Multi-project scheduling under mode duration uncertainties

In this study, we investigate the multi-mode multi-project resource constrained project scheduling problem under uncertainty. We assume a multi-objective setting with 2 objectives : minimizing multi-project makespan and minimizing total sum of absolute deviations of scheduled starting times of activities from their earliest starting times found through simulation. We develop two multi-objective genetic algorithm (MOGA) solution approaches. The first one, called decomposition MOGA, decomposes the problem into two-stages and the other one, called holistic MOGA, combines all activities of each project into one big network and does not require that activities of a project are scheduled consecutively as a benchmark. Decomposition MOGA starts with an initial step of a 2-stage decomposition where each project is reduced to a single macro-activity by systematicaly using artificial budget values and expected project durations. Generated macro-activities may have one or more processing modes called macro-modes. Deterministic macromodes are transformed into random variables by generating disruption cases via simulation. For fitness computation of each MOGA two similar 2-stage heuristics are developed. In both heuristics, a minimum target makespan of overall projects is determined. In the second stage minimum total sum of absolute deviations model is solved in order to find solution robust starting times of activities for each project. The objective value of this model is taken as the second objective of the MOGA's. Computational studies measuring performance of the two proposed solution approaches are performed for different datasets in different parameter settings. When non-dominated solutions of each approach are combined to a final population, overall results show that a larger ratio of these solutions are genetared by decomposition MOGA. Additionally, required computational effort for decompositon MOGA is much less than holistic approach as expected

Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation

Wind farm layout optimisation is a challenging real-world problem which requires the discovery of trade-off solutions considering a variety of conflicting criteria, such as minimisation of the land area usage and maximisation of energy production. However, due to the complexity of handling multiple objectives simultaneously, many approaches proposed in the literature often focus on the optimisation of a single objective when deciding the locations for a set of wind turbines spread across a given region. In this study, we tackle a multi-objective wind farm layout optimisation problem. Different from the previously proposed approaches, we are applying a high-level search method, known as selection hyper-heuristic to solve this problem. Selection hyper-heuristics mix and control a predefined set of low-level (meta)heuristics which operate on solutions. We test nine different selection hyper-heuristics including an online learning hyper-heuristic on a multi-objective wind farm layout optimisation problem. Our hyper-heuristic approaches manage three well-known multi-objective evolutionary algorithms as low-level metaheuristics. The empirical results indicate the success and potential of selection hyper-heuristics for solving this computationally difficult problem. We additionally explore other objectives in wind farm layout optimisation problems to gain a better understanding of the conflicting nature of those objectives