We present a mixed integer linear programming (MILP) approach in order to model the non-linear problem of minimizing the tire noise function. In a recent work, we proposed an exact solution for the Tire Noise Optimiza...
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We present a mixed integer linear programming (MILP) approach in order to model the non-linear problem of minimizing the tire noise function. In a recent work, we proposed an exact solution for the Tire Noise Optimization Problem, dealing with an APproximation of the noise (TNOP-AP). Here we study the original non-linear problem modeling the EXact- or real-noise (TNOP-EX) and propose a new scheme to obtain a solution for the TNOP-EX. Relying on the solution for the TNOP-AP, we use a Branch&Cut framework and develop an exact algorithm to solve the TNOP-EX. We also take more industrial constraints into account. Finally, we compare our experimental results with those obtained by other methods.
This paper proposes several schemes for optimal scheduling of power producers in a shipboard power system for a typical offshore supply vessel, having a multiple number of possibly varying capacity gensets. The propos...
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This paper proposes several schemes for optimal scheduling of power producers in a shipboard power system for a typical offshore supply vessel, having a multiple number of possibly varying capacity gensets. The proposed scheduling methods are illustrated using four alternative power system configurations, ranging from a few large gensets to many gensets of smaller ratings. mixed integer linear programming is used to formulate the optimization problems. Three formulations are presented: one for minimizing online capacity without further objectives, one that includes a redundancy constraint for loosing a group of gensets (to account for a worst-case failure scenario), and one to also balance running hours and minimizing connect/disconnect of the gensets. These different objectives can be combined and weighted based on importance, with or without redundancy constraints. Simulations are carried out to demonstrate the properties of the three different scheduling methods. The first method, minimizing online capacity only, is also used to illustrate the differences between the four genset configurations. This shows, for instance, that using more small gensets ensures generally a lower online available power (the connected capacity matches better the prevailing load) and near optimal loading of each genset around 80% (assuming equal loadsharing), at the same time as resilience to genset failures is preserved.
The growing complexity of power system operation demands a greater performance of the modeling approaches, even for generation technologies already consolidated as hydroelectricity. Five mixedintegerlinear programmi...
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The growing complexity of power system operation demands a greater performance of the modeling approaches, even for generation technologies already consolidated as hydroelectricity. Five mixed integer linear programming formulations for modeling the hydro production function (HPF) are compared: four based on previous references (the traditional method based on a single concave piecewise linear flow-power function, the rectangle method, the logarithmic independent branching 6-stencil method, and the quadrilateral method), and one firstly presented in the paper (the parallelogram method). The comparison is made in the context of daily and weekly hydro scheduling problems of a hypothetical three-plant system on different national day-ahead markets and using different levels of detail in the HPF discretization and time limits. The discussion of results is focused around the relative accuracy, effectiveness, and speed of the analyzed methods to solve the scheduling problems in order to aid in making the most appropriate choice depending on the time horizon. This discussion shows the logarithmic independent branching 6-stencil method as one of the most accurate, the parallelogram method as one of the most effective, and the traditional method based on a single concave piecewise linear flow-power function as the fastest one.
A standard quadratic program is an optimization problem that consists of minimizing a (nonconvex) quadratic form over the unit simplex. We focus on reformulating a standard quadratic program as a mixedintegerlinear ...
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A standard quadratic program is an optimization problem that consists of minimizing a (nonconvex) quadratic form over the unit simplex. We focus on reformulating a standard quadratic program as a mixed integer linear programming problem. We propose two alternative formulations. Our first formulation is based on casting a standard quadratic program as a linear program with complementarity constraints. We then employ binary variables to linearize the complementarity constraints. For the second formulation, we first derive an overestimating function of the objective function and establish its tightness at any global minimizer. We then linearize the overestimating function using binary variables and obtain our second formulation. For both formulations, we propose a set of valid inequalities. Our extensive computational results illustrate that the proposed mixed integer linear programming reformulations significantly outperform other global solution approaches. On larger instances, we usually observe improvements of several orders of magnitude.
The Traveling Salesman Problem (TSP) is a well known problem in operations research with various studies and applications. In this paper, we address a variant of the TSP in which the customers are divided into several...
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The Traveling Salesman Problem (TSP) is a well known problem in operations research with various studies and applications. In this paper, we address a variant of the TSP in which the customers are divided into several priority groups and the order of servicing groups can be flexibly changed with a rule called the d-relaxed priority rule. The problem is called the Clustered Traveling Salesman Problem with Relaxed Priority Rule (CTSP-d). We propose two new mixed integer linear programming (MILP) models for the CTSP-d and a metaheuristic based on Iterated Local Search (ILS) with operators designed for or adapted to the problem. The experimental results obtained on the benchmark instances show that two new models performs better than previous ones, and ILS also proves its performance with 13 new best known solutions found and significant stability compared to existing metaheuristics.
This paper presents a new free-form topology optimization framework for highly materially restricted design situations. Highly materially restricted design situations are herein defined as being limited significantly ...
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This paper presents a new free-form topology optimization framework for highly materially restricted design situations. Highly materially restricted design situations are herein defined as being limited significantly by the combined demands on material use and manufacturability. The new framework uses the ground structure approach with frame elements and casts the design problem as a mixedintegerlinear Program (MILP), ensuring a solution with a manufacturable discrete material distribution. This paper also presents an extension to hybrid mesh ground structures containing both frame and thin solid elements. In both cases, the user can easily tailor the resultant design to discrete fabrication requirements as e.g. relevant when using an extrusion -based Additive Manufacturing (AM) process that requires the design features to adhere to a discrete number of bead depositions. The new framework is numerically demonstrated on topology optimization benchmark examples. In addition, numerical and experimental comparisons are made to the conventional density-based topology optimization approach (with continuous density variables). When the design is highly restricted, the new MILP framework is in most tested cases found to generate solutions with improved numerical predictions on the compliance. For the experimentally validated case study, the numerical prediction is seen to significantly underestimate the experimentally observed performance gain. The experimental investigation additionally cements the high performance of density-based topology optimization in design situations with low levels of design restrictions. In these cases (6 out of 7 tested herein), the solutions generated with density-based topology optimization have comparable or preferable performance.
The problem of scheduling surgeries consists of allocating patients and resources to each surgical stage, considering the patient's needs, as well as sequencing and timing constraints. This problem is classified a...
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ISBN:
(数字)9783030588083
ISBN:
(纸本)9783030588083;9783030588076
The problem of scheduling surgeries consists of allocating patients and resources to each surgical stage, considering the patient's needs, as well as sequencing and timing constraints. This problem is classified as NP-hard and has been widely discussed in the literature for the past 60 years. Nevertheless, many authors do not take into account the multiple stages and resources required to address the complex aspects of operating room management. The general goal of this paper is to propose a mathematical model to represent and solve this problem. Computational tests were also performed to compare the proposed model with a similar model from the literature, with a 64% average reduction in computational time.
Long-term portfolio optimization is commonly used to find the most cost-effective design and operation of a district heating system, subject to technical, financial, and environmental restrictions. Optimizing a distri...
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Long-term portfolio optimization is commonly used to find the most cost-effective design and operation of a district heating system, subject to technical, financial, and environmental restrictions. Optimizing a district heating system is not trivial and demands high accuracy and high computational speed. However, existing methods addressing this problem offer one or the other but not both at the same time. The state-of-the-art method for portfolio optimization is mixed integer linear programming (MILP), which is extensively used in industry and academia but can be computing and resource-intensive for large portfolio models. This limitation has motivated the development of various options to reduce the computation time while maintaining the ac-curacy to a large extent. An alternative method to MILP is the merit order (MO) method, which has been used especially for power generation applications due to its simplicity and faster computation but somewhat reduced accuracy. The aim of this paper is to investigate the potential advantages and disadvantages of MO models compared to MILP models in the context of optimizing the portfolio of assets supplying a district heating network. As a study case, we analyze a large portion of the district heating network in Berlin. Four MO model variants with different levels of complexity are proposed and compared to a reference MILP model. Results suggest that MO models variants including heat storage and describing CHP plants with significant detail have the potential to reduce calculation time by nearly three orders of magnitude compared to the reference MILP model, without significantly sacrificing accuracy. In fact, differences in heat generation and net present value (NPV) between the most accurate MO model and the reference MILP model account for +/- 4% and-6%, respectively. Moreover, results show that combining MO and MILP models is advantageous and offers high computational speed and at the same time high accuracy, especially wh
This paper describes a method for solving task planning and motion planning problems simultaneously. We target a fetch-and-carry of a small item by a single-arm mobile manipulator and introduce a method that can gener...
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ISBN:
(数字)9798331531614
ISBN:
(纸本)9798331531621
This paper describes a method for solving task planning and motion planning problems simultaneously. We target a fetch-and-carry of a small item by a single-arm mobile manipulator and introduce a method that can generate a sequence of actions and motions required for each action, even in environments with narrow open spaces such as corridors. In addition, to deal with a case where another object is already placed at the target location, we introduce a push-aside action and extend the previous method to include this action. As our method is formulated using mixed integer linear programming (MILP), the calculation time is relatively short, irrespective of the complexity of the target problem. To verify the efficiency of the proposed method, we performed a quantitative evaluation through simulation and conducted experiments on an actual mobile manipulator to verify feasibility of the methods.
Developing a minimum backbone grid in the power system planning is beneficial to improve the power system's resilience. To obtain a minimum backbone grid, a mixed integer linear programming (MILP) model with netwo...
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Developing a minimum backbone grid in the power system planning is beneficial to improve the power system's resilience. To obtain a minimum backbone grid, a mixed integer linear programming (MILP) model with network connectivity constraints for a minimum backbone grid is proposed. In the model, some constraints are presented to consider the practical application requirements. Especially, to avoid islands in the minimum backbone grid, a set of linear constraints based on single-commodity flow formulations is proposed to ensure connectivity of the backbone grid. The simulations on the IEEE-39 bus system and the French 1888 bus system show that the proposed model can be solved with higher computational efficiency in only about 30 min for such a large system and the minimum backbone grid has a small scale only 52% of the original grid. Compared with the improved fireworks method, the minimum backbone grid from the proposed method has fewer lines and generators.
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