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ASCE International Conference on Transportation and Development (ICTD)

作者： Seghayati, Majid Nickkar, Amirreza Khaksar, Hasan Iran Univ Sci & Technol Dept Civil Engn Tehran Iran Morgan State Univ Ctr Built Environm & Infrastruct Studies Baltimore MD 21239 USA

ISBN: (数字)9780784482582

ISBN: (纸本)9780784482582

In this paper, two categories have been considered: optimizing economic issues in long-term planning of regional airline network structure;and the importance of changes in demand in different months of the year in airline network design. A mixed integer linear programming model has been provided in this study which, compared to previous models, considers issues such as capacity constraints and network planning issues. Moreover, the most important initiative of this article is entering two parameters: cost (non-profit) due to losing customers and expenditures of the unused capacity. These two parameters will help the model identify the best supply in each path. Various demand functions have been used in solving the model. Based on conducted analyses, using the expected value of monthly demand in comparison with high and low demand and seasonal changes in the network is optimized. The results of study also show that in times of high demand, the airline tends to aggregate passengers and increase density in hub paths.

关键词： Airline network design Small size airlines Hub and spoke mixed integer linear programming

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A Prioritization Tool for SUDS Planning in Large Cities by Coupling an Urban Drainage Model with mixed integer linear programming 11th

A Prioritization Tool for SUDS Planning in Large Cities by C...

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11th International Conference on Urban Drainage Modelling (UDM)

作者： Narine Torres, Maria Zhu, Zhenduo Pablo Rodriguez, Juan Univ Andes CIIA Bogota Colombia SUNY Buffalo Dept Civil Struct & Environm Engn Buffalo NY 14260 USA

ISBN: (纸本)9783319998671;9783319998664

Decision making regarding Sustainable Urban Drainage Systems (SUDS) sitting is an analytical complex process, as it involves the evaluation of a high number of environmental, physical and technical considerations. Additionally, as the resources are always limited (e.g. budget and land availability), the prioritization of sub-catchments for SUDS placement would help decision makers to determine where is most beneficial to locate these infrastructures. This study proposes a methodology that couples an urban drainage model and mixed integer linear programming (MILP) to determine where is more beneficial to locate SUDS to reduce both runoff volumes and combined sewer overflows (CSOs). To achieve this goal, the City Drain model is used to model the runoff generation and the flows among sub-catchments. Three indexes are proposed to quantify the reduction on both CSO and runoff volumes, and a lexicographic multi-objective model is used to find the prioritized sub-catchments within a city. The methodology was applied to sub-catchments of Bogota (Colombia), comprising an area of 38 km(2). Preliminary results showed that the prioritized sub-catchments were highly dependent on land availability and the optimal solution did not necessarily involve the selection of the sub-catchments that yield most runoff. This study demonstrates the importance and usefulness of the prioritization tool for SUDS planning, which can be used by other large cities like Bogota.

关键词： Sustainable urban drainage systems Spatial allocation mixed integer linear programming

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Distributed mixed-integer linear programming via Cut Generation and Constraint Exchange

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2020年第4期65卷 1456-1467页

作者： Testa, Andrea Rucco, Alessandro Notarstefano, Giuseppe Univ Salento Dept Engn I-73100 Lecce Italy Univ Bologna Dept Elect Elect & Informat Engn I-40136 Bologna Italy

Many problems of interest for cyber-physical network systems can be formulated as mixed-integer linear programs in which the constraints are distributed among the agents. In this paper, we propose a distributed algorithmic framework to solve this class of optimization problems in a peer-to-peer network with no coordinator and with limited computation and communication capabilities. At each communication round, agents locally solve a small linear program, generate suitable cutting planes, and communicate a fixed number of active constraints. Within the distributed framework, we first propose an algorithm that, under the assumption of integer-valued optimal cost, guarantees finite-time convergence to an optimal solution. Second, we propose an algorithm for general problems that provides a suboptimal solution up to a given tolerance in a finite number of communication rounds. Both algorithms work under asynchronous, directed, unreliable networks. Finally, through numerical computations, we analyze the algorithm scalability in terms of the network size. Moreover, for a multi-agent multi-task assignment problem, we show, consistently with the theory, its robustness to packet loss.

关键词： Distributed algorithms Approximation algorithms Optimization Task analysis mixed integer linear programming Peer-to-peer computing Convergence Cutting planes distributed optimization mixed-integer multi-agent multi-task assignment

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A New Operation-Oriented mixed integer linear programming Model for Energy-Efficient Train Operations

A New Operation-Oriented Mixed Integer Linear Programming Mo...

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IEEE International Conference on Power and Energy Systems (ICPES)

作者： Minling Feng Chaoxian Wu Shaofeng Lu South China University of Technology Guangzhou China University of Liverpool UK

ISBN: (数字)9781665404945

ISBN: (纸本)9781665404952

The study on the train speed profile optimization has attracted many attentions from the industry and academia since it leads to energy reduction for greener railway transportation. One of the gaps existing between the academic research and industry application is that the speed profile cannot be accurately tracked due to high inertia and large time-delay of the railway traction systems. In both manual and automatic operations, it is considered more practical to provide train operation strategies such as traction or coasting rather than offering an accurate train speed to follow. In this paper, we introduce a new modelling process using a series of binary variables to represent the corresponding operation strategies as the decision variables, so that it is easy to be adopted in real operations. The train operation process is discretized based on distance and the model is solved by mixed integer linear programming. Instead of providing train speeds, this modelling outputs offer selective operations for various distance sections. Based on the results, computational efficiency of the model relaxing the restriction on the applied force are almost the same compared to the previous work using the speed as the decision variable and the calculation time increases from 50 ms to 650 ms when the range of applied force is limited to 85 kN under the maximum tractive efforts. The operation-oriented decisions lead to more flexible adoptions in rail industry applications as the train only needs to switch between different operation modes at the corresponding locations rather than tracking the train speeds.

关键词： Rails Computational modeling Force Transportation Switches Rail transportation mixed integer linear programming

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mixed-integer linear programming and constraint programming formulations for solving distributed flexible job shop scheduling problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 142卷 106347-106347页

作者： Meng, Leilei Zhang, Chaoyong Ren, Yaping Zhang, Biao Lv, Chang Liaocheng Univ Sch Comp Sci Liaocheng 252059 Shandong Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Jinan Univ Sch Intelligent Syst Sci & Engn Zhuhai Campus Zhuhai 519070 Peoples R China

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.

关键词： Distributed flexible job shop scheduling problem mixed integer linear programming Constraint programming Makespan

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A mixed integer linear programming Approach for Soft Graph Clustering

A Mixed Integer Linear Programming Approach for Soft Graph C...

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IEEE International Conference on Data Mining (ICDM)

作者： Vicky Mak-Hau John Yearwood School of Information Technology Deakin University Geelong Vic Australia

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

This paper proposes a mixed-integer linear programming (MILP) formulation for Soft Graph Clustering that can be applied to both weighted and unweighted graphs and is polynomial in size. It can provide proven optimal solutions or solutions with proven optimality gap. The solutions will partition the set of vertices in a graph into K (possibly overlapping) clusters, for K predetermined. The MILP approach can also simultaneously allocate membership proportion for vertices that lie in multiple clusters, and handle restrictions such as cardinality constraints on the size of the overlaps and equal balance of cluster memberships. Our approach requires neither a threshold on edge weights to be predetermined nor the finding of clique neighbourhoods of any size. It does not require the maximum number of clusters to be predetermined. We compare our approach numerically with four existing algorithms: one that requires the finding of clique neighbourhoods, one that performs well on weighted graphs only, one that requires the maximum number of clusters be predetermined, and one that is statistical analysis-based; and discuss in what way our approach outperform these methods.

关键词： Conferences Clustering algorithms mixed integer linear programming Data mining

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Generating Longest Counterexample: On the Cross-roads of mixed integer linear programming and SMT

Generating Longest Counterexample: On the Cross-roads of Mix...

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American Control Conference

作者： Manish Goyal David Bergman Parasara Sridhar Duggirala Department of Computer Science University of North Carolina Chapel Hill NC USA School of Business University of Connecticut Storrs CT USA

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

We present a technique for obtaining the longest counterexample - the execution that stays in the unsafe set for the longest (not necessarily contiguous) time, for a safety specification of linear hybrid systems. Given that hybrid systems are infinite state systems, the number of counterexamples for safety violations are potentially infinite. Therefore, searching for the right counterexample is very challenging. We employ two frameworks for solving this problem: first is an mixed integer linear Program (MILP) formulation and second is to encode counterexamples using Satisfiability Module Theory (SMT) solvers. We evaluate these frameworks on several linear hybrid systems with up to 30 dimensions.

关键词： hybrid systems Counter example mixed integer linear programming safety specification Frameworks pull rope Unsafe

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Optimal Load Control and Scheduling through Distributed mixed-integer linear programming

Optimal Load Control and Scheduling through Distributed Mixe...

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Mediterranean Conference on Control and Automation (MED)

作者： Vassilios Yfantis William Motsch Nico Bach Achim Wagner Martin Ruskowski Department of Mechanical and Process Engineering Chair of Machine Tools and Control Systems Technische Universit&#x00E4 t Kaiserslautern Kaiserslautern Germany Technologie-Initiative SmartFactory KL e.V. Kaiserslautern Germany German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

This paper presents a mixed-integer linear programming-based optimization model for simultaneous optimal load control and scheduling of distributed systems coupled through their energy consumptions. The subsystems are able to adjust their energy consumption during the execution of a task and aim at minimizing their completion time and energy cost. The overall problem is solved in a distributed fashion, where each subsystem optimizes its individual operation without sharing sensitive information. To this end, dual decomposition is employed and a new algorithm to update the dual variables is presented. It relies on a transformation of the gradient of the quadratically approximated dual function and the subsequent solution of a regression problem. The proposed algorithm makes efficient use of information collected in previous iterations. The solution obtained from the distributed optimization of the subsystems is compared to both a decentral and a system-wide solution, showing that the distributed solution lies close to the global optimum of the process.

关键词： Energy consumption Costs Automation Approximation algorithms mixed integer linear programming Task analysis Optimization

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mixed-integer linear programming based Maintenance Scheduling of Generating Units

Mixed-Integer Linear Programming based Maintenance Schedulin...

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International Conference on Environment and Electrical Engineering (EEEIC)

作者： Ali Esmaeel Nezhad Pedro H.J. Nardelli Farideh Ghanavati Subham Sahoo Gerardo J. Osó rio Department of Electrical Engineering School of Energy Systems LUT University Lappeenranta Finland Department of Industrial Engineering and Management University of Aveiro Aveiro Portugal Department of Energy Technology Aalborg University Aalborg Denmark R. Dr. Ant&#x00F3 nio University Portucalense Infante D. Henrique (UPT) Porto Portugal

ISBN: (数字)9781665485371

ISBN: (纸本)9781665485388

This paper presents a mixed-integer linear programming model for the maintenance scheduling of generating units in the power system. The proposed model is investigated for weekly scheduling for one year addressing the crew availability constraint. The maintenance scheduling problem is modeled as an optimization problem to determine the optimal timing for handling the technical constraints of the power generation sector. In addition, the technical constraints for optimal scheduling of the tasks, like sequential tasks and rest time of the crews have been addressed in the scheduling management framework. The weekly peak power and spinning reserve have been considered in line with the economic issues for power generation in the whole system. The historical market clearing price (MCP) and mid-term load forecasting have been considered in the developed model.

关键词： Simulation Optimal scheduling Maintenance engineering Power systems mixed integer linear programming Timing Spinning

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mixed-integer linear programming for enterprise's inventory pledge financing decision

Mixed-integer linear programming for enterprise's inventory ...

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IEEE International Conference on Networking, Sensing and Control

作者： Junheng Cheng Yanhong Lin Xiaoyu He School of Economics Fujian Normal University Fuzhou China

ISBN: (纸本)9781665472449

Small and medium-sized enterprises (SMEs) are the backbone of most countries' economy. However, they often face financing difficulties and high financing costs. In recent years, supply chain finance develops rapidly and provides a good way for SMEs to alleviate their financing problems. Inventory pledge financing is one of the most widely used supply chain financing modes. This paper studies an assets optimization problem for the companies that adopt inventory pledge financing, which involves collaterals selection, purchasing and selling decisions for multiple periods during the entire pledge horizon. For the problem, we construct a mixed-integer linear programming model and verify its effectiveness by CPLEX with a practice-based case and randomly generated instances.

关键词： Costs Supply chains Stochastic processes Finance Companies Inventory management mixed integer linear programming

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