Application of Genetic Algorithm to The Job Assignment Problem with Dynamics Constraints

The process of giving out an assignment to an individual that results to delay, or non-performance of the job is from the cause of not evaluating the minimum cost of the work and the right person to perform the assignment. Assignment problem entails assigning a precise person or thing to an exact task or job. The optimal result is to assign one person to one job. The most common method to solve assignment problem is the Hungarian method. In this paper, Genetic Algorithm is applied to solve assignment problems to attain an optimal solution. The “N men – N jobs” issue is the core task issue, where the general expense of tasks is limited as a result of allocating a single job to just an individual. In deciphering this issue, Genetic Algorithm (GA) and Partially Matched Crossover (PMX) are been utilized as an exceptional encoding plan. GA was evaluated alongside the Hungarian method and the results clearly showed that it performed better than the Hungarian method

Design of System Architecture and Thermal Management Components for an Underwater Energy Storage Facility

The electricity industry is currently experiencing a significant paradigm shift in managing electrical resources. With the onset of aging infrastructure and growing power demands, and the influx of intermittent renewable energy generation, grid system operators are looking towards energy storage as a solution for mitigating industry challenges. An emerging storage solution is underwater compressed air energy storage (UWCAES), where air compressors and turbo-expanders are used to convert electricity to and from compressed air stored in submerged accumulators. This work presents three papers that collectively focus on the design and optimization of an UWCAES system. In the first paper, the field performance of a distensible air accumulator is studied for application in UWCAES systems. It is followed by a paper that analyzed the energetic and exergetic performance of a theoretical UWCAES system. The final paper presents a multi-objective UWCAES optimization model utilizing a genetic algorithm to determine optimum system configurations