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检索条件"主题词=MIXED integer linear programming"

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Open Vehicle Routing Problem with Time Windows in a Real Case Application

Open Vehicle Routing Problem with Time Windows in a Real Cas...

引用

Decision Aid Sciences and Application (DASA), International Conference on

作者： Rihem Nouir Imen Hamdi High Institute of Transport and Logistics University of Sousse Tunisia High Institute of Transport and Logistics University of Sousse TUNISIA MODILS Lab FSEG Sfax Tunisia

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

The vehicle routing problem is a complex yet crucial issue for companies' logistical optimization. Commonly, it is one of the most interesting problem as it is encountred frequently in real life while subjecting to different constraints to optimize various criteria. In this paper, we are interested in the Open Vehicle Routing Problem (OVRP) with a homogeneous fleet of vehicles and time windows constraints. It is proved to be strongly NP-hard. We propose a mixed integer linear programming (MILP) which is runned by CPLEX solver to obtain optimal solution for small sized problems. However, for large sized instances, we develop an innovative K-means based-algorithm. Then, the computational results prove its performance by testing real world instances.

关键词： Vehicle routing Companies Dispatching mixed integer linear programming Time factors Artificial intelligence Optimization Testing

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Virtual Power Plant Optimization Service-Benchmark of Solvers

Virtual Power Plant Optimization Service-Benchmark of Solver...

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Filipe Alves Rui Ribeiro Maria Petiz Ali Abbasi Pedro Carvalho Ricardo Faia Pedro Faria Zita Vaie Ricardo Rodrigues DTx—Digital Transformation CoLAB University of Minho Guimarães Portugal GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI-Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal

ISBN: (数字)9788396960160

ISBN: (纸本)9798350359718

This work provides a comprehensive analysis of the optimization of a Virtual Power Plant (VPP), that consider the presence of energy storage systems and controllable loads, through the benchmarking of various solvers. It delves into the development of a mixed integer linear programming (MILP) algorithm aiming at optimizing energy management and exchange within a VPP, that takes into account the operation of shift electric appliances and battery storage systems among different houses. The proposed model aims to minimize the overall electricity cost while ensuring that the energy demand of the system is met, the battery state of charge is maintained within safe operating limits, and the shift electrical appliance is scheduled. Furthermore, the experimental comparisons, the study evaluates the performance of commercial and open-source solvers in handling the complex dynamics of energy demand and supply. The findings highlight the importance of solver selection in enhancing the management, scalability, and reliability of VPP optimization strategies, offering insights into the optimal combination of programming interfaces and solvers for efficient VPP operation.

关键词： Energy consumption Scalability Virtual power plants programming Batteries mixed integer linear programming State of charge Reliability Optimization Load modeling

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Enhancing Data Processing Throughput in IoT-Edge-Cloud Systems Using Optimized Task Placement

Enhancing Data Processing Throughput in IoT-Edge-Cloud Syste...

引用

International Conference on Distributed Computing Systems

作者： Vishal Choudhary Peng Wang Suman Sourav Binbin Chen Center for Smart Systems (CS2) Singapore University of Technology and Design Singapore Information Systems Technology and Design Pillar (ISTD) Singapore University of Technology and Design Singapore

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

The rapid growth of Internet-of- Things (IoT) systems demands higher throughput to process sensor data. Existing data processing platforms use simple heuristics for task placement, which perform poorly. We proposed a Permutation-based Task Placement Optimizer (PTPO) that constructs a set of valid task placement permutations to formulate a mixed-integer linear programming problem. PTPO enables efficient real-time task placement for multiple dynamic applications. Our study highlights three key design factors: joint consideration of compute and network constraints, accurate profiling of resource needs, and fine-grained splitting of tasks across nodes. We demonstrate more than 80% throughput gain compared to state-of-the-art schemes using real-world IoT Applications.

关键词： Accuracy Throughput Data processing Real-time systems mixed integer linear programming Internet of Things Task analysis

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An integer programming Approach for Angular Coverage under Uncertainty

An Integer Programming Approach for Angular Coverage under U...

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IEEE Conference on Decision and Control

作者： Yuntian Zhang Chen Chen Shuxin Ding Fang Deng National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing China China Academy of Railway Sciences Corporation Limited Signal and Communication Research Institute Beijing China Traffic Management Laboratory for High-Speed Railway National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail Transit Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper investigates angular coverage under uncertainty (ACU). A compact integer programming (IP) formulation is developed to model the angular field-of-view (FoV) of sensors and probabilistic coverage under uncertainty. The IP formulation minimizes the weighted non-coverage probability over the target set as well as considering the practical colocation and budget constraints. Recognizing the non-linearity, non-convexity, and non-separability of ACU, we first introduce the reformulation-linearisation technique (RLT) to obtain a tractable mixed-integer linear programming model which provides a tight lower bound for the original problem. Further, we exploit the structure of the mathematical model and customize a branch-and-cut (B&C) algorithm to solve the derived problem exactly. We show that the solution for the derived problem can also solve the original problem based on the bounding scheme. Computational experiments on a series of problem instances ranging from moderate to large size scaling up to 4,000 dimensional decision variables reveal the effectiveness and efficiency of the proposed exact approach.

关键词： integer programming Lower bound Uncertainty Probabilistic logic Mathematical models Distance measurement Sensors mixed integer linear programming Computational efficiency IP networks

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MILP and Metaheuristic Approaches for HHCRSP with Optional Starting Point: Enhancing Efficiency in Home Healthcare

MILP and Metaheuristic Approaches for HHCRSP with Optional S...

引用

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Leo Schwartz Olivier Grunder Amir Hajjam El Hassani SINERGIES Université de Technologie de Belfort Montbéliard Belfort France

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This study presents an innovative extension to Home Healthcare Scheduling and Routing Problem (HHCRSP), introducing an Optional Starting Point (OSP) that allows for personalization of routes from the caregivers’ homes directly to patients. We developed a model using mixed integer linear programming (MILP) and a metaheuristic method inspired by the Greedy Randomized Adaptive Search Procedure (GRASP). This combination aims to make scheduling and routing more efficient, saving time and improving the quality of care for patients at home. Our results show that this new method can significantly enhance how home healthcare is delivered, making it more flexible and effective for both caregivers and patients.

关键词： Adaptation models Metaheuristics Medical services Routing mixed integer linear programming Information technology Research and development

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Resource Allocation and Service Provisioning for Softwarized Network Slicing Using Game Theory: A Techno-Economic Approach

Resource Allocation and Service Provisioning for Softwarized...

引用

IEEE India Conference (INDICON)

作者： Deborsi Basu Samriddha Sanyal Goutam Das Uttam Ghosh Raja Datta G. S. Sanyal School of Telecommunications Indian Institute of Technology Kharagpur India Dept. of Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur India Dept. of Computer Science and Data Science School of Applied Computational Sciences MMC TN USA

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Softwarized Network Slicing (SNS) is a forthcoming technological advancement that is going to transform conventional resource allocation techniques. Optimizing the flow of slice resources among the key network players, namely MVNOs (Mobile Virtual Network Operators), MIPs (Mobile Infrastructure Providers), and UEs (User Entities), is a complex and challenging task due to its complicated combinatorial nature. Moreover, maintaining the concurrent quality of service of end-users based on UE fairness is also difficult. Considering these factors, in this work, we have proposed a novel resource allocation strategy using a two-tier matching game between MIPs, MVNOs, and UEs, which not only minimizes the resource allocation cost but also maintains a decent UE satisfaction by providing desired UE SINR. An MILP (mixed integer linear programming) is formed for cost minimization, and the same is solved using a modified differed acceptance stable algorithm. We have compared our improvement with existing and latest state-of-the-art techniques.

关键词： Costs Network slicing Games Transforms Quality of service Interference Minimization mixed integer linear programming Resource management Signal to noise ratio

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DER Control: Harnessing DDPG and MILP for Enhanced Performance in Active Energy Management

DER Control: Harnessing DDPG and MILP for Enhanced Performan...

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Intelligent Systems, International Workshop on

作者： Paul Bauer Jonathan Kapfinger Maren Konrad Thomas Leopold Stefan Wilker Thilo Sauter TU Wien ICT Vienna Austria Dep. for Integrated Sensor Systems TU Wien ICT University for Continuing Education Krems

ISBN: (数字)9798350353303

ISBN: (纸本)9798350353310

Energy communities (EC) have emerged as a significant approach for enhancing the efficiency of renewable energy use in the low-power private sector. The increasing penetration of decentralized renewable energy resources (DERs), such as battery storage, photovoltaic (PV) systems, and heat pumps, has generated a need for advanced community-wide control mechanisms. Traditional model-based controllers, while effective, often require labor-intensive mathematical modeling and linear approximations, limiting their flexibility and accuracy. This paper introduces a deep reinforcement learning (DRL) approach, specifically the Deep Deterministic Policy Gradient (DDPG) algorithm, for the autonomous and optimal management of DERs within an EC. The algorithm's ability to enhance DER control is demonstrated by comparing its performance against the results of a model-derived optimization task solved by mixed integer linear programming (MILP). The results yielded by both algorithms are found to be comparable in quality in shaving demand peaks and power grid-friendly power consumption curves. The study underscores the potential of EC power consumption optimization and intermediate energy storage, thereby contributing to grid stability and carbon emission reduction.

关键词： Photovoltaic systems Renewable energy sources Power demand linear approximation Power system stability Approximation algorithms Mathematical models Stability analysis mixed integer linear programming Optimization

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Bit-Based MILP Modelling of Non-Bit-Permutation linear Layers for linear Cryptanalysis

Bit-Based MILP Modelling of Non-Bit-Permutation Linear Layer...

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Asia Joint Conference on Information Security (Asia JCIS)

作者： Abhilash Das Indian Institute of Technology Jammu India

ISBN: (数字)9798350380149

ISBN: (纸本)9798350380156

In recent years, there has been a notable increase in the adoption of mixed integer linear programming (MILP) by designers for formulating security arguments, while cryptan-alysts utilize it to find new distinguishers. Within cryptographic primitives, the linear layer plays a crucial role in resisting various attacks, thereby an efficient modelling of linear layers is a fundamental aspect of MILP modelling. The modelling of a linear layer is often related to how the XOR operation is modelled. This paper begins by demonstrating the naïve approach to model the XOR operation of $n$ binary variables using $2^{n-1}$ inequalities with no dummy variables. Subsequently, we introduce a new MILP modelling for the XOR operation of $n$ binary variables and establish that this approach requires only one inequality. We also reveal that XOR modelling of the MDS linear layers is inadequate for bit-based MILP in linear cryptanalysis. In response, we propose a bit-based MILP modelling for non-bit-permutation linear layers in the context of linear cryptanalysis because sometimes bit-based MILP models allow a more detailed analysis of cryptographic properties as compared to word-oriented models.

关键词： Analytical models Ciphers Toy manufacturing industry Asia Information security mixed integer linear programming Encryption Context modeling

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Integrated Scheduling and Location Considering Machine Mobility

Integrated Scheduling and Location Considering Machine Mobil...

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IEEE International Conference on Networking, Sensing and Control

作者： Jiangyuan Hu Peng Wu School of Economics and Management Fuzhou University Fuzhou P. R. China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

In this study, we address a new dynamic scheduling-location problem (DSLP), which is that incorporates movable machines. The objective is designed to optimize job scheduling and machine placement dynamically. For this strongly NP-hard problem, we formulate the problem as a mixed-integer linear programming (MILP) model. To further improve its computational efficiency and tighten the lower bounds for faster convergence to optimal solutions, we propose several valid inequalities based on the characteristics of the DSLP. Numerical experimental results for a case and randomly generated instances demonstrate significant improvements in system efficiency by allowing machine movement. The findings highlight the potential of extending traditional location scheduling frameworks to include dynamic elements, offering new insights and practical implications for complex industrial applications.

关键词： Job shop scheduling Operations research Processor scheduling Computational modeling Production Dynamic scheduling mixed integer linear programming Computational efficiency Sensors Convergence

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Community-Based Resilience of Distribution Systems Against Extreme Weather: A Time-Sharing Framework

Community-Based Resilience of Distribution Systems Against E...

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IEEE Green Energy and Systems Conference (IGESC)

作者： Bo Tu Kenneth McDonald Zhihua Qu Marwan A. Simaan Kristopher Davis Kelly Stevens RISES Center University of Central Florida Orlando Florida

ISBN: (数字)9798350379617

ISBN: (纸本)9798350379624

This paper introduces a time-sharing framework designed to maximize the use of cost-effective reclosers for reconfiguring the topology of power distribution systems, thereby energizing all yielded smaller segments in a rolling manner. The primary goal is to minimize the total installation capital required for distributed energy resources (DERs) to achieve a specified level of community resilience. The problem is formulated as a mixed-integer linear programming (MILP) problem, with the objective of reducing the installation costs of both DERs and reclosers. Given the radial topology of distribution power systems, this NP-hard problem is analytically tackled using two coupled approaches. The first approach presents an algorithm that optimizes the allocation of reclosers based on the power capacity of DERs. The second approach discusses an optimal DER selection algorithm that considers budgetary constraints and the per-unit cost of DERs. The proposed framework's effectiveness is validated through numerical simulations using a modified IEEE 33-bus distribution system. The results demonstrate that the time-sharing framework can enhance resilience during outages while operating within limited community budgets.

关键词： Costs NP-hard problem Power distribution Smart systems Numerical simulation Topology Distributed power generation mixed integer linear programming Resource management Resilience

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