Optimal Ordering of Activities of New Product Development Projects with Time and Cost Considerations

This chapter presents a Discrete Multi-objective Particle Swarm Optimization (MOPSO) algorithm that determines the optimal order of activities execution within a design project that minimizes project total iterative time and cost. Numerical Design Structure Matrix (DSM) was used to model project activities’ execution order along with their interactions providing a base for calculating the objective functions. Algorithm performance was tested on a hypothetical project data and results showed its ability to reach Pareto fronts on different sets of objective functions.

The Use of Soft Computing for Optimization in Business, Economics, and Finance

Optimization methods have had successful applications in business, economics, and finance. Nowadays the new theories of soft computing are used for these purposes. The applications in business, economics, and finance have specific features in comparison with others. The processes are focused on private corporate attempts at money making or decreasing expenses; therefore the details of applications, successful or not, are not published very often. The optimization methods help in decentralization of decision-making processes to be standardized, reproduced, and documented. The optimization plays very important roles especially in business because it helps to reduce costs that can lead to higher profits and to success in the competitive fight.

Application of Meta-Heuristic Optimization Algorithms in Electric Power Systems

Optimization of solutions on expansion of electric power systems (EPS) and their control plays a crucial part in ensuring efficiency of the power industry, reliability of electric power supply to consumers and power quality. Until recently, this goal was accomplished by applying classical and modern methods of linear and nonlinear programming. In some complicated cases, however, these methods turn out to be rather inefficient. Meta-heuristic optimization algorithms often make it possible to successfully cope with arising difficulties. State estimation (SE) is used to calculate current operating conditions of EPS using the SCADA measurements of state variables (voltages, currents etc.). To solve the SE problem, the Energy Systems Institute of Siberian Branch of Russian Academy of Sciences (ESI of SB RAS) has devised a method based on test equations (TE), i.e. on the steady state equations that contain only measured parameters. Here, a technique for EPS SE using genetic algorithms (GA) is suggested. SE is the main tool for EPS monitoring. The quality of SE results determines largely the EPS control efficiency. An algorithm for exclusion of wrong SE calculations is described. The algorithm using artificial neural networks (ANN) is based on the analysis of results of the calculation performed solving the SE problem with different combinations of constants. The proposed procedure is checked on real data.

Hybrid Metaheuristics Algorithms for Inventory Management Problems

The hybrid metaheuristics algorithms (HMHAs) have gained a considerable attention for their capability to solve difficult problems in different fields of science. This chapter introduces some applications of HMHAs in solving inventory theory problems. Three basic inventory problems, joint replenishment EOQ problem, newsboy problem, and stochastic review problem, in certain and uncertain environments such as stochastic, rough, and fuzzy environments with six different applications, are considered. Several HMHAs such as genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), harmony search (HS), variable neighborhood search (VNS), and bees colony optimization (BCO) methods are used to solve the inventory problems. The proposed metaheuristics algorithms also are combined with fuzzy simulation, rough simulation, Pareto selecting and goal programming approaches. The computational performance of all of them, on solving these three optimization problems, is compared together.

Analysing the Returns-Earnings Relationship

This chapter considers the problem of understanding the relationship between company stock returns and earnings components, namely accruals and cash flows. The problem is of interest, because earnings are a key output of the accounting process, and investors have been shown to depend heavily on earnings in their valuation models. This chapter offers an elucidation on the application of a nascent data analysis technique, the Classification and Ranking Belief Simplex (CaRBS) and a recent development of it, called RCaRBS, in the returns-earnings relationship problem previously described. The approach underpinning the CaRBS technique is closely associated with uncertain reasoning, with methodological rudiments based on the Dempster-Shafer theory of evidence. With the analysis approach formed as a constrained optimisation problem, details on the employment of the evolutionary computation based technique trigonometric differential evolution are also presented. Alongside the presentation of results, in terms of model fit and variable contribution, based on a CaRBS classification-type analysis, a secondary analysis is performed using a development RCaRBS, which is able to perform multivariate regression-type analysis. Comparisons are made between the results from the two different types of analysis, as well as briefly with more traditional forms of analysis, namely binary logistic regression and multivariate linear regression. Where appropriate, numerical details in the construction of results from both CaRBS and RCaRBS are presented, as well emphasis on the graphical elucidation of findings.

Investigating of Hybrid Meta-Heuristics to Solve the Large-Scale Multi-Source Weber Problems and Performance Measuring of them with Statistical Tests

The location–allocation problems are a class of complicated optimization problems that requires finding sites for m facilities and to simultaneously allocate n customers to those facilities to minimize the total transportation costs. Indeed, these problems, belonging to the class NP-hard, have a lot of local optima solutions. In this chapter, three hybrid meta-heuristics: genetic algorithm, variable neighborhood search and particle swarm optimization, and a hybrid local search approach. These are investigated to solve the uncapacitated continuous location-allocation problem (multi-source Weber problem). In this regard, alternate location allocation and exchange heuristics are used to find the local optima of the problem within the framework of hybrid algorithms. In addition, some large-scale problems are employed to measure the effectiveness and efficiency of hybrid algorithms. Obtained results from these heuristics are compared with local search methods and with each other. The experimental results show that the hybrid meta-heuristics produce much better solutions to solve large-scale problems. Moreover, the results of two non-parametric statistical tests detected a significant difference in hybrid algorithms such that the hybrid variable neighborhood search and particle swarm optimization algorithm outperform the others.

Usage of Metaheuristics in Engineering

A metaheuristic is conventionally described as an iterative generation process which guides a servient heuristic by combining intelligently different concepts for exploring and exploiting the search space, learning strategies are used to structure information in order to find efficiently near-optimal solutions. In the literature, usage of metaheuristic in engineering problems is increasing in a rapid manner. In this study; a survey of the most important metaheuristics from a conceptual point of view is given. Background knowledge for each metaheuristics is presented. The publications are classified with respect to the used metaheuristic techniques and application areas. Advantages and disadvantages of metaheuristics can be found in this chapter. Future directions of metaheuristics are also mentioned.

ANN-Based Self-Tuning Frequency Control Design for an Isolated Microgrid

The increasing need for electrical energy, limited fossil fuel reserves, and the increasing concerns with environmental issues call for fast development in the area of distributed generations (DGs) and renewable energy sources (RESs). A Microgrid (MG) as one of the newest concepts in the power systems consists of several DGs and RESs that provides electrical and heat power for local loads. Increasing in number of MGs and nonlinearity/complexity due to entry of MGs to the power systems, classical and nonflexible control structures may not represent desirable performance over a wide range of operating conditions. Therefore, more flexible and intelligent optimal approaches are needed. Following the advent of optimization/intelligent methods, such as artificial neural networks (ANNs), some new potentials and powerful solutions for MG control problems such as frequency control synthesis have arisen. The present chapter addresses an ANN-based optimal approach scheduling of the droop coefficients for the purpose of frequency regulation in the MGs.

Instance-Specific Parameter Tuning for Meta-Heuristics

Meta-heuristics are of significant interest to decision-makers due to the capability of finding good solutions for complex problems within a reasonable amount of computational time. These methods are further known to perform according to how their algorithm-specific parameters are set. As most practitioners aim for an off-the-shelf approach when using meta-heuristics, they require an easy applicable strategy to calibrate its parameters and use it. This chapter addresses the so-called Parameter Setting Problem (PSP) and presents new developments for the Instance-specific Parameter Tuning Strategy (IPTS). The IPTS presented only requires the end user to specify its preference regarding the trade-off between running time and solution quality by setting one parameter p (0 = p =1), and automatically returns a good set of algorithm-specific parameter values for each individual instance based on the calculation of a set of problem instance characteristics. The IPTS does not require any modification of the particular meta-heuristic being used. It aims to combine advantages of the Parameter Tuning Strategy (PTS) and the Parameter Control Strategy (PCS), the two major approaches to the PSP. The chapter outlines the advantages of an IPTS and shows in more detail two ways in which an IPTS can be designed. The first design approach requires expert-based knowledge of the meta-heuristic’s performance in relation to the problem at hand. The second, automated approach does not require explicit knowledge of the meta-heuristic used. Both designs use a fuzzy logic system to obtain parameter values. Results are presented for an IPTS designed to solve instances of the Travelling Salesman Problem (TSP) with the meta-heuristic Guided Local Search (GLS).

A Gravitational Search Algorithm Approach for Optimizing Closed-Loop Logistics Network

Since late in the 20th century, various heuristic and metaheuristic optimization methods have been developed to obtain superior results and optimize models more efficiently. Some have been inspired by natural events and swarm behaviors. In this chapter, the authors illustrate empirical applications of the gravitational search algorithm (GSA) as a new optimization algorithm based on the law of gravity and mass interactions to optimize closed-loop logistics network. To achieve these aims, the need for a green supply chain will be discussed, and the related drivers and pressures motivate us to develop a mathematical model to optimize total cost in a closed-loop logistic for gathering automobile alternators at the end of their life cycle. Finally, optimizing total costs in a logistic network is solved using GSA in MATLAB software. To express GSA capabilities, a genetic algorithm (GA), as a common and standard metaheuristic algorithm, is compared. The obtained results confirm GSA’s performance and its ability to solve complicated network problems in closed-loop supply chain and logistics.