Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

A fuzzy multiobjective algorithm for multiproduct batch plant: Application to protein production

This paper addresses the problem of the optimal design of batch plants with imprecise demands and proposes an alternative treatment of the imprecision by using fuzzy concepts. For this purpose, we extended a multiobjective genetic algorithm (MOGA) developed in previousworks, taking into account simultaneously maximization of the net present value (NPV) and two other performance criteria, i.e. the production delay/advance and a flexibility criterion. The former is computed by comparing the fuzzy computed production time to a given fuzzy production time horizon and the latter is based on the additional fuzzy demand that the plant is able to produce. The methodology provides a set of scenarios that are helpful to the decision’s maker and constitutes a very promising framework for taken imprecision into account in new product development stage

A decision-based perspective for the design of methods for systems design

Organization of material, a definition of decision based design, a hierarchy of decision based design, the decision support problem technique, a conceptual model design that can be manufactured and maintained, meta-design, computer-based design, action learning, and the characteristics of decisions are among the topics covered

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

BUILDING TRUST FOR SERVICE ASSESSMENT IN INTERNET-ENABLED COLLABORATIVE PRODUCT DESIGN & REALIZATION ENVIRONMENTS

Reducing costs, increasing speed and leveraging the intelligence of partners involved during product design processes are important benefits of Internet-enabled collaborative product design and realization environments. The options for cost-effective product design, re-design or improvement are at their peak during the early stages of the design process and designers can collaborate with suppliers, manufacturers and other relevant contributors to acquire a better understanding of associated costs and product viability. Collaboration is by no means a new paradigm. However, companies have found distrust of collaborative partners to be the most intractable obstacle to collaborative commerce and Internet-enabled business especially in intellectual property environments, which handle propriety data on a constant basis. This problem is also reinforced in collaborative environments that are distributed in nature. Thus trust is the main driver or enabler of successful collaborative efforts or transactions in Internet-enabled product design environments. Focus is on analyzing the problem of ¡®trust for services¡¯ in distributed collaborative service provider assessment and selection, concentrating on characteristics specific to electronic product design (e-Design) environments. Current tools for such collaborative partner/provider assessment are inadequate or non-existent and researching network, user, communication and service trust problems, which hinder the growth and acceptance of true collaboration in product design, can foster new frontiers in manufacturing, business and technology. Trust and its associated issues within the context of a secure Internet-enabled product design & realization platform is a multifaceted and complex problem, which demands a strategic approach crossing disciplinary boundaries. A Design Environment Trust Service (DETS) framework is proposed to incorporate trust for services in product design environments based on client specified (or default) criteria. This involves the analysis of validated network (objective) data and non-network (subjective) data and the use of Multi Criteria Decision Making (MCDM) methodology for the selection of the most efficient service provision alternative through the minimization of distance from a specified ideal point and interpreted as a Dynamic (Design) Trust Index (DTI) or rank. Hence, the service requestor is provided with a quantifiable degree of belief to mitigate information asymmetry and enable knowledgeable decision-making regarding trustworthy service provision in a distributed environment

A three-stage model for closed-loop supply chain configuration under uncertainty

In this paper, a general closed-loop supply chain (CLSC) network is configured which consists of multiple customers, parts, products, suppliers, remanufacturing subcontractors, and refurbishing sites. We propose a three-stage model including evaluation, network configuration, and selection and order allocation. In the first stage, suppliers, remanufacturing subcontractors, and refurbishing sites are evaluated based on a new quality function deployment (QFD) model. The proposed QFD model determines the relationship between customer requirements, part requirements, and process requirements. In addition, the fuzzy sets theory is utilised to overcome the uncertainty in the decision-making process. In the second stage, the closed-loop supply chain network is configured by a stochastic mixed-integer nonlinear programming model. It is supposed that demand is an uncertain parameter. Finally in the third stage, suppliers, remanufacturing subcontractors, and refurbishing sites are selected and order allocation is determined. To this end, a multi-objective mixed-integer linear programming model is presented. An illustrative example is conducted to show the process. The main novel innovation of the proposed model is to consider the CLSC network configuration and selection process simultaneously, under uncertain demand and in an uncertain decision-making environment

Linking objective and subjective modeling in engineering design through arc-elastic dominance

Engineering design in mechanics is a complex activity taking into account both objective modeling processes derived from physical analysis and designers’ subjective reasoning. This paper introduces arc-elastic dominance as a suitable concept for ranking design solutions according to a combination of objective and subjective models. Objective models lead to the aggregation of information derived from physics, economics or eco-environmental analysis into a performance indicator. Subjective models result in a confidence indicator for the solutions’ feasibility. Arc-elastic dominant design solutions achieve an optimal compromise between gain in performance and degradation in confidence. Due to the definition of arc-elasticity, this compromise value is expressive and easy for designers to interpret despite the difference in the nature of the objective and subjective models. From the investigation of arc-elasticity mathematical properties, a filtering algorithm of Pareto-efficient solutions is proposed and illustrated through a design knowledge modeling framework. This framework notably takes into account Harrington’s desirability functions and Derringer’s aggregation method. It is carried out through the re-design of a geothermal air conditioning system