Optimization Models for Reducing Off-Cuts of Raw Materials in Construction Site

Management and Recycling of Construction Waste in Taiwan

Hierarchically partitioned coordinated operation of distributed integrated energy system based on a master-slave game

The growing share of intermittent renewable energy sources for power generation indicates an increasing demand for flexibility in the energy system. Energy storage technologies ensure a balance between demand and supply and increase the system flexibility. This study investigates increased application of renewable energy resources at a regional scale. Power-to-gas storage that interacts with a large-scale rooftop photovoltaic system is added to a regional energy system dominated by combined heat and power plants. The study addresses the influence of the storage system on the production planning of the combined heat and power plants and the system flexibility. The system is modeled and the product costs are optimized using the Mixed Integer Linear Programming method, as well as considering the effects on CO2 emissions and power import into the regional system. The optimization model is investigated by developing different scenarios for the capacity and cost of the storage system. The results indicate that the proposed storage system increases the system flexibility and can reduce power imports and the marginal emissions by around 53%, compared with the current energy system. There is a potential to convert a large amount of excess power to hydrogen and store it in the system. However, because of low efficiency, a fuel cell cannot significantly contribute to power regeneration from the stored hydrogen. Therefore, for about 70% of the year, the power is imported to the optimized system to compensate the power shortfalls rather than to use the fuel cell.

The considerable increase in the complexity associated with the formulation of maintenance plans has enabled the development of new techniques to bring maintenance scheduling optimization models to more realistic environments. In this sense, a previous optimization model was proposed considering the use of time windows for the formation of grouping schemes under an opportunistic strategy for maintenance activities considering non-negligible execution times, thus offering the possibility of analysing scenarios with limited resources. This article proposes a risk analysis based on the failure probability of each component involved in the maintenance scheduling optimization model, which has the particularity of enabling a greater number of combinations of grouped PM activities. Moreover, it seeks to identify the general behaviour of the optimization model against different scenarios of periodicities and execution times of each maintenance activity. The proposed optimization model is formulated under a mixed integer linear programming (MILP) paradigm and its objective function seeks to minimize the unavailability of the system associated with the execution times of the activities developed, generating different experimental cases, and varying the start time scheduling under a tolerance factor from 0% up to a maximum of 25% for advance or delay. Results show in contrast with the base optimization model, an 8% less unavailability when the tolerance factor is 10%. Finally, it was possible to quantify the risk present in each maintenance schedule, at the same time a behaviour towards advancing PM activities is evidenced by the optimization model proposed over the delay.

Quantifying the waste reduction potential of using prefabrication in building construction in Hong Kong

Software-defined networking provides a promising solution for traffic engineering (TE) by utilizing a centralized controller to remotely configure programmable network switches. In order to achieve optimal traffic distribution and meet the Quality of Service (QoS) requirements, the SDN controller must frequently solve complex optimization models. Applying an optimization model in ISP (Internet Service Provider) networks implies abundant routing reconfigurations, which adversely impact the network stability and QoS parameters. Namely, since routing updates usually cannot be applied on all switches at the same time, transient congestion and loops often happen as a result. Here, in this paper, we propose a new control framework that strives to maximize the network throughput and provide QoS with minimal reconfiguration costs. In contrast to the conventional TE approaches, which perform the network optimizations periodically and control the side effects of reconfigurations by carefully choosing the period length between the optimization cycles, we propose a multi-objective optimization model which jointly minimizes the routing cost function and the reconfiguration overhead. The Pareto frontier of the optimization model is generated by the augmented ∈-constrained method, whereas a Lyapunov drift-plus-penalty function is used to select the best compromise solution from the Pareto set. Since the reconfiguration overhead is reduced, the network controller could be allowed to optimize resource allocation more frequently, in order to respond quickly and efficiently to the network changes. Considering that the proposed optimization model is computationally intractable in large-scale networks, we also propose a heuristic algorithm to efficiently solve large instances of the problem. In simulations and Mininet experiments, we show that our solution brings performance improvement over the conventional periodic TE techniques. Moreover, the proposed heuristic achieves better QoS request acceptance ratio than the state-of-the-art multi-objective optimization solution, despite the significantly reduced computational complexity.

This paper proposes a joint optimization model of grid-tied residential microgrid (MG) planning and operation with the support of demand side management (DSM). In this model, the residential MG is included with various distributed energy resources, such as photovoltaic units, wind turbines, microturbines, diesel engine generators (DEs), and battery energy storage systems (BESSs). The proposed model is formulated as a bi-objective optimization problem in view of economic and environmental contemplation. Therefore, the minimization of MG total annual cost and total annual emission are considered as the primary objectives. This paper mainly analyzes the benefits of DSM assistance in the process of residential MG joint planning and operation optimization. The proposed bi-objective optimization problem is converted into a single-objective optimization problem and the single-objective problem is solved using a mixed integer linear programming method. To investigate the impacts of DSM on the MG planning and operation problem, the proposed model is applied to a residential MG consisting of 1000 smart homes with different DSM participation levels. The simulation results show that the proposed model is an efficient tool for residential MG planning and operation optimization from the perspective of economic and environmental considerations.

With the increasing development of low-carbon economy, the coupling degree of electric thermal gas system is deepening day by day. The operation mode of traditional energy supply system using electric heating separation mode and hierarchical dispatching of transmission and distribution network has been difficult to mine the whole network resources and realize the global optimal operation strategy. In order to increase the consumption of new energy and solve the multi-energy current coupling system, this paper puts forward a layered optimization operation strategy for the electric-thermal multi-energy current coupling system with Concentrated solar power (CSP) plant and carbon trading mechanism. The upper layer is the solution layer of multi-energy coupled flow system. In order to solve the matrix values of complex multi-energy coupled flow system, Newton's method, improved Newton’s method and improved second-order cone collaborative solution methods are proposed. The lower layer is the multi-energy flow optimization layer. According to the system solution values obtained from the upper layer, the lower layer optimization aims at economic minimization of the total user cost. The carbon trading mechanism model is introduced to optimize the time sequence output of the electric thermal unit under different scenarios, and the mixed integer linear programming method is adopted. Finally, the validity is verified by a simulation example. The results show that this method can optimize the operation of the system, improve the accuracy and rapidity of the system, and “carbon trading and CSP power station” method can better constrain the carbon emission of the multi-energy flow coupling system, reduce the energy pressure of the device, and improve the economy of the total cost of the user.If this method is adopted, it can be used for reference in the future energy management and system decision of multi-energy flow coupling system.

Little research has considered fuzzy scheduling and sequencing problem in operating rooms. Multiple-period fuzzy scheduling and sequencing of patients in operating rooms optimization models are proposed in this research taking into consideration patient‘s preference. The objective of the scheduling optimization model is obtaining minimal undertime and overtime and maximum patients' satisfaction about the assigned date. The objective of sequencing the optimization model is both to minimize overtime and to maximize patients' satisfaction about the assigned time. A real-life case study from a hospital that offers comprehensive surgical procedures for all surgical specialties is considered for illustration. Research results showed that the proposed models efficiently scheduled and sequenced patients while considering their preferences and hospitals operating costs. In conclusion, the proposed optimization models may result in improving patient satisfaction, utilizing hospital's resources efficiently, and providing assistance to decision makers and planners in solving effectively fuzzy scheduling and sequencing problems of operating rooms.

<p>In practice, manufacturing systems are highly dynamic and continuously facing unexpected events and interruptions. This puts production managers in charge of making frequent updates to the ongoing plans and schedules to cope with these changes. This is done by adopting different scheduling strategies and optimization models. Despite several attempts in the literature, the need for models that can minimize the effect of changes (i.e., stochastic and robust models) or react to them using real-time information (i.e., real- time optimization models) is still felt. </p> <p>This dissertation includes three main contributions with different optimization models and heuristic algorithms that can help managers deal with different unexpected events and interruptions on the shop floor. In the first contribution (Chapter 2), the case of stochastic deterioration-based failures in a single machine production system is considered. The machine’s degradation is modeled as a multi-state system. The obtained formulations are then integrated into an optimization model that jointly optimizes the production sequences, machine inspections, and condition-based maintenance actions. The results showed an average improvement of about 35% in total expected costs when information about the machine’s degradation level was used. </p> <p>In the second contribution (Chapter 3), the case of unexpected new job arrivals and random machine breakdowns in a flexible job shop production environment is considered. The effect of random machine breakdowns on the processing durations is formulated and then integrated into a dynamic optimization model. The proposed model investigates how real-time updates can be utilized to improve scheduling decisions based on unexpected arrivals, the availability of machines (downtimes and recovery times), and the completion times of operations. </p> <p>Finally, in Chapter 4, the case of integrating production scheduling and condition-based preventive maintenance (PM) planning in a flexible job shop production system is addressed. The study considers the case of stochastic machine degradation, random machine breakdowns, minimal repairs, condition-based PM, due date changes, and new job arrivals. The reliability of machines is modeled as a multi-state system, in which the obtained formulations are incorporated into an integrated dynamic optimization model. The developed model aims to study the effects of different RTS policies on each of the considered cases and empirically quantify the potential benefits of using real-time information to enhance scheduling decisions. </p>

During the last two decades, there has been a growing interest in component-based software engineering CBSE both in academia and industry. In component-based system development, it is common to identify software modules first. Once they are identified, we need to select appropriate software components for each module. These components can either be bought as commercial off-the-shelf COTS components and probably adapted to work in the software system or can be developed in-house. This is a 'build-or-buy' decision. This paper discusses a framework that helps a developer to decide whether to buy or to build software components while designing a fault-tolerant modular software system. This paper proposes optimisation models for optimal component selection for a fault-tolerant modular software system under the consensus recovery block scheme. It is necessary to identify critical modules in the design of a fault-tolerant modular software system and also to develop a system with a built in redundancy for critical modules. Therefore, the first optimisation model is developed for optimal component selection with the dual objective of reliability maximisation and cost minimisation of the overall system under the constraints on the delivery time and criticality of modules. The second optimisation model is an extension of the first optimisation model and discusses the issue of compatibility of components of modules. In practice, it is not possible for management to obtain precise value of reliability, cost, delivery time, etc., therefore both the models are formulated as fuzzy multi-objective optimisation models. A case study of developing a manufacturing system for medium-size enterprise is used to illustrate the proposed methodology.

Construction waste is generated from the various construction activities of building, maintaining, renovating, and demolishing infrastructure facilities. These materials include, but are not limited to, concrete, rubble, fiberglass, asphalt, bricks, wood, and scrap metals. In the last few decades, there has been a significant increase in the volume of construction and demolition (C&D) waste materials in the United States (U.S.). Between 1990 and 2017, the total increase in debris generation has surpassed 320%. According to U.S. solid waste management statistics, more than 50% of C&D waste has been and continues to be transported to landfills. The disposal of C&D waste materials in landfills can adversely affect the environment and is not viable economically. Also, C&D waste materials left on construction sites can threaten the safety of construction workers. The application of strategic C&D waste management practices through reducing, recycling, and reusing waste materials is essential for sustainable management of limited resources and thereby can benefit the environment, construction safety, and the economy. For example, the advantages of C&D waste management include reduced environmental pollution, expanded job opportunities, savings in construction project expenses, and enhanced safety for construction workers. The objective of this research was to explore various construction waste management practices to control C&D waste as well as the potential benefits they may bring to the environment, construction safety, and the economy. The findings of this research can be summarized as follows: 1) most construction companies do not deal with waste seriously that is the reason for high practice in landfilling. 2) the best way to minimize the harmful effect of construction waste is by following construction waste management practices, and 3) recycling construction waste is more cost-effective than dispose the waste.

Multipurpose linear programming optimization model for repetitive activities scheduling in construction projects

In this paper, the structure of the highest level of a hierarchical control architecture for microgrids is proposed. Such structure includes two sub-levels: the Energy Management System (EMS) and the tertiary regulation. The first devoted to energy resources allocation in each time slot based on marginal production costs, the latter aiming at finding the match between production and consumption satisfying the constraints set by the EMS level about the energy production in each time slot. Neglecting the efficiency of the different energy generation systems as well as that of the infrastructure for electrical energy distribution, the problem dealt with by the EMS sub-level is linear and can be solved by well known Linear Programming optimization procedures. The tertiary sub-level, below the EMS, optimizes mainly technical objectives and requires the solution of the Optimal Power Flow problem. After a review of the state of the art on the topic, the higher control sub-levels are described and an application is proposed. The application shows the efficiency of Mixed Integer Linear Programming methods for cost minimization of the energy production systems for microgrids.

Leakage power is becoming a dominant contributor to the total power consumption and dual-V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> assignment is an efficient technique to decrease leakage power, for which effective design methods have been proposed. However, due to the exponential relation of subthreshold current with process parameters, such as, the effective gate length, oxide thickness and doping concentration, process variations can severely affect both power and timing yields of the designs obtained by those methods. In this paper, we propose a mixed integer linear programming method for dual-V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> design that minimizes the leakage power and circuit delay in a statistical sense such that the impact of process variation on the respective yields is minimized. The experimental results show that 30% more leakage power reduction can be achieved by using statistical approach when compared with the deterministic approach