We study the convex hull of the feasible set of the semi-continuous knapsack problem, in which the variables belong to the union of two intervals. Besides being important in its own right, the semi-continuous knapsack...
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ISBN:
(纸本)3540221131
We study the convex hull of the feasible set of the semi-continuous knapsack problem, in which the variables belong to the union of two intervals. Besides being important in its own right, the semi-continuous knapsack problem is a relaxation of general mixed-integer programming. We show how strong inequalities that are valid for the semi-continuous knapsack polyhedron can be derived and used as cuts in a branch-and-cut scheme for mixed-integer programming anal problems with semi-continuous variables. We present computational results that demonstrate the effectiveness of these inequalities, which we call collectively semi-continuous cuts. Our computational experience also shows that dealing with semi-continuous constraints directly in the branch-and-cut algorithm through a specialized branching scheme and semi-continuous cuts is considerably more practical than the "textbook" approach of modeling semi-continuous constraints through the introduction of auxiliary binary variables in the model.
With the development of the smart grid, more load data can be collected and utilized to facilitate bidirectional communications between the supply-side and demand-side. More detailed data can achieve better results in...
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With the development of the smart grid, more load data can be collected and utilized to facilitate bidirectional communications between the supply-side and demand-side. More detailed data can achieve better results in applications, but it is not feasible due to sensor placement and data transmission limitations. Non-intrusive load monitoring (NILM) is an exceptionally low-cost solution for providing appliance-level load information of a building without installing extra sensors. While most current studies focus on NILM in residential buildings, the potential benefit of industrial NILM is preferable due to the grander power consumption scale and more professional management. This paper considers the challenges and strengths of industrial NILM, and a mixed-integer programming NILM approach is proposed for flowline industries. This approach separately models equipment with different load features, resulting in better performance in industrial scenarios containing various loads. Temporal dependencies between equipment are exploited by recognizing the statistical regularity of load fluctuation and are introduced as a novel flowline constraint in the model. A pulse width constraint that restricts the state duration of equipment is also introduced to improve performance. Several cases are designed and tested on two public industrial datasets to evaluate the effectiveness and transferability of the model. The classical factorial hidden Markov model (FHMM), a state-of-the-art matrix factorization method, and a deep learning model are used as benchmarks for comparison.
作者：
Ji, YuanshenLeite, FernandaUniv Texas Austin
Construct Engn & Project Management Program Dept Civil Architectural & Environm Engn 301 E Dean Keeton St C1752 Austin TX 78712 USA
It is common to implement multiple tower cranes on building construction projects. The plan for usage of multiple tower cranes should be optimized for better project performance, such as reduced cost or operation time...
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It is common to implement multiple tower cranes on building construction projects. The plan for usage of multiple tower cranes should be optimized for better project performance, such as reduced cost or operation time. Optimization of plans for multiple cranes is complex, especially when considering the supply of transported material (e.g., location, quantity, material type), as well as assigning lift tasks among tower cranes that are in range. This study developed a mathematic formulation that can solve this optimization problem using mixed-integer programming. The formulation introduces several binary variables and restricts the domain of the indices of these variables using an additional set of auxiliary variables. The proposed model contributes to the body of knowledge by showing the feasibility of using mixed-integer-programming techniques to solve the optimization problem of multiple tower cranes and their supply systems. The findings also demonstrate that when a multiple tower crane problem is concerned, optimizing each piece of equipment individually could lead to suboptimal solutions. Specifically, the operation time drops by 6.8% and the operation cost decreases by 3.6% in the two-tower crane case study example. The proposed model can also assist engineers with assessing a large number of alternative plans, which are heavily needed in preconstruction planning, where site layout is preliminary.
A decision support tool has been developed to evaluate energy-saving intervention investments for domestic buildings. Various potential interventions are considered, each affecting energy consumption and savings, as w...
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A decision support tool has been developed to evaluate energy-saving intervention investments for domestic buildings. Various potential interventions are considered, each affecting energy consumption and savings, as well as the total financial cost of the investment. The decision problem is formulated as a mixed-integer programming problem. The implemented methodologies increase the efficiency and efficacy of the solution algorithms and can be applied to most realistic cases. The tool allows users to customize the problem based on their own preferences and find the optimal combination of investments. Uncertainty complicating the decision process is addressed by using interval analysis;therefore, the robustness of the optimal decision can be evaluated to facilitate the decision-making process. A domestic building in the Mediterranean area is used as a case study to demonstrate the functionality of this tool and to evaluate the impact of the decision-maker's uncertainty on the optimal decision.
Systemic risk is concerned with the instability of a financial system whose members are interdependent in the sense that the failure of a few institutions may trigger a chain of defaults throughout the system. Recentl...
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Systemic risk is concerned with the instability of a financial system whose members are interdependent in the sense that the failure of a few institutions may trigger a chain of defaults throughout the system. Recently, several systemic risk measures have been proposed in the literature that are used to determine capital requirements for the members subject to joint risk considerations. We address the problem of computing systemic risk measures for systems with sophisticated clearing mechanisms. In particular, we consider an extension of the Rogers-Veraart network model where the operating cash flows are unrestricted in sign. We propose a mixed-integer programming problem that can be used to compute clearing vectors in this model. Because of the binary variables in this problem, the corresponding (set-valued) systemic risk measure fails to have convex values in general. We associate nonconvex vector optimization problems with the systemic risk measure and provide theoretical results related to the weighted-sum and Pascoletti-Serafini scalarizations of this problem. Finally, we test the proposed formulations on computational examples and perform sensitivity analyses with respect to some model-specific and structural parameters.
Metro maps are schematic diagrams of public transport networks that serve as visual aids for route planning and navigation tasks. It is a challenging problem in network visualization to automatically draw appealing me...
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Metro maps are schematic diagrams of public transport networks that serve as visual aids for route planning and navigation tasks. It is a challenging problem in network visualization to automatically draw appealing metro maps. There are two aspects to this problem that depend on each other: the layout problem of finding station and link coordinates and the labeling problem of placing nonoverlapping station labels. In this paper, we present a new integral approach that solves the combined layout and labeling problem (each of which, independently, is known to be NP-hard) using mixed-integer programming (MIP). We identify seven design rules used in most real-world metro maps. We split these rules into hard and soft constraints and translate them into an MIP model. Our MIP formulation finds a metro map that satisfies all hard constraints (if such a drawing exists) and minimizes a weighted sum of costs that correspond to the soft constraints. We have implemented the MIP model and present a case study and the results of an expert assessment to evaluate the performance of our approach in comparison to both manually designed official maps and results of previous layout methods.
Widely used distribution generations (DGs) and energy storage systems (ESSs) enable a distribution system to have a more flexible fault reconfiguration capability. In order to enhance the service reliability and the b...
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Widely used distribution generations (DGs) and energy storage systems (ESSs) enable a distribution system to have a more flexible fault reconfiguration capability. In order to enhance the service reliability and the benefit of distribution networks with DGs and ESSs, this paper proposes a novel distribution system reconfiguration (DSR) model including DGs and ESSs. Meanwhile, the impact of sectionalizing switches and tie switches on reliability is considered. The concept of "boundary switch" is introduced for quantifying the customer interruption duration. The DSR model is presented to minimize the sum of the customer interruption cost, the operation cost of switches, and the depreciation cost of DGs and ESSs. Furthermore, the proposed model is converted into a mixed-integer linear programming, which can be efficiently solved by commercial solvers. Finally, the validity and efficiency of the proposed DSR model are verified by a modified IEEE 33-bus system and a modified PG&E69-bus network. The obtained results indicate the advantages of DGs and ESSs in reducing outage time, and suggest that the types and locations of SSs have great effects on the resulting benefit of DGs and ESSs.
This paper investigates a new bi-objective parallel machine scheduling and location problem: selecting locations for available machines, assigning jobs to these located machines for processing, and sequencing the assi...
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This paper investigates a new bi-objective parallel machine scheduling and location problem: selecting locations for available machines, assigning jobs to these located machines for processing, and sequencing the assigned jobs on each machine to optimize the location cost and makespan, simultaneously. For the challenging NPhard problem, we first develop a novel bi-objective mixed-integer linear programming (MILP) model with fewer integer variables compared with the state-of-the-art one. Then, several valid inequalities are proposed to tighten it further. We develop an epsilon-constraint based on the fuzzy-logic method to solve the bi-objective model. Computational results for benchmark instances show that the proposed approach obtains more Pareto-optimal solutions compared with the state-of-the-art one and is more than 15 times faster than it. Valid inequalities can reduce average computation time by more than 90%.
mixed-integer Non-Linear programming (MINLP) is not rare in real-world applications such as portfolio invest-ment. It has brought great challenges to optimization methods due to the complicated search space that has b...
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mixed-integer Non-Linear programming (MINLP) is not rare in real-world applications such as portfolio invest-ment. It has brought great challenges to optimization methods due to the complicated search space that has both continuous and discrete variables. This paper considers the multi-objective constrained portfolio optimization problems that can be formulated as MINLP problems. Since each continuous variable is dependent to a discrete variable, we propose a Compressed Coding Scheme (CCS), which encodes the dependent variables into a contin-uous one. In this manner, we can reuse some existing search operators and the dependence among variables will be utilized while the algorithm is optimizing the compressed variables. CCS actually bridges the gap between the portfolio optimization problems and the existing optimizers, such as Multi-Objective Evolutionary Algorithms (MOEAs). The new approach is applied to two benchmark suites, involving the number of assets from 31 to 2235. The experimental results indicate that CCS is not only efficient but also robust for dealing with the multi-objective constrained portfolio optimization problems.
The German Armed Forces provide an operation contingent to support the North Atlantic Treaty Organization (NATO) Response Force (NRF). To fulfill a mission, the NRF operates a number of technical systems, mostly vehic...
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The German Armed Forces provide an operation contingent to support the North Atlantic Treaty Organization (NATO) Response Force (NRF). To fulfill a mission, the NRF operates a number of technical systems, mostly vehicles. Each system is composed of several parts which might fail over time, and it can only be used again in the mission if all broken parts are replaced. For short deployments (e.g., one month), the NRF troops bring with them a tightly constrained "warehouse"of spare parts. To ensure an optimal use of the space, we present a two-stage stochastic programming model where in the first stage spare parts are chosen, then failures occur at random, and in the second stage the parts are assigned to the broken systems. We carry out a scenario-based approach, where the failures are simulated by a Monte-Carlo approach. We demonstrate that the resulting mixed-integer linear program can be solved using standard numerical solvers. Using real-world input data provided by the German Armed Forces and simulated data, we analyze the sensitivity of the solutions with respect to the size of the warehouse, the service level, and the number of scenarios, and compare our approach with simpler, doctrine based warehousing strategies.
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