehouse/multi-retailer supply chain. In the IDP control system, a proposed mixed-integer linea...
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This paper proposes a new inventory control system called the inventory/distribution plan (IDP) control system for a one-warehouse/multi-retailer supply chain. In the IDP control system, a proposed mixed-integer linear programming model is solved to determine an optimal IDP that controls the inventories of the supply chain. The efficiency of the IDP control system is compared to that of the echelon-stock R,s,S control policy, where R is a periodic review interval, s is a reorder point, and S is an order-up-to level, at various fill rates. The experimental results show that when the system faces non-stationary demands, the IDP control system significantly outperforms the echelon-stock R,s,S control system because it can give lower total costs for all ranges of fill rates. (C) 2008 Elsevier B.V. All rights reserved.
This paper addresses the self-scheduling problem of determining the unit commitment status for power generation companies before submitting the hourly bids in a day-ahead market. The hydrothermal model is formulated a...
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This paper addresses the self-scheduling problem of determining the unit commitment status for power generation companies before submitting the hourly bids in a day-ahead market. The hydrothermal model is formulated as a deterministic optimization problem where expected profit is maximized using the 0/1 mixed-integer linear programming technique. This approach allows precise modelling of non-convex variable cost functions and non-linear start-up cost functions of thermal units, non-concave power-discharge characteristics of hydro units, ramp rate limits of thermal units and minimum up and down time constraints for both hydro and thermal units. Model incorporates long-term bilateral contracts with contracted power and price patterns, as well as forecasted market hourly prices for day-ahead auction. Solution is achieved using the homogeneous interior point method for linearprogramming as state of the art technique, with a branch and bound optimizer for integerprogramming. The effectiveness of the proposed model in optimizing the generation schedule is demonstrated through the case studies and their analysis. (C) 2008 Elsevier B.V. All rights reserved.
This paper presents a new robust approach to the task assignment of unmanned aerial vehicles (UAVs) operating in uncertain dynamic environments for which the optimization data, such as target cost and target-UAV dista...
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This paper presents a new robust approach to the task assignment of unmanned aerial vehicles (UAVs) operating in uncertain dynamic environments for which the optimization data, such as target cost and target-UAV distances, are time varying and uncertain. The impact of this uncertainty in the data is mitigated by tightly integrating two approaches for improving the robustness of the assignment algorithm. One approach is to design task assignment plans that are robust to the uncertainty in the data, which reduces the sensitivity to errors in the situational awareness (SA), but can be overly conservative for long duration plans. A second approach is to replan as the SA is updated, which results in the best plan given the current information, but can lead to a churning type of instability if the updates are performed too rapidly. The strategy proposed in this paper combines robust planning with the techniques developed to eliminate churning. This combination results in the robust filter-embedded task assignment algorithm that uses both proactive techniques that hedge against the uncertainty, and reactive approaches that limit churning behavior by the vehicles. Numerous simulations are shown to demonstrate the performance benefits of this new algorithm. Copyright (c) 2007 John Wiley & Sons, Ltd.
In an electricity market cleared by a merit-order economic dispatch we first identify the necessary conditions for the market outcomes supported by pure strategy Nash equilibria (NE) to exist when generating companies...
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In an electricity market cleared by a merit-order economic dispatch we first identify the necessary conditions for the market outcomes supported by pure strategy Nash equilibria (NE) to exist when generating companies (Gencos) game through their incremental cost offers or supply functions. A Genco may own any number of units, each offering to generate power through an incremental cost curve or supply function consisting of multiple blocks. Then, we develop a mixed-integer linear programming (MILP) scheme to find the NE without approximations or iterations. In Part II of this paper, we show how to use these NE to derive a dominant offer strategy in terms of gaming or not gaming that best meet the risk/benefit expectations of the participating Gencos. The MILP scheme is tested on several systems of up to 30 generating units, each with four incremental cost blocks. Finally, based on these results, we carry out a number of numerical analyses of how market power is influenced by the number and size of the competing Gencos.
Many practical problems of interest in chemical engineering and other fields can be formulated as bilinear programs (BLPs). For such problems, a local nonlinearprogramming solver often provides a suboptimal solution ...
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Many practical problems of interest in chemical engineering and other fields can be formulated as bilinear programs (BLPs). For such problems, a local nonlinearprogramming solver often provides a suboptimal solution or even fails to locate a feasible one. Numerous global optimization algorithms devised for bilinear programs rely on linearprogramming (LP) relaxation, which is often weak, and, thus, slows down the convergence rate of the global optimization algorithm. All interesting recent development is the idea of using an ab initio partitioning of the. search domain to improve the relaxation quality, which results in a relaxation problem that is a mixed-integerlinear program (MILP) rather than LP, called as piecewise MILP relaxation. However, much work is in order to fully exploit the potential of such approach. Several novel formulations are developed for piecewise MILP under- and overestimators for BLPs via three systematic approaches, and two segmentation schemes. As is demonstrated and evaluated the superiority of the novel models is shown, using a variety of examples. In addition, metrics are defined to measure the effectiveness of piecewise MILP relaxation within a two-level-relaxation framework, and several theoretical results are presented, as well as valuable insights into the properties of such relaxations, which may prove useful in developing global optimization algorithms. (c) 2008 American Institute of Chemical Engineers.
Protein structure determination and prediction has been a focal research subject in life sciences due to the importance of protein structure in understanding the biological and chemical activities of organisms. The ex...
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Protein structure determination and prediction has been a focal research subject in life sciences due to the importance of protein structure in understanding the biological and chemical activities of organisms. The experimental methods used to determine the structures of proteins demand sophisticated equipment and time. A host of computational methods are developed to predict the location of secondary structure elements in proteins for complementing or creating insights into experimental results. However, prediction accuracies of these methods rarely exceed 70%. In this paper, a novel two-stage method to predict the location of secondary structure elements in a protein using the primary structure data only is presented. In the first stage of the proposed method, the folding type of a protein is determined using a novel classification approach for multi-class problems. The second stage of the method utilizes data available in the Protein Data Bank and determines the possible location of secondary structure elements in a probabilistic search algorithm. It is shown that the average accuracy of the predictions is 74.1 % on a large structure dataset. (C) 2007 Elsevier Ltd. All rights reserved.
The increasing variety of products offered by the food industry has helped the industry to respond to market trends, but at the same time has resulted in a more complex production process, which requires flexibility a...
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The increasing variety of products offered by the food industry has helped the industry to respond to market trends, but at the same time has resulted in a more complex production process, which requires flexibility and an efficient coordination of existing resources. Especially in industrial yogurt production, there is a wide variety of products that differ in features like fat content, the whey used to produce the mixture, the flavor, the size of the container or the language on the label. The great diversification and the special features that characterize yogurt production lines (satisfaction of multiple due dates, variable processing times, sequence-dependent setup times and costs and monitoring of inventory levels), render generic scheduling methodologies impractical for real-world applications. In this work we present a customized mixedintegerlinearprogramming (MILP) model for optimizing yogurt packaging lines that consist of multiple parallel machines. The model is characterized by parsimony in the utilization of binary variables and necessitates the use of only a small pre-determined number of time periods. The efficiency of the proposed model is illustrated through its application to the yogurt production plant of a leading dairy product manufacturing company in Greece.
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