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In this manuscript, an overview is given of the activities done at the Operations Research and Scheduling (OR&S) research group of the faculty of Economics and Business Administration of Ghent University. Unlike the book published by [1] that gives a summary of all academic and professional activities done in the field of Project Management in collaboration with the OR&S group, the focus of the current manuscript lies on academic publications and the integration of these published results in teaching activities. An overview is given of the publications from the very beginning till today, and some of the topics that have led to publications are discussed in somewhat more detail. Moreover, it is shown how the research results have been used in the classroom to actively involve students in our research activities

A review of asset management literature on multi-asset systems

This article gives an overview of the literature on asset management for multi-unit systems with an emphasis on two multi-asset categories: fleet (a system of homogeneous assets) and portfolio (a system of heterogeneous assets). As asset systems become more complicated, researchers have employed different terms to refer to their specific problems. With an objective to facilitate readers in searching conducive studies to their interests, this paper establishes a novel classification scheme for multi-unit systems in accordance with essential features such as diversity of assets and intervention options. Moreover, discerning differences in characteristics between cross-component and cross-asset interactions, we select three types of potential multi-component dependencies (performance, stochastic, and resource) and extend their notions to be applicable to multi-asset systems. The investigation into these dependencies enables the identification of problems that could exist in real industrial settings but are yet to be determined in academia. Ultimately, we delve into modelling approaches adopted by previous researchers. This comprehensive information allows us to offer the insights into the current trends in multi-asset maintenance. We expect that the output of this review paper will not only stress research gaps on multi-asset systems, but more importantly help systematise future studies on this aspect

A synthesis of logic and bio-inspired techniques in the design of dependable systems

Much of the development of model-based design and dependability analysis in the design of dependable systems, including software intensive systems, can be attributed to the application of advances in formal logic and its application to fault forecasting and verification of systems. In parallel, work on bio-inspired technologies has shown potential for the evolutionary design of engineering systems via automated exploration of potentially large design spaces. We have not yet seen the emergence of a design paradigm that effectively combines these two techniques, schematically founded on the two pillars of formal logic and biology, from the early stages of, and throughout, the design lifecycle. Such a design paradigm would apply these techniques synergistically and systematically to enable optimal refinement of new designs which can be driven effectively by dependability requirements. The paper sketches such a model-centric paradigm for the design of dependable systems, presented in the scope of the HiP-HOPS tool and technique, that brings these technologies together to realise their combined potential benefits. The paper begins by identifying current challenges in model-based safety assessment and then overviews the use of meta-heuristics at various stages of the design lifecycle covering topics that span from allocation of dependability requirements, through dependability analysis, to multi-objective optimisation of system architectures and maintenance schedules

Solving Many-Objective Car Sequencing Problems on Two-Sided Assembly Lines Using an Adaptive Differential Evolutionary Algorithm

The car sequencing problem (CSP) is addressed in this paper. The original environment of the CSP is modified to reflect real practices in the automotive industry by replacing the use of single-sided straight assembly lines with two-sided assembly lines. As a result, the problem becomes more complex caused by many additional constraints to be considered. Six objectives (i.e. many objectives) are optimised simultaneously including minimising the number of colour changes, minimising utility work, minimising total idle time, minimising the total number of ratio constraint violations and minimising total production rate variation. The algorithm namely adaptive multi-objective evolutionary algorithm based on decomposition hybridised with differential evolution algorithm (AMOEA/D-DE) is developed to tackle this problem. The performances in Pareto sense of AMOEA/D-DE are compared with COIN-E, MODE, MODE/D and MOEA/D. The results indicate that AMOEA/D-DE outperforms the others in terms of convergence-related metrics

State of the art in simulation-based optimisation for maintenance systems

Recently, more attention has been directed towards improving and optimising maintenance in manufacturing systems using simulation. This paper aims to report the state of the art in simulation-based optimisation of maintenance by systematically classifying the published literature and outlining main trends in modelling and optimising maintenance systems. The authors investigate application areas and published real case studies as well as researched maintenance strategies and policies. Much of the research in this area is focusing on preventive maintenance and optimising preventive maintenance frequency that will lead to the minimum cost. Discrete event simulation was the most reported technique to model maintenance systems whereas modern optimisation methods such as Genetic Algorithms was the most reported optimisation method in the literature. On this basis, the paper identifies the current gaps and discusses future prospects. Further research can be done to develop a framework that guides the experimenting process with different maintenance strategies and policies. More real case studies can be conducted on multi-objective optimisation and condition based maintenance especially in a production context

A practical guide to multi-objective reinforcement learning and planning

Real-world sequential decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems. © 2022, The Author(s)

A Practical Guide to Multi-Objective Reinforcement Learning and Planning

Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems

Multi-objective optimisation methods applied to aircraft techno-economic and environmental issues

Engineering methods that couple multi-objective optimisation (MOO) techniques with high fidelity computational tools are expected to minimise the environmental impact of aviation while increasing the growth, with the potential to reveal innovative solutions. In order to mitigate the compromise between computational efficiency and fidelity, these methods can be accelerated by harnessing the computational efficiency of Graphic Processor Units (GPUs). The aim of the research is to develop a family of engineering methods to support research in aviation with respect to the environmental and economic aspects. In order to reveal the non-dominated trade-o_, also known as Pareto Front(PF), among conflicting objectives, a MOO algorithm, called Multi-Objective Tabu Search 2 (MOTS2), is developed, benchmarked relative to state-of-the-art methods and accelerated by using GPUs. A prototype fluid solver based on GPU is also developed, so as to simulate the mixing capability of a microreactor that could potentially be used in fuel-saving technologies in aviation. By using the aforementioned methods, optimal aircraft trajectories in terms of flight time, fuel consumption and emissions are generated, and alternative designs of a microreactor are suggested, so as to assess the trade-offs between pressure losses and the micro-mixing capability. As a key contribution to knowledge, with reference to competitive optimisers and previous cases, the capabilities of the proposed methodology are illustrated in prototype applications of aircraft trajectory optimisation (ATO) and micromixing optimisation with 2 and 3 objectives, under operational and geometrical constraints, respectively. In the short-term, ATO ought to be applied to existing aircraft. In the long-term, improving the micro-mixing capability of a microreactor is expected to enable the use of hydrogen-based fuel. This methodology is also benchmarked and assessed relative to state-of-the-art techniques in ATO and micro-mixing optimisation with known and unknown trade-offs, whereas the former could only optimise 2 objectives and the latter could not exploit the computational efficiency of GPUs. The impact of deploying on GPUs a micro-mixing _ow solver, which accelerates the generation of trade-off against a reference study, and MOTS2, which illustrates the scalability potential, is assessed. With regard to standard analytical function test cases and verification cases in MOO, MOTS2 can handle the multi-modality of the trade-o_ of ZDT4, which is a MOO benchmark function with many local optima that presents a challenge for a state-of-the-art genetic algorithm for ATO, called NSGAMO, based on case studies in the public domain. However, MOTS2 demonstrated worse performance on ZDT3, which is a MOO benchmark function with a discontinuous trade-o_, for which NSGAMO successfully captured the target PF. Comparing their overall performance, if the shape of the PF is known, MOTS2 should be preferred in problems with multi-modal trade-offs, whereas NSGAMO should be employed in discontinuous PFs. The shape of the trade-o_ between the objectives in airfoil shape optimisation, ATO and micro-mixing optimisation was continuous. The weakness of MOTS2 to sufficiently capture the discontinuous PF of ZDT3 was not critical in the studied examples … [cont.]