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Combining Geographic Information Systems for Transportation and mixed integer linear programming in Facility Location-Allocation Problems

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Journal of Software Engineering and Applications 2014年第10期7卷 844-858页

作者： Silvia Maria Santana Mapa Renato da Silva Lima Federal Office for Education Science and Technology of Minas GeraisCongonhasBrazil Federal University of Itajuba Industrial Engineering and Management InstituteItajubáBrazil

In this study, we aimed to assess the solution quality for location-allocation problems from facilities generated by the software TransCAD®?, a Geographic Information System for Transportation (GIS-T). Such facilities were obtained after using two routines together: Facility Location and Transportation Problem, when compared with optimal solutions from exact mathematical models, based on mixed integer linear programming (MILP), developed externally for the GIS. The models were applied to three simulations: the first one proposes opening factories and customer allocation in the state of Sao Paulo, Brazil;the second involves a wholesaler and a study of location and allocation of distribution centres for retail customers;and the third one involves the location of day-care centers and allocation of demand (0 - 3 years old children). The results showed that when considering facility capacity, the MILP optimising model presents results up to 37% better than the GIS and proposes different locations to open new facilities.

关键词： Geographic Information Systems for Transportation Location-Allocation Problems mixed integer linear programming Transportation TransCAD^(█)

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The Flores mixed integer linear programming model for the optimal response of hydro cascades in the electricity spot market

ELEKTROTEHNISKI VESTNIK-ELECTROCHEMICAL REVIEW

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ELEKTROTEHNISKI VESTNIK-ELECTROCHEMICAL REVIEW 2006年第5期73卷 261-266页

作者： Bregar, Zvonko Elektroinst Milan Vidmar Hajdrihova 2 Ljubljana 1000 Slovenia

mixed integer linear programming (MILP) models have in the last decade gained considerable interest in simulation of deregulated electricity markets [1, 2]. They are based on an in-depth knowledge of all relavant technical characteristics (for example a cascade of hydro power plants), of the current state (for example the regulating basins states), and the forecast of external parameters (for example the 24 hours inflows forecast) of the system under study, and the hour-by-hour forecast of the electricity marginal price for the following day [8]. Similar as in the linear programming (LP) models, in MILPs, too, all technical operation limitations are expressed as a set of linear equations and inequations. The optimization function in MILPs remains linear and is usually expressed as the maximum of the expected revenue from the electricity market. It is very important to note that some variables in MILPs can be of the integer type which allows far better modelling of system technical characteristics then in LPs. When applying MILPs, one does not ussually do the math since it suffices to use the available computer solvers. This paper presents the theory of the new MILP Flores model that has been developed at the Milan Vidmar Electric Power Research Institute. It is based on the Institute's PRAK model that calculates the hydro cascades production [17]. The know-how follows the general LP energy flow MESSAGE model of the International Atomic Energy Agency [18]. Two examples are added: the cascade of three existing run-of-river plants on the Soea river including the new pumping hydro plant Avee [14] and the weekly-cycle cascade of the two rehabilitated plants Moste II+III on the upper Sava river [15].

关键词： mixed integer linear programming hydro productibility cascades of hydro power plants electricity market hydro pumping plants

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A finite-time cutting plane algorithm for distributed mixed integer linear programming

A finite-time cutting plane algorithm for distributed mixed ...

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IEEE Annual Conference on Decision and Control

作者： Andrea Testa Alessandro Rucco Giuseppe Notarstefano Department of Engineering Università del Salento Lecce 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 algorithm to solve this class of optimization problems in a peer-to-peer network with no coordinator and with limited computation and communication capabilities. In the proposed algorithm, at each communication round, agents solve locally a small LP, generate suitable cutting planes, namely intersection cuts and cost-based cuts, and communicate a fixed number of active constraints, i.e., a candidate optimal basis. We prove that, if the cost is integer, the algorithm converges to the lexicographically minimal optimal solution in a finite number of communication rounds. Finally, through numerical computations, we analyze the algorithm convergence as a function of the network size.

关键词： Distributed algorithms Optimization Convergence Algorithm design and analysis mixed integer linear programming Routing

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A mixed-integer linear programming model for integrated production and preventive maintenance scheduling in the capital goods industry

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2019年第1期57卷 61-82页

作者： Chansombat, Sirikarn Pongcharoen, Pupong Hicks, Christian Naresuan Univ Ctr Operat Res & Ind Applicat Fac Engn Phitsanulok Thailand Newcastle Univ Business Sch Newcastle Upon Tyne Tyne & Wear England

The scheduling literature is extensive, but much of this work is theoretical and does not capture the complexity of real world systems. Capital goods companies produce products with deep and complex product structures, each of which requires the coordination of jobbing, batch, flow and assembly processes. Many components require numerous operations on multiple machines. Integrated scheduling problems simultaneously consider two or more simultaneous decisions. Previous production scheduling research in the capital goods industry has neglected maintenance scheduling and used metaheuristics with stochastic search that cannot guarantee an optimal solution. This paper presents a novel mixed integer linear programming model for simultaneously solving the integrated production and preventive maintenance scheduling problem in the capital goods industry, which was tested using data from a collaborating company. The objective was to minimise total costs including: tardiness and earliness penalty costs;component and assembly holding costs;preventive maintenance costs;and set-up, production, transfer and production idle time costs. Thus, the objective function and problem formulation were more extensive than previous research. The tool was successfully tested using data obtained from a collaborating company. It was found that the company's total cost could be reduced by up to 63.5%.

关键词： manufacturing and maintenance integrated scheduling capital goods mixed integer linear programming

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A practical mixed integer linear programming-based short-term hydro scheduling

A practical mixed integer linear programming-based short-ter...

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IEEE/PES Transmission and Distribution Conference and Exhibition: Asia and Pacific

作者： G.W. Chang C.T. Su Department of Electrical Engineering National Chung Cheng University Chiayi Taiwan

This paper presents a practical method by using mixed integer linear programming for the short-term hydro scheduling (STHS). The STHS is used to determine the optimal or near-optimal schedules for the dispatchable hydro plants in a hydro-dominant system for a user-definable study period at each time step while respecting all system and hydraulic constraints. The problem can be modeled in detail for a hydro system that contains both conventional and pumped-storage units. Discrete and dynamic constraints such as plant startup and shutdown statuses are also included in the model. The STHS problem is solved with a commercial-grade MILP solver. The usefulness of the proposed solution algorithm is illustrated by testing the problem with actual hydraulic system data. Numerical experiences show that the solution technique is computationally efficient, simple, and suitable for decision support of short-term hydro operations planning.

关键词： mixed integer linear programming Processor scheduling Character generation Dynamic programming Power generation System testing Hydraulic systems linear programming Mathematical model Power system modeling

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Data-driven mixed-integer linear programming-based optimisation for efficient failure detection in large-scale distributed systems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第1期303卷 337-353页

作者： Er-Rahmadi, Btissam Ma, Tiejun Univ Southampton Southampton Business Sch Dept Decis Analyt & Risk Ctr Risk Res Bldg 2Univ Rd Southampton SO17 1BJ Hants England Univ Edinburgh Artificial Intelligence Applicat Inst Sch Informat Informat Forum Crichton St Edinburgh EH8 9AB Midlothian Scotland Edinburgh Res Ctr Huawei Technol R&D 2 Semple St Edinburgh EH3 8BL Midlothian Scotland

Failure detectors (FDs) are fundamental building blocks for distributed systems. An FD detects whether a process has crashed or not based on the reception of heartbeats' messages sent by this process over a communication channel. A key challenge of FDs is to tune their parameters to achieve optimal performance which satisfies the desired system requirements. This is challenging due to the complexities of large-scale networks. Existing FDs ignore such optimisation and adopt ad-hoc parameters. In this paper, we propose a new mixed integer linear programming (MILP) optimisation-based FD algorithm. We obtain the MILP formulation via piecewise linearisation relaxations. The MILP involves obtaining optimal FD parameters that meet the optimal trade-off between its performance metrics requirements, network conditions and system parameters. The MILP maximises our FD's accuracy under bounded failure detection time while considering network and system conditions as constraints. The MILP's solution represents optimised FD parameters that maximise FD's expected performance. To adapt to real-time network changes, our proposed MILP-based FD fits the probability distribution of heartbeats' inter-arrivals. To address our FD scalability challenge in large-scale systems where the MILP model needs to compute approximate optimal solutions quickly, we also propose a heuristic algorithm. To test our proposed approach, we adopt Amazon Cloud as a realistic testing environment and develop a simulator for robustness tests. Our results show consistent improvement of overall FD performance and scalability. To the best of our knowledge, this is the first attempt to combine the MILP-based optimisation modelling with FD to achieve performance guarantees.

关键词： Nonlinear programming mixed integer linear programming Distributed systems Failure detection Heartbeats

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Design of non-uniformly spaced linear phase FIR filters using mixed integer linear programming

Design of non-uniformly spaced linear phase FIR filters usin...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Joon Tae Kim Woo Jin Oh Yong Hoon Lee Department of Electrical Engineering Korea Advanced Institute of Science and Technology Taejon South Korea

An optimization problem for designing a non-uniformly spaced, linear phase FIR filter with minimal complexity is formulated, and solved by mixed integer linear programming (MILP). Design examples illustrate that the proposed method is useful for designing a wide range of filter types, and can outperform the subset selection-based design methods.

关键词： Finite impulse response filter Nonlinear filters mixed integer linear programming Costs Design optimization Design methodology Arithmetic Delay Space technology Narrowband

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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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An integrated data envelopment analysis and mixed integer non-linear programming model for linearizing the common set of weights

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CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2019年第4期27卷 887-904页

作者： Toloo, Mehdi Tavana, Madjid Santos-Arteaga, Francisco J. VSB Tech Univ Ostrava Fac Econ Dept Syst Engn Ostrava Czech Republic La Salle Univ Business Syst & Analyt Dept Business Analyt Philadelphia PA 19141 USA Univ Paderborn Fac Business Adm & Econ Business Informat Syst Dept D-33098 Paderborn Germany Free Univ Bolzano Fac Econ & Management Bolzano Italy

The problem of ranking efficient decision making units (DMUs) is of interest from both theoretical and practical points of view. In this paper, we propose an integrated data envelopment analysis and mixed integer non-linear programming (MINLP) model to find the most efficient DMU using a common set of weights. We linearize the MINLP model to an equivalent mixed integer linear programming (MILP) model by eliminating the non-linear constraints in which the products of variables are incorporated. The formulated MILP model is simpler and computationally more efficient. In addition, we introduce a model for finding the value of epsilon, since the improper choice of the non-Archimedean epsilon may result in infeasible conditions. We use a real-life facility layout problem to demonstrate the applicability and exhibit the efficacy of the proposed model.

关键词： Data envelopment analysis Common set of weights mixed integer linear programming Most efficient DMU

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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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