A prototype knowledge based fuzzy analytic network process system for sustainable manufacturing indicator

Sustainable manufacturing is a relatively new but a very complex manufacturing paradigm. The complexity arises as this paradigm covers three interdependent yet mutually supporting sustainability dimensions of economic, environmental and social. In a further step to embark on the essence of sustainable manufacturing, the development of appropriate indicators needs to be emphasized as compared to other efforts. Regrettably, the existing indicators have several drawbacks that may hamper the accuracy of sustainability performance assessment of an organization. As such, there are only a few standardized indicator mechanisms which can suit specific requirements of various manufacturing organizations. Hence, this study suggests a novel Knowledge-Based Fuzzy Analytic Network Process (KBFANP) system which can assist the decision making process of sustainable manufacturing by developing a new indicator mechanism. The KBFANP system comprises of four major phases, namely Initialization, Selection, Evaluation and Prioritization. The system incorporates the advantages of Knowledge-Based System Fuzzy Set Theory and Analytic Network Process into a single unified approach as a standardized indicator, which is applicable to all types of problem setting. A prototype of KBFANP system was developed, tested and analyzed on three experimental data sets and two real manufacturing settings. The system was able to provide solutions on the areas that need improvement with different levels of priority. This study also supports the notion of lean and green manufacturing as the elementary foundation of sustainable manufacturing implementation. The proposed KBFANP system can act as an advisory Decision Support System which is beneficial to both academia and industrial practitioners

Conceptual model of sustainable lean and green manufacturing management system: Initial stage

The concept of lean and green manufacturing management has recently gained interest among the researchers and practitioners in the area of manufacturing management because it can promote good business sense and being environmentally friendly at the same time.The manufacturing process in automotive industry is exceptionally challenging, characterized by increasing complexity and significant relationship with the deterioration of environment.This paper proposes a framework for a sustainable lean and green manufacturing management system with specific emphasize on the automotive industry.The framework consists of a conceptual model which focuses on the initial stages in moving towards planning,design and implementation phases of sustainable lean and green manufacturing management system

A reliability based consistent fuzzy preference relations for risk assessment in oil and gas industry

In decision making, linguistic variables tend to be complex to handle but they make more sense than classical fuzzy numbers. Fuzziness is not sufficient enough to deal with information and degree of reliability of information is critical. Z-numbers is proposed to model the uncertainty produced by human judgment when eliciting information. Therefore, the implementation of z-numbers is taken into consideration, where it has more authority to describe the knowledge of human being and extensively used in the uncertain information development. This issue has motivated the authors to propose fuzzy multi criteria decision making methodology using z-numbers. The proposed methodology is demonstrated the capability to handle knowledge of human being and uncertain information for risk assessment in oil and gas industry. This assessment is due to periodic basis, which will give insights from the operational until the strategic level of decision making process that is capable of dealing with uncertainty in human judgment. The consistent fuzzy preference relations is developed to calculate the preference-weights of the criteria related based on the derived network structure and to resolve conflicts arising from differences in information and opinions provided by the decision makers. The proposed methodology is constructed without losing the generality of the consistent fuzzy preference relations under fuzzy environment

A study of vehicle routing problem via trade-off ranking method

Vehicle routing defines selecting the minimum cost, distance, and/or time path from a depot to several alternatives for a goods or service to reach its destination. The objective of most routing problem is to minimize the total cost of providing the service. But other objectives also may come into play, particularly in the public sector. For emergency services, such as ambulance, police, and fire engine, minimizing the response time to an incident is of primary importance. A few routing algorithms do not use a deterministic algorithm to find the "best" route for a goods to get from its original source to its destination. Instead, to avoid congestion, a few algorithms use a randomized algorithm that routes a path to a randomly picked intermediate destination, and from there to its true destination. In this paper, the trade-off ranking method is used to solve for the vehicle routing treated as a conflicting multi-criteria problem. The integration of the trade-off ranking method into the vehicle routing problem gives another perspective on how to solve the problem, hence broadened the decision support system for the vehicle routing problem

Hybrid Knowledge-Based System for Collaborative Green Automotive Manufacturing Management

The objective of this research paper is to demonstrate the application of hybrid knowledge-based system, gauging absences of pre-requisites (GAP), and analytic hierarchy process (AHP) approaches for selecting the improvement programs for Collaborative Green Manufacturing Management (CGMM) system. In this research, a generic knowledge-based system is developed to measure the level of CGMM adoption in automotive manufacturers compared to the ideal system. Using the GAP and AHP tools, the key green manufacturing improvement programs can be prioritized and demonstrated with an illustrative example

Four types of dependence relationship in two consecutive stage data envelopment analysis model

Data Envelopment Analysis (DEA) model usually does not consider the interaction between the decision making units (DMU). The interaction can be represented in the form of two consecutive stages in which the outputs from the precedent stage will be the inputs for the latter stage. The two consecutive stage DEA model can be represented as Non-separable DEA (NSDEA) which integrates both desirable and undesirable output. The undesirable output unlike desirable output, indicates a higher efficiency if the output is lower or not productive. The different orientation between desirable and undesirable output may affect the efficiency score especially if it was formed in two consecutive stages. Thus, this research attempts to address four different types of dependence relationship which can occur in the formation of two consecutive stage DEA models and to investigate the impact towards the overall efficiency of the DMUs. The finding shows that the determination of positive or negative correlation between the two stages which combines both desirable and undesirable output, are more likely to be influenced by the orientation of the first precedent stage

An analysis on a public university final year engineering students’ stress levels

Stress is one of the serious issues that affect university students’ life and has been identified to cause academic decline, poor relationships with peers and family members and overall dissatisfaction with life. As for final year university students, academic workloads, expectations from societies and future career uncertainties are some of the common factors that create stress. Therefore, this case study aims to investigate the stress experienced by the final year engineering students at a public university. The main objectives are to identify factors that cause stress and its effects on the students. A five-dimension set of questionnaires i.e. Interpersonal, Intrapersonal, Motivation, Environment, and Workload was distributed to 260 final year students of an engineering faculty before they participated in a Stress Management Awareness Program. The program which was part of the Final Year Project (FYP), exposed the students with stress management strategies. Later, interviews were conducted to explore the extent of stress effects on the students’ academic success, and lifestyle in general, and the effect of the workshop. The mean scores of the dimensions and its items were analysed descriptively and findings from the interviews were compared after the students had undergone the workshop. The quantitative results indicated a moderate level of stress among the students with some significance in ‘Environment’ and ‘Workloads’ dimensions. Qualitatively, the workshop had made the students aware of their stress experiences, and educated them with various stress management strategies. Interestingly, there were similar continuous patterns of stress experiences from 33 volunteered interviewees, which have brought to light the actual circumstances of students’ stress. Evidently, these results call for a context-driven stress management module that can provide the students with resourceful self regulated strategies in coping with the demanding life as final year engineering students

Preliminary study of malaysian eco-friendly car selection by using analytic hierarchy process

As the global community is moving towards the usage of a cleaner technology, automotive industry has enforced the car to be produced to become environmentally friendly, or eco-friendly. Malaysia aims to produce 200,000 electric vehicles (EV) by the year 2020 but this effort might be halted due to the fact of electric vehicle’s sales which is not really promising for the time being. This paper attempts to investigate the current preference of Malaysian to buy their car, and whether they really need to buy the eco-friendly car. An Analytic Hierarchy Process (AHP) model for Malaysian eco-friendly car selection is developed by this research which involves group judgment of 22 respondents. The result indicates that safety is the highest priority for the Malaysian to buy cars, followed by fuel economy, services, performance, affordable price, emission and design. Two of the criteria which are closely related with eco-friendly factor which are fuel economy is ranked at 2nd whereas emission is ranked at 6th. This concludes that Malaysian still consider eco-friendly factor, which also justify the selection of AHP model for the best eco-friendly car to be Nissan Leaf (2016), followed by Mercedez Benz C350e (2016), Hyundai Ionic HEV (2017), BMW i8 eDrive (2017) and Toyota Camry Hybrid

A knowledge based fuzzy analytic network process for sustainable manufacturing indicator

Sustainable manufacturing is a relatively new but a very complex manufacturing paradigm as it encompasses three interdependent sustainability dimensions of economic, environmental and society. To embark on the essence of sustainable manufacturing, the development of sustainability indicators needs to be highlighted. Regrettably, there are only a few standardized indicator mechanisms which can suit specific requirements of various manufacturing organizations. Hence, this research proposes a novel Knowledge-Based Fuzzy Analytic Network Process (KBFANP) system which are able to assist the decision-making process of sustainable manufacturing by the development of a new indicator mechanism. The KBFANP system consists of four major phases, namely Initialization, Selection, Evaluation and Prioritization. The system integrates the advantages of Knowledge-Based System, Fuzzy Set Theory and Analytic Network Process into a single unified standardized indicator, which is applicable to all types of manufacturing settings. The system is developed, implemented and analyzed on two manufacturing companies. The proposed KBFANP system can be made as the advisory Decision Support System which is able to provide solutions on the areas that need improvement, with different levels of priority

Planning and Design of a Knowledge Based System for Green Manufacturing Management

This paper presents a conceptual design approach to the development of a hybrid Knowledge Based (KB) system for Green Manufacturing Management (GMM) at the planning and design stages. The research concentrates on the GMM by using a hybrid KB system, which is a blend of KB system and Gauging Absences of Pre-requisites (GAP). The hybrid KB/GAP system identifies all potentials elements of green manufacturing management issues throughout the development of this system. The KB system used in the planning and design stages analyses the gap between the existing and the benchmark organizations for an effective implementation through the GAP analysis technique. The proposed KBGMM model at the design stage explores two components, namely Competitive Priority and Lean Environment modules. Through the simulated results, the KBGMM System has identified, for each modules and sub-module, the problem categories in a prioritized manner. The System finalized all the Bad Points (BP) that need to be improved to achieve benchmark implementation of GMM at the design stage. The System provides valuable decision making information for the planning and design a GMM in term of business organization