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Per-Instance Algorithm Configuration in Homogeneous Instance Spaces: A Use Case in Reconfigurable Assembly Systems

APPLIED SCIENCES-BASEL 2024年第14期14卷 6035页

作者： Vargas, Daniel Guzman Gautama, Sidharta Uzunosmanoglu, Mehmet Raa, Birger Limere, Veronique Univ Ghent Dept Ind Syst Engn & Prod Design B-9000 Ghent Belgium Flanders MakeUGent ISyE CoreLab B-3920 Lommel Belgium Flanders MakeUGent CVAMO CoreLab B-3920 Lommel Belgium Univ Ghent Dept Business Informat & Operat Management B-9000 Ghent Belgium

The physical capabilities of a reconfigurable assembly system (RAS) increase the agility and responsiveness of the system in highly volatile market conditions. However, achieving optimal RAS utilization entails solving complex optimization problems effectively and efficiently. These optimizations often define homogenous sets of problem instances. While algorithm configuration in such homogeneous contexts traditionally adopts a "one-size-fits-all" approach, recent studies have shown the potential of per-instance algorithm configuration (PIAC) methods in these settings. In this work, we evaluate and compare the performance of different PIAC methods in this context, namely Hydra-a state-of-the-art PIAC method-and a simpler case-based reasoning (CBR) approach. We evaluate the impact of the tuning time budget and/or the number of unique problem instances used for training on each of the method's performance and robustness. Our experiments show that whilst Hydra fails to improve upon the default algorithm configuration, the CBR method can lead to 16% performance increase using as few as 100 training instances. Following these findings, we evaluate Hydra's methodology when applied to homogenous instance spaces. This analysis shows the limitations of Hydra's inference mechanisms in these settings and showcases the advantages of distance-based approaches used in CBR.

关键词： per-instance algorithm configuration reconfigurable assembly systems homogeneous instance spaces integer quadratic programming

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An integer programming Based Approach to Delivery Drone Routing under Load-Dependent Flight Speed

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DRONES 2023年第5期7卷 320-320页

作者： Nishira, Mao Ito, Satoshi Nishikawa, Hiroki Kong, Xiangbo Tomiyama, Hiroyuki Ritsumeikan Univ Grad Sch Sci & Engn Kusatsu Shiga 5258577 Japan Osaka Univ Grad Sch Informat Sci & Technol Suita Osaka 5650871 Japan Toyama Prefectural Univ Fac Engn Dept Intelligent Robot Imizu Toyama 9390398 Japan

Delivery drones have been attracting attention as a means of solving recent logistics issues, and many companies are focusing on their practical applications. Many research studies on delivery drones have been active for several decades. Among them, extended routing problems for drones have been proposed based on the Traveling Salesman Problem (TSP), which is used, for example, in truck vehicle routing problems. In parcel delivery by drones, additional constraints such as battery capacity, payload, and weather conditions need to be considered. This study addresses the routing problem for delivery drones. Most existing studies assume that the drone's flight speed is constant regardless of the load. On the other hand, some studies assume that the flight speed varies with the load. This routing problem is called the Flight Speed-Aware Traveling Salesman Problem (FSTSP). The complexity of the drone flight speed function in this problem makes it difficult to solve the routing problem using general-purpose mathematical optimization solvers. In this study, the routing problem is reduced to an integer programming problem by using linear and quadratic approximations of the flight speed function. This enables us to solve the problem using general-purpose mathematical optimization solvers. In experiments, we compared the existing and proposed methods in terms of solving time and total flight time. The experimental results show that the proposed method with multiple threads has a shorter solving time than the state-of-the-art method when the number of customers is 17 or more. In terms of total flight time, the proposed methods deteriorate by an average of 0.4% for integer quadratic programming and an average of 1.9% for integer cubic programming compared to state-of-the-art methods. These experimental results show that the quadratic and cubic approximations of the problem have almost no degradation of the solution.

关键词： delivery drones traveling salesman problem flight speed-aware traveling salesman problem integer quadratic programming integer cubic programming

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Aggregate Planning for Multi-product Assembly Lines with Reconfigurable Cells

Aggregate Planning for Multi-product Assembly Lines with Rec...

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IFIP WG 5.7 International Conference on Advances in Production Management Systems (APMS)

作者： Uzunosmanoglu, Mehmet Raa, Birger Limere, Veronique De Cock, Alexander Singh, Yogang Lopez, Angel J. Gautama, Sidharta Cottyn, Johannes Univ Ghent Dept Ind Syst Engn & Prod Design Ghent Belgium Flanders Make ISyE CoreLab Lommel Belgium Univ Ghent Dept Business Informat & Operat Management Ghent Belgium Flanders Make CodesingS CoreLab Lommel Belgium

ISBN: (纸本)9783030859022;9783030859015

This paper deals with aggregate planning of Reconfigurable Assembly Lines (RAL). The assembly line considered in this paper consists of hexagonal cells. These have multiple slots where processing modules can be inserted to perform certain operations. In addition, each cell has a single central slot where a central module can be inserted for inter-cellular and intra-cellular transportation of parts. Multiple products with different assembly sequences must be handled over multiple planning periods. An integer quadratic programming (IQP) model is proposed to solve the following problems simultaneously: (i) assigning processing modules and a central module to the cells;(ii) installation of the cells and conveyors between the cells;and (iii) routing products, ensuring that availability of the resources is not exceeded. The assembly line should be reconfigured over time to adapt to possible product functionality and demand changes at minimum reconfiguration, operational and material handling costs while ensuring the demand is met within each period. The IQP model is implemented and solved for an illustrative problem and its extensions using Gurobi.

关键词： Reconfigurable assembly lines Aggregate planning integer quadratic programming Optimisation models

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The Parameterised Complexity of Computing the Maximum Modularity of a Graph

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ALGORITHMICA 2020年第8期82卷 2174-2199页

作者： Meeks, Kitty Skerman, Fiona Univ Glasgow Sch Comp Sci Glasgow Lanark Scotland Univ Bristol Heilbronn Inst Math Res Dept Math Bristol Avon England

The maximum modularity of a graph is a parameter widely used to describe the level of clustering or community structure in a network. Determining the maximum modularity of a graph is known to be NP\-complete in general, and in practice a range of heuristics are used to construct partitions of the vertex-set which give lower bounds on the maximum modularity but without any guarantee on how close these bounds are to the true maximum. In this paper we investigate the parameterised complexity of determining the maximum modularity with respect to various standard structural parameterisations of the input graph G. We show that the problem belongs to FPT when parameterised by the size of a minimum vertex cover for G, and is solvable in polynomial time whenever the treewidth or max leaf number of G is bounded by some fixed constant;we also obtain an FPT algorithm, parameterised by treewidth, to compute any constant-factor approximation to the maximum modularity. On the other hand we show that the problem is W[1]-hard (and hence unlikely to admit an FPT algorithm) when parameterised simultaneously by pathwidth and the size of a minimum feedback vertex set.

关键词： Modularity Community detection integer quadratic programming Vertex cover Pathwidth

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Feature Matching With Intra-Group Sparse Model

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IEEE TRANSACTIONS ON MULTIMEDIA 2020年第8期22卷 2074-2085页

作者： Jiang, Bo Tang, Jin Luo, Bin Anhui Univ Sch Comp Sci & Technol Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230601 Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Hefei 230601 Peoples R China

Feature matching is a fundamental problem in computer vision area. In many real applications, one can usually obtain some potential (candidate) matches C by using some discriminative feature descriptors, such as SIFT descriptor. Then, the feature matching problem can be formulated as the problem of trying to select the correct matches S from the potential match set C. In this paper, we propose to solve matches selection by developing a novel intra-group sparse matching (IGSM) model. Our IGSM is motivated by a simple observation that the potential match set C can be divided into several non-overlapping groups C-i, among which the correct matches S are uniformly distributed. We thus develop an intra-group selection model to conduct matches selection at the intra-group level to incorporate the one-to-one matching constraint more in matches selection process. Our IGSM model has three main advantages: (1) The selection mechanism is parameter-free;(2) it generates an intra-group sparse solution which better maintains the one-to-one matching constraint in nature;(3) a simple yet effective update algorithm has been derived to solve IGSM model. The optimality and convergence of the algorithm are theoretically guaranteed. Experimental results on several image feature matching datasets show the effectiveness and efficiency of the proposed IGSM matching method.

关键词： Feature matching integer quadratic programming matches selection sparse models

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Ascent with quadratic assistance for the construction of exact experimental designs

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COMPUTATIONAL STATISTICS 2020年第2期35卷 775-801页

作者： Filova, Lenka Harman, Radoslav Comenius Univ Fac Math Phys & Informat Bratislava Slovakia Johannes Kepler Univ Linz Linz Austria

In the area of statistical planning, there is a large body of theoretical knowledge and computational experience concerning so-called optimal approximate designs of experiments. However, for an approximate design to be realizable, it must be converted into an exact, i.e., integer, design, which is usually done via rounding procedures. Although rapid, rounding procedures often yield worse exact designs than heuristics that do not require approximate designs at all. In this paper, we build on an alternative principle of utilizing optimal approximate designs for the computation of optimal, or nearly-optimal, exact designs. The principle, which we call ascent with quadratic assistance (AQuA), is an integer programming method based on the quadratic approximation of the design criterion in the neighborhood of the optimal approximate information matrix. To this end, we present quadratic approximations of all Kiefer's criteria with an integer parameter, including D- and A-optimality and, by a model transformation, I-optimality. Importantly, we prove a low-rank property of the associated quadratic forms, which enables us to use AQuA efficiently and apply it to large design spaces. We numerically demonstrate the robustness and superior performance of the proposed method for selected statistical models under various types of experimental constraints. We also show how can iterative application of AQuA be used for a stratified information-based subsampling of large datasets under a lower bound on the quality and an upper bound on the cost of the subsample.

关键词： Optimal design integer quadratic programming Experimental constraints Mixture designs Subsampling

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Detection of Code Spread OFDM Based on 0-1 integer quadratic programming

Detection of Code Spread OFDM Based on 0-1 Integer Quadratic...

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Conference on Wireless Sensing, Localization and Processing VII

作者： Elghariani, Ali Zoltowski, Michael D. Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (纸本)9780819490827

In this paper we introduce integer quadratic programming (MIQP) approach to optimally detect QPSK Code Spread OFDM (CS-OFDM) by formulating the problem as a combinatorial optimization problem. The Branch and Bound (BB) algorithm is utilized to solve this integer quadratic programming problem. Furthermore, we propose combined preprocessing steps that can be applied prior to BB so that the computational complexity of the optimum receiver is reduced. The first step in this combination is to detect as much as possible symbols using procedures presented in [9], which is basically based on the gradient of quadratic function. The second step detects the undetected symbols from the first step using MMSE estimator. The result of the latter step will be used to predict the initial upper bound of the BB algorithm. Simulation results show that the proposed preprocessing combination when applied prior to BB provides optimal performance with a significantly reduced computational complexity.

关键词： CSOFDM integer quadratic programming Branch and Bound MMSE Computational complexity

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Detection of Code Spread OFDM Based on 0-1 integer quadratic programming

Detection of Code Spread OFDM Based on 0-1 Integer Quadratic...

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46th Asilomar Conference on Signals, Systems and Computers

作者： Elghariani, Ali Zoltowski, Michael D. Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (纸本)9781467350518

In this paper we introduce integer quadratic programming (IQP) approach to detect QPSK Code Spread OFDM (CSOFDM) signal by formulating the problem as a combinatorial optimization problem. The Branch and Bound (BB) algorithm is utilized to solve this integer quadratic programming problem. Furthermore, we propose preprocessing procedures that can be applied prior to BB so that the computational complexity of the optimum receiver is reduced. The basic idea of the proposed preprocessing is to introduce a good choice of upper bound for the BB search tree and/or reduce the search space of the BB problem. Performance and complexity computations of these preprocessing are investigated. Simulation results show that the proposed preprocessing when applied prior to BB provides optimal or near optimal performance with a significantly reduced computational complexity.

关键词： CSOFDM integer quadratic programming Branch and Bound MMSE LLR Computational complexity

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A decision space algorithm for multiobjective convex quadratic integer optimization

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COMPUTERS & OPERATIONS RESEARCH 2021年 134卷 105396-105396页

作者： De Santis, Marianna Eichfelder, Gabriele Sapienza Univ Roma Dipartimento Ingn Informat Automat & Gest Via Ariosto 25 I-00185 Rome Italy Tech Univ Ilmenau Inst Math Po 10 05 65 D-98684 Ilmenau Germany

We present a branch-and-bound algorithm for minimizing multiple convex quadratic objective functions over integer variables. Our method looks for efficient points by fixing subsets of variables to integer values and by using lower bounds in the form of hyperplanes in the image space derived from the continuous relaxations of the restricted objective functions. We show that the algorithm stops after finitely many fixings of variables with detecting both the full efficient and the nondominated set of multiobjective strictly convex quadratic integer problems. A major advantage of the approach is that the expensive calculations are done in a preprocessing phase so that the nodes in the branch-and-bound tree can be enumerated fast. We show numerical experiments on biobjective instances and on instances with three and four objectives.

关键词： Multiobjective optimization Convex quadratic optimization integer quadratic programming Branch-and-bound algorithm

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Maximizing Signal to Interference Noise Ratio for Massive MIMO: A Mathematical programming Approach 2

Maximizing Signal to Interference Noise Ratio for Massive MI...

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2nd South American Colloquium on Visible Light Communications (IEEE SACVLC)

作者： Adasme, Pablo Soto, Ismael San Juan, Enrique Seguel, Fabian Firoozabadi, Ali Dehghan Univ Santiago de Chile Dept Elect Engn Estn Cent Ave Ecuador 3519 Santiago Chile Univ Tecnol Metropolitana Dept Elect Av Jose Pedro Alessandri 1242 Santiago Chile

ISBN: (纸本)9781728163413

In this paper, we consider the problem of maximizing the worst user signal to interference noise ratio (SINR) for Massive Multiple Input Multiple Output (MaMIMO) systems subject to antenna assignment and multiuser interference constraints. In particular, we aim to choose a subset of antennas from a larger set while imposing a maximum interference value allowed in the system. Notice that MaMIMO technology has recently been considered as a strong candidate for 5G wireless communications by the research community as it provides better performance in terms of data rate. It also allows to transmit in higher frequency bands with strong signal performance and reliability. In order to propose new optimization models for this problem, we model SINR by using Manhattan, Euclidean and Infinity distance norms. Thus, we obtain integer quadratic and linear programming models that allow to obtain optimal solutions for the problem. As such, the proposed models can be used as a source of comparison for any exact or approximation method to be developed as part of future research. Finally, we propose a local search algorithm that allows to obtain feasible solutions in short CPU time. Our preliminary numerical results indicate that the linear models are preferable and that the local search approach obtains better solutions than the quadratic model for some of the tested instances in significantly less CPU time.

关键词： integer quadratic programming Mixed integer linear programming Resource allocation of Massive MIMO systems Manhattan Euclidean and Infinity distance norms

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