Abstract

Abstract
The ability to select the most appropriate materials for a given application is the fundamental challenge faced by a design engineer. The objective of any material selection procedure is to identify appropriate selection criteria and to obtain the most appropriate combination of criteria in conjunction with requirements. Hence, selection of material is a multicriteria decision making problem. This study investigates and evaluates critical material selection criteria in a priority framework using the fuzzy Delphi-analytical hierarchy process method to overcome all shortcomings from AHP and Delphi methods that are common in material selection problem. 75 of the most important criteria for material selection have been collected from the literature. These criteria have been questioned in automobile interior design firms in Iran for car dashboard design. This ranking method would help product designers to decide on appropriate materials in a consistent method. Results indicate that "general" criteria such as availability, quality, risk, and technology are the most important criteria from the viewpoint of Iranian car manufacturers. Other criteria such as financial, technical, social and environmental, and sensorial criteria are relatively important in subsequent ranks.

Abstract
Given an event log L, a control-flow discovery algorithm f, and a quality metric m, this paper faces the following problem: what are the parameters in f that mostly influence its application in terms of m when applied to L? This paper proposes a method to solve this problem, based on sensitivity analysis, a theory which has been successfully applied in other areas. Clearly, a satisfactory solution to this problem will be crucial to bridge the gap between process discovery algorithms and final users. Additionally, recommendation techniques and meta-techniques like determining the representational bias of an algorithm may benefit from solutions to the problem considered in this paper. The method has been evaluated over a set of logs and the flexible heuristic miner, and the preliminary results witness the applicability of the general framework described in this paper.

Abstract
Objective - The purpose of this paper is to build a framework for an international outsourcing process in the apparel industry that can serve to support managerial decisions and actions regarding outsourcing choices and implementation. Design/methodology/approach - We developed of a straightforward and flexible framework describing the main stages of the international outsourcing process and its main activities with application in the context of the apparel industry. A case study approach was adopted with primary data collected through in-depth interviews and secondary data aggregated from company reports and documents. Theoretical foundation - Some research gaps in the outsourcing literature and most specifically on the matter of international outsourcing were identified by Hatonen and Eriksson (2009) and Kakabadse and Kakabadse (2000), among others. Specifically, these authors claim that there is not enough research on developing and offering decision models, tools or guidelines to support managerial decisions with the appropriate empirical evidence. This study aims to address this gap. Findings - We found that the international outsourcing process can be described using the proposed framework. Apparel companies can use this framework to support and supervise international outsourcing processes. Practical implications - This study provides a simple model that can help companies in the apparel industry to enhance their outsourcing activities and operations, and also contributes to a broader academic understanding of the matter.

Abstract
In the last decade, public transportation providers have focused on improving infrastructure efficiency as well as providing travellers with relevant information. Ubiquitous environments have enabled traveller information systems to collect detailed transport data and provide information. In this context, journey prediction becomes a pivotal component to anticipate and deliver relevant information to travellers. Thus, in this work, to achieve this goal, three steps were defined: (i) firstly, data from smart cards were collected from the public transport network in Porto, Portugal; (ii) secondly, four different traveller groups were defined, considering their travel patterns; (iii) finally, decision trees (J48), Naive Bayes (NB), and the Top-K algorithm (Top-K) were applied. The results show that the methods perform similarly overall, but are better suited for certain scenarios. Journey prediction varies according to several factors, including the level of past data, day of the week and mobility spatiotemporal patterns.

Abstract
The adoption of smart cards in urban public transport has fundamentally changed how transport providers manage and plan their networks. Traveller information services, in particular, have leveraged this contextual data for targeting passengers and providing relevant information. Thus, it becomes increasingly relevant for the next generation of services to obtain on-time contextual passenger information, to support the development of intelligent information services. In this paper an adaptation of the Top-K algorithm is proposed for predicting journey destination, applied to different scenarios in public transport. The performance and efficiency are analysed and compared to a decision tree classifier. Finally, the feasibility and potential of applying the proposed methods to large-scale systems in a real-world environment is discussed.