Modelling the effect of valve point loadings on the performance and cost of power generators for electricity dispatch problems is necessary. For the past 20 years, the development of computer based methods for the ide...
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Modelling the effect of valve point loadings on the performance and cost of power generators for electricity dispatch problems is necessary. For the past 20 years, the development of computer based methods for the identification of optimal designs have been based on a single model, introduced by Walters and Sheble (1993)[1]. This model approximates the non-monotonic incremental cost curve using a sine function. This note explores the properties of this model, highlighting one critical deficiency for use within an automated optimization based design system and proposes a new model. (C) 2011 Elsevier Ltd. All rights reserved.
In this paper, a converge-diverge supply network is considered. Under deterministic conditions, this network is modeled in a partially new framework as an MILP model. Some strategic aspects like supplier partnership c...
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In this paper, a converge-diverge supply network is considered. Under deterministic conditions, this network is modeled in a partially new framework as an MILP model. Some strategic aspects like supplier partnership consideration are presented as groups of constraint in the model. Then, the model is solved by a simple and flexible heuristic method and the results are compared with the results obtained from solving the model using ILOG CPLEX. (C) 2012 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.
This paper introduces an original planning model which integrates production, human resources and cash management decisions, taking into account the consequences that decisions in one area may have on other areas and ...
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This paper introduces an original planning model which integrates production, human resources and cash management decisions, taking into account the consequences that decisions in one area may have on other areas and allowing all these areas to be coordinated. The most relevant characteristics of the planning problem are: (1) production capacity is a non-linear function of the size of the staff;(2) firing costs may depend on the worker who is fired;(3) working time is managed under a working time account (WA) scheme, so positive balances must be paid to workers who leave the company;(4) there is a learning period for hired workers;and (5) cash management is included. A mixedintegerlinear program is designed to solve the problem. Despite the size and complexity of the model, it can be solved in a reasonable time. A numerical example, the main results of a computational experiment and a sensibility analysis illustrate the performance and benefits of the model. (C) 2011 Elsevier B.V. All rights reserved.
In this paper we propose a new heuristic framework, called Kernel Search, to solve the complex problem of portfolio selection with real features. The method is based on the identification of a restricted set of promis...
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In this paper we propose a new heuristic framework, called Kernel Search, to solve the complex problem of portfolio selection with real features. The method is based on the identification of a restricted set of promising securities (kernel) and on the exact solution of the MILP problem on this set. The continuous relaxation of the problem solved on the complete set of available securities is used to identify the initial kernel and a sequence of integer problems are then solved to identify further securities to insert into the kernel. We analyze the behavior of several heuristic algorithms as implementations of the Kernel Search framework for the solution of the analyzed problem. The proposed heuristics are very effective and quite efficient. The Kernel Search has the advantage of being general and thus easily applicable to a variety of combinatorial problems.
One of the main services of National Statistical Agencies (NSAs) for the current Information Society is the dissemination of large amounts of tabular data, which is obtained from microdata by crossing one or more cate...
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One of the main services of National Statistical Agencies (NSAs) for the current Information Society is the dissemination of large amounts of tabular data, which is obtained from microdata by crossing one or more categorical variables. NSAs must guarantee that no confidential individual information can be obtained from the released tabular data. Several statistical disclosure control methods are available for this purpose. These methods result in large linear, mixedintegerlinear, or quadratic mixedintegerlinear optimization problems. This paper reviews some of the existing approaches, with an emphasis on two of them: cell suppression problem (CSP) and controlled tabular adjustment (CTA). CSP and CTA have concentrated most of the recent research in the tabular data protection field. The particular focus of this work is on methods and results of practical interest for end-users (mostly, NSAs). Therefore, in addition to the resulting optimization models and solution approaches, computational results comparing the main optimization techniques - both optimal and heuristic - using real-world instances are also presented. (C) 2011 Elsevier B.V. All rights reserved.
This paper presents a novel strategy for speeding up the classical Benders decomposition for large-scale mixed integer linear programming problems. The proposed method is particularly useful when the optimality cut is...
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This paper presents a novel strategy for speeding up the classical Benders decomposition for large-scale mixed integer linear programming problems. The proposed method is particularly useful when the optimality cut is difficult to obtain. A ratio of distances from a feasible point to an infeasible point and a feasibility cut is used as a metric to determine the tightest constraint for the region located by the feasible point, thus improving the convergence rate. Application of the proposed approach to a multi-product batch plant scheduling problem shows substantial improvement both in the computational time and the number of iterations. (C) 2012 Elsevier Ltd. All rights reserved.
This paper presents a mixed-integerlinearprogramming (MILP) super-structure model for the optimal design of distributed energy generation systems that satisfy the heating and power demand at the level of a small nei...
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This paper presents a mixed-integerlinearprogramming (MILP) super-structure model for the optimal design of distributed energy generation systems that satisfy the heating and power demand at the level of a small neighborhood. The objective is the optimal selection of the system components among several candidate technologies (micro combined heat and power units, photovoltaic arrays, boilers, central power grid), including the optimal design of a heating pipeline network, that allows heat exchange among the different nodes. The objective function to be minimised contains the annualised overall investment cost and the annual operating cost of the system. We show that besides the usual energy balance and unit operations constraints, additional equations must be included in the model to guarantee correctness of the produced heating pipeline designs. A special instance of the problem where a single centralised combined heat and power unit is installed in the neighborhood is also considered. The efficiency of the proposed model is evaluated through illustrating examples. (C) 2012 Elsevier Ltd. All rights reserved.
We consider a biodiesel production company that collects waste vegetable oil from source points that generate waste in large amounts. The company uses the collected waste as raw material for biodiesel production. The ...
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We consider a biodiesel production company that collects waste vegetable oil from source points that generate waste in large amounts. The company uses the collected waste as raw material for biodiesel production. The manager of this company needs to decide which of the present source points to include in the collection program, which of them to visit on each day, which periodic routing schedule to repeat over an infinite horizon and how many vehicles to operate such that the total collection, inventory and purchasing costs are minimized while the production requirements and operational constraints are met. For this selective and periodic inventory routing problem, we propose two different formulations, compare them and apply the better performing one on a real-world problem with 36 scenarios. We generate lower bounds using a partial linear relaxation model, and observe that the solutions obtained through our model are within 3.28% of optimality on the average. Several insights regarding the customer selection, routing and purchasing decisions are acquired with sensitivity analysis.
Video surveillance systems are becoming increasingly important for crime investigation and deterrence, and the number of cameras installed in public space is increasing. However, many cameras installed at fixed positi...
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Video surveillance systems are becoming increasingly important for crime investigation and deterrence, and the number of cameras installed in public space is increasing. However, many cameras installed at fixed positions are required to observe a wide and complex area. In order to efficiently observe such a wide area at lower cost, mobile robots are an attractive option. In this paper, we propose a method for determining the traveling route of a mobile surveillance camera. Our method is based on mixed integer linear programming and obtains an optimum traveling route such that a camera with a certain visual angle and visual distance can observe the entire region at the shortest intervals. Through our experiments, we apply this method to several artificially generated data and data for a real university campus and demonstrate that effective patrol courses for specified mobile surveillance cameras can be generated.
This paper focuses on the flow shop sequence dependent group scheduling (FSDGS) problem with minimisation of total completion time as the criterion (Fm|f(mls), prmu, S-plk|Sigma C-j). The research problem is formulate...
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This paper focuses on the flow shop sequence dependent group scheduling (FSDGS) problem with minimisation of total completion time as the criterion (Fm|f(mls), prmu, S-plk|Sigma C-j). The research problem is formulated in form of two different mixed integer linear programming (MILP) models. Comparing with the latest MILP model for the proposed problem in the literature, the complexity size of the proposed models are significantly reduced. One of the proposed mathematical models is so effective that even medium-sized instances (problems up to 60 jobs in all groups) are solved to optimality in a reasonable amount of time. Moreover, a metaheuristic hybridising genetic and simulated annealing algorithm, called GSA, is proposed to solve the problems heuristically. All the results and analyses show the high performance of the proposed mathematical models as well as the proposed metaheuristic algorithm compared to the available ones in literature.
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