A multi-objective evolutionary approach to simulation-based optimisation of real-world problems.

This thesis presents a novel evolutionary optimisation algorithm that can improve the quality of solutions in simulation-based optimisation. Simulation-based optimisation is the process of finding optimal parameter settings without explicitly examining each possible configuration of settings. An optimisation algorithm generates potential configurations and sends these to the simulation, which acts as an evaluation function. The evaluation results are used to refine the optimisation such that it eventually returns a high-quality solution. The algorithm described in this thesis integrates multi-objective optimisation, parallelism, surrogate usage, and noise handling in a unique way for dealing with simulation-based optimisation problems incurred by these characteristics. In order to handle multiple, conflicting optimisation objectives, the algorithm uses a Pareto approach in which the set of best trade-off solutions is searched for and presented to the user. The algorithm supports a high degree of parallelism by adopting an asynchronous master-slave parallelisation model in combination with an incremental population refinement strategy. A surrogate evaluation function is adopted in the algorithm to quickly identify promising candidate solutions and filter out poor ones. A novel technique based on inheritance is used to compensate for the uncertainties associated with the approximative surrogate evaluations. Furthermore, a novel technique for multi-objective problems that effectively reduces noise by adopting a dynamic procedure in resampling solutions is used to tackle the problem of real-world unpredictability (noise). The proposed algorithm is evaluated on benchmark problems and two complex real-world problems of manufacturing optimisation. The first real-world problem concerns the optimisation of a production cell at Volvo Aero, while the second one concerns the optimisation of a camshaft machining line at Volvo Cars Engine. The results from the optimisations show that the algorithm finds better solutions for all the problems considered than existing, similar algorithms. The new techniques for dealing with surrogate imprecision and noise used in the algorithm are identified as key reasons for the good performance.University of Skövde Knowledge Foundation Swede

Open Source Software in Complex Domains: Current Perceptions in the Embedded Systems Area

With Nokia’s 770 and N800 Internet Tablets heavily utilising Open Source software, it is timely to ask whether – and if so to what extent – Open Source has made ingress into complex application domains such as embedded systems. In this paper we report on a qualitative study of perceptions of Open Source software in the secondary software sector, and in particular companies deploying embedded software. Although the sector is historically associated in Open Source software studies with uptake of embedded Linux, we find broader acceptance. The level of reasoning about Open Source quality and trust issues found was commensurate with that expressed in the literature. The classical strengths of Open Source, namely mass inspection, ease of conducting trials, longevity and source code access for debugging, were at the forefront of thinking. However, there was an acknowledgement that more guidelines were needed for assessing and incorporating Open Source software in products

Bridging the Hype Cycle of Collaborative Robot Applications

This paper investigates manufacturing companies’ current and planned usage of collaborative robots along with possible reasons for the observed slow growth in implementing Collaborative Robot Applications (CRAs) in the industry. The paper also discusses whether similarities can be seen in the Gartner Hype Cycle for technology adoption. Findings from an industrial survey suggest increasingly positive attitudes towards using CRAs in manufacturing and final assembly operations as tools and support mechanisms aiding human operators. Better methodologies and best practices are urgently needed for successful CRA implementation and efficient manufacturing human-robot collaboration design

Support Systems on the Industrial Shop-floors of the Future – Operators’ Perspective on Augmented Reality

AbstractWith augmented reality, virtual information can be overlaid on the real world in order to enhance a human's perception of reality. In this study, we aim to deepen the knowledge of augmented reality in the shop-floor context and analyze its role within smart factories of the future. The study evaluates a number of approaches for realizing augmented reality and discusses advantages and disadvantages of different solutions from a shop-floor operator's perspective. The evaluation is done in collaboration with industrial companies, including Volvo Cars and Volvo GTO amongst others. The study also identifies important future research directions for utilizing the full potential of the technology and successfully implement it on industrial shop-floors

A serious game for understanding artificial intelligence in production optimization

This paper describes a serious game that can be used to teach and demonstrate production optimization using artificial intelligence techniques. The game takes place in a physical Lego factory and is designed to be an enjoyable learning experience

Using Heuristic Search for Initiating the Genetic Population in Simulation-Based Optimization of Vehicle Routing Problems

Genetic algorithms are nowadays commonly used in simulation-based optimization of vehicle routing problems. These algorithms work with a population of solutions that are iteratively improved in an evolutionary process. Usually, the initial population is created randomly. In general, this is not very efficient since it takes unnecessarily long time before sufficiently good solutions have evolved. For a better performance of genetic algorithms, this work describes the use of heuristic search for creating the initial population. A new heuristic search procedure is described in the paper and evaluated using a real-world problem of garbage collection. The results from the evaluation show that the new procedure is able to improve a genetic algorithm

A Framework for Discrete Event Simulation of Pick Up and Delivery Problems

So called pick-up-and-delivery problems are encountered in everyday life –for example parcel transportation, dial-a-ride services, and collection of mail from mail boxes.For modeling these problems, discrete-event simulation is a powerful technique that is able to handle complexties in a transportation system.In this paper, a new framework for discrete-event simulation of pick-up-and-deliviery problems are proposed.The framework includes ageneral and extensible library of simulation modeling components applicable to different types of pick-up-and-delivery problems.For evaluation, the proposed framework is used in the simulation-optimization on a real-world pick-up-and-delivery problem of waste collection