Build Optimization Model Research Articles

Logistics Distribution Route Optimization in Artificial Intelligence and Internet of Things Environment

With the increasing challenges facing the logistics industry, especially in meeting the growing demand for distribution efficiency and accuracy, the use of modern technology to optimize logistics distribution routes has become a key issue. This study explores the application of artificial intelligence (AI) and Internet of Things (IoT) technologies in the optimization of logistics distribution routes. The research first focused on collecting and processing logistics-related data, including historical delivery records, real-time traffic data, and cargo tracking information. Then, by building optimization models based on AI and IoT technologies, the study explores the potential of these technologies to improve logistics distribution efficiency and reduce costs. In addition, through cost-benefit analysis and discussion of challenge-coping strategies, this study not only verified the effectiveness of the proposed scheme in theory but also demonstrated its feasibility in practical application. Finally, the study presents implications for industry practice and recommendations for future research, emphasizing the importance of continuous technology evaluation and adaptation to market changes.

A mixed integer programming approach to improve oil spill response resource allocation in the Canadian arctic

Determining proper locations to establish emergency response facilities is a critical strategic element of pollution preparedness and response planning for oil spills in remote areas. Many location-allocation models are available in the literature, but Arctic contexts such as remoteness and environmental sensitivities are still inadequately investigated while building optimization models. A Mixed Integer Programming (MIP) based optimization model is developed to devise a location-allocation problem: maximizing weighted spill coverage considering spill size, environmental sensitivity, and response time. Strategic decisions - e.g. allocation of stockpiling resources to resource stations and which response stations to open - are incorporated into the model as decision variables. Input parameters of the model are estimated using numerical and geospatial data of potential oil spills and response stations. The model is illustrated for hypothetical oil spill scenarios in the Canadian Arctic. The model provides optimal allocation of resources and recommends best-suited locations to build response facilities. Data visualization tools including Network Diagrams and sensitivity analysis on different model configurations, show the adequacy of the proposed mathematical modelling approach to solve the given problem. Multiple facility locations have been compared to cover all possible oil spills along Arctic shipping routes, further revealing a few better locations considering realistic constraints. Decision makers can use such optimization modelling information – e.g., how many stations to build in the Arctic to adequately cover potential oil spills – to aid strategic decision-making of maritime shipping.

Sustainable Building Optimization Model for Early-Stage Design

Buildings represent the largest potential for carbon reduction worldwide. This highlights the need for a simulation and optimization method for energy management. The early design stage of buildings represents an important phase in which choices can be made to optimize design parameters. These parameters can focus on multiple areas, including energy and thermal comfort. This paper introduces the optimization of early-stage sustainable building design considering end-user energy consumption. It proposes an optimization model that integrates multiple layers, which consist of a parametric energy simulation, artificial neural network, and genetic algorithm. The proposed optimization model considers a single objective function to obtain the optimal design. The targeted goal is to obtain minimal energy consumption for residential buildings during the early design stages. Key design parameters of the building were identified for optimization and feasible ranges for them were obtained using genetic algorithms. Finally, the results of this paper include the identification of the optimal building design for the thermal comfort analysis and optimal energy performance. The model was applied to a case study in Egypt and the results showed that using the developed optimization model can lead to a 25% reduction in energy consumption.

Optimization of renewable energy for buildings with energy storages and 15-minute power balance

When planning renewable hybrid energy solutions in buildings, it is important to consider both investment and operating costs. This study develops a novel building optimization model based on the coming 15 min power balance settlement. It utilizes multiple energy storages, including hot water tank and flow and lead-acid batteries. We apply the model to plan the retrofitting of an office building in Helsinki and a residential building in Tallinn, with photovoltaics and a ground source heat pump. The model is a large dynamic linear or mixed-integer linear programming model (LP/MILP) for an entire year. The results determine both the optimal dimensioning and the optimal operation of the different production and storage technologies for each building. The optimized configurations caused significant savings in energy costs for both buildings while reducing non-renewable primary energy consumption. Heat storage is highly cost-efficient, but power storages are not. Photovoltaics is cost-efficient in the Helsinki building but slightly unprofitable in the Tallinn building. Power and heat storages do not interact strongly, even in the presence of the ground source heat pump. The heat storage operates in concert with district heating and the ground source heat pump, while power storages operate together with photovoltaics and power trade.

Deposit and Withdrawal Dynamics: A Data-Based Mutually-Exciting Stochastic Model

This paper proposes a novel self- and mutual-exciting stochastic model to capture two essential features underlying a general type of discrete-time event data motivated by the practice: the dependence on the past event arrivals and associated sizes (i.e., self-exciting) and the behavioral interdependence between multiple activities (i.e., mutual-exciting). Despite the technical challenges in capturing these two features under a general discrete-time framework, we are able to fully characterize the probability distribution for the proposed model (i.e., the closed-form characteristic functions), theoretically quantify customer performance measures, and establish efficient maximum likelihood estimation. To illustrate the applicability of our model and validate its performance, we calibrate it with a customer deposit and withdrawal data set from one leading online money market fund. We analytically quantify the churn probability and expected activity level of customers as an illustration of performance measures and then compare our model with classic time-series and machine-learning models and show that our model can achieve high prediction accuracy. The theoretical tractability and predictive accuracy of the proposed framework enable us to build optimization models to improve firm performance, and we illustrate one application through a personalized interest-rate optimization problem. On a broader note, our model framework is generally applicable to characterize any discrete-time event data with self and mutual excitation in nature and to inform optimal policies for decision-makers.

Environmental impacts assessment for Australian buildings: thermal resistance and environmental impacts relationship

Selecting suitable construction designs for efficient energy management and moderating greenhouse gas emissions regarding to a construction project's life-cycle of are typically demanding to designers in the fight with climate change across the global. Scientists are striving to develop building optimization models so that strategies are ably devised to moderate energy consumption and greenhouse gas emissions simultaneously. An inclusive greenhouse gas emissions valuation model is established to utilise several types of buildings envelop, which is adhered to the National Construction Building Code of Australia - energy efficiency requirement. The extracted results are exported from GaBi version 8.1 and consolidated by Visual Basic in Microsoft Excel to calculate greenhouse gas emissions amount in regard to building's energy consumption in 50 years. Accordingly, Credit 15A which is one of five pathways in the category: Green-Star's greenhouse gas emissions reduction (Green-Star is the rating tool to enhance a project's sustainability during its life-cycle) will be used to assess by selecting the wall structures following the guidance in the Insulation Council of Australia and New Zealand (2014) handbook. With the model, designers and planners probably apply the optimum envelope structure types with the relevant R-value options for their design.

Medial-ABC: an algorithm for the correspondence between myocardium and coronary artery mesh models based on the medial axis of coronary artery

AbstractThe role of coronary arteries is to supply sufficient blood to myocardium. Obstruction of coronary arteries limits blood supply and causes myocardial ischemia or acute myocardial infarction, a major cause of human death. Hence, the quantification of the regional amount of heart muscle subtended by obstructed coronary arteries is of critical value in clinical medicine. However, conventional methods are inaccurate and frequently disagree with clinical practice. This study proposes a novel medial-axis-based correspondence (Medial-ABC) algorithm to find the correspondence between myocardium and coronary artery in order to segment regional myocardium at risk subtended by any potentially obstructed coronary artery. Given the triangular mesh models of coronary artery and myocardium, the proposed algorithm (i) computes the medial axis of coronary artery, (ii) finds the correspondence using the medial axis of coronary artery, and (iii) segments the coronary artery and myocardium. The proposed algorithm provides a robust mathematical linkage between myocardium at risk and supplying coronary arteries so that ischemic myocardial regions can be accurately identified. Hence, both the extent and severity of myocardial ischemia can be quantified effectively, efficiently, and accurately. Furthermore, the constructed mesh model of segmented coronary artery and myocardium can be post-processed for applications such as building optimization models of cardiac systems. The CardiacVis program, which implements the Medial-ABC algorithm, is freely available at Voronoi Diagram Research Center (http://voronoi.hanyang.ac.kr/software/cardiacvis) and will be an invaluable tool for quantitative patient-specific risk stratification in clinical practice.

Intelligent Optimization based on Machine Learning: State of Art and Perspectives (A Survey)

This survey focuses on the following problem: it is necessary, observing the behaviour of the object, automatically figure out how to improve (optimize) the quality of his functioning and to identify constraints to the improvement of this quality. In other words, build the objective function (or set of objective functions in multiobjective case) and constraints - i.e. the mathematical model of optimization - by mean machine learning. We present the main developed to date methods and algorithms that enable the automatic construction of mathematical models of planning and management objects by the use of arrays of precedents. The construction of empirical optimization models by reliable case information allows us to obtain an objective control model that reflects real-world processes. This is their main advantage compared to the traditional, subjective approach to the construction of control models. Relevant to the task a set of mathematical methods and information technologies called ``Extraction optimization models from data'', ``BOMD: Building Optimization Models from Data'', ``Building Models from Data'', ``The LION Way: Learning plus Intelligent Optimization'', ``Data-Driven Optimization''. The incompleteness of information and uncertainty are understood in different ways. Significantly different are the problem settings - deterministic, stochastic, parametric, mixed. Therefore, the consideration of a wider range of tasks leads to a variety of (primarily statistical) and other formulations of the problem and interpretations of uncertainty and incompleteness of initial information. The survey contains the following sections: Empirical synthetic of pseudoBoolean models; Empirical linear models with real variables; Empirical neural network optimization models; Iterative models; Models, including statistical statements; Problems, associated with the lack of the training set of points not belonging to the region of feasible solutions.

Energy-Efficient Design Optimization of a Building Envelope using DOE-2

This paper focuses on various design parameters targeted to improve building considering numerous variable such as efficient energy measures, cost effective parameters, indoor & outdoor comfort, environmental problems based on seasonal variations etc. The reported data deals with various optimization parameters of the building envelope located at the GIET University campus, Gunupur, Odisha. The research focuses on optimal choices of cost, energy consumption and efficiency measures. The data analyzed provide qualitative information on optimal cost, energy efficiency solutions, renewable energy production onsite and energy saving potential. This work is intended for optimizing the energy performance of building using Energy Plus and Open studio which is a simulation-based optimization methodology which links the data related to the building optimization model targeting towards net zero energy building.

A simulation approach to sizing batteries for integration with net-zero energy residential buildings

Utilities have greatly increased the use of time-of-use (TOU) rate structures for residential customers to more closely match the cost of delivered electricity as well as demand tariffs to incentivize a temporal shift in residential energy use. However current research tends to focus on a particular climate or location. This study analyzes four residences across four different climate zones in Arizona and explores the value of adding battery storage to a net-zero energy (NZE) photovoltaic (PV) system. Using the National Renewable Energy Lab's Building Optimization (BEopt) and System Advisor Model (SAM) tools, this study performs parametric analysis by varying battery size to determine a capacity that produces a maximum net present value (NPV). A sensitivity analysis on three possible future battery price trends is also performed. This study finds the NZE PV systems are only able to mitigate 37–44% of the peak electricity purchases and 4–12% of demand charges due to mismatch between solar potential and on-peak hours. Adding large batteries, 1.5–1.6 times larger than required to meet the annual peak energy purchase requirements, provides a maximum NPV for PV-battery systems at locations with favorable utility rates. We conclude that the best economics are achieved, at both current and expected future battery prices, when batteries are sized to never require replacement during the PV system lifetime.

BOMD: Building Optimization Models from Data (Neural Networks based Approach)

This article aims to develop mathematical methods and algorithms that automatically build nonlinear models of planning and management of economic objects based on the use of empirical samples (observations). We call the relevant new information technology "Building Optimization Models from Data (BOMD)". The offered technology BOMD allows to obtain an objective control models that reflect the real economic processes. This is its main advantage over commonly employed subjective approach to management. To solve the problems posed in the article, the methods of artificial intelligence were used, in particular, the training of neural networks and construction of decision trees. If the learning sample contains simultaneously the values of the objective function and the values of characteristic function of constraints, it is proposed to use an approach based on the training of two neural networks: NN1 - for the synthesis of the objective function and NN2 - for the synthesis of the approximating characteristic function of constraints (instead of a neural network NN2, a decision tree can be used). The solution of the problem presented by such synthesized neural model may end up finding, generally speaking, a local conditional extremum. To find the global extremum of the multiextremal neural objective function, a heuristic algorithm based on a preliminary classification of the search area by using the decision tree is developed. Presented in the paper approach to an extraction of conditionally optimization model from the data for the case when there is no information on the points not belonging to the set of admissible solutions is fundamentally novel. In this case, a heuristic algorithm for approximating the region of admissible solutions based on the allocation of regular (non-random) empty segments of the search area is developed.

A review on contemporary computational programs for Building's life-cycle energy consumption and greenhouse-gas emissions assessment: An empirical study in Australia

Choosing an effective and efficient building design option for energy management as well as for reducing greenhouse-gas (GHG) emissions associated with a building life-cycle is always highly challenging to any designers in the battle to tackle global climate change issues. Researchers around the world are thriving in building optimization models for developing strategies that would result in an overall reduction of a building's energy consumption aside from decreasing GHG emissions.Despite using similar input data sources, challenges to get similar results which are simulated from different software to assess environmental and financial aspects in a life-cycle of a system are still the dilemma to all sustainable development designers. Another difficulty, which happens in the progress of sustainable development in Australia, is that designers may have to follow a variety of green building rating tools to minimize environmental footprint and to increase the efficiency of buildings operation during their lifetime. Time will be consumed if design options are not appropriately and automatically calculated and assessed. In order to obtain building's life-cycle assessment (LCA) along with to achieve Green Star's certificate, choosing an appropriate program to calculate life-cycle energy consumption, life-cycle cost and life-cycle GHG emissions are a necessity to designers. This paper presents a comprehensive review of all considerable researches focus on the biologically inspired algorithms to different fields of sustainable building designs and their module modelling development in recent years (up to 2016). Major fields are reviewed in detail, including building envelope design, comprehensive optimization of several areas simultaneously as well as configuration and control of building energy consumption.The outcomes of this study can assist researchers to choose which program is the best suitable for their study on sustainable assessment. In Australia, based on these design, practitioners can decide the optimum option for their project which can achieve their sustainability goals as well as can be granted the Green Star's certificate less sophisticated.

Estimation of Deprivation Level Functions using a Numerical Rating Scale

Evaluating and quantifying human suffering in humanitarian operations offers an innovative and potentially powerful way to assess the performance of humanitarian logistics (HL) and help build optimization models. Previous studies have suggested deprivation cost as a metric and have estimated deprivation cost functions for water using willingness‐to‐pay. Our study proposes deprivation levels, defined as the degree of human suffering caused by lack of access to a good or service, and estimates deprivation level functions using a numerical rating scale. Analyzing data collected from respondents with and without disaster experience, we find that individuals in the latter category estimate deprivation differently from the beneficiaries of disaster relief. Our study demonstrates that deprivation levels can be expressed as logistic growth functions with a typical S‐shape, and that these can be integrated into HL optimization models to better account for human suffering.

Application of Integrated Concept in Industries for Maximizing Energy Saving Potential

An enormous amount of reusable energy, especially heat, steam, water, oil and so on can be generated or retrieved during the production in industries. It is profitable for companies to efficiently recycle this kind of energy for cutting down on energy consumption. To achieve this, the integrated concept is applied to maximizing energy saving potential in industries. A brief introduction to the development of the process integration technology is given at first. A modeling approach dedicated to build process model and optimization model is then proposed, which integrates all the production activities through the formation of energy, material, and information and emission flow. Sine chloralkali production is a typical industry process involving with considerable energy worthwhile to reuse, the proposed modeling approach are carried out to this case for demonstrating the implementation of the integrated concept in industries.

An integrated tool for optimizing rehabilitation programs of highways pavement

Modeling pavement performance and optimizing resources represent two challenges for decision makers responsible for maintenance and rehabilitation of road networks pavement. This paper presents the developments made in a stochastic performance prediction model and optimization model as two major parts of an integrated pavement management system. Markov modeling is used to create a transition process model that is implemented to predict pavement condition throughout the life time of road networks. With the use of the Pavement Condition Index (PCI), the steps of performing the prediction of deterioration are presented, showing the process of creating the elements of Markov matrix. The obtained results are used to set the priorities for maintenance planning and budgeted cost allocations on the network level. The proposed model advises decision makers on the status of network level with the guidelines to keep road conditions in acceptable level of performance according to the predefined strategies. Genetic algorithms technique is adopted to build optimization model. Three objective functions are constructed for budgeted cost of maintenance and rehabilitation program, quality of work performed, and selected area for program implementation. A brief description of the developed pavement management systems, including the prediction and the optimization models, are presented. A numerical example is worked out to illustrate the practical use of both models.

A differentiated service scheme to optimize website revenues

We consider a website host server with web quality of service (QoS) capabilities to offer differentiated services. A quantitative modelling framework is set up to analyse the economic benefits of differentiated services and to build optimization models for managing the website host's connection bandwidth to the Internet (which is assumed to be the bottleneck factor determining the QoS). Three models are formulated corresponding to three operational scenarios to provide differentiated services. The first is for the marketing manager to classify visit requests as premium or basic when the information technology (IT) manager has already reserved bandwidths for the two classes, and the second is for the IT manager to allocate the total available bandwidth to each class when the marketing manager has already designated which visit requests are premium and which are basic. The third is for the joint optimization of request classification and bandwidth allocation when centralized coordination is possible. Analytic results are obtained for a special case that corresponds to very impatient customers requesting large amounts of data. Qualitative insights gained and numerical results obtained strongly support the implementation of differentiated services. More interestingly, the decentralized models that use simple and rough-cut rules yield solutions almost as good as the joint optimization model.

Optimization and Suboptimization: The Method of Extended Models in the Non-Linear Case

Let us consider some particular optimization problem. Most of ita data is determined by the economic “environment”. However, this environment is evolutive, and may be the subject of an optimization research. The particular problem first considered then appears to be a suboptimization, and its results will be valid only if it is separable from its environment. This point of view can be expressed in mathematical terms by considering a “principal” programming problem from which one tries to separate a “reduced” subproblem. The concept of “separability” is defined and studied in the first part of this paper. In the second part, it is used to justify the “Method of Extended Models” as a tool for building optimization models.