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Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

The purpose of this paper is to improve current design processes by proposing a new approach based on multi‐criteria optimization of the designed asset. Management of design in construction projects is a complex task since it involves collaboration between professionals in multiple disciplines. Traditionally, designers work with a single solution at a time which is iteratively modified according to the view points of all the consultants. This results in sub‐optimal solutions. A multi‐criteria approach is able to accommodate diverse view points of specialist consultants in a construction project, aiming at a better optimized building system.

The shortcomings of current design practices are analyzed based on a literature review. It is found that current approaches involving single objective optimization or Pareto optimization are not adequate for supporting collaborative design processes. A new approach to managing multiple objectives in design is proposed. This involves performing multi‐objective optimization, presenting a population of good solutions to the design consultants and selecting the best solution through an algorithm called RR‐PARETO2 (Relaxed‐Restricted Pareto) filtering. A software tool with a graphical user interface was developed. An example of the design of a building façade is taken to evaluate the application of this approach.

The paper provides empirical evidence that a multi‐objective optimization approach is able to provide support for the task of accommodating multiple viewpoints in design. The proposed methodology allows navigation through the solution space and pruning it visually by applying constraints. It is shown that the RR‐PARETO2 is able to select a good compromise solution with the best trade‐offs among all the objectives.

The idea of visualizing and filtering a population of design solutions has been proposed by design researchers for a long time, but is not currently adopted in practice in construction projects. The idea of collaborative filtering of the solution space according to the viewpoints of all the consultants by visually applying constraints on design variables and objectives is a new concept, the ultimate aim being a better balanced built asset. This is the first time the RR‐PARETO2 algorithm has been applied to building design.

Reliability is a major quality characteristic which has grown in importance as products/systems have become ever more sophisticated. Neglecting it could spell great losses in terms of patronage, revenue, and even lives. The purpose of this paper is to present a multi‐criteria optimisation model and methodology for the Pareto optimal assignment of reliability to the components of a series‐parallel system in order to maximise its reliability.

The subsystems' reliabilities are maximised independently but simultaneously in order to maximise the overall system reliability, while a penalty function modelling cost of reliability improvement is minimised. The resultant continuous and nonlinear optimisation problem is scalarised by a convex combination of the criteria and the MATLAB Optimisation Toolbox is used to generate the solutions.

The results for an illustrative example problem extracted from the literature show that: higher reliabilities could be assigned to the components, in order to achieve or exceed target system reliability; cost increased sharply with slight improvements in the component reliabilities, and the model was stable under the weighting scheme used.

The novelty of this work is in: the multi‐criteria optimisation view taken of the design problem; the focus on the subsystems' reliabilities and cost as the criteria to be optimised; the use of the two aforementioned qualities for the purpose of Pareto assignment of component reliabilities in a system's design; and the use of the model and methodology in the context of series‐parallel systems.

In this paper, the authors investigated a proposed radio-frequency identification (RFID)-based meat supply chain to monitor quality and safety of meat products we purchase from supermarkets. The supply chain consists of farms, abattoirs and retailers. The purpose of this paper is to determine a cost-effective trade-off decision obtained from a developed multi-criteria optimization model based on three objectives. These objectives include customer satisfaction in percentage of product quantity as requested by customers, product quality in numbers of meat products and the total implementation cost. Furthermore, this work was aimed at determining the number and locations of farms and abattoirs that should be established and quantities of products that need to be transported between entities of the proposed supply chain.

To this aim, a tri-criteria optimization model was developed. The considered criteria were used for minimizing the total implementation cost and maximizing customer satisfaction and product quality. In order to obtain Pareto solutions based on the developed model, four solution approaches were employed. Subsequently, a new decision-making algorithm was developed to select the superior solution approach in terms of values of the three criteria.

A case study was applied to examine the applicability of the developed model and the performance of the proposed solution approaches. The computational results proved the applicability of the developed model in obtaining a trade-off among the considered criteria and solving the RFID-based meat supply chain design problem.

The developed tri-criteria optimization model can be used by decision makers as an aid to design and optimize food supply chains.

This paper presents a development of first, a cost-effective optimization approach for a proposed RFID-based meat supply chain seeking a trade-off among three conflicting criteria; and second, a new decision-making algorithm which can be used for any multi-criteria problem to select the best Pareto solution.

This study produced epoxy-filled urea-formaldehyde (UF) microcapsules (MCs) and T-403 amine MCs using the in situ technique. The Taguchi method was used to determine the effects of the control factors (temperature, stirring speed, core-shell ratio and surfactant concentration) affecting MCs’ core diameter and core content and optimizing their optimum levels with a single criterion. Optimum control factor levels, which simultaneously provide maximum core diameter and core content of MCs, were determined by the PROMETHEE-GAIA multi-criteria optimization method. In addition, the optimized MC yield was analyzed by thermal camera images and compression test.

Microcracks in materials used for aerospace vehicles and automotive parts cause serious problems, so research on self-healing in materials science becomes critical. The damages caused by micro-cracks need to heal themselves quickly. The study has three aims: (1) production of self-healing MCs, mechanical and chemical characterization of produced MCs, (2) single-criteria and multi-criteria optimization of parameters providing maximum MC core diameter and core content, (3) investigation of self-healing property of produced MCs and evaluation. Firstly, MCs were produced to achieve these goals.

The optimized micro cures are buried in the epoxy matrix at different concentrations. Thermal camera images after damage indicate the presence of healing. An epoxy-amine MC consisting of a 10% by weight filled aluminum sandwich panel was prepared and subjected to a quasi-static compression test. It was determined that there is a strong bond between the UF shell and the epoxy resin.

The optimization of production factors has been realized to produce the most efficient MCs that heal using less expensive and more accessible methods.

A multi‐criteria model is developed for analyzing the land‐use strategies for reducing the future social and economic costs in an area with potential natural hazard. A multi‐criteria decision‐making procedure consists of generating alternatives, establishing criteria, assessment of criteria weights and application of the compromise ranking method (VIKOR). The alternatives are the scenarios of sustainable hazard effects mitigation, generated in the form of comprehensive land‐use plans. The alternative plans should consider the redevelopment of urban areas and infrastructures, multi‐purpose land use, including restrictions on building in hazardous areas. The evaluation of alternatives is implicated with imprecision (or uncertainty) of established criteria, and the fuzzy multi‐criteria model is developed to deal with “qualitative” (unquantifiable or linguistic) or incomplete information. The planning goal is to compromise competitive land uses through choice of the best (compromise) combination of uses. An application of this model is illustrated with the post‐earthquake regional planning problem in central Taiwan.

The most difficult tasks in the design and development of products for diverse engineering applications were the selection of suitable materials. Choice of inappropriate process variables leads to poor performance, which increases the cost of the product. The selection of the best option of available alternatives is important to improve the performance and productivity of the manufacturing enterprises.

The paper aims to develop Hybrid Multi-Criteria Decision Making (HMCDM) by integrating two potential optimization techniques Elimination Et Choix Traduisant la REalité and multi-objective optimization on the basis of ratio analysis. The weight of the criteria was calculated using the critic weight method.

The efficiency and flexibility of the proposed HMCDM technique were illustrated and validated by two examples. In the first case, the best electrode material among the five available alternatives was selected for the electrical discharge machining of AZ91/B4Cp magnesium composites. In the second case, the optimum weight percentage of composites providing the best tribological properties was chosen.

It was noted that the HMCDM methodology was quite simple to comprehend, easy to apply and provided reliable rankings of the material alternatives. The proposed hybrid algorithm is suitable for product optimization as well as design optimization.

In this chapter, a case of reverse supply chain is considered, where a product recovery facility receives sensors and Radio Frequency Identification (RFID) tags embedded End-Of-Life (EOL) products. Sensors and RFID tags can capture and store component’s life cycle information during its economic life. This technology can provide data about contents and conditions of products and components without the need of actual disassembly and inspection. It also determines the remaining lives of the components which eventually translate into their quality levels.

The example considered here presents an advanced-repair-to-order-and-disassembly-to-order system. It disassembles the components to meet the components’ demands, repairs the products to meet the products’ demands and recycles the materials to meet the materials’ demands. The received EOL products may have different design alternatives. The objective of the proposed multi-criteria decision-making model is to determine which of the design alternatives is best in fulfilling the various criteria.

The purpose of this paper is to present how multi-criteria methods can be used in the process of National Energy Programme development and analysis of the electricity generation planning in relatively small energy systems.

Due to dependence on many input and output parameters which can be very complex and uncertain, energy systems have become extremely difficult to analyse and use of multi-criteria decision-making methods is essential. This paper presents and compares results of the analysis of electricity generation scenarios with three widely used multi-criteria methods, analytic hierarchy process (AHP), preference ranking organisation method for enrichment evaluation (PROMETHEE) and multi-attribute utility theory (MAUT).

AHP, due to its transparency and simplicity in comparison with PROMETHEE and MAUT, is the most suitable method for the analysis of the electricity generation decision alternatives in a small energy system, such as Slovenian. The case study proposed in this research confirms that transition from environment unfriendly fossil-fuelled economy to sustainable and climate friendly development requires a realistic approach, which must be based on excellent knowledge of alternative possibilities of development and especially awareness of new opportunities in exploitation of energy efficiency and renewable energy sources.

The case study presented in this research represents a repeatable and practical application of three widely used multi-criteria methods in the process of national energy policy development. It provides a methodological approach for finding realistic solutions to the problem of the future development challenges in small energy systems.

This study aims to deal with supplier selection problem. The supplier selection problem has significantly become attractive to researchers and practitioners in recent years. Many real-world supply chain problems are assumed as multiple objectives combinatorial optimization problems.

In this paper, the authors propose a multi-objective model with fuzzy parameters to select suppliers and allocate orders considering multiple periods, multiple resources, multiple products and two-echelon supply chain. The objective functions consist of total purchase costs, transportation, order and on-time delivery, coverage and the weights of suppliers. Distance-based partial and general coverage of suppliers makes the number of orders of products more realistic. In this model, the weights of suppliers are determined by fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, as a multi-criteria decision analysis method, in the objective function. Also, the authors consider the parameters related to delays as triangular fuzzy numbers.

A small-sized numerical example is provided to clearly show the proposed model. The exact epsilon constraint method is used to solve this given multi-objective combinatorial optimization problem. Subsequently, the sensitivity analysis is conducted to testify the proposed model. The obtained results demonstrate the validity of the proposed multiple objectives mixed integer mathematical programming model and the efficiency of the solution approach.

In real-life situations, supplier selection parameters are uncertain and incomplete. Hence, the fuzzy set theory is used to tackle uncertainty. In this paper, a multi-objective supplier selection problem is formulated taking into consideration the coverage of suppliers and suppliers’ weights. Integrating coverage of suppliers to select and allocate the order to them can be mentioned as the main contribution of this study. The proposed model considers the delay from suppliers as fuzzy parameters.