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Solving the nuclear dismantling project scheduling problem by combining mixed-integer and constraint programming techniques and metaheuristics

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JOURNAL OF SCHEDULING 2021年第3期24卷 269-290页

作者： Hubner, Felix Gerhards, Patrick Stuerck, Christian Volk, Rebekka Karlsruhe Inst Technol KIT Inst Ind Prod IIP Karlsruhe Germany Helmut Schmidt Univ Inst Comp Sci Hamburg Germany Helmut Schmidt Univ Inst Management Sci & Operat Res Hamburg Germany

Scheduling of megaprojects is very challenging because of typical characteristics, such as expected long project durations, many activities with multiple modes, scarce resources, and investment decisions. Furthermore, each megaproject has additional specific characteristics to be considered. Since the number of nuclear dismantling projects is expected to increase considerably worldwide in the coming decades, we use this type of megaproject as an application case in this paper. Therefore, we consider the specific characteristics of constrained renewable and non-renewable resources, multiple modes, precedence relations with and without no-wait condition, and a cost minimisation objective. To reliably plan at minimum costs considering all relevant characteristics, scheduling methods can be applied. But the extensive literature review conducted did not reveal a scheduling method considering the special characteristics of nuclear dismantling projects. Consequently, we introduce a novel scheduling problem referred to as the nuclear dismantling project scheduling problem. Furthermore, we developed and implemented an effective metaheuristic to obtain feasible schedules for projects with about 300 activities. We tested our approach with real-life data of three different nuclear dismantling projects in Germany. On average, it took less than a second to find an initial feasible solution for our samples. This solution could be further improved using metaheuristic procedures and exact optimisation techniques such as mixed-integer programming and constraint programming. The computational study shows that utilising exact optimisation techniques is beneficial compared to standard metaheuristics. The main result is the development of an initial solution finding procedure and an adaptive large neighbourhood search with iterative destroy and recreate operations that is competitive with state-of-the-art methods of related problems. The described problem and findings can be transferred

关键词： Project scheduling Multi-mode resource investment problem mixed-integer programming Constraint programming Metaheuristic Nuclear dismantling project scheduling problem

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A mixed-integer linear programming model for the continuous casting planning

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2006年第2期104卷 260-270页

作者： Bellabdaoui, A. Teghem, J. Fac Polytech Mons B-7000 Mons Belgium

The development of optimization models for planning and scheduling is one of the most useful tools for improving productivity of a large number of manufacturing companies. This paper presents a mixed-integer programming model for scheduling steelmaking-continuous casting production. We first review the recent works in continuous casting planning. We focus on a model inspired from an application of steelmaking-continuous casting by Arcelor Group in Liege, Belgium. The process scheduling is characterized by several constraints: job grouping, technological interdependence, no dead time inside the same group of jobs and dynamic processing time of jobs. We present a formulation with mixed-integer linear programming which can be solved using standard software packages. Finally, we treat a few examples to illustrate this application and we conclude this paper with some comments and directions for future extensions. (c) 2004 Elsevier B.V. All rights reserved.

关键词： steelmaking continuous casting mixed-integer programming production planning

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On the use of mixed-integer linear programming for predictive control with avoidance constraints

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2009年第7期19卷 822-828页

作者： Maia, Marcelo H. Galvao, Roberto K. H. Inst Tecnol Aeronaut CTA Div Engn Eletron BR-12228900 Sao Jose Dos Campos SP Brazil

This technical note concerns the predictive control of discrete-time linear models subject to state, input and avoidance polyhedral constraints. Owing to the presence of avoidance constraints, the optimization associated with the predictive control law is non-convex, even though the constraints themselves are convex. The inclusion of the avoidance constraints in the predictive control law is achieved by the use of a modified version of a mixed-integer programming approach previously derived in the literature. The proposed modification consists of adding constraints to ensure that linear segments of the system trajectories between consecutive sampling times do not cross existing obstacles. This avoids the significant extra computation that would be incurred if the sampling time was reduced to prevent these crossings. Simulation results show that the inclusion of these additional constraints successfully prevents obstacle collisions that would otherwise occur. Copyright (C) 2008 John Wiley & Sons, Ltd.

关键词： constrained predictive control vehicle maneuvering mixed-integer programming

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AN OPTIMAL GAS-SUPPLY FOR A POWER-PLANT USING A mixed integer programming-MODEL

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AUTOMATICA 1991年第3期27卷 513-518页

作者： AKIMOTO, K SANNOMIYA, N NISHIKAWA, Y TSUDA, T KYOTO INST TECHNOL KYOTO 606JAPAN KYOTO UNIV DEPT ELECT ENGNKYOTO 606JAPAN FUJIFACOM CORP HINDTOKYOJAPAN

At Kawasaki Steel Mizushima Works, a new energy control system has been established in order to deal with energy and utilities in the steelworks. The system consists of the works' central computer, online computer, process computer and digital instrumentation system. The software system is divided into the planning system, execution system and evaluation system. As the representative topic of the execution system, an optimal gas supply amount for the joint electric power plant is determined. An optimal guidance for the gas supply amount is given to operators by solving a mixed-integer program by a decomposition method in process computer online realtime. This new energy control system has brought a satisfactory energy saving effect.

关键词： OPTIMAL PLANNING mixed-integer programming COMPUTER CONTROL PRODUCTION CONTROL ENERGY SAVING STEEL INDUSTRY

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Energetic reasoning and mixed-integer linear programming for scheduling with a continuous resource and linear efficiency functions

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OR SPECTRUM 2016年第2期38卷 459-492页

作者： Nattaf, Margaux Artigues, Christian Lopez, Pierre Rivreau, David CNRS LAAS 7 Ave Colonel Roche F-31400 Toulouse France Univ Toulouse UPS F-31400 Toulouse France Univ Toulouse LAAS F-31400 Toulouse France LUNAM Univ Univ Catholique Ouest LISA 3 Pl Andre Leroy F-49008 Angers France

This paper addresses a scheduling problem with a continuously divisible, cumulative and renewable resource with limited capacity. During its processing, each task consumes a part of this resource, which lies between a minimum and a maximum requirement. A task is finished when a certain amount of energy is received by it within its time window. This energy is received via the resource and an amount of resource is converted into an amount of energy with a non-decreasing and continuous function. The goal is to find a feasible schedule, which is already NP-complete, and then to minimize the resource consumption. For the case where all functions are linear, we present two new mixed-integer linear programs (MILP), as well as improvements of an existing formulation. We also present a detailed version of the adaptation of the well-known "left-shift/right-shift" satisfiability test for the cumulative constraint and the associated time-window adjustments to our problem. For this test, three ways of computing relevant intervals are described. Finally, a hybrid branch-and-bound using both the satisfiability test and the MILP is presented with a new heuristic for choosing the variable on which the branching is done. Computational experiments on randomly generated instances are reported in order to compare all of these solution methods.

关键词： Continuous scheduling Continuous resources Linear efficiency functions Energy constraints Energetic reasoning Branching scheme mixed-integer programming

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TWO PENALIZED mixed-integer NONLINEAR programming APPROACHES TO TACKLE MULTICOLLINEARITY AND OUTLIERS EFFECTS IN LINEAR REGRESSION MODELS

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2021年第6期17卷 3475-3491页

作者： Roozbeh, Mahdi Babaie-Kafaki, Saman Aminifard, Zohre Semnan Univ Fac Math Stat & Comp Sci POB 35195-363 Semnan Iran

In classical regression analysis, the ordinary least-squares estimation is the best strategy when the essential assumptions such as normality and independency to the error terms as well as ignorable multicollinearity in the covariates are met. However, if one of these assumptions is violated, then the results may be misleading. Especially, outliers violate the assumption of normally distributed residuals in the least-squares regression. In this situation, robust estimators are widely used because of their lack of sensitivity to outlying data points. Multicollinearity is another common problem in multiple regression models with inappropriate effects on the least-squares estimators. So, it is of great importance to use the estimation methods provided to tackle the mentioned problems. As known, robust regressions are among the popular methods for analyzing the data that are contaminated with outliers. In this guideline, here we suggest two mixed-integer nonlinear optimization models which their solutions can be considered as appropriate estimators when the outliers and multicollinearity simultaneously appear in the data set. Capable to be effectively solved by metaheuristic algorithms, the models are designed based on penalization schemes with the ability of down-weighting or ignoring unusual data and multicollinearity effects. We establish that our models are computationally advantageous in the perspective of the flop count. We also deal with a robust ridge methodology. Finally, three real data sets are analyzed to examine performance of the proposed methods.

关键词： Regression analysis multicollinearity breakdown point mixed-integer programming metaheuristic algorithm

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Firefly penalty-based algorithm for bound constrained mixed-integer nonlinear programming

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OPTIMIZATION 2016年第5期65卷 1085-1104页

作者： Costa, M. Fernanda P. Rocha, Ana Maria A. C. Francisco, Rogerio B. Fernandes, Edite M. G. P. Univ Minho Ctr Math Braga Portugal Univ Minho Algoritmi Res Ctr Braga Portugal

In this article, we aim to extend the firefly algorithm (FA) to solve bound constrained mixed-integer nonlinear programming (MINLP) problems. An exact penalty continuous formulation of the MINLP problem is used. The continuous penalty problem comes out by relaxing the integrality constraints and by adding a penalty term to the objective function that aims to penalize integrality constraint violation. Two penalty terms are proposed, one is based on the hyperbolic tangent function and the other on the inverse hyperbolic sine function. We prove that both penalties can be used to define the continuous penalty problem, in the sense that it is equivalent to the MINLP problem. The solutions of the penalty problem are obtained using a variant of the metaheuristic FA for global optimization. Numerical experiments are given on a set of benchmark problems aiming to analyze the quality of the obtained solutions and the convergence speed. We show that the firefly penalty-based algorithm compares favourably with the penalty algorithm when the deterministic DIRECT or the simulated annealing solvers are invoked, in terms of convergence speed.

关键词： mixed-integer programming firefly algorithm penalty function 90C30 90C26 90C11 90C56 90C59

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A mixed integer linear programming based approach for unit commitment in smart grid environment

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JOURNAL OF ENGINEERING RESEARCH 2014年第3期2卷 131-157页

作者： Ghadirianari, R. Rashidinejad, M. Fotuhi-Firuzabad, M. Islamic Azad Univ Dept Elect Engn Tehran Sci & Res Branch Tehran Iran Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran Sharif Univ Technol Ctr Excellence Power Syst Control & Management Dept Elect Engn Tehran Iran

The future of power systems known as smart grids is expected to involve an increasing level of intelligence and incorporation of new information and communication technologies in every aspect of the power grid. Demand response resources and gridable vehicle are two interesting programs which can be utilized in the smart grid environment. Demand response resources can be used as a demand side virtual power plant (resource) to enhance the security and reliability of utility and have the potential to offer substantial benefits in the form of improved economic efficiency in wholesale electricity markets. An economic model of incentive responsive loads is modelled based on price elasticity of demand and customers' benefit function. On the other hand, a gridable vehicle can be used as a small portable power plant to improve the reliability as well as security of the power system. This paper formulates a mixed-integer programming approach to solve the unit commitment problem with demand response resources and gridable vehicles. The objective function of the unit commitment problem has been modified to incorporate demand response resources and gridable vehicles. The proposed method is conducted on the conventional 10-unit test system to illustrate the impacts of smart grid environment on the unit commitment problem. Moreover the benefits of implementing demand response resources and gridable vehicle in electricity markets are demonstrated.

关键词： Demand response mixed-integer programming smart grid unit commitment

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mixed integer programming with dose-volume constraints in intensity-modulated proton therapy

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JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS 2017年第5期18卷 29-35页

作者： Zhang, Pengfei Fan, Neng Shan, Jie Schild, Steven E. Bues, Martin Liu, Wei Mayo Clin Dept Radiat Oncol Scottsdale AZ USA Univ Arizona Dept Syst & Ind Engn Tucson AZ USA Arizona State Univ Dept Biomed Informat Phoenix AZ USA

Background: In treatment planning for intensity-modulated proton therapy (IMPT), we aim to deliver the prescribed dose to the target yet minimize the dose to adjacent healthy tissue. mixed-integer programming (MIP) has been applied in radiation therapy to generate treatment plans. However, MIP has not been used effectively for IMPT treatment planning with dose-volume constraints. In this study, we incorporated dose-volume constraints in an MIP model to generate treatment plans for IMPT. Methods: We created a new MIP model for IMPT with dose volume constraints. Two groups of IMPT treatment plans were generated for each of three patients by using MIP models for a total of six plans: one plan was derived with the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method while the other plan was derived with our MIP model with dose-volume constraints. We then compared these two plans by dose-volume histogram (DVH) indices to evaluate the performance of the new MIP model with dose-volume constraints. In addition, we developed a model to more efficiently find the best balance between tumor coverage and normal tissue protection. Results: The MIP model with dose-volume constraints generates IMPT treatment plans with comparable target dose coverage, target dose homogeneity, and the maximum dose to organs at risk (OARs) compared to treatment plans from the conventional quadratic programming method without any tedious trial-and-error process. Some notable reduction in the mean doses of OARs is observed. Conclusions: The treatment plans from our MIP model with dose-volume constraints can meetall dose-volume constraints for OARs and targets without any tedious trial-and-error process. This model has the potential to automatically generate IMPT plans with consistent plan quality among different treatment planners and across institutions and better protection for important parallel OARs in an effective way.

关键词： dose-volume constraints intensity-modulated proton therapy mixed-integer programming

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Review of Nonlinear mixed-integer and Disjunctive programming Techniques

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OPTIMIZATION AND ENGINEERING 2002年第3期3卷 227-252页

作者： Grossmann, Ignacio E. Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA

This paper has as a major objective to present a unified overview and derivation of mixed-integer nonlinear programming (MINLP) techniques, Branch and Bound, Outer-Approximation, Generalized Benders and Extended Cutting Plane methods, as applied to nonlinear discrete optimization problems that are expressed in algebraic form. The solution of MINLP problems with convex functions is presented first, followed by a brief discussion on extensions for the nonconvex case. The solution of logic based representations, known as generalized disjunctive programs, is also described. Theoretical properties are presented, and numerical comparisons on a small process network problem.

关键词： mixed-integer programming disjunctive programming nonlinear programming

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