This article is about the current agricultural scenario, where large-scale production causes large amounts of food to be transported to various points of consumption, causing substantial emissions of so-called greenho...
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This article is about the current agricultural scenario, where large-scale production causes large amounts of food to be transported to various points of consumption, causing substantial emissions of so-called greenhouse gases and increasing the carbon footprint. Land use optimization and land parcel allocation are essential areas of agriculture research that currently represent relevant challenges and are classified as combinatorial optimization problems. In this context, the Selection and Allocation of Land Parcels Problem (SA-LPP) is proposed;its goal is to optimize the selection and allocation of land parcels with rectangular shapes in small areas available for food production. We propose a reformulation of the SA-LPP as a variant of the two-dimensional orthogonal packing problem (2OPP), called Group-2OPP. This problem was solved through a mixed-integerlinearprogramming (MILP) model, but due to the model complexity, we also propose a Greedy Randomized Adaptive Search Procedure metaheuristic approach. Some sensitivity analyses were performed as well to evaluate the impact of parameters on the solutions. Computational results show that the proposed metaheuristic outperforms the MILP model in terms of solution quality and computational times.
The cyclotomic polynomial (CP) prefilter design problem is formulated as an optimization problem with linear objective functions by applying the logarithm to the transfer function of the CP prefilter. Then this proble...
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ISBN:
(纸本)0780331923
The cyclotomic polynomial (CP) prefilter design problem is formulated as an optimization problem with linear objective functions by applying the logarithm to the transfer function of the CP prefilter. Then this problem is solved by mixed integer linear programming (MILP). Design examples demonstrate that this method leads to more efficient cascaded FIR prefilter-equalizers than existing methods.
To remain competitive, the modern industry strive for flexibility. Recently, a method for automatic generation of control code from a 3D simulation model of a flexible manufacturing system was developed. Finite automa...
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To remain competitive, the modern industry strive for flexibility. Recently, a method for automatic generation of control code from a 3D simulation model of a flexible manufacturing system was developed. Finite automata and supervisory control theory (SCT) were used to guarantee the required behaviour of the system. This paper moves one step further. A method for automatic conversion between deterministic finite automata and mixed integer linear programming (MILP) formulation is presented. This allows to efficiently combine SCT and MILP to automatically generate time-optimal, collision-free and non-blocking working schedules
This paper addresses a blending and shipping problem faced by a company that manages a wheat supply chain. The problem involves the delivery of bulk products from loading ports to destination ports, which may be serve...
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This paper addresses a blending and shipping problem faced by a company that manages a wheat supply chain. The problem involves the delivery of bulk products from loading ports to destination ports, which may be served by different vessel types. Since the products demanded by customers are mainly exported in bulk to overseas customers, the shipment planning is of great economic importance. The problem is formulated as a mixed-integerlinearprogramming model. The objective function seeks to minimize the total cost including the blending, loading, transportation and inventory costs. Constraints on the system include blending and demand requirements, availability of original and blended products;as well as blending, loading, draft and vessel capacity restrictions. When solved, the model produces: (1) the quantity of each original product to be used to make blended products, (2) the quantity of each product to be loaded at each port and to be transported from each port to each customer, and (3) the number of vessels of each type to be hired in each time period. Numerical results are presented to demonstrate the feasibility of the real world bulk grain blending and shipping model. (C) 2006 Elsevier B.V. All rights reserved.
Electric power systems are prone to disturbances and contingencies, which can trigger cascading failures with severe consequences for society. These undesirable events could disintegrate the electrical infrastructure ...
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Electric power systems are prone to disturbances and contingencies, which can trigger cascading failures with severe consequences for society. These undesirable events could disintegrate the electrical infrastructure in areas with disconnected elements. In this article, we propose a novel procedure to restore a collapsed power grid composed of multiple islands and isolated assets. The framework developed identifies the power lines to be closed during the electrical network recovery stages. In the latter, link overload limits and generation thresholds are taken into account. Two disintegrated networks based on the well-known IEEE 57-bus test system are built to demonstrate the performance of our proposal. In summary, this methodology provides a solution to recover the power system optimally and reliably.
This work considers the optimal scheduling problem for a campus central plant equipped with a bank of multiple electrical chillers and a thermal energy storage (TES). Typically, the chillers are operated in ON/OFF mod...
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This work considers the optimal scheduling problem for a campus central plant equipped with a bank of multiple electrical chillers and a thermal energy storage (TES). Typically, the chillers are operated in ON/OFF modes to charge TES and supply chilled water to satisfy the campus cooling demands. A bilinear model is established to describe the system dynamics of the central plant. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity cost. At each time step, the MPC problem is represented as a large-scale mixed-integer nonlinearprogramming problem. We propose a heuristic algorithm to obtain suboptimal solutions for it via dynamic programming (DP) and mixed integer linear programming (MILP). The system dynamics is linearized along the simulated trajectories of the system. The optimal TES operation profile is obtained by solving a DP problem at every horizon, and the optimal chiller operations are obtained by solving an MILP problem at every time step with a fixed TES operation profile. Simulation results show desired performance and computational tractability of the proposed algorithm. Note to Practitioners-This work was motivated by the supervisory control need for a campus central plant. Plant operators have to decide a scheduling strategy to mix and match various chillers with a thermal energy storage to satisfy the campus cooling demands, while minimizing the operation cost. This work mathematically characterizes the system dynamics of a campus central plant and establishes a linear model to predict campus cooling load. It proposes a model predictive control (MPC) strategy to optimally schedule the campus central plant based on plant system dynamics and predicted campus cooling load. A heuristic algorithm is proposed to obtain suboptimal solutions for the MPC problem. The effectiveness and efficiency of the proposed approach are well demonstrated for the central pl
This paper intends to address the distributed flexible job shop scheduling problem (DFJSP) with minimizing maximum completion time (makespan). In order to solve this problem, we propose four mixedintegerlinear progr...
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This paper intends to address the distributed flexible job shop scheduling problem (DFJSP) with minimizing maximum completion time (makespan). In order to solve this problem, we propose four mixed integer linear programming (MILP) models as well as a constraint programming (CP) model, among which four MILP models are formulated based on four different modeling ideas. MILP models are effective in solving small-scaled problems to optimality. DFJSP is NP-hard, therefore, we propose an efficient constraint programming (CP) model based on interval decision variables and domain filtering algorithms. Numerical experiments are conducted to evaluate the performance of the proposed MILP models and CP model. The results show that the sequence-based MILP model is the most efficient one, and the proposed CP model is effective in finding good quality solutions for the both the small-sized and large-sized instances. The CP model incomparably outperforms the state-of-the-art algorithms and obtains new best solutions for 11 benchmark problems. Moreover, the best MILP model and CP model have proved the optimality of 62 best-known solutions.
This paper presents a practical mixed integer linear programming (MILP) based approach for unit commitment (UC), which is suitable for both traditional and deregulated environments. In general, the UC problem is compl...
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This paper presents a practical mixed integer linear programming (MILP) based approach for unit commitment (UC), which is suitable for both traditional and deregulated environments. In general, the UC problem is complicated large-scale, combinatorial, and non-convex in nature, it is very difficult to solve by conventional approaches to achieve both solution accuracy and efficiency. With the recent development of solution techniques, there is a trend to tackle the UC problem by using MILP approaches. In this paper the authors propose a detailed procedure to formulate the UC problem in MILP manners. The problems are then solved via a state-of-the-art optimization package. The usefulness of the proposed solution technique is illustrated by testing the problem with actual system data. The solution obtained not only gives the unit on/off states and MW schedules, but also provides marginal price information associated with system constraints such as load demand requirement to assist strategic bidding in the power market.
This paper deals with the Steelmaking and Casting phases of a real integrated plant. We focus on the scheduling of the casts on the machines of the process and on the cranes that perform the internal transport. The ca...
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ISBN:
(纸本)9781467357159
This paper deals with the Steelmaking and Casting phases of a real integrated plant. We focus on the scheduling of the casts on the machines of the process and on the cranes that perform the internal transport. The casting phase is made by Continuous Casting and Ingot Casting machines. A mixed integer linear programming model is presented for the schedule of operations, with the objective of minimizing the makespan. The model is divided into four sub-models: the steelmaking and casting processes, the cranes, the continuous casting machines and the ingot casting machines. An example is presented to show the application of the proposed model.
In this paper a functional vector generation method to maximize the data path coverage of a combinational circuit is introduced. We present a new gate model based on sensitization requirements for transition propagati...
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In this paper a functional vector generation method to maximize the data path coverage of a combinational circuit is introduced. We present a new gate model based on sensitization requirements for transition propagation, and introduce a new methodology to obtain functional vectors of maximum coverage based on mixed integer linear programming (MILP). Performance comparison and results based on a large set of MCNC'91 benchmark circuits are given. Experimental results show significant speedups over a greedy SAT method.
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