This paper presents a vendor selection model for buyers practicing Just-in-time management strategy. Five vendor evaluation criteria including quality, delivery, net price, geographical location, and production capaci...
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This paper presents a vendor selection model for buyers practicing Just-in-time management strategy. Five vendor evaluation criteria including quality, delivery, net price, geographical location, and production capacity are utilized for the selection of vendors and determination of associated purchasing quantities. A soft time window mechanism is incorporated to allow purchased parts been delivered within a grace period of time with a specified penalty function. A mixed-integer programming model is developed for this vendor selection problem. The proposed model encourages the buyer to select vendors capable of providing quality in-time products which will minimize the total cost of purchasing, quality, transportation, and penalty charge for violating the delivery time window. An illustrative example is given to demonstrate the implementation of the time-window model.
Abstract In this paper, a transmission expansion planning formulation is proposed that simultaneously considers investment in phase shifters and in primary network assets, such as lines and transformers. Based on the ...
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Abstract In this paper, a transmission expansion planning formulation is proposed that simultaneously considers investment in phase shifters and in primary network assets, such as lines and transformers. Based on the classical network expansion planning formulation, a new mixed-integer linear programming (MILP) formulation is built to deal with the placement of phase shifter along with adding new branches, while the N-1 security criterion is considered. The curtailment of wind farm output is also discussed. The whole problem is solved by Benders’ decomposition method. Simulations have been done on the Garver 6-bus system. The planning schemes obtained from the proposed method showing cost reduction offered by phase shifters over the traditional circuit expansion.
The purpose of this erratum is to correct the computational results reported in [M. Di Summa and L. A. Wolsey, SIAM J. Discrete Math., 24 (2010), pp. 853–875].
The purpose of this erratum is to correct the computational results reported in [M. Di Summa and L. A. Wolsey, SIAM J. Discrete Math., 24 (2010), pp. 853–875].
With increasing wind farm integrations, unit commitment (UC) is more difficult to solve because of the intermittent and random nature of wind power outputs. A robust optimization model for UC is built to deal with the...
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ISBN:
(纸本)9781457710001
With increasing wind farm integrations, unit commitment (UC) is more difficult to solve because of the intermittent and random nature of wind power outputs. A robust optimization model for UC is built to deal with the errors on wind power predictions. The robust optimization method is based on scenario analysis, while the probability of each selected scenario is derived mathematically. In the proposed UC formulation, spinning reserve requirement is specially considered to support possible wind power change between any two successive periods. Network security constraints are considered by DC power flow equations and the whole UC formulation is a mixed-integer programming problem. Case studies on the IEEE 30-bus system demonstrate the effectiveness of the proposed method. The influences of wind power on UC results and network security are also discussed.
This paper presents a mathematical programming based clustering approach that is applied to a digital platform company's customer segmentation problem involving demographic and transactional attributes related to ...
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This paper presents a mathematical programming based clustering approach that is applied to a digital platform company's customer segmentation problem involving demographic and transactional attributes related to the customers. The clustering problem is formulated as a mixed-integer programming problem with the objective of minimizing the maximum cluster diameter among all clusters. In order to overcome issues related to computational complexity of the problem, we developed a heuristic approach that improves computational times dramatically without compromising from optimality in most of the cases that we tested. The performance of this approach is tested on a real problem. The analysis of our results indicates that our approach is computationally efficient and creates meaningful segmentation of data. (c) 2005 Elsevier B.V. All rights reserved.
Decomposition has proved to be one of the more effective tools for the solution of large-scale problems, especially those arising in stochastic programming. A decomposition method with wide applicability is Benders...
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Decomposition has proved to be one of the more effective tools for the solution of large-scale problems, especially those arising in stochastic programming. A decomposition method with wide applicability is Benders' decomposition, which has been applied to both stochastic programming as well as integerprogramming problems. However, this method of decomposition relies on convexity of the value function of linear programming subproblems. This paper is devoted to a class of problems in which the second-stage subproblem(s) may impose integer restrictions on some variables. The value function of such integer subproblem(s) is not convex, and new approaches must be designed. In this paper, we discuss alternative decomposition methods in which the second-stage integer subproblems are solved using branch-and-cut methods. One of the main advantages of our decomposition scheme is that Stochastic mixed-integer programming (SMIP) problems can be solved by dividing a large problem into smaller MIP subproblems that can be solved in parallel. This paper lays the foundation for such decomposition methods for two-stage stochastic mixed-integer programs.
This paper presents a new data classification method based on mixed-integer programming. Traditional approaches that are based on partitioning the data sets into two groups perform poorly for multi-class data classifi...
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This paper presents a new data classification method based on mixed-integer programming. Traditional approaches that are based on partitioning the data sets into two groups perform poorly for multi-class data classification problems. The proposed approach is based on the use of hyper-boxes for defining boundaries of the classes that include all or some of the points in that set. A mixed-integer programming model is developed for representing existence of hyper-boxes and their boundaries. In addition, the relationships among the discrete decisions in the model are represented using propositional logic and then converted to their equivalent integer constraints using Boolean algebra. The proposed approach for multi-class data classification is illustrated on an example problem. The efficiency of the proposed method is tested on the well-known IRIS data set. The computational results on the illustrative example and the IRIS data set show that the proposed method is accurate and efficient on multi-class data classification problems. (c) 2005 Elsevier B.V. All rights reserved.
The modern approach of delivering radiation treatments through intensity modulated radiotherapy (IMRT) requires a computationally complex planning process. Intensities must be chosen for the many small unit grids into...
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Energy consumption in commercial buildings accounts for a significant proportion of worldwide energy consumption. Any increase in the energy efficiency of the energy systems for commercial buildings would lead to sign...
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Energy consumption in commercial buildings accounts for a significant proportion of worldwide energy consumption. Any increase in the energy efficiency of the energy systems for commercial buildings would lead to significant energy savings and emissions reductions. In this work, we introduce an energy systems engineering framework towards the optimal design of such energy systems with improved energy efficiency and environmental performance. The framework features a superstructure representation of the various energy technology alternatives, a mixed-integer optimization formulation of the energy systems design problem, and a multi-objective design optimization solution strategy, where economic and environmental criteria are simultaneously considered and properly traded off. A case study of a supermarket energy systems design is presented to illustrate the key steps and potential of the proposed energy systems engineering approach. (c) 2010 Elsevier Ltd. All rights reserved.
Selection of supply contracts is a critical decision faced by manufacturing firms in a variety of industries. Manufacturing firms often have the option of selecting from several types of supply contracts that include ...
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Selection of supply contracts is a critical decision faced by manufacturing firms in a variety of industries. Manufacturing firms often have the option of selecting from several types of supply contracts that include long-term, medium-term, and short-term contracts. While extant literature has stressed the importance of such contracts, few methodologies have been proposed for optimally selecting contracts under various business conditions. To this end, this paper proposes a methodology for optimal contract selection based on a mixed-integer programming approach. We present specific insights to manufacturing managers on choosing the right contracts in the presence of market price uncertainty, supplier discounts, investment costs, and supplier capacity restrictions.
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