School of Innovation, Design and Engineering CDT 503 – Computer science, advanced level (15 hp) Master thesis 15 credits Goran Brestovac and Robi Grgurina Applying Multi-Criteria Decision Analysis Methods in Embedded Systems Design Västerås, Sweden September 2013 ABSTRACT In several types of embedded systems the applications are deployed both as software and as hardware components. For such systems, the partitioning decision is highly important since the implementation in software or hardware heavily influences the system properties. In the industry, it is rather common practice to take deployment decisions in an early stage of the design phase and based on a limited number of aspects. Often such decisions are taken based on hardware and software designers‟ expertise and do not account the requirements of the entire system and the project and business development constraints. This approach leads to several disadvantages such as redesign, interruption, etc. In this scenario, we see the need of approaching the partitioning process from a multiple decision perspective. As a consequence, we start by presenting an analysis of the most important and popular Multiple Criteria Decision Analysis (MCDA) methods and tools. We also identify the key requirements on the partitioning process. Subsequently, we evaluate all of the MCDA methods and tools with respect to the key partitioning requirements. By using the key partitioning requirements the methods and tools that the best suits the partitioning are selected. Finally, we propose two MCDA-based partitioning processes and validate their feasibility thorough an industrial case study. Date: September 2013 Carried out at: MDH and ABB Corporate Research, Sweden Advisor at MDH: Gaetana Sapienza Advisor at ABB: Gaetana Sapienza Examiner: Ivica Crnkovic iii ACKNOWLEDGEMENTS First of all we would like to thank Tihana Galinac Grbac, professor at Faculty of Engineering at University of Rijeka, for encouragement, advice and help for taking the opportunity to write our master thesis at the School of Innovation, Design and Engineering (IDT) at Mälardalen University (MDH). Special thanks to Gaetana Sapienza our thesis supervisor at IDT for doing a splendid job at providing a lot of valuable advices, good ideas and a great guidance throughout the master thesis. We would also like to thank Ivica Crnkovic, the master thesis examiner at MDH for providing a good environment, facilities for completing this master thesis, and all help and support while staying at MDH. Last but not least we would like to thank to our parents for the support. iv PREFACE In today‟s industrial applications based on component design, some of the components may be implemented in software or in hardware based on a number of different and conflicting requirements. The decision impacts performance, power consumption, price, reliability and other system properties. The system must fulfill all requirements taking into consideration all the constraints. Constraints could be project limitations like development time, budget, project deadline etc. or client strategic decisions like vendor loyalty. There are also a lot of interactions between components in the system which could lead to a multiple dependencies between them. This leads to an optimization problem with multiple criteria. The criteria could be defined differently for software and hardware components. The criteria values for some components could be defined as ranges, might be missing or estimated. Multiple criteria decision analysis (MCDA) methods could be useful approach for solving the hardware/software partitioning problem. Västerås, September 2013 Goran Brestovac Robi Grgurina v NOMENCLATURE Abbreviation Explanation AHP Analytic Hierarchy Process ANP Analytic Network Process CRSA Classical Rough Set Approach DM Decision Maker DRSA Dominance-based Rough Set Approach ELECTRE ELimination Et Choix Traduisant la REalité (ELimination and Choice Expressing the REality) ER Evidential Reasoning HW Hardware IDS Intelligent Decision System IT Information Technology MCDA Multi-Criteria Decision Analysis MCDM Multiple-Criteria Decision-Making NA Not Available NS Not Specified PROMETHEE Preference Ranking Organization Method for Enrichment Evaluations ROSE Rough Set Data Explorer RSA Rough Set Approach SMAA Stochastic Multicriteria Acceptability Analysis SW Software TOPSIS Technique for Order Preference by Similarity to Ideal Solution VIKOR VIseKriterijumska Optimizacija I Kompromisno Resenje (Multicriteria Optimization and Compromise Solution) vi CONTENTS Chapter 1 INTRODUCTION 1 1.1 Motivation ............................................................................................................1 1.2 Research questions ............................................................................................. 2 1.3 Contribution ........................................................................................................ 3 1.4 Structure of the Thesis ........................................................................................ 3 Chapter 2 PARTITIONING 4 2.1 Hardware/software partitioning ........................................................................ 4 Related works ............................................................................................................................ 5 Chapter 3 MCDA METHODS 6 3.1 About MCDA ....................................................................................................... 6 3.2 Ranking ............................................................................................................... 8 AHP ........................................................................................................................................... 8 ANP ......................................................................................................................................... 14 TOPSIS .................................................................................................................................... 16 VIKOR ..................................................................................................................................... 17 PROMETHEE I AND II .......................................................................................................... 20 EVIDENTIAL REASONING ................................................................................................... 22 3.3 Classification ..................................................................................................... 24 RSA .......................................................................................................................................... 24 DRSA ....................................................................................................................................... 27 3.4 Ranking and Classification ............................................................................... 29 Electre III ................................................................................................................................ 29 Electre IV ................................................................................................................................. 31 ELECTRE TRI ......................................................................................................................... 33 SMAA-2 ................................................................................................................................... 34 SMAA-TRI ............................................................................................................................... 36 Chapter 4 MCDA TOOLS 37 4.1 Ranking ............................................................................................................. 38 Electre III/IV .......................................................................................................................... 38 MakeItRational ....................................................................................................................... 39 ANP Solver .............................................................................................................................. 42 Web ANP Solver ...................................................................................................................... 43 TransparentChoice ................................................................................................................. 44 Triptych ................................................................................................................................... 47 SANNA .................................................................................................................................... 48 Visual PROMETHEE Academic ............................................................................................. 49 Intelligent Decision System .................................................................................................... 52 4.2 Classification ..................................................................................................... 55 Electre TRI .............................................................................................................................. 55 4eMka2 .................................................................................................................................... 56 jMAF .......................................................................................................................................