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Optimized Planning Approach for Multiple Tower Cranes and Material Supply Points Using mixed-integer programming

JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT 2020年第3期146卷

作者： Ji, Yuanshen Leite, Fernanda Univ 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. 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.

关键词： Optimization mixed-integer programming Tower crane plan Branch-and-cut (CBC)

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A mixed-integer programming Model for Assessing Energy-Saving Investments in Domestic Buildings under Uncertainty

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ENERGIES 2018年第4期11卷 989页

作者： Kontogiorgos, Panagiotis Chrysanthopoulos, Nikolaos Papavassilopoulos, George P. Natl Tech Univ Athens Sch Elect & Comp Engn 9 Iroon Polytexneiou Str Zografos 15773 Greece

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.

关键词： energy efficiency interval analysis mixed-integer programming decision support systems

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Computation of Systemic Risk Measures: A mixed-integer programming Approach

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OPERATIONS RESEARCH 2023年第6期71卷 2130页

作者： Ararat, Cagin Meimanjan, Nurtai Bilkent Univ Dept Ind Engn TR-06800 Ankara Turkiye Vienna Univ Econ & Business Inst Stat & Math A-1020 Vienna Austria

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.

关键词： systemic risk measure set-valued risk measure Eisenberg-Noe model Rogers-Veraart model mixed-integer programming vector optimization

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Drawing and Labeling High-Quality Metro Maps by mixed-integer programming

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2011年第5期17卷 626-641页

作者： Noellenburg, Martin Wolff, Alexander KIT Inst Theoret Informat D-76131 Karlsruhe Germany Univ Wurzburg Lehrstuhl Informat 1 D-97074 Wurzburg Germany

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.

关键词： Network visualization graph drawing graph labeling metro map octilinear layout mixed-integer programming

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A Reliability-Based Network Reconfiguration Model in Distribution System with DGs and ESSs Using mixed-integer programming

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ENERGIES 2020年第5期13卷 1219页

作者： Guo, Shanghua Lin, Jian Zhao, Yuming Wang, Longjun Wang, Gang Liu, Guowei South China Univ Technol Sch Elect Power Engn Guangzhou 510640 Peoples R China Shenzhen Power Supply Co Ltd Shenzhen 518001 Peoples R China

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.

关键词： distribution system reconfiguration distribution generation energy storage system switch customer interruption duration mixed-integer programming

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An improved mixed-integer programming approach for bi-objective parallel machine scheduling and location

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年 174卷

作者： Wu, Peng Wang, Yun Cheng, Junheng Li, Yantong Fuzhou Univ Sch Econ & Management Fuzhou Peoples R China Fujian Normal Univ Sch Econ Fuzhou Peoples R China Dalian Maritime Univ Sch Maritime Econ & Management Dalian Peoples R China

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%.

关键词： Parallel machine scheduling and location Bi-objective optimization mixed-integer programming Valid inequalities

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Utilizing dependence among variables in evolutionary algorithms for mixed-integer programming: A case study on multi-objective constrained portfolio optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2021年 66卷 100928-100928页

作者： Chen, Yi Zhou, Aimin Das, Swagatam East China Normal Univ Shanghai Key Lab Multidimens Informat Proc 3663 North Zhongshan Rd Shanghai 200062 Peoples R China East China Normal Univ Dept Comp Sci & Technol 3663 North Zhongshan Rd Shanghai 200062 Peoples R China Indian Stat Inst Elect & Commun Sci Unit 203 BT Rd Kolkata 700108 India

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.

关键词： Evolutionary computations mixed-integer programming Coding scheme Multi-objective constrained portfolio optimization

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Stochastic mixed-integer programming for a spare parts inventory management problem

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COMPUTERS & OPERATIONS RESEARCH 2022年 138卷 105568-105568页

作者： Johannsmann, Leonie M. Craparo, Emily M. Dieken, Thor L. Fugenschuh, Armin R. Seitner, Bjoern O. Helmut Schmidt Univ Univ Fed Armed Forces Hamburg Holstenhofweg 85 D-22043 Hamburg Germany Naval Postgrad Sch Operat Res Dept 1411 Cunningham Rd Monterey CA 93943 USA Logistikzentrum Bundeswehr Anton Dohrn Weg 59 D-26389 Wilhelmshaven Germany Brandenburg Univ Technol Cottbus Senftenberg Pl Deutsch Einheit 1 D-03046 Cottbus Germany

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.

关键词： Logistics Warehouse management Spare parts management Uncertainty Scenario generation mixed-integer programming Two-stage stochastic optimization Operations research

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Fast Non-Parametric Learning to Accelerate mixed-integer programming for Hybrid Model Predictive Control ⁎

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IFAC-PapersOnLine 2020年第2期53卷 5239-5245页

作者： Jia-Jie Zhu Georg Martius Empirical Inference Department Max Planck Institute for Intelligent Systems Tübingen Germany Autonomous Learning Group Max Planck Institute for Intelligent Systems Tübingen Germany

Today’s fast linear algebra and numerical optimization tools have pushed the frontier of model predictive control (MPC) forward, to the efficient control of highly nonlinear and hybrid systems. The field of hybrid MPC has demonstrated that exact optimal control law can be computed, e.g., by mixed-integer programming (MIP) under piecewise-affine (PWA) system models. Despite the elegant theory, online solving hybrid MPC is still out of reach for many applications. We aim to speed up MIP by combining geometric insights from hybrid MPC, a simple-yet-effective learning algorithm, and MIP warm start techniques. Following a line of work in approximate explicit MPC, the proposed learning-control algorithm, LNMS, gains computational advantage over MIP at little cost and is straightforward for practitioners to implement.

关键词： Model Predictive Control of Hybrid System Machine Learning Learning for Control Nonparametric learning mixed-integer programming

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Reinforcement learning and mixed-integer programming for power plant scheduling in low carbon systems: Comparison and hybridisation

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APPLIED ENERGY 2023年第1期349卷

作者： O'Malley, Cormac de Mars, Patrick Badesa, Luis Strbac, Goran Imperial Coll London London SW7 2AZ England UCL Gower St London WC1E 6BT England Tech Univ Madrid UPM Ronda Valencia 3 Madrid 28012 Spain

Decarbonisation is driving dramatic growth in renewable power generation. This increases uncertainty in the load to be served by power plants and makes their efficient scheduling, known as the unit commitment (UC) problem, more difficult. UC is solved in practice by mixed-integer programming (MIP) methods;however, there is growing interest in emerging data-driven methods including reinforcement learning (RL). In this paper, we extensively test two MIP (deterministic and stochastic) and two RL (model-free and with lookahead) scheduling methods over a large set of test days and problem sizes, for the first time comparing the state-of-the-art of these two approaches on a level playing field. We find that deterministic and stochastic MIP consistently produce lower-cost UC schedules than RL, exhibiting better reliability and scalability with problem size. Average operating costs of RL are more than 2 times larger than stochastic MIP for a 50-generator test case, while the cost is 13 times larger in the worst instance. However, the key strength of RL is the ability to produce solutions practically instantly, irrespective of problem size. We leverage this advantage to produce various initial solutions for warm starting concurrent stochastic MIP solves. By producing several near-optimal solutions simultaneously and then evaluating them using Monte Carlo methods, the differences between the true cost function and the discrete approximation required to formulate the MIP are exploited. The resulting hybrid technique outperforms both the RL and MIP methods individually, reducing total operating costs by 0.3% on average.

关键词： Unit commitment Reinforcement learning mixed-integer programming Renewable power uncertainty

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