A Novel Project Risk Assessment Method Development via AHP-TOPSIS Hybrid Algorithm

In project planning, risk assessment method plays vital role. Poorly assessed project risks cause degeneration at project cost, project completion time, and project output quality and project scope. Each project activity risk influence these project success factors. Implementation performance of a project activity triggers or smooth of its successor’s activity risks. Because of this; employing robust and detailed risk assessment methods is important to reach those project goals. In project risk assessment literature, when it is investigated, it is noticed that risk assessment and evaluation methods are only developed at whole project level. Actually, they are not comprehensive enough to evaluate the project risks at activity level. Besides that traditional risk assessment methods such as risk matrix does not able analyze project risk quantitatively. With this motivation, main aim of this study is developing a multi-criteria based decision method which prioritizing project risks at activity level. AHP and TOPSIS method are combined to developed novel method. In this hybrid method, Constructing AHP model is to prioritize work packages with respect to relative importance of project time, project output quality and project cost. Broken down structure of these work packages are used as input for weighted criteria for TOPSIS method. In second layer of this decision method, TOPSIS model is used for prioritizing predetermined activity risks according weighted project work packages success criteria. In the application of this method, a case study approach is followed. In this sense, “Global Furniture Ltd.” which is established in Istanbul, Turkey is chosen as a case to apply newly developed model. Results showed that application of AHP-Stochastic TOPSIS Hybrid Algorithm provides a platform that project risks could be analyzed as quantitative and also at project activity level

An intelligent group decision-support system and its application for project performance evaluation

Purpose: In any organization there are main goals, with lots of projects designed to achieve these goals. It is important for any organization to determine how much these projects affect the achievement of these goals. The purpose of this paper is to develop a fuzzy multiple attribute-based group decision-support system (FMAGDSS) to evaluate projects' performance in promoting the organization's goals utilizing simple additive weighting (SAW) algorithm and technique for order of preference by similarity to ideal solution (TOPSIS) algorithm. The proposed FMAGDSS deals with choosing the most appropriate fuzzy ranking algorithm for solving a given fuzzy multi attribute decision making (FMADM) problem with both qualitative and quantitative criteria (attributes), and uncertain judgments of decision makers. Design/methodology/approach: In this paper, a FMAGDSS model is designed to determine scores and ranks of every project in promoting the organization's goals. In the first step of FMAGDSS model, all projects are assessed by experts based on evaluation criteria and the organization's goals. The proposed FMAGDSS model will then choose the most appropriate fuzzy ranking method to solve the given FMADM problem. Finally, a sensitivity analysis system is developed to assess the reliability of the decision-making process and provide an opportunity to analyze the impacts of "criteria weights" and "projects" performance' on evaluating projects in achieving the organizations' goals, and to assess the reliability of the decision-making process. In addition, a software prototype has been developed on the basis of FMAGDSS model that can be applied to solve every FMADM problem that needs to rank alternatives according to certain attributes. Findings: The result of this study simplifies and accelerates the evaluation process. The proposed system not only helps organizations to choose the most efficient projects for sustainable development, but also helps them to assess the reliability of the decision-making process, and decrease the uncertainty in final decision caused by uncertain judgment of decision makers. Research limitations/implications: Future studies are suggested to expand this system to evaluate and rank the project proposals. To achieve this goal, the efficiency of the projects in line with organization's goals, should be predicted.Originality/value: This study contributes to the relevant literature by proposing a FMAGDSS model to evaluate projects in promoting organization's goals. The proposed FMAGDSS has ability to choose the most appropriate fuzzy ranking algorithm to solve a given FMADM problem based on the type and the number of attributes and alternatives, considering the least computation and time consumption for ranking alternatives. © Emerald Group Publishing Limited

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

Modeling and Solving Project Portfolio and Contractor Selection Problem Based on Project Scheduling under Uncertainty

AbstractIn this paper a new formulation of the project portfolio selection problem based on the project schedules in uncertain circumstances have been proposed. The project portfolio selection models usually disregard the project scheduling, whereas is an element of the project selection process. We investigate a project portfolio selection problem based on the schedule of the projects, so that the minimum expected profit would be met in the shortest possible time period. Also due to uncertain nature of durations of the activities, this duration considered as the semi-trapezoidal fuzzy numbers. Finally, a fuzzy linear programming model is developed for the problem, where the results indicated the validity of the presented model

Criticality evaluation to support maintenance management of manufacturing systems

This paper focuses on criticality evaluation for supporting daily equipment maintenance management and the definition of medium and long-term maintenance actions to improve equipment and, therefore, productivity. These two different purposes led to the development of two different methods for criticality evaluation, using criteria adjusted for each case. The first method is based on rules for defining priorities for corrective and preventive maintenance tasks. Since a failure mode of critical equipment is not necessarily critical, priorities for maintenance tasks are assigned to tasks rather than to equipment. The second method uses Analytic Hierarchy Process to prioritize equipment based on its performance. This method is based on the indicators commonly monitored by maintenance departments. In addition to assessing equipment performance, it considers the maintenance effort made to achieve the evaluated performance. The selection of the criticality criteria and the development of the methods was based on literature review and triggered by a case study in a multinational automotive company. With the integration of the proposed methods in a computerized maintenance management system, maintenance technicians and managers are able to know in real time the tasks that should be performed first and to monitor the overall performance of equipment in the plant, focusing improvements where they are more required.POFC - Programa Operacional Temático Factores de Competitividade (UID/CEC/00319/2013

A Hybrid Fuzzy TOPSIS – Best Worst Method for Risk Prioritization in Megaprojects

Megaprojects are usually complex and in many cases encounter failure in terms of finish late or overspent. This study aims to investigate the critical risk factors behind these projects as well as their priority. Project risk management is a mature research stream. But when focus on megaprojects the amount of research decreases significantly. This research provides a hierarchy of risk structure in Tehran-Rasht railway megaproject and prioritizes the risk factors through a two-phase methodology. This method is a new hybrid MCDM technique consist of group fuzzy TOPSIS and fuzzy Best-Worst Method. BWM is the latest MCDM technique which in this paper, its fuzzy version combined with fuzzy TOPSIS is employed.  This research also considers all the project success criteria including time, cost and quality simultaneously and calculates the risk priority Index (RPI) accordingly. The results imply that quality is the most important project success factor and the risk elements with greater impact on project quality, get higher PRI. The identified and ranked risk factors help practitioners and academics to follow the subsequent steps of the risk management process of Iranian transportation megaprojects

Decision-making through Fuzzy TOPSIS and COPRAS approaches for lean tools selection: A case study of automotive accessories manufacturing industry

Similarity in prioritization of lean tools (LTs) by different frameworks on the same problem is a point of contention. The goal of the present research is to address LTs selection problem through two commonly used multi-criteria decision making approaches, namely the technique for order preference by similarity to ideal solution (TOPSIS) and complex proportional assessment(COPRAS). A framework involving value stream mapping and plant layout through TOPSIS and COPRAS approaches to find the best possible LTs for an automotive accessories manufacturing plant is developed and assessed in this research. The obtained similarity of rankings between TOPSIS and COPRAS is 71.42% and the difference is 28.58%. Based on the assessment, systematic layout planning (SLP) is selected as the most suitable LT and its implementation is elaborated in detail. Significant reductions were obtained in lead time (16.44%), non-value added time (61.03%), transportation distances (40.42%), and waiting time (86%). Additionally, lean implementation resulted in reduced inventory, reduced internal traffic, improved productivity, and improved customer service.The LTs selection framework presented in this research work addresses the computational complexity associated with the existing models and allows the segregation of most preferable and least preferable criterion which eliminates the criteria weight generation methods

A Modified FMEA Approach to Enhance Reliability of Lean Systems

Purpose - The purpose of this thesis is to encourage the integration of Lean principles with reliability models to sustain Lean efforts on long term basis. This thesis presents a modified FMEA that will allow Lean practitioners to understand and improve the reliability of Lean systems. The modified FMEA approach is developed based on the four critical resources required to sustain Lean systems: personnel, equipment, material and schedule. Design/methodology/approach – A three phased methodology approach is presented to enhance the reliability of Lean systems. The first phase compares actual business and operational conditions with conditions assumed in Lean implementation. The second phase maps potential deviations of business and operational conditions to their root cause. The third phase utilizes a modified Failure Mode and Effects Analysis (FMEA) to prioritize issues that the organization must address. Findings – A literature search shows that practical methodologies to improve the reliability of Lean systems are non existent. Research Limitations/Implications –The knowledge database involves tedious calculations and hence it needs to be automated. Originality/Value • Defined Lean system reliability • Developed conceptual model to enhance the Lean system reliability • Developed knowledge base in the form of detailed hierarchical root trees for the four critical resources that support our Lean system reliability • Developed Risk Assessment Value (RAV) based on the concept of effectiveness of detection using Lean controls when Lean designer implements Lean change. • Developed modified FMEA for the four critical resources • Developed RPLS tool to prioritize Lean failures • Developed case study to analyze RPN and RAV approac

A methodology for project portfolio selection under criteria prioritisation, uncertainty and projects interdependency – combination of fuzzy QFD and DEA

© 2018 Elsevier Ltd Resources of an organisation (people, time, money, equipment, etc) are never endless. As such, a constant and continuous challenge for decision makers is to decide which projects should be given priority in terms of receiving critical resources in a way that the organisation's productivity and profitability is best guaranteed. Previous literature has already developed a plenitude of project portfolio selection methodologies ranging from simple scoring to complex mathematical models. However, most of them too often fail to propose one integrated and seamless method that can simultaneously take into account three important elements: (1) prioritisation of selection criteria over each other, (2) uncertainty in decision-making, and (3) projects interdependencies. This paper aims to fill this gap by proposing an integrated method that can simultaneously address all these three aspects. The proposed method combines Quality Function Development (QFD), fuzzy logic, and Data Envelopment Analysis (DEA) to accounts for prioritisation, uncertainty and interdependency. We then apply this method in a numerical example from a real world case to illustrate the applicability and efficacy of the proposed methodology