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Supply Chain Analytics 2024年 8卷

作者： Mayadunne, Sanjaya Rajagopalan, Hari K. Sharer, Elizabeth Georgia Gwinnett College United States Francis Marion University United States Dean School of Business Eugene A. Fallon Jr. Professor of Management at Francis Marion University United States

Warehouse layout optimization is critical to inventory and logistics management in organizations. In many instances, limited warehouse space is a constraint and a barrier to expanding operations and increasing demand. We present a multi-step solution using mixed integer programming to improve space utilization and increase order retrieval and fulfillment efficiency. We present a real-world case study to demonstrate the applicability and efficiency of the proposed mixed integer programming heuristics at a large distribution center. © 2024

关键词： mixed integer programming Order Retrieval and Fulfillment, Multi step heuristic, Throughput capacity Warehouse Optimization

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A mixed-integer programming Formulation for a Simplified Model of the Double Row Layout Problem

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Wuhan University Journal of Natural Sciences 2023年第5期28卷 433-440页

作者： GUAN Jian LIN Geng FENG Huibin RUAN Zhiqiang Modern Educational Technology Center Minjiang UniversityFuzhou 350121FujianChina College of Mathematics and Data Science Minjiang UniversityFuzhou 350121FujianChina College of Computer and Control Engineering Minjiang UniversityFuzhou 350121FujianChina

The double row layout problem(DRLP)is to assign facilities on two rows in parallel so that the total cost of material handling among facilities is *** it is vital to save cost and enhance productivity,the DRLP plays an important role in many application ***,it is very hard to handle the DRLP because of its complex *** this paper,we consider a new simplified model for the DRLP(SM-DRLP)and provide a mixed integer programming(MIP)formulation for *** continuous decision variables of the DRLP are divided into two parts:start points of double rows and adjustable clearances between adjacent *** former one is considered in the new simplified model for the DRLP with the purpose of maintaining solution quality,while the latter one is not taken into account with the purpose of reducing computational *** evaluate its performance,our SM-DRLP is compared with the model of a general DRLP and the model of another simplified *** experimental results show the efficiency of our proposed model.

关键词： facility layout mixed integer programming double row layout problem production optimization manufacturing design

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A Method for the Inverse QSAR/QSPR Based on Artificial Neural Networks and mixed integer Linear programming 20

A Method for the Inverse QSAR/QSPR Based on Artificial Neura...

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10th International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB)

作者： Chiewvanichakorn, Rachaya Wang, Chenxi Zhang, Zhe Shurbevski, Aleksandar Nagamochi, Hiroshi Akutsu, Tatsuya Kyoto Univ Sakyo Grad Sch Informat Dept Appl Math & Phys Kyoto Japan Kyoto Univ Uji Bionformat Ctr Inst Chem Res Kyoto Japan

ISBN: (纸本)9781450376761

In this study,we propose a novel method for the inverse QSAR/QSPR. Given a set of chemical compounds G and their values a(G) of a chemical property, we define a feature vector f(G) of each chemical compound G. By using a set of feature vectors as training data, the first phase of our method constructs a prediction function psi with an artificial neural network (ANN) so that psi(f(G)) takes a value nearly equal to a(G) for many chemical compounds G in the set. Given a target value a* of the chemical property, the second phase infers a chemical structure G* such that a(G*) = a* in the following way. We compute a vector f* such that psi(f*) = a*, where finding such a vector f* is formulated as a mixed integer linear programming problem (MILP). Finally we generate a chemical structure G* such that f(G*) = f*. For acyclic chemical compounds and some chemical properties such as heat of formation, boiling point, and retention time, we conducted some computational experiments with our method.

关键词： QSAR/QSPR artificial neural networks mixed integer programming feature vectors chemical graphs

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A mixed-integer programming analysis of the structure of a Florida-based cattle feeding industry.

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Southern Journal of Agricultural Economics 1986年第2期18卷 125-137页

作者： Moseley, A.E. Spreen, T.H. Pheasant, J.W. Food & Resource Economics Dept. Univ. of Florida USA.

The objective of this paper is to estimate the cost of retaining feeder cattle in Florida, feeding these cattle to slaughter weights, slaughtering them, and distributing the meat to retail outlets. A mixed integer programming model is developed. The optimal number and location of feedlots and slaughter plants are determined. The results indicate that at production levels exceeding 600 000 head, the cost of producing carcass beef in the State is comparable to the average for the United States. -Authors

关键词： beef cattle plant location mixed integer programming economic-engineering

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OAMIP: Optimizing ANN Architectures Using mixed-integer programming 1

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20th International Conference on the Integration of Constraint programming, Artificial Intelligence, and Operations Research (CPAIOR)

作者： ElAraby, Mostafa Wolf, Guy Carvalho, Margarida Mila Quebec AI Inst Montreal PQ Canada CIRRELT Montreal PQ Canada Univ Montreal Dept Comp Sci & Operat Res Montreal PQ Canada Univ Montreal Dept Math & Stat Montreal PQ Canada

ISBN: (数字)9783031332715

ISBN: (纸本)9783031332708;9783031332715

In this work, we concentrate on the problem of finding a set of neurons in a trained neural network whose pruning leads to a marginal loss in accuracy. To this end, we introduce Optimizing ANN Architectures using mixed-integer programming (OAMIP) to identify critical neurons and prune non-critical ones. The proposed OAMIP uses a mixed-integer Program (MIP) to assign importance scores to each neuron in deep neural network architectures. The impact of simultaneous neuron pruning on the main learning tasks guides the neurons' scores. By carefully devising the objective function of the MIP, we drive the solver to minimize the number of critical neurons (i.e., with high importance score) that maintain the overall accuracy of the trained neural network. Our formulation identifies optimized sub-network architectures that generalize across different datasets, a phenomenon known as lottery ticket optimization. This optimized architecture not only performs well on a single dataset but also generalizes across multiple ones upon retraining of network weights. Additionally, we present a scalable implementation of our pruning methodology by decoupling the importance scores across layers using auxiliary networks. Finally, we validate our approach experimentally, showing its ability to generalize on different datasets and architectures.

关键词： Pruning Neural Networks mixed integer programming Neurons Ranking Sparse Neural Networks

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mixed integer Linear programming model for Crude Palm Oil Supply Chain Planning 4

Mixed Integer Linear Programming model for Crude Palm Oil Su...

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4th International Conference on Operational Research (InteriOR)

作者： Sembiring, Pasukat Mawengkang, Herman Sadyadharma, Hendaru Bu'ulolo, F. Fajriana Univ Sumatera Utara Dept Math Medan Indonesia Univ HKBP Nommensen Medan Indonesia Malaikulsalah Univ Lhokseumawe Aceh Indonesia

The production process of crude palm oil (CPO) can be defined as the milling process of raw materials, called fresh fruit bunch (FFB) into end products palm oil. The process usually through a series of steps producing and consuming intermediate products. The CPO milling industry considered in this paper does not have oil palm plantation, therefore the FFB are supplied by several public oil palm plantations. Due to the limited availability of FFB, then it is necessary to choose from which plantations would be appropriate. This paper proposes a mixed integer linear programming model the supply chain integrated problem, which include waste processing. The mathematical programming model is solved using neighborhood search approach.

关键词： Optimization Crude Palm Oil mixed integer programming Supply Chain Neighborhood Search

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A Novel Method for the Inverse QSAR/QSPR based on Artificial Neural Networks and mixed integer Linear programming with Guaranteed Admissibility 11

A Novel Method for the Inverse QSAR/QSPR based on Artificial...

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13th International Joint Conference on Biomedical Engineering Systems and Technologies

作者： Azam, Naveed Ahmed Chiewvanichakorn, Rachaya Zhang, Fan Shurbevski, Aleksandar Nagamochi, Hiroshi Akutsu, Tatsuya Kyoto Univ Dept Appl Math & Phys Kyoto Japan Kyoto Univ Bioinformat Ctr Inst Chem Res Uji Japan

ISBN: (纸本)9789897583988

Inverse QSAR/QSPR is a well-known approach for computer-aided drug design. In this study, we propose a novel method for inverse QSAR/QSPR using artificial neural network (ANNs) and mixed integer linear programming In this method, we introduce a feature function f that converts each chemical compound G into a vector f (G) of several descriptors of G. Next, given a set of chemical compounds along with their chemical properties, we construct a prediction function iv with an ANN so that psi( f (G)) takes a value nearly equal to a given chemical property for many chemical compounds G in the set. Then, given a target value y* of the chemical property, we conversely infer a chemical structure G K having the desired property y* in the following way. We formulate the problem of finding a vector x* such that (i) psi(x*) = y and (ii) there exists a chemical compound G* such that f(G*) = x* (if one exists over all vectors x* in (i)) as a mixed integer linear programming problem (MILP). In an existing method for the inverse QSAR/QSPR, the second condition (ii) was not guaranteed. For acyclic chemical compounds and some chemical properties such as heat of formation, boiling point, and retention time, we conducted computational experiments.

关键词： QSAR/QSPR Artificial Neural Networks mixed integer programming Feature Vectors Chemical Graphs

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A mixed integer Linear programming Approach to Minimum-Time Trajectory Generation Considering Traffic Lights for Class 8 Vehicles 4

A Mixed Integer Linear Programming Approach to Minimum-Time ...

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4th Modeling, Estimation, and Control Conference (MECC)

作者： Ward, Jacob Ellison, Evan Bevly, David Brown, Lowell Auburn Univ Dept Mech Engn Auburn AL 36849 USA Daimler Truck North Amer Portland OR 97217 USA

This work presents a new methodology for planning and tracking a time-optimal trajectory for class 8 vehicles on roads in which traffic lights exist. This approach takes the non-convex nature of the problem and simplifies it by introducing a set of integer decision variables. Due to the planned trajectory taking place over distances greater than a kilometer, an inner loop controller is developed to refine the high-level trajectory and guarantee that the ego vehicle does not cross into intersections during a red light phase. In order to account for model differences between the inner loop and outer loop systems an event triggered replan methodology is introduced. This event triggered replan allows for the high-level plan to adjust the trajectory to the current states of the ego vehicle without creating an unnecessary computational burden. This full system is then evaluated on three test scenarios in which the ego vehicle must navigate through two intersections with varying red light phases. The tests successfully demonstrate the feasibility of this method for generating safe, time-optimal trajectories for class 8 vehicles. Copyright (c) 2024 The Authors.

关键词： Trajectory Optimization Smart Transportation mixed integer programming V2X

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Sparse Signal Processing for Massive Connectivity via mixed-integer programming

Sparse Signal Processing for Massive Connectivity via Mixed-...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Liang, Shuang Shi, Yuanming Zhou, Yong ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China

ISBN: (纸本)9781665443852

Massive connectivity is a critical challenge of Internet of Things (IoT) networks. In this paper, we consider the grant-free uplink transmission of an IoT network with a multi-antenna base station (BS) and a large number of single-antenna IoT devices. Due to the sporadic nature of IoT devices, we formulate the joint activity detection and channel estimation (JADCE) problem as a group-sparse matrix estimation problem. Although many algorithms have been proposed to solve the JADCE problem, most of them are developed based on compressive sensing technique, yielding suboptimal solutions. In this paper, we first develop an efficient weighted l(1)-norm minimization algorithm to better approximate the group sparsity than the existing mixed l(1)/l(2)-norm minimization. Although an enhanced estimation performance in terms of the mean squared error (MSE) can be achieved, the weighted l(1)-norm minimization algorithm is still a convex relaxation of the original group-sparse matrix estimation problem, yielding a suboptimal solution. To this end, we further reformulate the JADCE problem as a mixed integer programming (MIP) problem, which can be solved by using the branch-and-bound method. As a result, we are able to obtain an optimal solution of the JADCE problem, which can be adopted as an upper bound to evaluate the effectiveness of the existing algorithms. Moreover, we also derive the minimum pilot sequence length required to fully recover the estimated matrix in the noiseless scenario. Simulation results show the performance gains of the proposed optimal algorithm over the proposed weighted l(1)-norm algorithm and the conventional mixed l(1)/l(2)-norm algorithm. Results also show that the proposed algorithms require a short pilot sequence than the conventional algorithm to achieve the same estimation performance.

关键词： Massive connectivity joint activity detection and channel estimation mixed integer programming optimal solution

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Budget-Constrained Regression Model Selection Using mixed integer Nonlinear programming

Budget-Constrained Regression Model Selection Using Mixed In...

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作者： Zhang, Jingying University of Arkansas

学位级别：Ph.D.

Regression analysis fits predictive models to data on a response variable and corresponding values for a set of explanatory variables. Often data on the explanatory variables come at a cost from commercial databases, so the available budget may limit which ones are used in the final model. In this dissertation, two budget-constrained regression models are proposed for continuous and categorical variables respectively using mixed integer Nonlinear programming (MINLP) to choose the explanatory variables to be included in solutions. First, we propose a budget-constrained linear regression model for continuous response variables. Properties such as solvability and global optimality of the proposed MINLP are established, and a data transformation is shown to signicantly reduce needed big-Ms. Illustrative computational results on realistic retail store data sets indicate that the proposed MINLP outperforms the statistical software outputs in optimizing the objective function under a limit on the number of explanatory variables selected. Also our proposed MINLP is shown to be capable of selecting the optimal combination of explanatory variables under a budget limit covering cost of acquiring data sets. A budget-constrained and or count-constrained logistic regression MINLP model is also proposed for categorical response variables limited to two possible discrete values. Alternative transformations to reduce needed big-Ms are included to speed up the solving process. Computational results on realistic data sets indicate that the proposed optimization model is able to select the best choice for an exact number of explanatory variables in a modest amount of time, and these results frequently outperform standard heuristic methods in terms of minimizing the negative log-likelihood function. Results also show that the method can compute the best choice of explanatory variables affordable within a given budget. Further study adjusting the objective function to minimize the Bayesi

关键词： Budget Constraint mixed integer programming Nonlinear programming Regression

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