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The paper presents a technique for predicting the energy consumption of unregulated energy loads (UELs) in office buildings. It also presents an approach for determining a set of optimum values required by the technique.

The proposed technique uses the optimum power drawn and optimum usage period in three modes of device operation, for the prediction. The usage modes are active mode, idle (low active) mode and off mode. The optimum powers and usage times are inserted into a linear mathematical equation to predict the energy consumption. Regarding the approach for determining the optimum values, the non-dominated sorting genetic algorithm II (NSGA-II) is applied to a range of values obtained from field measurements. The proposed prediction method and approach for determining optimum values were tested using data of energy consumption of UELs in a case study facility.

Test results show that the method for predicting the energy consumption of UELs in offices is highly accurate and suitable for adoption by energy modelers, building designers and building regulatory agencies. The approach for determining the optimum values is also effective and can aid the establishment of workable benchmark values.

A new and simple model has been developed for the prediction of unregulated energy. A method for determining a set of optimum values of power and usage periods required by the model has also been developed. Furthermore, optimum values have been suggested that can be fine-tuned for use as benchmark values. The proposed approaches are the first of their kind.

Genetic algorithm (GA) is a model of machine learning. The algorithm can be used to find sub‐optimum, if not optimum, solution(s) to a particular problem. It explores the solution space in an intelligent manner to evolve better solutions. The algorithm does not need any specific programming efforts but requires encoding the solution as strings of parameters. The field of application of genetic algorithms has increased dramatically in the last few years. A large variety of possible GA application tools now exist for non‐computer specialists. Complicated problems in a specific optimization domain can be tackled effectively with a very modest knowledge of the theory behind genetic algorithms. This paper reviews the technique briefly and applies it to solve some of the optimization problems addressed in construction management literature. The lessons learned from the application of GA to these problems are discussed. The result of this review is an indication of how the GA can contribute in solving construction‐related optimization problems. A summary of general guidelines to develop solutions using this optimization technique concludes the paper.

The purpose of this paper is to propose a portfolio procurement framework to response to uncertain customer demand and purchasing price volatility in a simultaneous manner. Then it aims to obtain optimal procurement and production decisions under the portfolio framework to maximize profit.

The portfolio procurement problem is modeled as a dynamic Stackelberg game and Nash equilibrium solutions are obtained. The portfolio procurement framework is analyzed in the settings, with both risk-neutral objective and downside risk constraints measure of contract prices.

By obtaining the Nash equilibrium solutions for both the buyer’s ordering decisions and the supplier’s optimum production decisions, Stackelberg game model for portfolio procurement is proved to be feasible. Additionally, downside risk constrains are proposed to help supply chain participants’ to evaluate the profitability and risk probabilities of the designed procurement contracts under the uncertain customer demand and spot market.

This paper assumes the supplier is risk averse and the buyer is risk neutral, and it would be interesting to examine the performances of portfolio procurement strategy with different risk attitudes participants.

This research could help the buyer respond to not only demand uncertainty but also the volatile spot price in the procurement process. Related optimal portfolio procurement strategy can be carried out to improve the enterprise’ procurement plan by adjusting the order of long-term contract, option contract and the spot market. The proposed framework could also help suppliers design and evaluate contracts for buyers with different risk preference, and on the other hand help the buyers decide if she should accept the contracts from the supplier.

This research should also increase awareness in both academia and industry on the opportunities of using the dynamic portfolio procurement approach to enhance flexibility and to mitigate the inventory as well as price risks in the procurement process. Effective downside risk constrains on contract prices could also help to protect the bottom line of companies with different risk preference.

The portfolio procurement framework proposed in this research can mitigate inventory and price risks simultaneously. Also, instead of solving the portfolio procurement planning problem in computational simulation experiments as in previous research, this paper proposed a dynamic game model for this portfolio-based procurement problem and obtained its Nash equilibrium solutions for both the buyer’s ordering decisions and the supplier’s optimum production decisions. Finally, an innovative and simple downside risk constraints has been designed to help the buyer evaluate supplier’s contract prices according to their individual risk preference.

This paper attempts to establish the general formula for computing the inverse of grey matrix, and the results are applied to solve grey linear programming. The inverse of a grey matrix and grey linear programming plays an important role in establishing a grey computational system.

Starting from the fact that missing information often appears in complex systems, and therefore that true values of elements are uncertain when the authors construct a matrix, as well as calculate its inverse. However, the authors can get their ranges, which are called the number-covered sets, by using grey computational rules. How to get the matrix-covered set of inverse grey matrix became a typical approach. In this paper, grey linear programming was explained in detail, for the point of grey meaning and the methodology to calculate the inverse grey matrix can successfully solve grey linear programming.

The results show that the ranges of grey value of inverse grey matrix and grey linear programming can be obtained by using the computational rules.

Because the matrix and the linear programming have been widely used in many fields such as system controlling, economic analysis and social management, and the missing information is a general phenomenon for complex systems, grey matrix and grey linear programming may have great potential application in real world. The methodology realizes the feasibility to control the complex system under uncertain situations.

The paper successfully obtained the ranges of uncertain inverse matrix and linear programming by using grey system theory, when the elements of matrix and the coefficients of linear programming are intervals and the results enrich the contents of grey mathematics.

The problem of structural optimization of trusses subject to stress and frequency constraints is considered from a practical viewpoint. Assuming that the choice of members has to be from a discrete set of available sections, the solution is attempted using a mathematical programming approach and an approximate two‐step procedure involving a continuous variable optimization followed by a discrete programming algorithm. The latter approach is highly promising for problems involving stress and frequency constraints. Detailed results are presented using several benchmark problems.

This paper aims to propose a dynamically balanced mechanism for cleaning unit used in agricultural thresher machine using a dynamically equivalent system of point masses.

The cleaning unit works on crank-rocker Grashof mechanism. This mechanism can be balanced by optimizing the inertial properties of each link. These properties are defined by the dynamic equivalent system of point masses. Parameters of these point masses define the shaking forces and moments. Hence, the multi-objective optimization problem with minimization of shaking forces and shaking moments is formulated by considering the point mass parameters as the design variables. The formulated optimization problem is solved using a posteriori approach-based algorithm i.e. the non-dominated sorting Jaya algorithm (NSJAYA) and a priori approach-based algorithms i.e. Jaya algorithm and genetic algorithm (GA) under suitable design constraints.

The mass, center of mass and inertias of each link are calculated using optimum design variables. These optimum parameters improve the dynamic performance of the cleaning unit. The optimal Pareto set for the balancing problem is measured and outlined in this paper. The designer can choose any solution from the set and balance any real planar mechanism.

The efficiency of the proposed approach is tested through the existing cleaning mechanism of the thresher machine. It is found that the NSJAYA is computationally more efficient than the GA and Jaya algorithm. ADAMS software is used for the simulation of the mechanism.

In this paper, a set of safe adaptive genetic algorithms (sGAs) is proposed based on the Splicing/Decomposable encoding scheme and the efficient speed‐up strategies developed by Xu et al.. The proposed algorithms implement the self‐adaptation of the problem representation, selection and recombination operators at the levels of population, individual and component which commendably balance the conflicts between “reliability” and “efficiency”, as well as “exploitation” and “exploration” existed in the evolutionary algorithms. It is shown that the algorithms converge to the optimum solution in probability one. The proposed sGAs are experimentally compared with the classical genetic algorithm (CGA), non‐uniform genetic algorithm (nGA) proposed by Michalewicz, forking genetic algorithm (FGA) proposed by Tsutsui et al. and the classical evolution programming (CEP). The experiments indicate that the new algorithms perform much more efficiently than CGA and FGA do, comparable with the real‐coded GAs — nGA and CEP. All the algorithms are further evaluated through an application to a difficult real‐life application problem: the inverse problem of fractal encoding related to fractal image compression technique. The results for the sGA is better than those of CGA and FGA, and has the same, sometimes better performance compared to those of nGA and CEP.

The purpose of this paper is to apply the developed systematic mechanical design methodologies, that are obtained in part I, to investigate their success in designing mechanics of a flexible printed circuit board assembly (PCBA) rework cell.

The decision of soldering and desoldering tool, which is the most critical function of a PCBA rework or remanufacturing cell, significantly influences overall design concept. Therefore, the paper starts by applying the design methodology to the soldering and desoldering function. The same study is repeated for the rest of the sub‐functions but only the results are provided.

An application of rework machine design methodology for the design of a PCBA rework cell has been made available. In addition to this, the embedded knowledge, such as the requirements list, the function structure, the function/means tree, the weighted objective tree and evaluation chart for the soldering and desoldering function are provided.

The paper is the first work providing both embedded knowledge and the application of the systematic design methodology for the design of a fully automated flexible PCBA rework cell. The methodology leads rework machine designers in a well‐controlled and structured design environment.

The design methodology can be applied to all functions or targeted on key weighted areas to ensure that the designed rework machine meets the key areas of concerns. Furthermore, the methodology is generic and may be used to develop other complex manufacturing sytems.

The generic design environment for a flexible printed‐circuit board assemblies (PCBA) remanufacturing cell contains four interrelated complex design domains. Mechanical design domains are really complex and the use of well‐proven mechanical product design methodologies does not help the designer. Hence, this paper aims to develop a generic systematic design methodology for a flexible PCBA remanufacturing cell.

The study investigates the use of conventional mechanical product design techniques for the design of a flexible PCBA rework (remanufacturing) cell. It indicates problems and the weaknesses when conventional product design techniques are used for the development of flexible manufacturing systems (FMS). It then provides a new systematic mechanical design methodology for designing a flexible PCBA rework (remanufacturing) cell. The design methodology is intended to be generic in order to apply successfully to any FMS design.

Conventional product design methodology cannot be used directly for the design of a flexible PCBA remanufacturing cell. Hence, two design methodologies were developed: the generic FMS mechanical design methodology and a specific FMS design methodology for a PCBA rework cell. The first one was developed based on the tasks of the conventional product design process integrated with new design tools. The generic design methodology was then extended to obtain the second methodology for a PCBA rework cell design. Both of the methodologies were applied to a flexible PCBA rework cell design problem. Both design methodologies eliminated unusable design solutions at the early design stages of the conceptual design process and made the design process easier.

The generic and specific design methodologies provide a better design environment, even for less specialized FMS designers.

The design methodologies may help for the commercialization of a flexible PCBA remanufacturing cell that may be used for SM rework and assembly.

This paper deals with the optimisation of engineering problems using genetic algorithms. The process is discussed and the various stages of the genetic algorithm described. In conjunction with a finite element analysis program the process is then applied to a realistic problem of extraction of a pollutant from an aquifer. The genetic algorithm suggests sensible solutions for optimum extraction well positions and pumping rates to minimise the overall cost, based upon the results of a series of finite element analyses. The discontinuous nature of the problem is handled easily. The conclusions drawn are that a genetic algorithm optimiser, in conjunction with a finite element analysis program, generates solutions to engineering problems that are sensible and efficient.