Evolutionary Multiobjective Design in Automotive Development

This paper describes the use of evolutionary algorithms to solve multiobjective optimization problems arising at different stages in the automotive design process. The problems considered are black box optimization scenarios: definitions of the decision space and the design objectives are given, together with a procedure to evaluate any decision alternative with regard to the design objectives, e.g., a simulation model. However, no further information about the objective function is available. In order to provide a practical introduction to the use of multiobjective evolutionary algorithms, this article explores the three following case studies: design space exploration of road trains, parameter optimization of adaptive cruise controllers, and multiobjective system identification. In addition, selected research topics in evolutionary multiobjective optimization will be illustrated along with each case study, highlighting the practical relevance of the theoretical results through real-world application examples. The algorithms used in these studies were implemented based on the PISA (Platform and Programming Language Independent Interface for Search Algorithm) framework. Besides helping to structure the presentation of different algorithms in a coherent way, PISA also reduces the implementation effort considerably.     

Comparing alternatives for analyzing requirements trade-offs – In the absence of numerical data

ContextChoosing a design solution most often involves dealing with trade-offs and conflicts among requirements and design objectives. Making such trade-offs during early stages of requirements and design is challenging because costs and benefits of alternatives are often hard to quantify.ObjectiveThe objective of this work is to develop a decision analysis method that assists in making trade-offs in the absence of quantitative data.MethodIn this method, stakeholders qualitatively compare consequences of alternatives on decision criteria. We propose an algorithm that generates all possible consequences of alternatives on requirements, according to the rough qualitative comparisons that stakeholders made. The possible consequences generated by the algorithm are then analyzed by the Even Swaps Multi-Criteria Decision Analysis method to determine the best solution. The Even Swaps method is a technique developed in management science to assist in multi-criteria decision making when explicit value trade-offs are not available.Results and conclusionsOur algorithm teases out the need to accurately measure or estimate costs and benefits of alternative design solutions. The algorithm automates the Even Swap process, and reuses stakeholders’ value trade-offs throughout the Even Swaps process. We applied the prototype tool in several case studies to evaluate the utility of the method. The results of case studies provide evidence that our decision aid method selects the optimum solution correctly compared to results of other similar quantitative methods, while our method does not rely on detailed numerical assessment of alternatives and importance weights of criteria.     

3M and Imation: Demerger as a Source of Innovation

The case documents an eventful year in the history of 3M, a company that until November 1995 had been considered a paragon of corporate consistency. The particular emphasis of this case is on the spin-off or demerger of 3M's data storage and imaging businesses into a new company called Imation. Although the decision to demerge was taken in a global context, the detailed comments and reflections of participants involved in this process are drawn from Europe and, in particular, from the UK. The case makes extensive use of quotes from these managers in order to bring to life decisions and events. This case explores the strategy behind 3M's decision to demerge, and considers the myriad of structural and cultural issues arising from the decision. Finally, the case examines the results of the demerger and assesses the position of the fledgling company.     

Design of a data-driven environmental decision support system and testing of stakeholder data-collection

The aims of this paper are to present the requirements and top level design of a decision support system that facilitates the exchange of environmental information between local level and higher levels of government, as well as to assess the possibility to include the local individual in the decision making process. The design of a tool for data collection and exchange of available data also aims to predict impacts of small-scale locally oriented actions by the local administration and residents on incomes and biodiversity, monitor results of the decisions that follow such prediction and inform central policy assessors to enable appropriate tuning of regulatory and fiscal incentives. The potential of data gathering for use in a DSS was tested by case studies across Europe. The main challenges for implementing effective environmental decision support are now more socio-economic than technical, requiring also a more local-orientated attitude of researchers and government.     

A case retrieval method combined with similarity measurement and multi-criteria decision making for concurrent design

In current practice, the design is still based on the sequential design methodology, which makes the process or manufacture information not considered at the preliminary design stage. In addition, the quality of design and the time to perform them are largely dependent on the experience of the engineers. Case-based design is an intelligent method which involves retrieving the most similar previous cases to provide a solution of a new decision problem. Therefore, case retrieval is the most crucial process in case-based design. However, few studies attempt to research case retrieval in concurrent design. This paper proposes a hybridization of fuzzy similarity measurement (FSM) and fuzzy multi-criteria decision making (FMCDM) for case retrieval from historical cases for concurrent design. In FSM stage, triangular function is used to represent the different fuzzy requirements, respectively, and meanwhile calculate the similarity. Less valuable case is filtered out by defined threshold. Then FMCDM is developed to evaluate the most similar cases in terms of product criteria to pick out the most suitable case. Furthermore, the FSM–FMCDM model is applied to power transformer concurrent design, and the experiment indicates that the FSM–FMCDM retrieval is more reasonable than the prior SM only case retrieval.     

Evaluating choice in universal access: an example from rehabilitation robotics

This paper describes a technique using dimensional analysis that so far has been rarely, if ever, used in studies of universal access. However, this procedure can produce a robust decision support process for inclusive designs. In this paper, it is applied to a case study of a wheelchair robot computer interface with choices in data input methods, addressing disparate requirements of different groups of intended users. The main underlying issue is to combine technical measurements, speed of operation and cost with subjective opinion. It turns out that Braglia’s method has greater robustness in determining rank compared to standard techniques. In this, case a simple switch was rated better than a mouse or voice operation for control.     

Seeking common ground while reserving differences in gesture elicitation studies

Gesture elicitation studies have been frequently conducted in recent years for gesture design. However, most elicitation studies adopted the frequency ratio approach to assign top gestures derived from end-users to the corresponding target tasks, which may cause the results get caught in local minima, i.e., the gestures discovered in an elicitation study are not the best ones. In this paper, we propose a novel approach of seeking common ground while reserving differences in gesture elicitation research. To verify this point, we conducted a four-stage case study on the derivation of a user-defined mouse gesture vocabulary for web navigation and then provide new empirical evidences on our proposed method, including 1) gesture disagreement is a serious problem in elicitation studies, e.g., the chance for participants to produce the same mouse gesture for a given target task without any restriction is very low, below 0.26 on average; 2) offering a set of gesture candidates can improve consistency; and 3) benefited from the hindsight effect, some unique but highly teachable gestures produced in the elicitation study may also have a chance to be chosen as the top gestures. Finally, we discuss how these findings can be applied to inform all gesture-based interaction design.

     

Progressive design methodology for complex engineering systems based on multiobjective genetic algorithms and linguistic decision making

This work focuses on a design methodology that aids in design and development of complex engineering systems. This design methodology consists of simulation, optimization and decision making. Within this work a framework is presented in which modelling, multi-objective optimization and multi criteria decision making techniques are used to design an engineering system. Due to the complexity of the designed system a three-step design process is suggested. In the first step multi-objective optimization using genetic algorithm is used. In the second step a multi attribute decision making process based on linguistic variables is suggested in order to facilitate the designer to express the preferences. In the last step the fine tuning of selected few variants are performed. This methodology is named as progressive design methodology. The method is applied as a case study to design a permanent magnet brushless DC motor drive and the results are compared with experimental values.     

一种决策模型的自动生成及管理系统

本文说明了一种决策模型的自动生成及管理系统的设计与实现方法。该系统能够辅助人们去建立一种基于决策表的决策模型，自动进行各种检验，并能将此模型转换成另外两种决策模型。此外，它还具有决策模型库和应用案例库的管理功能。该系统已成功地运用到多个大型信息系统的开发之中。     

A multi-agent system based on reactive decision rules for solving the caregiver routing problem in home health care

Home Health Care (HHC) services are growing worldwide. HHC providers that employ their caregivers have to manage operational decisions such as assigning patients to caregivers and planning the caregivers’ routes. Centralized “off-line” approaches are generally used to deal with both these problems. In this paper, we solved the caregiver routing problem in a dynamic and distributed way using a Multi-agent system (MAS) to simulate caregiver behavior. Four decision rules were developed for the caregivers: NPR (Nearest Patient Rule), NRR (No-wait Route Rule), SRR (Shortest Route Rule), and BRR (Balanced Route Rule). These decision rules were implemented and tested on a multi-agent platform to assess their performances. We designed an experimental plan based on case studies that represent different sizes of HHC provider inspired from real-world examples. The results obtained show the relevance of using local decision rules to plan the caregiver's route.     

A decision framework for cultural and creative products based on IF-TODIM method and group consensus reaching model

Along with increasing the emphasis on cultural attributes, product design is not only satisfied with the realization of function and appearance, but also considers the embodiment of human emotion and social style. As a result, the number of creative product based on cultural style is increasing. However, existing product decision studies do not consider this style-oriented product- ranking problem, ignore the influence of cultural style and fuzzy decision-making environment on the limited psychological behavior of decision makers (DMs). Decision is a worthwhile research topic in order to facilitate ranking for cultural and creative products (CCPs). Therefore, this paper provides a decision framework based on intuitionistic fuzzy TODIM (IF-TODIM) method and group consensus reaching (GCR) model to fill this gap. Benefit from the theory of intuitionistic fuzzy set, IF-TODIM method can deal with the limited psychological behavior of DMs and the fuzziness of decision environment. This method bases on CCPs characteristics and cultural hierarchy theory (CHT) to select the decision criteria, and applies IF-TODIM method to quantify the relationship of DMs’ preferences and establish the dominance matrix for the alternatives. Furthermore, the GCP model is introduced to improve the group consensus of DMs, and the modified overall dominance matrixes are adopted to determine the alternatives scores and ranking results. The new Chinese style decorations are used as a case study to demonstrate the practicality and feasibility of the proposed method. Moreover, comparing with IF-TOPSIS method are further to verify its effectiveness and superiority.     

Modeling climate change uncertainties in water resources management models

The impact of climate change on hydrologic design and management of hydrosystems could be one of the important challenges faced by future practicing hydrologists and water resources managers. Many water resources managers currently rely on the historical hydrological data and adaptive real-time operations without consideration of the impact of climate change on major inputs influencing the behavior of hydrologic systems and the operating rules. Issues such as risk, reliability and robustness of water resources systems under different climate change scenarios were addressed in the past. However, water resources management with the decision maker’s preferences attached to climate change has never been dealt with. This short paper discusses issues related to impacts of climate change on water resources management and application of a soft-computing approach, fuzzy set theory, for climate-sensitive management of hydrosystems. A real-life case study example is presented to illustrate the applicability of a soft-computing approach for handling the decision maker’s preferences in accepting or rejecting the magnitude and direction of climate change.     

Multiple-attribute decision making methods for plant layout design problem

The layout design problem is a strategic issue and has a significant impact on the efficiency of a manufacturing system. Much of the existing layout design literature that uses a surrogate function for flow distance or for simplified objectives may be entrapped into local optimum; and subsequently lead to a poor layout design due to the multiple-attribute decision making (MADM) nature of a layout design decision. The present study explores the use of MADM approaches in solving a layout design problem. The proposed methodology is illustrated through a practical application from an IC packaging company. Two methods are proposed in solving the case study problem: Technique for order preference by similarity to ideal solution (TOPSIS) and fuzzy TOPSIS. Empirical results showed that the proposed methods are viable approaches in solving a layout design problem. TOPSIS is a viable approach for the case study problem and is suitable for precise value performance ratings. When the performance ratings are vague and imprecise, the fuzzy TOPSIS is a preferred solution method.     

Ergodynamics and macroergonomics in analysis of decision‐making efficiency and complexity

The principles of macroergonomics and the laws of ergodynamics are reviewed. The theories of mutual adaptation and transformation dynamics are presented as a complex basis of the ergodynamics, and they are offered as paradigms for macroergonomic evaluation and design of sociotechnical or human‐machine‐environment decision‐making systems (HMES). Criteria and factors of decision‐making efficiency are studied for different cognitive strategies and their transformations in the course of long‐term training and short‐term decision making in emergencies. Sample system criteria and design guidelines, based on the results of both laboratory studies and actual research and design applications, are provided. A combination of macroergonomics and ergodynamics helps ergonomists to meet rapidly growing requirements of practice and overcome a narrow approach to the workstations analysis when a broader view of complex multilevel system leads to success and instead of traditional static approach the analysis of transformations in technologies, management structures, and work skills is needed.     

Participatory design with train drivers — a process analysis

A participatory design process involving train drivers is analyzed and described in this paper. A group of six drivers were involved in the design process, and within a short period, four design iterations were completed. The present case study was the final part of a larger research project (TRAIN) investigating the train driving task including the drivers' information environment, number and nature of hours worked, work situation and work environment, and their effect on the drivers' behaviour and the train driver system safety.Although usability activities are widely used in IT development today, the users are not involved to the desired extent. This paper argues that to produce usable systems, quality time has to be spent initially to acquire knowledge of a work domain and establishing a common ground in terms of shared knowledge and a better understanding of the work context between the parties involved in system development. Our suggestions on participatory analysis and design that conclude the paper are based on the present case study including train drivers, as well as our experiences from previous case studies.     

Applying variable precision rough set model for clustering student suffering study’s anxiety

Computational models of the artificial intelligence such as rough set theory have several applications. Data clustering under rough set theory can be considered as a technique for medical decision making. One possible application is the clustering of student suffering study’s anxiety. In this paper, we present the applicability of variable precision rough set model for clustering student suffering studies anxiety. The proposed technique is based on the mean of accuracy of approximation using variable precision of attributes. The datasets are taken from a survey aimed to identify of studies anxiety sources among students at Universiti Malaysia Pahang (UMP). At this stage of the research, we show how variable precision rough set model can be used to groups student in each study’s anxiety. The results may potentially contribute to give a recommendation how to design intervention, to conduct a treatment in order to reduce anxiety and further to improve student’s academic performance.     

Fuzzy logic-based decision support system for automating ergonomics risk assessments

Workers in the modular construction industry are frequently exposed to ergonomic risks, which may lead to injuries and lower productivity. In light of this, researchers have proposed a number of ergonomics risk assessment methods to identify design flaws in work systems, thereby reducing ergonomic discomfort and boosting workplace productivity. However, organizations often disregard ergonomics risk assessments due to a lack of convenient tools and knowledge. Therefore, this study proposes a fuzzy logic-based decision support system to help practitioners to automatically and comprehensively assess the ergonomic performance of work systems. For comprehensive assessment of ergonomic risk, the proposed decision support system considers physical, environmental, and sensory factors. Specifically, the decision support system comprises eight fuzzy expert systems that output a composite risk score, called an “ergonomic risk indicator”, that indicates the overall level of ergonomic risk present in a given work system. The performance of the proposed decision support system is then evaluated using a real-world case study in a modular construction facility by comparing the results of the decision support system with the facility's occupational injury reports. The results prove the effectiveness of the decision support system. Overall, the decision support system is capable of generating a composite risk score, the ergonomic risk indicator, and the proposed high-level architecture and design represent significant contributions for the enhancement of health and safety in the modular construction industry.     

Multiobjective performance-based designs in fault estimation and isolation for discrete-time systems and its application to wind turbines

In this work, we develop a performance-based design of model-based observes and statistical-based decision mechanisms for achieving fault estimation and fault isolation in systems affected by unknown inputs and stochastic noises. First, through semidefinite programming, we design the observers considering different estimation performance indices as the covariance of the estimation errors, the fault tracking delays and the degree of decoupling from unknown inputs and from faults in other channels. Second, we perform a co-design of the observers and decision mechanisms for satisfying certain trade-off between different isolation performance indices: the false isolation rates, the isolation times and the minimum size of the isolable faults. Finally, we extend these results to a scheme based on a bank of observers for the case where multiple faults affect the system and isolability conditions are not verified. To show the effectiveness of the results, we apply these design strategies to a well-known benchmark of wind turbines which considers multiple faults and has explicit requirements over isolation times and false isolation rates.