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8th Workshop on Engineering Applications (WEA)

作者： Morillo-Torres, Daniel Torres Baena, Mauricio Wilmer Escobar, John Romero-Conrado, Alfonso R. Coronado-Hernandez, Jairo R. Gatica, Gustavo Pontificia Univ Javeriana Cali Cali Colombia Univ Valle Cali Colombia Univ Costa Barranquilla Colombia Univ Andres Bello Santiago Chile

ISBN: (纸本)9783030867027;9783030867010

A mixed-integer linear programming (MILP) model is proposed for solving a one dimension cutting stock problem (1D-CSP) in the steel industry. A case study of a metallurgical company is presented and the objective is to minimize waste in the cutting process of steel bars, considering inventory constraints and the potential use of the resulting leftovers. The computational results showed that an optimal solution was always found with an average improvement in waste reduction of 80%. There was no significant difference when comparing results between the complete model and the model without inventory constraints.

关键词： Cutting stock problem mixed-integer linear programming Steel bars Industrial application

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Learning Presolver Selection for mixed-integer linear programming 24

Learning Presolver Selection for Mixed-Integer Linear Progra...

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16th International Conference on Machine Learning and Computing (ICMLC)

作者： Song, Wentao Gu, Naijie Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798400709234

Presolving has become an important component of modern mixed-integer linear programming (MILP) solvers. Empirically, it has been observed that the performance of the solver is significantly influenced by the presolving algorithm (presolver), and selecting an appropriate combination of presolvers can improve solving efficiency. In industry, it is common to manually control the switch of presolvers to find a more efficient combination. However, with the emergence of new presolvers, it has become increasingly challenging to manually implement an optimal selection strategy in the vast combination space. Therefore, this paper proposes presolver selection, which needs to consider two key issues: (P1) How many presolvers should be selected? (P2) Which presolvers should be preferred among them? To address this challenge, this paper uses a hierarchical sequence model (HEM) to learn the presolver selection strategy through reinforcement learning. Specifically, the high-level model learns how many presolvers should be selected, and the low-level model learns to select a subset of presolvers within the determined size. The experimental results show that the method used can solve (P1) and (P2) better scompared to the designed baseline. It effectively improves the performance of the solver on real-world and synthetic MILPs.

关键词： mixed-integer linear programming Presolver Selection Hierarchical Sequence Model Reinforcement Learning

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A mixed-integer linear programming approach for circular economy-led closed-loop supply chains in green reverse logistics network design under uncertainty

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JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT 2021年第1期38卷 1-31页

作者： Gharibi, Kazhal Abdollahzadeh, Sohrab Urmia Univ Technol Orumiyeh Iran

Purpose To maximize the network total profit by calculating the difference between costs and revenue (first objective function). To maximize the positive impact on the environment by integrating GSCM factors in RL (second objective function). To calculate the efficiency of disassembly centers by SDEA method, which are selected as suppliers and maximize the total efficiency (third objective function). To evaluate the resources and total efficiency of the proposed model to facilitate the allocation resource process, to increase resource efficiency and to improve the efficiency of disassembly centers by Inverse DEA. Design/methodology/approach The design of a closed-loop logistics network for after-sales service for mobile phones and digital cameras has been developed by the mixed-integer linear programming method (MILP). Development of MILP method has been performed by simultaneously considering three main objectives including: total network profit, green supply chain factors (environmental sustainability) and maximizing the efficiency of disassembly centers. The proposed model of study is a six-level, multi-objective, single-period and multi-product that focuses on electrical waste. The efficiency of product return centers is calculated by SDEA method and the most efficient centers are selected. Findings The results of using the model in a case mining showed that, due to the use of green factors in network design, environmental pollution and undesirable disposal of some electronic waste were reduced. Also, with the reduction of waste disposal, valuable materials entered the market cycle and the network profit increased. Originality/value (1) Design a closed-loop reverse logistics network for after-sales services;(2) Introduce a multi-objective multi-echelon mixed integer linear programming model;(3) Sensitivity analysis use Inverse-DEA method to increase the efficiency of inefficient units;(4) Use the GSC factors and DEA method in reverse logistics network.

关键词： Green reverse supply chain Inverse data envelopment analysis mixed-integer linear programming Circular economy Electrical and electronic waste Closed-loop supply chain

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A Comparative Study of mixed-integer linear programming and Genetic Algorithms for Solving Binary Problems 5

A Comparative Study of Mixed-Integer Linear Programming and ...

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5th International Conference on Industrial Engineering and Applications (ICIEA)

作者： Kuendee, Punyisa Janjarassuk, Udom King Mongkuts Inst Technol Ladkrabang Fac Engn Bangkok Thailand

ISBN: (纸本)9781538657485

This paper aims to investigate the capability of mixed-integer linear programming (MILP) method and genetic algorithm (GA) to solve binary problem (BP). A comparative study on the MILP method and GA with default and tuned setting to find out an optimal solution is presented. The mixed-integer programming library (MIPLIB 2010) is used to test and evaluate algorithms. The evaluation is shown in quality of the solution and the execution time of computation. The results show that GA is superior to MILP in execution time with inconsistent results. However, MILP is superior to GA in quality of the solution with more stable results.

关键词： mixed-integer linear programming genetic algorithm binary problem mixed integer programming library

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Optimal Power Flow Integrated Electrical Gas System Using mixed-integer linear programming

Optimal Power Flow Integrated Electrical Gas System Using Mi...

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2024 IEEE Sustainable Power and Energy Conference, iSPEC 2024

作者： Wati, Trisna Risamasu, Yasmin Anggraeni Wibowo, Rony Seto Penangsang, Ontoseno Uman Putra, Dimas Fajar Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9798350395075

As time passes, the number generators gas-fired electricity will continue to increase. Power plants Gas fuel is very reliable for fulfilling load requirements and supporting peak loads. Besides its high efficiency, gas fired units are very profitable because of low capital costs, fast installation and minimal pollutant emissions, as well the start and shutdown speeds are the characteristics of the unit gas fuel is suitable for basic, medium, and heavy loads. Peaks provide backup for renewable generators. Intermittent an increases interdependence thus the electricity and gas energy sectors. Optimal and Gas Flow (OPGF) was the most basic problem in the mix these two systems. Gas consumption rates vary form over time, causing pressure fluctuations in the tissue gas transmission pipelines, which can have a negative impact on security and reliability of gas delivery. In addition, the gas system has slower dynamics than the system electricity and requires a longer stabilization time processing load changes and disturbances. Therefore, the optimal power and gas flow at integrated electrical and natural gas system for minimize operating cost. The OPGF problems were solved using mixed-integer linear programming (MILP) with Piecewise RMSE values the linear approximation is 5.57 © 2024 IEEE.

关键词： mixed-integer linear programming

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Optimization of Factory Energy Costs through Solar Energy Forecasting and Machine Scheduling Using mixed-integer linear programming 8

Optimization of Factory Energy Costs through Solar Energy Fo...

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8th International Conference on Information Technology, InCIT 2024

作者： Peerasantikul, Korawee Auwatanamongkol, Surapong National Institute of Development Administration Thailand

ISBN: (纸本)9798350366303

This study utilizes machine learning and optimization techniques to lower energy costs in factory operations. By incorporating weather forecast data and machine learning techniques, we aim to accurately predict solar energy production. Various machine learning models, including XGBoost, Random Forest, Support Vector Regression (SVR), Ridge Regression, and Long Short-Term Memory (LSTM) networks, were employed. Among these, XGBoost emerged as the most effective model for solar energy forecasting. Utilizing the solar energy production predicted by the models, a mixed-integer linear programming (MILP) optimization algorithm is used to determine the optimal scheduling for Sand Milling Machines. The model strategically schedules machine operations during intervals when solar energy is underutilized and during off-peak periods to minimize energy costs. This approach also ensures that the sand milling process meets critical constraints by maintaining sand levels within desired minimum and maximum thresholds. The results underscore the potential for significant cost savings and enhanced energy efficiency in factory operations through the integration of machine learning predictions and optimization techniques. © 2024 IEEE.

关键词： mixed-integer linear programming

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Conflict-Free Navigation Optimization at Unsignalized Intersections Using mixed-integer linear programming for Connected and Autonomous Vehicles 22

Conflict-Free Navigation Optimization at Unsignalized Inters...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Chu, Shuanglong Guo, Jinfeng Mu, Chen Chang'an University School of Information Engineering Xi'an China

ISBN: (纸本)9798331509712

The advent of Connected and Autonomous Vehicles (CAVs) offers significant potential to mitigate traffic accidents and reduce congestion through advanced unsignalized intersection technologies. However, this potential also introduces substantial modeling and computational challenges. This paper establishes a mixed integer linear programming (MILP) model and a Simulated Annealing Algorithm (SAA) to solve the unsignalized intersection problem. It begins with an indepth analysis of the conflict relationships among vehicles at intersections, laying the foundation for the model. Building on this, a novel vehicle trajectory planning model is introduced, leveraging the MILP model to ensure faster and more practical trajectory calculations, meeting real-time computation requirements. Finally, this paper designs a SAA to determine the optimal vehicle passing order, enhancing both safety and efficiency at intersections. Through numerical simulations, the research verifies the efficiency of the proposed MILP model and the effectiveness of the SAA in optimizing vehicle trajectories. © 2024 IEEE.

关键词： mixed-integer linear programming

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Research on New Energy Consumption Prediction and Access Planning Based on Particle Swarm Optimization and mixed-integer linear programming 24

Research on New Energy Consumption Prediction and Access Pla...

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9th International Conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Zhao, Lu Fan, Li Li, Yuanlin Power China Hubei Electric Engineering Co. Ltd China

ISBN: (纸本)9798400718137

With the rapid development of machine learning, it has been applied to solve various problems in social life because of its unique advantages. To address the current new energy consumption prediction and access planning methods of peak shifting capacity is insufficient. Particle swarm optimisation algorithm and mixed integer linear programming algorithm are proposed and applied to the models of new energy consumption prediction and photovoltaic access planning respectively. The new energy consumption prediction model based on particle swarm optimisation is applied for comparative analysis, and the results show that its average prediction accuracy and precision rate are 94.7% and 95.8%, respectively, which are significantly better than the comparison model. The mixed-integer linear programming-based PV access model is used for comparative analysis, and the results show that it is significantly better than the comparison model in terms of comprehensive cost and PV access volume dimensions, which is practical. The above results show that the two models proposed in the study have good performance and practicality, which are conducive to maximising the consumption of new energy and saving costs. © 2024 Copyright held by the owner/author(s).

关键词： mixed-integer linear programming

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Optimizing Healthcare Delivery: A mixed-integer linear programming Model for Integrating Face-to- Face and Virtual Consultations

Optimizing Healthcare Delivery: A Mixed-Integer Linear Progr...

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2024 Global Digital Health Knowledge Exchange and Empowerment Conference, gDigiHealth.KEE 2024

作者： Oshan, Tareq Sharkawi, Mahmoud Hamada, Nuha College Of Engineering Al Ain University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331531331

Digital health (or telehealth) appears to be one of the most active areas of study in today's world and has been showing substantial evidence of effectiveness in different aspects of modern society. Most significantly, in the healthcare systems, where availability of quality services is the cornerstone, digital health solutions are transforming practices for patients. This paper proposes a mathematical model for healthcare systems in which a comprehensive approach is taken to patient need to be hospitalized. An objective function is established aiming to maximizing the number of patients needing hospitalization, either through face-to-face consultations or through online portals while taking into consideration the attitude of all patients towards online consultations. To achieve this, we use the preferences in the model to make sure that patient satisfaction is not compromised while resources are used efficiently. With a formal breakdown and analysis of the problem and considerable reliance on complex optimization algorithms, the model outlines a set of steps that will ensure the optimal distribution of patients in terms of face-to-face and virtual consultations. This in turn not only increases effectiveness of the healthcare delivery systems but it also improves the efficiency of the health care delivery systems as well. As a confirmation of our model, we tested it on an example data and used LINGO software to obtain the best solution of the problem. The findings from this example pinpoint the possibility of applying the model for developing effective solutions to patient distribution and, therefore, enhance the overall benefits of the healthcare system. This systematic approach is aimed at demonstrating the positive changes which are possible in the delivery of patient care through the introduction of technology supports in modern health care infrastructures. © 2024 IEEE.

关键词： mixed-integer linear programming

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Optimal Design of a Biofuel Supply Chain Considering Government Subsidies: mixed-integer linear programming Model

Optimal Design of a Biofuel Supply Chain Considering Governm...

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IISE Annual Conference and Expo 2024

作者： Akbarian-Saravi, Niloofar Akbarian-Saravi, Ali Rabbani, Masoud School of Industrial Engineering College of Engineering University of Tehran Tehran Iran School of Industrial Engineering Amirkabir University of Technology Tehran Iran

ISBN: (纸本)9781713877851

Renewable energies have gained a significant attraction due to the increasing growing demand for energy and environmental concerns. As one of the alternatives to fossil fuels, bioethanol from switchgrass is considered. To realize the strategic and tactical biofuel supply chain decisions, in this study, we developed a different mixed-integer linear programming model aiming to minimize total annualized costs over a 5-year planning horizon by incorporating financial incentives and comparing the models with and without budget consideration. Using this model, the optimal allocation of material flows is determined along with optimal supply and pre-processing locations for producing a gasoline (95%)-ethanol (5%) blend. The monetary incentives include i) a constant budget for the whole Bioethanol Supply Chain (BSC) planning;and ii) treating the distribution of the budget as a decision variable. To illustrate the application of the proposed model, a case study of Iran is investigated. Results indicate the requirement of a monetary incentive to produce bioethanol and establish plants for bioethanol processing. The cost savings resulting from the optimal allocation of monetary incentives were higher than those from constant monetary incentives. The sensitivity analysis of the model indicates that the biomass-to-ethanol conversion rate has the highest impact on the total costs. © IISE Annual Conference and Expo *** rights reserved.

关键词： mixed-integer linear programming

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