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A parallel hybrid PSO-GA algorithm for the flexible flow-shop scheduling with transportation

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年第0期173卷

作者： Amirteimoori, Arash Mahdavi, Iraj Solimanpur, Maghsud Ali, Sadia Samar Tirkolaee, Erfan Babaee London Sch Econ & Polit Sci Dept Math London England Mazandaran Univ Sci & Technol Dept Ind Engn Babol Iran Urmia Univ Dept Ind Engn Orumiyeh Iran King Abdulaziz Univ Fac Engn Dept Ind Engn Jeddah Saudi Arabia Istinye Univ Dept Ind Engn Istanbul Turkey

In this paper, a mixed-integer linear programming (MILP) model to simultaneously schedule jobs and transporters in a flexible flow shop system is suggested. Wherein multiple jobs, finite transporters, and stages with parallel unrelated machines are considered. In addition to the mentioned technicalities, the jobs are able to omit one or more stages, and may not be executable by all the machines, and similarly, transportable by all the transporters. To the best of our knowledge, no study in the literature has featured efficacy of the parallel computing in simultaneous scheduling of jobs and transporters in the flexible flow shop system which remarkably shortens run time if the solution approaches are designed accordingly. To this end, we employ Gurobi solver, Parallel Genetic Algorithm (PGA), Parallel Particle Swarm Optimization (PPSO) and hybrid Parallel PSO-GA Algorithm (PPSOGA) to deal with the problem instances. Furthermore, a parallel version of Ant Colony Optimization (ACO) algorithm adapted from the state-of-the-art literature is developed to verify the performance of our suggested solution methods. Using 60 problem instances generated via uniform distribution, the suggested solution approaches are compared against one another. After assessing the results of the computational experiments, it is deduced that PPSOGA algorithm outperforms PGA, PPSO, Parallel Ant Colony Optimization (PACO) and Gurobi solver in terms of the quality of the solutions. The efficiency and run time of the suggested approaches are then assessed through two prominent statistical tests (i.e., Wald and Analysis of Variance (ANOVA)). Eventually, it comes to spotlight that PPSOGA algorithm is computationally rewarding and dependable.

关键词： Flexible flow shop scheduling Metaheuristics mixed-integer linear programming Parallel computing Hybrid parallel PSO-GA algorithm

Analytical Heterogeneous Die-to-Die 3D Placement with Macros

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arXiv 2024年

作者： Zhao, Yuxuan Liao, Peiyu Liu, Siting Jiang, Jiaxi Lin, Yibo Yu, Bei Department of Computer Science and Engineering The Chinese University of Hong Kong NT Hong Kong School of Integrated Circuits Peking University China Institute of Electronic Design Automation Peking University Wuxi China Beijing Advanced Innovation Center for Integrated Circuits Beijing China

This paper presents an innovative approach to 3D mixed-size placement in heterogeneous face-to-face (F2F) bonded 3D ICs. We propose an analytical framework that utilizes a dedicated density model and a bistratal wirelength model, effectively handling macros and standard cells in a 3D solution space. A novel 3D preconditioner is developed to resolve the topological and physical gap between macros and standard cells. Additionally, we propose a mixed-integer linear programming (MILP) formulation for macro rotation to optimize wirelength. Our framework is implemented with full-scale GPU acceleration, leveraging an adaptive 3D density accumulation algorithm and an incremental wirelength gradient algorithm. Experimental results on ICCAD 2023 contest benchmarks demonstrate that our framework can achieve 5.9% quality score improvement compared to the first-place winner with 4.0× runtime speedup. Additional experiments on modern RISC-V designs further validate the generalizability and superiority of our framework. © 2024, CC BY-NC-ND.

关键词： mixed-integer linear programming

mixed-integer linear Optimization for Cardinality-Constrained Random Forests

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arXiv 2024年

作者： Burgard, Jan Pablo Pinheiro, Maria Eduarda Schmidt, Martin Trier University Department of Economic and Social Statistics Universitätsring 15 Trier54296 Germany Trier University Department of Mathematics Universitätsring 15 Trier54296 Germany

Random forests are among the most famous algorithms for solving classification problems, in particular for large-scale data sets. Considering a set of labeled points and several decision trees, the method takes the majority vote to classify a new given point. In some scenarios, however, labels are only accessible for a proper subset of the given points. Moreover, this subset can be non-representative, e.g., due to collection bias. Semi-supervised learning considers the setting of labeled and unlabeled data and often improves the reliability of the results. In addition, it can be possible to obtain additional information about class sizes from undisclosed sources. We propose a mixed-integer linear optimization model for computing a semi-supervised random forest that covers the setting of labeled and unlabeled data points as well as the overall number of points in each class for a binary classification. Since the solution time rapidly grows as the number of variables increases, we present some problem-tailored preprocessing techniques and an intuitive branching rule. Our numerical results show that our approach leads to better accuracy and a better Matthews correlation coefficient for biased samples compared to random forests by majority vote, even if only a few labeled points are *** Codes 90C11, 90C90, 90-08, 68T99 © 2024, CC BY.

关键词： mixed-integer linear programming

Bound Contraction Strategy Applied to Network Synthesis with Regeneration and Reuse to Minimize Water Consumption

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SSRN

SSRN 2024年

作者： Pedroza, Ivone Cristina Barros da Silva Sá Ravagnani, Mauro Antônio The State University of Bahia Portugal The State University of Maringá Brazil Campus IX Department of Human Sciences Department of Chemical Engineering Universidade Estadual de Maringá Brazil University of Maringá Main Campus Department of Chemical Engineering Brazil

SUMMARY The present work uses the global bound contraction optimization technique to solve problems of synthesis of water networks with regeneration and reuse in industrial processes. The objective is to obtain viable solutions that allow the reduction in the consumption of fresh water and the generation of effluents. The problems addressed have a formulation of mixed integer nonlinear programming (MINLP), containing bilinear terms. The results always presented the solution to the global optimum in all the cases studied. The costs of the analyzed networks were reduced, and the models generated always presented an increasingly smaller distance between the lower and upper limits for the variables studied. It can be concluded that the strategy used can contribute to a reassessment of existing water networks, as well as to the application in the design of new networks. © 2024, The Authors. All rights reserved.

关键词： mixed-integer linear programming

A functional equation for monomial functions

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arXiv 2024年

作者： Gselmann, Eszter Iqbal, Mehak Department of Analysis University of Debrecen P.O. Box 400 DebrecenH-4002 Hungary Doctoral School of Mathematical and Computational Sciences University of Debrecen P.O. Box 400 DebrecenH-4002 Hungary

Let F Ă K be fields with characteristic zero, n be a positive integer and κ ∊ K. In this paper, we determine those monomials f: F Ñ K of degree n for which (Formula presented) holds for all x ∊ F. We show that similar to the classical results, where additive functions were considered, the monomial functions in the equation can be represented with the aid of homomorphisms and higher-order *** Codes Primary 39B55, Secondary 39B72 © 2024, CC BY.

关键词： mixed-integer linear programming

A CONVEXIFICATION-BASED OUTER-APPROXIMATION METHOD FOR CONVEX AND NONCONVEX MINLP

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arXiv 2024年

作者： Peng, Zedong Cao, Kaiyu Furman, Kevin C. Li, Can Grossmann, Ignacio E. Bernal Neira, David E. Davidson School of Chemical Engineering Purdue University 480 Stadium Mall Drive West LafayetteIN47907 United States ExxonMobil Upstream Research Co 22777 Springwoods Village Parkway SpringTX77389 United States Department of Chemical Engineering Carnegie Mellon University PittsburghPA15213 United States Research Institute for Advanced Computer Science Universities Space Research Association Mountain ViewCA United States NASA Ames Research Center Mountain ViewCA United States

The advancement of domain reduction techniques has significantly enhanced the performance of solvers in mathematical programming. This paper delves into the impact of integrating convexification and domain reduction techniques within the Outer- Approximation method. We propose a refined convexification-based Outer-Approximation method alongside a Branch-and-Bound method for both convex and nonconvex mixed-integer Nonlinear programming problems. These methods have been developed and incorporated into the open-source mixed-integer Nonlinear Decomposition Toolbox for Pyomo-MindtPy. Comprehensive benchmark tests were conducted, validating the effectiveness and reliability of our proposed algorithms. These tests highlight the improvements achieved by incorporating convexification and domain reduction techniques into the Outer-Approximation and Branch-and-Bound methods. © 2024, CC BY-NC-ND.

关键词： mixed-integer linear programming

The mixed integer Trust Region Problem

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arXiv 2024年

作者： Pia, Alberto Del Department of Industrial and Systems Engineering Wisconsin Institute for Discovery University of Wisconsin-Madison MadisonWI United States

In this paper we consider the problem of minimizing a general quadratic function over the mixed integer points in an ellipsoid. This problem is strongly NP-hard, NP-hard to approximate within a constant factor, and optimal solutions can be irrational. In our main result we show that an arbitrarily good solution can be found in polynomial time, if we fix the number of integer variables. This algorithm provides a natural extension to the mixed integer setting, of the polynomial solvability of the trust region problem proven by Ye, Karmarkar, Vavasis, and Zippel. As a result, our findings pave the way for designing efficient trust region methods for mixed integer nonlinear optimization problems. The techniques that we introduce are of independent interest and can be used in other mixed integer nonlinear optimization problems. As an example, we consider the problem of minimizing a general quadratic function over the mixed integer points in a polyhedron. For this problem, we show that a solution satisfying weak bounds with respect to optimality can be computed in polynomial time, provided that the number of integer variables is fixed. It is well known that finding a solution satisfying stronger bounds cannot be done in polynomial time, unless P=NP. Copyright © 2024, The Authors. All rights reserved.

关键词： mixed-integer linear programming

Fair and Accurate Regression: Strong Formulations and Algorithms

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arXiv 2024年

作者： Deza, Anna Gómez, Andrés Atamtürk, Alper Industrial Engineering and Operations Research University of California United States Industrial and System Engineering University of Southern California United States

This paper introduces mixed-integer optimization methods to solve regression problems that incorporate fairness metrics. We propose an exact formulation for training fair regression models. To tackle this computationally hard problem, we study the polynomially-solvable single-factor and single-observation subproblems as building blocks and derive their closed convex hull descriptions. Strong formulations obtained for the general fair regression problem in this manner are utilized to solve the problem with a branch-and-bound algorithm exactly or as a relaxation to produce fair and accurate models rapidly. Moreover, to handle large-scale instances, we develop a coordinate descent algorithm motivated by the convex-hull representation of the single-factor fair regression problem to improve a given solution efficiently. Numerical experiments conducted on fair least squares and fair logistic regression problems show competitive statistical performance with state-of-the-art methods while significantly reducing training times. Copyright © 2024, The Authors. All rights reserved.

关键词： mixed-integer linear programming

Cycle-Configuration: A Novel Graph-theoretic Descriptor Set for Molecular Inference

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arXiv 2024年

作者： Song, Bowen Zhu, Jianshen Azam, Naveed Ahmed Haraguchi, Kazuya Zhao, Liang Akutsu, Tatsuya

In this paper, we propose a novel family of descriptors of chemical graphs, named cycle-configuration (CC), that can be used in the standard "two-layered (2L) model" of mol-infer, a molecular inference framework based on mixed integer linear programming (MILP) and machine learning (ML). Proposed descriptors capture the notion of ortho/meta/para patterns that appear in aromatic rings, which has been impossible in the framework so far. Computational experiments show that, when the new descriptors are supplied, we can construct prediction functions of similar or better performance for all of the 27 tested chemical properties. We also provide an MILP formulation that asks for a chemical graph with desired properties under the 2L model with CC descriptors (2L+CC model). We show that a chemical graph with up to 50 non-hydrogen vertices can be inferred in a practical time. © 2024, CC BY.

关键词： mixed-integer linear programming