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The article presents two variants of the project portfolio selection and scheduling problem (PPSSP). The primary objective of the PPSSP is to maximise the total portfolio value through the selection and scheduling of a subset of projects subject to various operational constraints. This article describes two recently-proposed, generalised models of the PPSSP [1,2] and proposes a set of synthetically generated problem instances for each. These datasets can be used by researchers to compare the performance of heuristic and meta-heuristic solution strategies. In addition, the Python program used to generate the problem instances is supplied, allowing researchers to generate new problem instances.

Resource-constrained project scheduling is one of the most widely studied research problems. Although a large number of algorithms have been developed for solving these problems, many of them ignored several practical issues such as resource unavailability and disruptions, and a recovery plan for after a disruption. This paper investigates different cost-effective measures for project scheduling problems under resource disruptions, in which the disrupted resources are dynamically recovered as the project progresses. Firstly, a new proactive scheduling technique is proposed to determine the make-span with an emphasis on maximizing the floating resources that can be used as a buffer to handle any future disruption effectively. Secondly, we introduce a bi-objective approach for reactive scheduling if a disruption occurs, in which both the revised make-span and recovery cost are minimized. Both proactive and reactive models are solved using a specially designed multi-method based evolutionary optimization algorithm, with the results obtained showing the benefits of the proposed method in comparison to state-of-the-art algorithms.

In many real-world decision problems, there are specific objective, decision variables, conditions, data and/or parameters that may vary over time. When these problems are solved through an optimization process, they are generally known as dynamic optimization. Dynamic optimization is a challenging research topic as the optimal solution usually moves with any change in the problem environment. In locating the optimal point in any optimization problems, the effectiveness of the search process is influenced by the nature of the problem landscape. In order to improve the effectiveness of the search process, in this article, a new approach is developed by integrating a landscape-based strategy with appropriately designed evolutionary algorithms for solving dynamic problems. The proposed approach is named as Landscape Influenced Dynamic Optimization Algorithm (LIDOA). LIDOA checks the similarity before and after the changed environment which is then used as an input to guide the evolutionary search process. The dynamic benchmark functions from IEEE-CEC2009 are solved using LIDOA. LIDOA was able to enhance the performance of the evolutionary algorithms in their adaptation to dynamic changes, which in turn enhanced their ability to attain better results.

The solution of large-scale optimization problems is the key to many decision-making processes in practice. However, it is a challenging research topic when considered both the quality of solutions and the required computational time. One of the popular approaches for these problems is to divide the problems into a number of smaller sub-problems, that are then solved separately with an exchange of some information using the cooperative co-evolution (CC) concept. However, the characteristics of sub-components could be different, and their contributions to the overall performance can also be different while solving the problem. In the CC approach, it usually applies one optimizer and allocates equal computational budget to all sub-components. In this article, a new algorithm is proposed with the use of multiple optimizers, along with a need-based allocation of computational budget for the sub-components. In the proposed algorithm, a group of optimizers cooperate in an effective way to evolve the sub-components, depending on heuristic fuzzy rules. The performance of our proposed algorithm was evaluated by solving a number of large-scale global optimization benchmark functions. The empirical results show that the proposed algorithm outperforms equal allocation CC, a single selection characteristic, a single candidate optimizer and state-of-the-art algorithms.

Although several multi-operator and multi-method approaches for solving optimization problems have been proposed, their performances are not consistent for a wide range of optimization problems. Also, the task of ensuring the appropriate selection of algorithms and operators may be inefficient since their designs are undertaken mainly through trial and error. This research proposes an improved optimization framework that uses the benefits of multiple algorithms, namely, a multi-operator differential evolution algorithm and a co-variance matrix adaptation evolution strategy. In the former, reinforcement learning is used to automatically choose the best differential evolution operator. To judge the performance of the proposed framework, three benchmark sets of bound-constrained optimization problems (73 problems) with 10, 30 and 50 dimensions are solved. Further, the proposed algorithm has been tested by solving optimization problems with 100 dimensions taken from CEC2014 and CEC2017 benchmark problems. A real-world application data set has also been solved. Several experiments are designed to analyze the effects of different components of the proposed framework, with the best variant compared with a number of state-of-the-art algorithms. The experimental results show that the proposed algorithm is able to outperform all the others considered.

Breaching of coastal barriers is a three-dimensional process induced by complex interactions between hydrodynamics, sediment transport and soil avalanching processes. Although numerous coastal barriers are breached every year in many coastal countries, causing dramatic inundations of the nearshore areas, the understanding of the processes and interactions associated with both breaching and subsequent flood propagation is still poor. This might explain why their combined modelling and prediction has not yet been sufficiently addressed. Consequently, barrier breaching and subsequent inundation are still often modelled separately, thus ignoring the strong interaction between breaching and flooding. However, the combined modelling of such strongly coupled processes is crucial. Since the open-source model system “XBeach” consists, among others, of a nonlinear shallow water solver coupled with a morphodynamic model, also including a soil avalanching module, it has the potential to simulate both breaching and subsequent flood propagation together. Indeed, the mutual interactions between hydrodynamics and morphodynamics (including soil avalanching) are properly accounted for. This paper, therefore, aims to examine the applicability of XBeach for modelling coastal barrier breaching and inundation modelling in combination, instead of the current approaches, which address the modelling of each of these two processes separately. The performance of XBeach, in terms of inundation modelling, is assessed through comparisons of the results from this model system (i) with the results from common 1D and 2D flood propagation models and (ii) with observations for barrier breaching and subsequent inundation from a real case study. Besides providing an improved understanding of the breaching process, the results of this study demonstrate a new promising application of XBeach and its potential for calculating time-varying inland discharges, as well as for combined modelling of both dune breaching and subsequent flood propagation in coastal zones.