The machine loading (ML) problem of flexible manufacturing systems (FMS) has been recognised as one of the most important planning problems in the industry. This study aims to minimise the system unbalance by developi...
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The machine loading (ML) problem of flexible manufacturing systems (FMS) has been recognised as one of the most important planning problems in the industry. This study aims to minimise the system unbalance by developing and testing a modified binary bat algorithm (MBBA), which satisfies the technological constraints such as the availability of machine time and tool slots. The proposed algorithm, coded in Matlab (R), is tested on a case study referring to a major Italian company, which manufactures equipment and plants for the food industry. Two scenarios are evaluated to this end: an AS IS scenario, reflecting the current configuration of the production system, and a TO BE one, in which the MBBA is implemented for improving the system's performance, by determining a new sequence of jobs, able to minimise the variance of the processing time across the various machines. The application of the MBBA reveals significant improvements in processing time compared to the approach currently used by the company. The results of the TO BE scenario allow deriving useful indications to operations managers, helping them to identify an alternative strategy to enhance the efficiency of the targeted production department.
The paper presents a methodology based on a modified binary bat algorithm (MBBA) and Improved Seed Population search that provides nearly optimal solutions to the power loss minimization problem, considering network r...
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The paper presents a methodology based on a modified binary bat algorithm (MBBA) and Improved Seed Population search that provides nearly optimal solutions to the power loss minimization problem, considering network reconfiguration and a large number of switches. The existence of many switches leads to a very large number of combinations, making it hard for algorithms to find a good solution. The proposed method is based on eliminating non-feasible solutions and defining an initial matrix with improved seed population for searching the optimal solution. This seed is used for the random process of the algorithm to produce new solutions and is continually updated to obtain better results close to the optimal solutions found during the searching process of the metaheuristic algorithm. This algorithm was tested against the Genetic algorithm (GA), Particle Swarm Optimization (PSO), and the Seed Population search alone on the modified versions of the IEEE 13-node test and IEEE 123-node test feeders. From several runs, the proposed method reached the optimal solution more times than the other algorithms and the remainder achieved near-optimal solutions. With this result, the MBBA provides good options to improve the solutions in the network reconfiguration problem with a large number of switches.
In this paper, a metaheuristic algorithm has been introduced for software usability feature selection and evaluation. Usability is becoming one of the most significant aspects of quality of software. The term 'usa...
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In this paper, a metaheuristic algorithm has been introduced for software usability feature selection and evaluation. Usability is becoming one of the most significant aspects of quality of software. The term 'usability' has already been defined by the authors in their previous work in a reference to the hierarchical software usability model. This model combines various usability factors and features in a hierarchical manner. Here, we introduced MBbat (modified binary bat algorithm) for usability feature selection to get an optimal solution for the search of useful usability features out of a given set of usability features. MBbat is an extension of binarybatalgorithm(BBA) which is based on the bat's behavior and to the best of our knowledge, this algorithm is introduced for the first time in software engineering practices. The selected number of features and accuracy of proposed MBbatalgorithm is compared with the original BBA and the proposed metaheuristic algorithm outperforms the original BBA as it generates a fewer number of selected features and having low accuracy. (C) 2017 Elsevier B.V. All rights reserved.
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