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A mixed integer linear programming model for quarantine-based home healthcare scheduling under uncertainty

Healthcare Analytics 2024年 6卷 100356-100356页

作者： Nabavizadeh, Najmeh Kayvanfar, Vahid Rafiee, Majid Department of Industrial Engineering Sharif University of Technology Tehran Iran Division of Engineering Management and Decision Sciences College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha 34110 Qatar

Home healthcare companies (HHC) have emerged as vital alternatives to traditional hospitals, particularly in meeting the healthcare needs of individuals within the comfort of their homes. The COVID-19 pandemic has amplified the significance of HHC services, offering a crucial alternative for patients and the elderly to follow quarantine protocols while receiving essential healthcare at home. Consequently, HHC companies must align their planning strategies with the World Health Organization (WHO) health guidelines. This research introduces a mixed integer linear programming (MILP) model tailored for home healthcare services during COVID-19, aiming to ensure strict adherence to quarantine protocols while enhancing service efficiency and quality. The proposed vehicle routing problem with pickup/delivery and time window formulation incorporates critical elements such as patient and caregiver classification, work and break regulations adherence, workload balancing, and multi-depot capabilities. The model addresses uncertain demand and service times through a stochastic programming approach to enhance practicality. K-means clustering is applied to streamline scenarios, with a sensitivity analysis determining the optimal number of clusters. Additionally, measures intrinsic to stochastic programming, such as the Expected Value of Perfect Information (EVPI) and Value of Stochastic Solution (VSS), are computed for comprehensive analysis. © 2024 The Authors

关键词： COVID-19 Home healthcare mixed integer linear programming Pickup and delivery Stochastic programming Vehicle routing

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mixed-integer-linear-programming Model for the Charging Scheduling of Electric Vehicle Fleets 10

Mixed-Integer-Linear-Programming Model for the Charging Sche...

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10th International Conference on Advanced Computer Information Technologies (ACIT)

作者： Bodenschatz, Nicki Eider, Markus Berl, Andreas Tech Hsch Deggendorf Fac Appl Comp Sci Deggendorf Germany

ISBN: (数字)9781728167602

ISBN: (纸本)9781728167602

The number of electric vehicles is steadily increasing of the past few years. This transition to electric vehicles bears the challenge, to integrate the charging processes into the grid without overstressing it. To prevent this, research has tackled lately the scheduling of electric vehicle charging. Especially the charging of electric vehicle fleets is in the focus of research. There are already different solution approaches to increase the grid stability, to increase the intake of locally produced renewable energy or simply to reduce the cost. However, all these solution approaches use different mathematical models with different parameters to represent the charging scheduling problem. This results in the problem that each model is applicable for a special use case only, other use cases might need other parameters for the scheduling of the electric vehicle fleet. To ease this problem, this paper provides a detailed mathematical model for the cost minimization of a general electric fleet in the form of a mixed-integer-linear-program. In order to do this, the paper shows that different research approaches use different parameters in their solutions. Afterwards, the paper presents a general overview of technical limitations for the electric fleets. On foundation of these limitations a mixed-integer-linear-program model for a wide range of electric fleets is established. Also, the paper provides options to extend the model in order to improve the result of an optimal schedule.

关键词： Mathematical model Electric vehicle fleet charging Scheduling mixed integer linear programming

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Grossone Methodology for Lexicographic mixed-integer linear programming Problems 3rd

Grossone Methodology for Lexicographic Mixed-Integer Linear ...

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3rd International Conference on Numerical Computations - Theory and Algorithms (NUMTA)

作者： Cococcioni, Marco Cudazzo, Alessandro Pappalardo, Massimo Sergeyev, Yaroslav D. Univ Pisa Pisa Italy Univ Calabria Arcavacata Di Rende Italy Lobachevsky State Univ Nizhnii Novgorod Russia

ISBN: (纸本)9783030406165

In this work we have addressed lexicographic multi-objective linear programming problems where some of the variables are constrained to be integer. We have called this class of problems LMILP, which stands for Lexicographic mixed integer linear programming. Following one of the approach used to solve mixed integer linear programming problems, the branch and bound technique, we have extended it to work with infinitesimal/infinite numbers, exploiting the Grossone Methodology. The new algorithm, called GrossBB, is able to solve this new class of problems, by using internally the GrossSimplex algorithm (a recently introduced Grossone extension of the well-known simplex algorithm, to solve lexicographic LP problems without integer constraints). Finally we have illustrated the working principles of the GrossBB on a test problem.

关键词： Multi-objective optimization Lexicographic optimization mixed integer linear programming Numerical infinitesimals

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Optimal Load Control and Scheduling through Distributed mixed-integer linear programming 30

Optimal Load Control and Scheduling through Distributed Mixe...

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30th Mediterranean Conference on Control and Automation (MED)

作者： Yfantis, Vassilios Motsch, William Bach, Nico Wagner, Achim Ruskowski, Martin Tech Univ Kaiserslautern Chair Machine Tools & Control Syst Dept Mech & Proc Engn D-67663 Kaiserslautern Germany Technol Initiat SmartFactory KL eV D-67663 Kaiserslautern Germany German Res Ctr Artificial Intelligence DFKI D-67663 Kaiserslautern Germany

ISBN: (纸本)9781665406734

This paper presents a mixed-integer linear programming-based optimization model for simultaneous optimal load control and scheduling of distributed systems coupled through their energy consumptions. The subsystems are able to adjust their energy consumption during the execution of a task and aim at minimizing their completion time and energy cost. The overall problem is solved in a distributed fashion, where each subsystem optimizes its individual operation without sharing sensitive information. To this end, dual decomposition is employed and a new algorithm to update the dual variables is presented. It relies on a transformation of the gradient of the quadratically approximated dual function and the subsequent solution of a regression problem. The proposed algorithm makes efficient use of information collected in previous iterations. The solution obtained from the distributed optimization of the subsystems is compared to both a decentral and a system-wide solution, showing that the distributed solution lies close to the global optimum of the process.

关键词： Energy consumption Costs Automation Approximation algorithms mixed integer linear programming Task analysis Optimization

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Floorplanning for Partially-Reconfigurable FPGA Systems via mixed-integer linear programming 22

Floorplanning for Partially-Reconfigurable FPGA Systems via ...

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22nd IEEE Annual International Symposium on Field-Programmable Custom Computing Machines ((FCCM)

作者： Rabozzi, Marco Lillis, John Santambrogio, Marco D. Politecn Milan Milan Italy Univ Illinois Chicago IL USA

ISBN: (纸本)9781479951116

The aim of this paper is to show a novel floorplanner based on mixed-integer linear programming (MILP), providing a suitable formulation that makes the problem tractable using state-of-the-art solvers. The proposed method takes into account an accurate description of heterogeneous resources and partially reconfigurable constraints of recent FPGAs. A global optimum can be found for small instances in a small amount of time. For large instances, with a time limited search, a 20% average improvement can be achieved over floorplanners based on simulated annealing. Our approach allows the designer to customize the objective function to be minimized, so that different weights can be assigned to a linear combination of metrics such as total wire length, aspect ratio and area occupancy.

关键词： Algorithm design and analysis Field programmable gate arrays Kernel linear programming Measurement Simulated annealing Wires FPGA Floorplanning Reconfigurable Computing Field programmable gate arrays Algorithm design and analysis simulated annealing circuit layout Rope ELECTRIC WIRE linear programming Kernel mixed integer linear programming

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A mixed-integer linear programming Formulation for Human Multi-Robot Task Allocation 30

A Mixed-Integer Linear Programming Formulation for Human Mul...

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30th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

作者： Lippi, Martina Marino, Alessandro Roma Tre Univ Rome Italy Univ Cassino & Southern Lazio Cassino Italy

ISBN: (纸本)9781665404921

In this work, we address a task allocation problem for human multi-robot settings. Given a set of tasks to perform, we formulate a general mixed-integer linear programming (MILP) problem aiming at minimizing the overall execution time while optimizing the quality of the executed tasks as well as human and robotic workload. Different skills of the agents, both human and robotic, are taken into account and human operators are enabled to either directly execute tasks or play supervisory roles;moreover, multiple manipulators can tightly collaborate if required to carry out a task. Finally, as realistic in human contexts, human parameters are assumed to vary over time, e.g., due to increasing human level of fatigue. Therefore, online monitoring is required and re-allocation is performed if needed. Simulations in a realistic scenario with two manipulators and a human operator performing an assembly task validate the effectiveness of the approach.

关键词： Collaboration Switches Manipulators Fatigue Real-time systems mixed integer linear programming Resource management

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mixed-integer linear programming for transition-independent decentralized MDPs 06

Mixed-integer linear programming for transition-independent ...

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Fifth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS

作者： Jianhui, Wu Durfee, Edmund H. EECS Department University of Michigan Ann Arbor MI 48109 United States

No abstract available

ISBN: (纸本)1595933034

No abstract available

关键词： mixed integer linear programming transition-independent decentralized MDP MDP piecewise linear approximation

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Benchmark of mixed-integer linear programming formulations for district heating network design

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ENERGY 2024年 308卷

作者： Lambert, Jerry Ceruti, Amedeo Spliethoff, Hartmut Tech Univ Munich Chair Energy Syst TUM Sch Engn & Design Boltzmannstr 15 D-85747 Garching Germany

Optimal routing and investment decisions are key design criteria to reduce the high investment costs of district heating systems. However, these optimization problems have prohibitively high computational costs for large districts. Four different mixed-integer linear optimization frameworks are benchmarked in this study in order to compare their computational scaling. The frameworks exhibit significant differences in solving times for synthetic benchmarks and real-world urban districts of up to 9587 potential edges. The new open-source framework topotherm, developed for this work, exhibits the best computational performance when only one time step is optimized. The comparison between the models R & eacute;simont, DHmin, DHNx, and topotherm shows two main trends. First, fewer integer variables do not necessarily translate to lower solving times, and second, using redundant binary variables, which introduce symmetries into the constraints, leads to higher solving times. None of the considered optimization frameworks is able to solve the largest benchmark problems for five time steps within the allowed time limit and tolerance. These findings highlight the challenges of and pressing need to develop efficient models for simultaneous optimization of district heating network topology, pipe sizing, and operation.

关键词： Energy system optimization District heating mixed integer linear programming Benchmark Computational scaling

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mixed-integer linear programming models for the paint waste management problem

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2021年 151卷 102343-102343页

作者： Wang, Juyoung Cevik, Mucahit Amin, Saman Hassanzadeh Parsaee, Amir Ali Univ Toronto Dept Mech & Ind Engn Toronto ON Canada Ryerson Univ Dept Mech & Ind Engn Toronto ON Canada

Hazardous wastes have significant negative impacts on environment and people, which make their management a prominent task. A general hazardous waste reverse logistics network consists of sources, collection centers, treatment centers, processing/recycling facilities, and disposal facilities. We study a reverse logistics network specifically for household hazardous wastes and examine its difference from a non-hazardous and industrial waste network. We consider multiobjective mixed-integer deterministic and stochastic mathematical models that are designed to answer the following questions: which facilities or centers should be opened, which routes should be utilized, and how much waste should be transported to each location in order to minimize the transportation cost, transportation/site risk, and to maximize household convenience for the purpose of participation increase at collection stage. Specifically, we propose an optimization framework for the management of hazardous household wastes, and consider the waste paint network in the City of Toronto, Ontario, Canada as a test bed for our analysis. Finally, we provide easy-to-interpret visualization tools for the problem, which help interpreting the model outcomes and identifying policy insights.

关键词： Reverse logistics Household hazardous waste mixed integer linear programming Paint Stochastic programming Multi-objective optimization

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Modeling and evaluating agile assembly systems using mixed-integer linear programming

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Procedia CIRP 2020年 93卷 1073-1078页

作者： Peter Burggräf Matthias Dannapfel Tobias Adlon Esben Schukat Hannes Kahmann Lucian Holtwiesche Laboratory for Machine Tools and Production Engineering (WZL) RWTH Aachen University Campus Boulevard 30 52074 Aachen Germany Chair of International Production Engineering and Management (IPEM) University of Siegen Paul-Bonatz-Straße 9-11 57076 Siegen Germany

Agile assembly systems in automotive production are predominantly characterized by decoupled and independent workstations. This raises the question how such assembly systems can be evaluated and compared against the predominantly used ‪‪assembly lines. Job-shop-problems as a promising formulation do not yet offer a model that covers all the intended degrees of freedom. This research work thus presents an extended job-shop-problem and its validation while considering parallel and multifunctional workstations, sequence-dependent set-up times, routing and sequence flexibility as well as a multivariable objective function. The resulting model based on mixed integer linear programming contributes to the evaluation of agile assembly systems and supports the development of real-time capable algorithms for practical applications.

关键词： Agile assembly modular assembly job shop problem mixed integer linear programming assembly control

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