Abstract
To address the need for evaluation techniques for complex business processes, also known as workflows, this paper proposes an approach based on generalized stochastic Petri nets (GSPNs). We review ten related approaches published in the last fifteen years and compare them to our approach using a wide range of criteria. On the basis of this evaluation, we observe that the newly proposed approach provides results that are at least as good as those from the most accepted alternatives and holds a number of additional advantages, such as modeling simplicity, improved precision, and model reuse for qualitative analyses. The overall approach is formally defined in this paper, along with the definition of several performance metrics. Part of these metrics can be computed analytically, while the remainder can be obtained by simulating the GSPN. Furthermore, a tool has been developed to translate automatically business process execution language processes into GSPNs. Finally, we present a case study in which we applied the proposed approach, colored Petri net tools, and an industrial tool to obtain performance insights into a realistic workflow. The results were highly similar, demonstrating the feasibility and the accuracy of our approach.

Abstract
Advance technology development and wide use of the World Wide Web have made it possible for new product development organizations to access multi-sources of data-related customer complaints. However, the number of customer plaints of highly innovative consumer electronic products is still increasing; that is, product quality and reliability is at risk. This article aims to understand why existing solutions from literature as well as from industry to deal with these increasingly complex multiple data sources are not able to manage product quality and reliability. Three case studies in industry are discussed. On the basis of the case study results, this article also identifies a new research agenda that is needed to improve product quality and reliability under this circumstance. Copyright (c) 2011 John Wiley & Sons, Ltd.

Abstract
Graphs are a very important abstraction to model complex structures and respective interactions, with a broad range of applications including web analysis, telecommunications, chemical informatics and bioinformatics. In this work we are interested in the application of graph mining to identify abnormal behavior patterns from telecom Call Detail Records (CDRs). Such behaviors could also be used to model essential business tasks in telecom, for example churning, fraud, or marketing strategies, where the number of customers is typically quite large. Therefore, it is important to rank the most interesting patterns for further analysis. We propose a vertex relevant ranking score as a unified measure for focusing the search of abnormal patterns in weighted call graphs based on CDRs. Classical graph-vertex measures usually expose a quantitative perspective of vertices in telecom call graphs. We aggregate wellknown vertex measures for handling attribute-based information usually provided by CDRs. Experimental evaluation carried out with real data streams, from a local mobile telecom company, showed us the feasibility of the proposed strategy. © 2012 Springer-Verlag.

Abstract
This paper presents and evaluates an order release decision rule in a job-shop, based on the input-output control concept. The order release mechanism is part of a global decision-making scheme that includes four main decisions: accept/reject orders, define the order's due date, release the accepted jobs and dispatch it on the shop floor. This paper also presents an evaluation of two acceptance rules, four release mechanisms, and two dispatching rules, using four levels of due date tightness. Extensive simulation experiments were performed to compare the different decision rules, using several criteria: mean tardiness, percent tardy, mean absolute deviation, mean queue time in the shop floor and in the system, and machine usage. We conclude that considering the four decisions simultaneously improve the job-shop performance, and planning both the input and the output when deciding to release a job, leads to an improvement of the operational performance measures. Copyright © 2012 Inderscience Enterprises Ltd.

Abstract
In this paper we present three hybrid heuristics for the single machine scheduling problem with quadratic earliness and tardiness costs, and no machine idle time. Our heuristic is a combination of a steady-state genetic algorithm and three improvement procedures. The two computationally less expensive of these three improvement procedures are used inside the genetic algorithm to improve the schedule obtained after the application of genetic operators, whereas the more expensive one is used to improve the best solution returned by the genetic algorithm. We have compared our hybrid approaches against existing recovering beam search and genetic algorithms. The computational results show the effectiveness of our hybrid approaches. Indeed, our hybrid approaches outperformed the existing heuristics in terms of solution quality as well as running time.

Abstract
This paper focuses on the integration of multiple computational tools towards the objective of assessing emission impacts of different routes. Data from real life GPS tracks was integrated with traffic emission modelling for multiple pollutants (NOx, HC, CO and PM10) to investigate different routing strategies. The main conclusion is that different pollutants dictate different best routes. Hence, strategies for assigning relative weights to pollutants are devised in order to be able to select the best environment-friendly route. © 2012 ACM.