Engineering Applications of Artificial Intelligence
Advances in Engineering Software
International Journal of Intelligent Systems Technologies and Applications
Engineering Applications of Artificial Intelligence
Robotics and Computer-Integrated Manufacturing
International Journal of Computer Applications in Technology
Computers and Operations Research
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Addressing lot sizing and warehousing scheduling problem in manufacturing environment
Expert Systems with Applications: An International Journal
Solving the design of distributed layout problem using forecast windows: A hybrid algorithm approach
Robotics and Computer-Integrated Manufacturing
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Production planning of a flexible manufacturing system (FMS) is plagued by two interrelated problems, namely 1) part-type selection and 2) operation allocation on machines. The combination of these problems is termed a machine loading problem, which is treated as a strongly NP-hard problem. In this paper, the machine loading problem has been modeled by taking into account objective functions and several constraints related to the flexibility of machines, availability of machining time, tool slots, etc. Minimization of system unbalance (SU), maximization of system throughput (TH), and the combination of SU and TH are the three objectives of this paper, whereas two main constraints to be satisfied are related to time and tool slots available on machines. Solutions for such problems even for a moderate number of part types and machines are marked by excessive computational complexities and thus entail the application of some random search optimization techniques to resolve the same. In this paper, a new algorithm termed as constraints-based fast simulated annealing (SA) is proposed to address a well-known machine loading problem available in the literature. The proposed algorithm enjoys the merits of simple SA and simple genetic algorithm and is designed to be free from some of their drawbacks. The enticing feature of the algorithm is that it provides more opportunity to escape from the local minimum. The application of the algorithm is tested on standard data sets, and superiority of the same is witnessed. Intensive experimentations were carried out to evaluate the effectiveness of the proposed algorithm, and the efficacy of the same is authenticated by efficiently testing the performance of algorithm over well-known functions