Optimization Model For Management Research Articles

Machine learning-based optimal temperature management model for safety and quality control of perishable food supply chain

The management of a food supply chain is difficult and complex because of the product's short shelf-life, time-sensitivity, and perishable nature which must be carefully considered to minimize food waste. Temperature-controlled perishable food supply chain provides the highly crucial facilities necessary to maintain the quality and safety of the product. The storage temperature is the most vital factor in maintaining both the quality and shelf-life of a perishable food. Adequate storage temperature control ensures that perishable foods are transported to the end-users in good quality and safe to consume. This paper presents perishable food storage temperature control through mathematical optimal control model where the storage temperature is regarded as the control variable and the deterioration of the perishable food’s quality follows the first-order reaction. The optimal storage temperature for a single perishable food is determined by applying the Pontryagin's maximum principle to solve the optimal control model problem. For multi-temperature commodities supply chain, an unsupervised machine learning (ML) method, called k-means clustering technique is used to determine the temperature clusters for a range of perishables. Based on descriptive analysis, it is observed that the k-means clustering technique is effective in identifying the best suitable storage temperature clusters for quality control of multi-commodity supply chain.

Methodology for 3D Management of University Faculties Using Integrated GIS and BIM Models: A Case Study

Three-dimensional virtual modeling is one of the tools being rapidly implemented in the construction industry, leading to the need for strategies based on intelligent 3D models of cities and/or digital twins, which allow simulation by interacting with their real physical counterparts, anticipating the outcomes of decision making. In practice, problems arise when creating and managing these twins, as different data, models, technology, and tools must be used, and they cannot all be combined as desired due to certain incompatibilities. On the other hand, today’s traditional building management demands a more optimized process to prevent errors and enable timely reactions to failures and defects. Managing and using a large amount of complex and disparate data are required, which is why the use of CMMS-type software is common (Computerized Maintenance Management System). However, such software is rarely designed for management in a 3D format, often due to the absence of three-dimensional models of the assets. This research aims to contribute to the technological development of the digitalization of the built environment, providing a simple methodology for generating and managing 3D models of cities. To achieve this, the tools and information useful for generating an integrated GIS 3D and BIM model, and for Computer-Aided Maintenance Management in a three-dimensional format (CMMS-3D), are identified. The final model obtained is used to optimize the three-dimensional management of a classroom building on the “Campus de Las Llamas” at the University of Cantabria in Spain. The results demonstrate that it is possible to integrate digital models with simple linking mechanisms between the existing tools, thus achieving an optimal three-dimensional management model.

Multi-objective robust optimization for optimal operation of gas-electricity networks with the penetration of integrated P2G and electric vehicle parking lots

Energy is considered one of the most important human needs. Therefore, various energy resources are exploited to meet human needs. Among all available resources, electricity and natural gas have accounted for the largest share in meeting these needs. Accordingly, management of these resources is one of the most important issues in different societies. This paper presents a multi-objective optimization model for optimal management and operation of the power distribution network and natural gas network as well as reducing carbon emissions. In this modeling, the natural gas network and distribution power network are connected through two electric vehicle parking lots (EVPLs) and two gas-fired units (GFUs), and these two EVPLs and GFUs exchange energy with each other. The EVPLs are equipped with various facilities including power-to-gas (P2G) systems and natural gas storage that facilitate energy exchange between the two networks. The modeling presented in this article is based on mixed integer linear programming, so the nonlinear equations including power flow equations, the Weymouth equation and the cost functions of GFUs are also linearized using various methods. It should be noted that the price of purchased electricity poses uncertainty which is modeled using robust optimization techniques. The proposed model is tested on the 33-bus power distribution network and the 20-node natural gas network. The results show that the presence of EVPLs significantly reduces the operational costs of the natural gas network and power distribution network and carbon emissions.

Physical model-assisted deep reinforcement learning for energy management optimization of industrial electric-hydrogen coupling system with hybrid energy storage

Utilizing renewable energy sources (RESs), such as wind and solar, to convert electrical energy into hydrogen energy for industrial users with different types of energy storage can enhance the integration of green electricity, thereby replacing fossil fuels like natural gas. In the energy management optimization of industrial electric‑hydrogen coupling system (EHCS) with high RESs integration, the traditional optimization methods cannot effectively address the nonlinear characteristics of equipment and stochastic RES generation, while the model-free reinforcement learning methods struggle to improve the training efficiency and may lead to constraint violation. To address this challenge, this paper proposes a short time scale energy management approach for EHCS based on physical models assisted deep reinforcement learning (DRL). Firstly, the energy management optimization model of the EHCS is developed based on the operation characteristics of the equipment within the system. Secondly, the energy management model of EHCS is formulated as a DRL framework. The DRL agent is efficiently trained with the assistance of the action transformation which takes into account the physical characteristics of the equipment during operation. Finally, a case study is conducted to verify the effectiveness of the proposed model for achieving high-efficiency agent training, reducing decision-making time for equipment scheduling, addressing the stochastic RES generation, and realizing the economic operation of EHCS with good performance based on the equipment characteristics.

Revolutionizing photovoltaic consumption and electric vehicle charging: A novel approach for residential distribution systems

AbstractElectric vehicles (EVs) and small photovoltaic (PV) installations advance residential power grids by lowering charging costs and fostering eco‐friendly operations. Yet, the variable nature of EV charging presents challenges to grid reliability. This research introduces a Monte Carlo‐based simulation for predicting EV charging loads and a systematic charging method that integrates a ‘green electricity’ pricing scheme with a joint optimization model for PV and EV management. By applying an improved ant lion optimizer (IALO) algorithm enriched with differential evolution features, an optimization strategy that markedly enhances grid performance is devised. In a park scenario, this ‘green electricity’ model reduced the mean square error of EV charging load by 11.82%, smoothed the power load curve, and improved grid stability. When compared with particle swarm optimization (PSO) and grey wolf optimizer (GWO) algorithms, the IALO algorithm boosted overall revenue by 16.8% and 12.8%, increased PV utilization by 162.3% and 37.1%, and significantly cut carbon emissions by 159.6% and 31.6%, respectively. These outcomes affirm the financial, environmental, and functional benefits of our proposed approach.

OPTIMIZATION OF WASTE MANAGEMENT IN SUMBAWA DISTRICT USING A DYNAMIC SYSTEM MODEL

The main problem that occurs in society today apart from basic needs is the problem of waste. This waste problem certainly needs a solution in good, effective, sustainable and long-term management. The waste issue not only occur in big cities but also in developing areas. Sumbawa Regency as one of the regions that continues to develop and grow requires optimum and long-term planning of solid waste management and systems. In 2021, the waste handled in Sumbawa Regency will only be around 17.5% of the total waste. This shows that the waste management system in Sumbawa Regency is not optimal. Optimal waste management can be achieved by taking an academic approach through model development. One such model is the dynamic system model. This can describe the causal relationship between variables that affect solid waste problems and to provide an overview of the waste management system through simulations based on the factors that influence the processing model. Therefore, it is necessary to develop an optimization model for waste management specifically in Sumbawa Regency using a dynamic systems approach. The purpose of this study is to describe waste management in the form of an optimization model for waste management in Sumbawa Regency with a dynamic system. Optimization results show that the optimistic model yields a total of 127.01 tons/day, the moderate model yields a total of 98.23 tons/day and the pessimistic model produces 62.5 tons/day

Energy Management Strategy for Wind Solar Storage Microgrid Based on Improved Ant Lion Optimizer

The energy management of microgrids involves optimizing the capacity configuration, which significantly impacts the economic and stable operation of microgrids. This paper presents a control strategy for microgrid operation that effectively manages distributed power sources and energy storage to optimize capacity configuration. A mathematical optimization model for microgrid energy management is established considering minimum annual cost and optimal scale constraints. The traditional Ant Lion Optimizer (ALO) is improved by using dynamic weight coefficients and chaotic mapping to enhance the diversity of the population and improve the convergence of the algorithm. This can effectively avoid local optimal solutions and premature problems, and improve the convergence speed and search ability of the ALO algorithm. Based on this control strategy and improved ALO algorithm, simulations and tests were conducted using actual data from an independent microgrid, resulting in the optimal solution for the microgrid capacity allocation model. The case study results validate the practicality of the proposed microgrid operation control strategy as well as the superiority of the improved ant colony optimization algorithm.

Multi-objective Optimization Model for Medical Waste Supply Chain Management Without Landfill Tax During COVID-19 In Indonesia

The COVID-19 pandemic has brought various problems into our daily life, and one of them is an increase in the volume of medical waste, which poses a risk to the environment and health.An evaluation of how optimal the supply chain management of medical waste as a whole can assist in the development of medical waste management and policy making. In this paper, the study on observing and reformulating the existing medical waste supply chain optimization model using Mixed Integer Linear Programming (MILP) is discussed. This study is done by modifying the objective function and the constraints with considering elimination of the landfill tax variable. The resulted model is also a multi-objective optimization problem. To this end, the Lexicographic Method is employed to obtained the optimal solution. In this model, the most prioritizes that is considered in the environmental condition to the medical waste supply chain. A numerical experiment is done in this paper which it results suggests two things, first, the cost of medical waste processing is much greater than its revenue, and second, the waste processing through incineration is not favorable. Thus, it can be concluded that this study provides a modified medical waste optimization model and decisions in determining the combination of the amount and method for each subtype of medical waste to be processed by maximizing profit and the amount of waste sent to the landfill.

Mixed-Integer Nonlinear Programming Model for Optimal Field Management for Carbon Capture and Storage

Mixed-Integer Nonlinear Programming Model for Optimal Field Management for Carbon Capture and Storage

ВЫЯВЛЕНИЕ И ОПТИМИЗАЦИЯ МОДЕЛЕЙ УПРАВЛЕНИЯ В ОТНОШЕНИИ НАУЧНЫХ ЦЕНТРОВ МИРОВОГО УРОВНЯ

In accordance with the Decree of the Government of the Russian Federation dated 30.04.2019. No.538 "On measures of state support for the creation and development of world-class scientific centers", the terms of the tender documentation for the competitive selection for grants in the form of subsidies from the federal budget for state support for the creation and development of world-class scientific centers (NCMU), including world-class international mathematical centers and world-class scientific centers that carry out research and development according to the priorities of scientific and technological development and requirements, as well as other regulatory documents for the programs of creation and development of NCMU, there are no requirements for the formation of internal models of project management and the system of management bodies of NCMU. In this regard, the project management model and the composition of management bodies are determined by the organizations that initiated the creation of the NCMU independently, considering the number of participants and the specifics of corporate governance. Purpose: Taking into account the lack of a unified approach to the formation of an internal management model of NCMU, the purpose of this article is to identify models of internal management of consortia of NCMU participating organizations for the implementation of the program for the creation and development of NCMU (by types of NCMU: mathematical, genomic, by priorities of scientific and technological development) and to prepare proposals for optimizing project management models in relation to NCMU Methods: The article uses the analysis method to identify existing NCMU internal management models, the induction method to identify and assess the criticality of deficiencies inherent in NCMU management models, the synthesis method to prepare proposals for optimizing project management models in relation to NCMU. Results: Considering the identified shortcomings of the NCMU management models, as well as the disadvantages associated with attracting qualified customers, an optimal management model for NCMU is proposed.

Blockchain-enabled framework for transactive home energy management with cloud energy storage under uncertainties

In modern paradigm of smart grid, prosumers can trade energy peer-to-peer in a transactive energy market to reduce energy costs and improve energy efficiency. In addition, cloud energy storage (CES) is a type of shared energy storage systems with high economic efficiency that can provide energy storage services for prosumers and become an active player in energy trading. However, transactive energy implementation in power systems has several challenges such as data privacy and security. In existing researches, energy trading mechanisms rely on central authorities, where security and privacy cannot be maintained. Therefore, this paper presents a decentralized stochastic optimization model of transactive energy management based on blockchain in the presence of CES. In this framework, the smart contract based on Alternating Direction Method of Multipliers technique is deployed on the Ganache and Ethereum blockchains, and the YALMIP toolbox along with CPLEX solver handle decentralized optimization in the MATLAB software. The numerical results demonstrate that not only the capacity of distributed generations is fully utilized, but also total costs of smart homes are reduced by more than 27%, and the total grid’s delivery power to smart homes is decreased by more than 24% compared to the inflexible scheduling.

Proposal of Set of Optimization Criteria and Their Specific Calculation for Effective Inventory Management in an Industrial Enterprise

Currently, there are many mathematical models and tools for inventory management and optimization. Based on the literature review, the authors conclude that inventory management tools and models are mostly one-sided and focused on the cost aspect of the inventory management process. The research goal was to propose a set of criteria for inventory optimization as a basis for the development of an inventory management model whose structure would consider both the economic aspect and the stochasticity of demand and the reliability of delivery. Optimization criteria are based on extensive analyses of influences and correlations between logistic chain subsystems in relation to inventory. The interactions of the subsystems are then mathematically expressed, which represents the main output of the created set of optimization criteria. The defined criteria can be used as a basis for creating a new optimization model of inventory management.

Predictive Models for Optimal Irrigation Scheduling and Water Management: A Review of AI and ML Approaches

Purpose: Maintaining agricultural output, protecting water supplies, and lessening environmental effects all depend on effective water management. Through a comprehensive review of the literature and an in-depth analysis of various AI and ML techniques, this paper aims to put light on the cutting-edge approaches used in irrigation scheduling predictive modeling. The goal of the research is to determine the advantages, disadvantages, and future directions of AI and ML-based irrigation management systems by means of a methodical analysis of various algorithms, data sources, and applications. Additionally, the study seeks to demonstrate how data-driven methods can enhance irrigation systems' sustainability, accuracy, and precision. Stakeholders in agriculture, water resource management, and environmental conservation can make well-informed decisions to maximize irrigation scheduling techniques by having a thorough understanding of the theoretical underpinnings and practical applications of predictive models. The study also attempts to tackle issues like scalability, model interpretability, and lack of data when implementing AI and ML solutions for practical irrigation management. In final form, this review's conclusions advance our understanding of how to use AI and ML to improve agricultural systems' resilience and water use efficiency, supporting adaptive and sustainable water management strategies in the face of rising water scarcity concerns and climate change. Design/Methodology/Approach: In order to gather information for this review study, several research articles from reliable sources were analyzed and compared. Objective: To provide the current research gaps in prediction models for the best irrigation scheduling and water management, and suggest using AI and ML techniques to fill in these gaps. Results/ Findings: In response to the growing challenges of water scarcity and climate change, the paper's findings highlight the transformative potential of AI and ML techniques in optimizing irrigation scheduling, enhancing agricultural resilience, increasing water use efficiency, and supporting adaptive and sustainable water management strategies. Originality/Value: This paper's uniqueness and significance come from its thorough analysis of AI and ML approaches in predictive modeling for ideal water management and irrigation scheduling. It also provides insights into new methods and their possible effects on resource optimization and agricultural sustainability. Type of Paper: Literature Review.

Lifetime prediction and replacement optimization for a standby system considering storage failures of spare parts

Spare parts redundancy is an effective way to improve system reliability and prolong system lifetime. Generally, preventive maintenance and inventory management of a standby system can significantly reduce operating and maintenance costs, particularly crucial in industrial systems. Additionally, during storage, spare parts may experience degradation failure due to internal mechanisms and sudden failure due to external shocks, complicating the health management of the standby system. Unfortunately, few existing studies consider the impact of spare parts’ competing failures in standby systems. To address this gap, this paper proposes a novel method for predicting the lifetime and optimizing the maintenance policy of nonlinear degradation standby systems, considering both degradation and sudden failures of spare parts. Firstly, we established the degradation model under the competing failure of spare parts and derived the analytical formula for life prediction of the standby system in the presence of competing failures, assuming that sudden failure follows the Weibull distribution. Furthermore, we developed a joint optimization model for replacement maintenance and inventory management based on predictive information, aiming to minimize the expected cost, in which the block replacement interval and the number of spare parts are treated as decision variables. Finally, the effectiveness and potential application value of the proposed method are verified through numerical simulation and a case study.

Data-driven Wasserstein distributionally robust dual-sourcing inventory model under uncertain demand

Dual-sourcing inventory management, which is aimed at replenishing inventory through two supply sources, has been extensively incorporated across various industries as it can mitigate supply chain related operational risks. Given the practical relevance of this framework, many dual-sourcing inventory models based on stochastic and robust optimization approaches have been developed. However, these approaches encounter challenges such as the curse of dimensionality or solution conservativeness. In this study, we developed a data-driven distributionally robust optimization model for dual-sourcing inventory management under uncertain demand conditions, in which partial information regarding the distribution of the uncertain demand is available. A tractable model was constructed to solve the problem, and an optimal solution was derived in a closed-form expression. Numerical experiments were conducted to evaluate the performance of the proposed model in comparison with benchmark models in terms of the order-, stock-, and rolling-horizon-related parameters and demand distributions. The results demonstrated the benefit of adopting the dual-sourcing strategy in inventory management based on the distributionally robust optimization approach. In addition, the proposed model outperformed the benchmark models in terms of mitigating the bullwhip effect.

Capacity optimization of independent hybrid renewable energy system under different operational strategies based on improved gray wolf algorithm

Renewable energy sources such as wind and solar power exhibit strong stochasticity and volatility, resulting in decreased power supply security and sustainability. A strategically optimized hybrid renewable energy system (HRES) is crucial for maintaining stable load operations and achieving sustainable energy development. This paper introduces an energy optimization management model for an independent HRES consisting of wind turbines, photovoltaic systems, diesel generators, and energy storage units. Operational strategies focus on energy storage-led loads following diesel generator-led load prioritizations. The model aims to optimize objectives to include economic, environmental, and power supply reliability indices. A dynamic adaptive parameter approach balances the parameters of the objective function at various instances. The optimal capacity allocation of the model is solved using the improved gray wolf optimization (IGWO) algorithm. This approach incorporates the golden sine strategy, the levy flight strategy, and the dynamic inverse learning strategy into the traditional GWO algorithm. Analyzing different test functions, evaluation metrics, and actual load data indicates that the proposed algorithm excels in global optimization capabilities and search speeds. The model significantly reduces the economic and environmental costs of the HRES microgrids and improves the sustainable development of renewable energy in various scenarios.

Application of Deep Learning Algorithm in Optimization of Engineering Intelligent Management Control

Currently, there are a series of problems in the management of the construction industry, such as resource waste, substandard quality, and low construction efficiency. In response to this phenomenon, the author proposes a multi-objective optimization control method for construction engineering management projects using deep learning algorithms. This method analyzes the relationship between cost, duration, and quality, and constructs an optimization management model for these three factors. At the same time, the improved SULSTM neural network algorithm is used to optimize the model parameters. The experimental results indicate that, when the value coefficient is 0.2211, the total investment cost and quality coefficient are 412700 yuan and 0.99496 yuan, respectively. When the value coefficient is 0.1976, the total cost and quality coefficient are 456300 yuan and 0.98798 yuan, respectively. When the value coefficient is 0.1990, the total cost and quality coefficient are 456300 yuan and 0.99496 yuan, respectively. Proved that the SUSTM neural network algorithm has faster convergence speed and lower loss values compared to the improved LSTM neural network algorithm. The cost of improving quality has a greater impact on the quality coefficient than the duration, and the total investment cost has a greater impact on the value coefficient than the quality coefficient.

Optimal scheduling of a microgrid based on renewable resources and demand response program using stochastic and IGDT-based approach

Optimal economic scheduling of microgrids with photovoltaic (PV) and wind generation has gained increased attention during recent years. Integration of renewable energy resources in microgrids requires increasingly active control and management of energy storages and demand response (DR). In this paper, a risk-based stochastic optimal energy management model is developed for microgrid with renewables, energy storage and load control by time-of-use-based DR programs. Microgrid includes PV system, wind system, micro-turbine, fuel cell, electric vehicle (EV), and energy storage. Information-gap decision theory (IGDT) is employed to address the uncertainty of loads and to provide the operating strategies for the microgrid controllable energy resources. This proposed model has been solved as a mixed-integer non-linear programming (MINLP) in General Algebraic Modeling System (GAMS) software and simulation results in different conditions are studied and discussed. Three different risk management strategies have been studied such as risk-averse, risk-neutral and risk-seeker mode. The simulation results indicate that the impacts of risk-averseness or risk-seeker of the decision maker affect the system operation. For instance, the results showed the DR program's role in risk-averse and risk-taking strategies, impacting consumption and costs. The proposed model ensures the risk-averse decision-maker that if the uncertain parameter deviates within the optimum robustness region, the final cost will not exceed the critical cost. On the other hand, the risk-seeking decision-maker can reach lower final costs by accepting the risks if the uncertain parameter deviates favorably within the opportunity region. Decision-makers can manage risks by adjusting consumption. Thus, considering the cost of risk management is crucial, as it increases with robust or opportunistic approaches.

Chance-constrained UAM traffic flow optimization with fast disruption recovery under uncertain waypoint occupancy time

Trajectory-based operations offer a promising solution for effective urban air mobility (UAM) traffic management with conflict-free four-dimensional trajectories. However, these trajectories generated in strategic phases by existing methods could be significantly disrupted due to uncertainties such as flight delays and trajectory deviations. This paper develops a chance-constrained UAM traffic flow management (UTFM) optimization model with fast disruption recovery to solve observed disrupted trajectories and improve their resilience. Our model introduces a novel concept of waypoint occupancy time to cope with the probabilistic separation constraints induced by variables, notably urban wind field. We then convert the probabilistic constraint into a deterministic one, incorporating risk-bounded separation guarantees derived from flight experiment data. Furthermore, we develop a hierarchical stochastic search algorithm for solving the redefined deterministic optimization problem. Our comprehensive numerical studies demonstrate the model's effectiveness in restoring disrupted flights and resolving conflicts within seconds. Additionally, our reliability testing showcases the resilience of the model in managing disruptions, even at levels as high as 50%, and its adaptability in addressing varying levels of uncertainty risk. We further demonstrate the ability of the UTFM model to capture tail risks across Gaussian and non-Gaussian uncertainty distributions. Lastly, our scalability analysis highlights the potential capacity of the model to support up to 3,000 flights per hour in Singapore's urban airspace below 400 ft. This study introduces an adaptable framework to facilitate the modelling of robust traffic flow management and performance-based separation for a variety of eVTOL types under uncertainties.

A BIM-integrated multi objective optimization model for sustainable building construction management

Purpose The construction industry is tasked with creating sustainable, efficient and cost-effective buildings. This study aims to develop a building information modeling (BIM)-based multiobjective optimization (MOO) model integrating the nondominated sorting genetic algorithm III (NSGA-III) to enhance sustainability. The goal is to reduce embodied energy and cost in the design process. Design/methodology/approach Through a case study research method, this study uses BIM, NSGA-III and real-world data in five phases: literature review, identification of factors, BIM model development, MOO model creation and validation in the architecture, engineering and construction sectors. Findings The innovative BIM-based MOO model optimizes embodied energy and cost to achieve sustainable construction. A commercial building case study validation showed a reduction of 30% in embodied energy and 21% in cost. This study validates the model’s effectiveness in integrating sustainability goals, enhancing decision-making, collaboration, efficiency and providing superior assessment. Practical implications This model delivers a unified approach to sustainable design, cutting carbon footprint and strengthening the industry’s ability to attain sustainable solutions. It holds potential for broader application and future integration of social and economic factors. Originality/value The research presents a novel BIM-based MOO model, uniquely focusing on sustainable construction with embodied energy and cost considerations. This holistic and innovative framework extends existing methodologies applicable to various buildings and paves the way for additional research in this area.