Project time–cost tradeoff problem is concerned with shortening project duration time with minimum cost. Due to uncertain environment conditions, such as inflation, climate changes, space congestion, productivity level, lack of accurate data, etc., there can be uncertainty in the parameters. The uncertainty in the parameters can be modeled using fuzzy set theory. In this study, a fuzzy time–cost tradeoff problem with fuzzy normal and crash durations, and fuzzy duration of activities and occurrence time of events is handled. The fuzzy time–cost tradeoff problem is solved directly by making use of a fuzzy ranking method and the tabu search (TS) algorithm.

There are many approaches in the literature to model and quantify manufacturing flexibility. Most of these models were developed to quantify several aspects of manufacturing flexibility like machine flexibility, routing flexibility, mix flexibility, volume flexibility, etc. This is mainly due to the fact that developing a generic model, which can be used to measure different types of flexibilities, is not straightforward. Recently, a generic flexibility measure, which is based on digraphs and permanent index, was proposed by the author. The main difficulty with that model like in all other flexibility models is the inability to collect precise data for computing the flexibility. In order to overcome this difficulty, a practical fuzzy linguistic approach is incorporated into the previous digraph model in this article. The extended fuzzy digraph model is explained in detail through an example in the present article.

The optimization of composite materials such as concrete deals with the problem of selecting the values of several variables which determine composition, compressive stress, workability and cost etc. This study presents multi-objective optimization (MOO) of high-strength concretes (HSCs). One of the main problems in the optimization of HSCs is to obtain mathematical equations that represents concrete characteristic in terms of its constitutions. In order to solve this problem, a two step approach is used in this study. In the first step, the prediction of HSCs parameters is performed by using regression analysis, neural networks and Gen Expression Programming (GEP). The output of the first step is the equations that can be used to predict HSCs properties (i.e. compressive stress, cost and workability). In order to derive these equations the data set which contains many different mix proportions of HSCs is gathered from the literature. In the second step, a MOO model is developed by making use of the equations developed in the first step. The resulting MOO model is solved by using a Genetic Algorithm (GA). GA employs weighted and hierarchical method in order to handle multiple objectives. The performances of the prediction and optimization methods are also compared in the paper.

There is an increasing trend in the scientific community to model and solve complex optimization problems by employing natural metaphors. This is mainly due to the inefficiency of classical optimization algorithms in modelling and solving larger scale combinatorial and/or highly non-linear problems. It has been shown that nature-inspired, meta-heuristic algorithms can provide far better solutions than classical algorithms. The branch of nature-inspired algorithms which are known as evolutionary algorithms are focused on evolutionary processes in order to develop some meta-heuristics which can mimic natural evolutionary processes. Genetic algorithms and genetic programming are some of the well-known algorithms that mimic evolutionary processes in problem modelling and solution. The present book is mainly focused on genetic algorithms and genetic programming, and successfully explains evolutionary computation through many different applications of these algorithms. The book comprises fifteen chapters. It starts by explaining evolutionary computation through analogies from biology. Then it explains designing evolutionary...

With their high potential, high motivation, great problem-solving ability and flexibility, project teams are important work structures for the business life. The success of these teams is highly dependent upon the people involved in the project team. This makes the project team selection an important factor for project success. The project team selection can be defined as selecting the right team members, which will together perform a particular project/task within a given deadline. In this article, an analytical model for the project team selection problem is proposed by considering several human and nonhuman factors. Because of the imprecise nature of the problem, fuzzy concepts like triangular fuzzy numbers and linguistic variables are used. The proposed model is a fuzzy multiple objective optimization model with fuzzy objectives and crisp constraints. The skill suitability of each team candidate is reflected to the model by suitability values. These values are obtained by using the fuzzy ratings method. The suitability values of the candidates and the size of the each project team are modeled as fuzzy objectives. The proposed algorithm takes into account the time and the budget limitations of each project and interpersonal relations between the team candidates. These issues are modeled as hard-crisp constraints. The proposed model uses fuzzy objectives and crisp constraints to select the most suitable team members to form the best possible team for a given project. A simulated annealing algorithm is developed to solve the proposed fuzzy optimization model. Software based on C + + computer programming language is also developed to experiment on the proposed model in forming project teams.

Successful implementation of TQM (Total Quality Management) in an organization requires “system approach.” ISO 9000 standard provides such a systematic approach. The ISO 9000 certification process is a cybernetic system where the feedback part is “quality auditing.” An effective auditing can therefore improve and accelerate the certification process. Value-added quality auditing is an effective auditing technique in which auditors not only evaluate quality a management system and processes in terms of compliance to a standard, but also they analyze their effectiveness. A value-added quality auditing approach is emerging as one of the most powerful tools for continuous quality improvement, especially after the introduction of the ISO 9001:2000 standard that requires effectiveness, process auditing, proper auditor skills, and team-based audits. In order to meet these requirements, formation of an appropriate quality audit team is essential. This article presents a team-oriented cybernetic model for the value-added quality auditing along with the procedure developed for the “quality-audit team formation.” The proposed model makes use of feedbacks to add value to the audited business where appropriate.