A driving simulation study to investigate the information threshold of graphical variable message signs based on visual perception characteristics of drivers

Graphical variable message signs (VMSs) are infrastructure-based advanced traveler information systems widely used to provide drivers with real-time traffic condition information about a road section or area. However, there is a lack of research on the suitable volume of information to be shown on graphical VMSs. In practice, an overload of VMS information commonly exists, especially on China’s highways. Building on our earlier findings obtained through surveys and static cognitive experiments, this study introduces the use of dynamic simulation experiments to assess the influence of the volume of information (i.e., number of roads displayed) on graphical VMSs from the perspective of drivers’ visual perception characteristics. Thirty-two drivers participated in the driving simulation experiment and questionnaires. Five indexes, including legibility speed, legibility distance, legibility time, comprehension accuracy, and driver subjective scoring, were thoroughly analyzed to evaluate their relationships to different volumes of information (i.e., four, five, and six roads shown on a VMS). The results show that the legibility distance notably decreased with increasing volumes of information. The comprehension accuracy decreased significantly when the number of roads shown increased to six. The legibility speed, legibility time, and subjective scoring also deteriorated as the number of roads displayed on the VMS increased. The index scores were evaluated, in combination with the data of the drivers’ subjective scoring, data-based statistical analyses, and comprehensive evaluations using the TOPSIS method, to recommend that five is the recommended maximum number of roads to be shown on a graphical VMS. The results of this study support the goal of providing understandable and effective messages for drivers by addressing issues relating to how much information should be displayed on a VMS. These findings provide a basis for policy development to ensure consistent and practical designs of graphical VMSs on highways.     

A Multicriteria Decision‐Making Application to the Conceptual Design of Mechanical Components

Early design is crucial for the success of the final product. In the conceptual design phase, several constraints, criteria, objectives and disciplines have to be considered. To this aim, multidisciplinary optimization has proven effective for the solution of engineering design problems, even in the industrial every‐day practice, to improve and simplify the work of designers in a successful quest of the best compromise solution. In this paper, a multicriteria decision‐making (MCDM)‐based design platform for early optimal design of industrial components is proposed. In a group decision‐making context, the selection of the most suitable component among several possible layouts is performed by means of a group Fuzzy Technique for Order of Preference by Similarity to Ideal Solution approach. Hence, a multi‐objective optimization is performed on the selected component by applying a multi‐objective particle swarm optimization for finding optimal component dimensions. An industrial case study is presented for showing the efficiency of the multicriteria decision‐making‐based design platform, regarding an innovative and low‐cost solution to increase the duration of heel tips in women's shoes. Copyright © 2016 John Wiley & Sons, Ltd.     

A Fuzzy Topsis Method for Prioritized Aggregation in Multi‐Criteria Decision Making Problems

Aggregation in a decision making environment requires the fusion of opinions of a group of decision makers. The group of decision makers are required to analyse a set of interrelated criteria that are usually measured on a linguistic scale. This process requires, in many instances, to capture experts experience, intuition and thinking that are traditionally expressed in a linguistic fashion rather than a numerical fashion. Furthermore, the necessity of considering the relationship between the criteria to the overall decision must be considered by the group of decision makers. This paper extends the application of fuzzy numbers, fuzzy relative importance scores (FRIS), fuzzy relative weights (FRW) and the fuzzy technique of order preference by similarity to ideal solution (TOPSIS) in prioritized aggregation. This extension provides a mean to systematically aggregate a group of decision makers' views for a set of interrelated criteria that are measured on a linguistic scale. First, an overview of the application of fuzzy numbers and the characteristics of aggregating fuzzy numbers in multi‐criteria decision making problems are presented. Then, the application of TOPSIS in fuzzy environments is presented. Next, past research is highlighted to present prioritized aggregation and the different aggregation operators' classes. Subsequently, a new prioritized aggregation method is presented. This method utilizes fuzzy TOPSIS with prioritized aggregation in fuzzy environments. Finally, the fuzzy prioritized aggregation method presented in this paper is applied on an actual case study. According to the results, the method presented in this paper provides a systematic approach to capture the uncertainty and imprecision associated with quantifying linguistic measurements in multi‐criteria decision making problems. Furthermore, it considers the relationship between the set of linguistically measured criteria undergoing prioritized aggregation in a fuzzy environment. Lastly, findings, conclusions and future work are presented. Copyright © 2016 John Wiley & Sons, Ltd.     

The Characteristic Objects Method: A New Distance‐based Approach to Multicriteria Decision‐making Problems

Multicriteria decision‐making (MCDM) methods are concerned with the ranking of alternatives based on expert judgements made using a number of criteria. In the MCDM field, the distance‐based approach is one popular method for obtaining a final ranking. The technique for order preference by similarity to the ideal solution (TOPSIS) is a commonly used example of this kind of MCDM method. The TOPSIS ranks the alternatives with respect to their geometric distance from the positive and negative ideal solutions. Unfortunately, two reference points are often insufficient, especially for nonlinear problems. As a consequence of this situation, the final result ranking is prone to errors, including the rank reversals phenomenon. This study proposes a new distance‐based MCDM method: the characteristic objects method. In this approach, the preferences of each alternative are obtained on the basis of the distance from the nearest characteristic objects and their values. For this purpose, we have determined the domain and Fuzzy number set for all the considered criteria. The characteristic objects are obtained as the combination of the crisp values of all the Fuzzy numbers. The preference values of all the characteristic object are determined on the basis of the tournament method and the principle of indifference. Finally, the Fuzzy model is constructed and is used to calculate preference values of the alternatives, making it a multicriteria model that is free of rank reversal. The numerical example is used to illustrate the efficiency of the proposed method with respect to results from the TOPSIS method. The characteristic objects method results are more realistic than the TOPSIS results. Copyright © 2014 John Wiley & Sons, Ltd.     

Ranking of Drivers for Green Manufacturing Implementation Using Fuzzy Technique for Order of Preference by Similarity to Ideal Solution Method

The governments are under tremendous pressure to sustain high manufacturing growth in emerging economies. Unfortunately, the manufacturing sector consumes much energy and other resources and emits a large amount of green house gases, which increases environmental problems such as climate change and global warming. One possible solution to this problem is green manufacturing (GM) implementation in industry. However, GM implementation faces many challenges. Various motivating factors named as ‘drivers’ should be facilitated by the government and industry to make this change possible. This paper investigates the drivers for GM implementation and their ranking based on fuzzy technique for order of preference by similarity to ideal solution method using government, industry and experts perspectives. The study concluded that competitiveness, incentives, organizational resources and technology are top ranked drivers and should be facilitated by the government and industry to help implement GM. The ranking of these drivers is expected to help the government and industry to focus on few important drivers to facilitate the GM implementation with limited resources. Copyright © 2014 John Wiley & Sons, Ltd.     

Prioritizing emergency shelter areas using hybrid multi‐criteria decision approach: A case study

Every year, more than 400 natural disasters affect global population. Adopting a resilient approach strengthens communities to survive and recover well from destabilizing events. Disasters destroy or damage houses and infrastructure, which calls for arrangement of temporary establishments to provide immediate evacuation and shelter to affected population. These sites are vital for an effective relief and must be strategically planned. Communities living in disaster‐prone areas should prepare themselves well in advance for any future catastrophic event. This study adopts a hybrid group decision support approach for emergency shelter site selection problem. Initially, relevant factors for locating potential sites are identified by reviewing extant literature and through consultation from a panel of disaster management experts. Next, fuzzy analytic hierarchy process theory and technique for order preference by similarity to ideal solution have been used to prioritize identified criteria and to evaluate potential locations for displacement sites. A case study of the recent Nepal earthquake has been used to illustrate the effectiveness of the proposed model. The model offers a resilience building approach to prepare communities for any future contingency by proposing and prioritizing a set of planned displacement sites.     

Strategic hybrid approach for selecting suppliers of high‐density polyethylene

Supplier selection is an important process for companies in the plastic sector due to its influence on firm performance and competitiveness. For a proper selection, a number of criteria from different aspects need to be considered by decision makers. Yet, as in different fields, because there are numerous criteria and alternatives to be considered in the plastic industry, choosing an appropriate multicriteria decision‐making approach has become a critical step for selecting suppliers. Therefore, the aim of this research is to define the most suitable supplier of high‐density polyethylene through the integration of powerful multicriteria decision‐making methods. For this purpose, the fuzzy analytic hierarchy process (FAHP) is initially applied to define initial weights of factors and subfactors under uncertainty, followed by the use of decision‐making trial and evaluation laboratory (DEMATEL) to evaluate interrelations between the elements of the hierarchy. Then, after combining FAHP and DEMATEL to calculate the final contributions of both factors and subfactors on the basis of interdependence, the technique for order of preference by similarity to ideal solution is used to assess the supplier alternatives. In addition, this paper also explores the differences between the judgments of decision makers for both AHP and DEMATEL methods. To do these, a case study is presented to demonstrate the validity of the proposed approach.     

Preference‐based Learning of Ideal Solutions in TOPSIS‐like Decision Models

Combining established modelling techniques from multiple‐criteria decision aiding with recent algorithmic advances in the emerging field of preference learning, we propose a new method that can be seen as an adaptive version of TOPSIS, the technique for order preference by similarity to ideal solution decision model (or at least a simplified variant of this model). On the basis of exemplary preference information in the form of pairwise comparisons between alternatives, our method seeks to induce an ‘ideal solution’ that, in conjunction with a weight factor for each criterion, represents the preferences of the decision maker. To this end, we resort to probabilistic models of discrete choice and make use of maximum likelihood inference. First experimental results on suitable preference data suggest that our approach is not only intuitively appealing and interesting from an interpretation point of view but also competitive to state‐of‐the‐art preference learning methods in terms of prediction accuracy. Copyright © 2014 John Wiley & Sons, Ltd.     

Performance oriented task-resource mapping and scheduling in fog computing environment

Resource allocation and task scheduling is a complex task in fog computing environment because of the inherent heterogeneity among the fog devices. The proposed work attempts to solve the problem by using the popular multi criteria decision making methods such as AHP and TOPSIS. The goal of this paper is to propose a model for performance oriented task - resource mapping in a fog computing environment. MIPS, RAM & storage, uplink latency, downlink latency, uplink bandwidth, downlink bandwidth, trust, cost per MIPS, cost per memory, cost per storage and cost per bandwidth are the various performance characteristics considered in this work for task – resource mapping. Two different multi-criteria decision making methods are employed in order to assess the performance characteristics of the fog devices. In the first method, Analytic Hierarchy Process (AHP) is used for both priority weight calculation and ranking of fog devices. In the second method, AHP is used for priority weight calculation, based on the weights yielded by AHP, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm is executed in order to rank the fog devices. Then the fog devices can be allocated to the tasks based on its rank. Furthermore, a motivational example is also demonstrated to validate the proposed method. Simulation results show that the proposed technique exhibits superior performance over other scheduling algorithms in the fog environment by incorporating performance, security, and cost metrics into scheduling decisions.     

A Topsis‐Based Approach for Prioritized Aggregation in Multi‐Criteria Decision‐Making Problems

Aggregation in multi‐criteria decision‐making environments is a process of combining the values of a set of attributes into one representative value for the entire set of attributes. Many aggregation methods—ranging from the simple averaging approach to more sophisticated methods, such as ordered weighted averaging—have been applied in previous research. One challenge in aggregation arises in special cases of prioritized aggregation, wherein the prioritized relationships between attributes must be considered during aggregation. This paper presents a new approach to aggregating attributes with prioritized relationships. First, an overview of past research is conducted to identify different aggregation methods, classes and properties. Next, the concept of prioritized aggregation is explained in detail. A prioritized aggregation method utilizing the technique of order preference by similarity to ideal solution is then presented. Subsequently, the presented prioritized aggregation method is applied on an actual case study. According to the results, the aggregation method presented in this paper is, through the application of technique of order preference by similarity to ideal solution, capable of quantifying and considering the prioritized relationship between a set of attributes undergoing aggregation. Finally, conclusions are stated, and a discussion describing future work pertinent to this paper is presented. Copyright © 2016 John Wiley & Sons, Ltd.