4 research outputs found
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Scheduling and Resource Efficiency Balancing. Discrete Species Conserving Cuckoo Search for Scheduling in an Uncertain Execution Environment
The main goal of a scheduling process is to decide when and how to execute each of the project’s activities. Despite large variety of researched scheduling problems, the majority of them can be described as generalisations of the resource-constrained project scheduling problem (RCPSP). Because of wide applicability and challenging difficulty, RCPSP has attracted vast amount of attention in the research community and great variety of heuristics have been adapted for solving it. Even though these heuristics are structurally different and operate according to diverse principles, they are designed to obtain only one solution at a time. In the recent researches on RCPSPs, it was proven that these kind of problems have complex multimodal fitness landscapes, which are characterised by a wide solution search spaces and presence of multiple local and global optima.
The main goal of this thesis is twofold. Firstly, it presents a variation of the RCPSP that considers optimisation of projects in an uncertain environment where resources are modelled to adapt to their environment and, as the result of this, improve their efficiency. Secondly, modification of a novel evolutionary computation method Cuckoo Search (CS) is proposed, which has been adapted for solving combinatorial optimisation problems and modified to obtain multiple solutions. To test the proposed methodology, two sets of experiments are carried out. Firstly, the developed algorithm is applied to a real-life software development project. Secondly, the performance of the algorithm is tested on universal benchmark instances for scheduling problems which were modified to take into account specifics of the proposed optimisation model. The results of both experiments demonstrate that the proposed methodology achieves competitive level of performance and is capable of finding multiple global solutions, as well as prove its applicability in real-life projects
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Discrete flower pollination algorithm for resource constrained project scheduling problem
YesIn this paper, a new population-based and nature-inspired metaheuristic algorithm, Discrete Flower Pollination Algorithm (DFPA), is presented to solve the Resource Constrained Project Scheduling Problem (RCPSP). The DFPA is a modification of existing Flower Pollination Algorithm adapted for solving combinatorial optimization problems by changing some of the algorithm's core concepts, such as flower, global pollination, Lévy flight, local pollination. The proposed DFPA is then tested on sets of benchmark instances and its performance is compared against other existing metaheuristic algorithms. The numerical results have shown that the proposed algorithm is efficient and outperforms several other popular metaheuristic algorithms, both in terms of quality of the results and execution time. Being discrete, the proposed algorithm can be used to solve any other combinatorial optimization problems.Innovate UKAwarded 'Best paper of the Month
Improved discrete cuckoo search for the resource-constrained project scheduling problem
YesAn Improved Discrete Cuckoo Search (IDCS) is proposed in this paper to solve resource-constrained project scheduling problems (RCPSPs). The original Cuckoo Search (CS) was inspired by the breeding behaviour of some cuckoo species and was designed specifically for application in continuous optimisation problems, in which the algorithm had been demonstrated to be effective. The proposed IDCS aims to improve the original CS for solving discrete scheduling problems by reinterpreting its key elements: solution representation scheme, Lévy flight and solution improvement operators. An event list solution representation scheme has been used to present projects and a novel event movement and an event recombination operator has been developed to ensure better quality of received results and improve the efficiency of the algorithm. Numerical results have demonstrated that the proposed IDCS can achieve a competitive level of performance compared to other state-of-the-art metaheuristics in solving a set of benchmark instances from a well-known PSPLIB library, especially in solving complex benchmark instances.Partially funded by the Innovate UK project HARNET – Harmonised Antennas, Radios and Networks under contract no. 100004607
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Interaction models for profiling assets in an extensible and semantic WoT framework,
NoThis paper addresses interoperability issues in an IoT-based cloud environment consisting of multiple WSN clusters
made up of connected objects embedded with smart devices which are fully integrated to the Web, forming the Web-ofThings
(WoT). Two levels of interoperability are considered: Device-level interoperability and semantic-level interoperability.
Eminent issues relating to device heterogeneity and platform dependencies are resolved by using an OSGi
(Open Service Gateway initiative) framework as the software fabric for IoT deployment. However, OSGi alone is not
enough to resolve data heterogeneity issues, and even less in providing a semantic mapping of devices and their data
streams in a generic deployment. To enable this level of interoperability, a novel system that envisages an all-purpose
collaboration framework for the WoT to deliver Sensing and Collaboration as a Service (SeaaS/CaaS) is presented