Artificial neural networks based predictive model for worker assignment into virtual cells

Abstract

Virtual cellular manufacturing systems (VCMS) have come into existence, replacing traditional cellular manufacturing systems (CMS), to meet highly dynamic production conditions in terms of demand, processing times, product mix and processing sequence. While cell formation phase of VCMS has been dealt quite voluminously, worker assignments phase has gained momentum recently after researchers started realizing the importance of workers' role during implementation of cell-based manufacturing. In the past, worker assignments have been analyzed with development of various heuristics/mathematical models in order to achieve reduced worker costs, improved productivity and quality, effective utilization of workforce and providing adequate levels of worker flexibility. In this paper, a new approach based on artificial neural networks (ANNs) has been proposed to assign workers into virtual cells since ANNs have the ability to model complex relationships between inputs and outputs and find similar patterns effectively. A framework of multilayered perceptron with feed forward (MLP-FF) neural network has been formulated on worker assignment for VCMS under dual resource constrained (DRC) context and its performance under two cell configurations with different time periods is analyzed. A worker assignment model has been developed and applied with cell formation solutions available in the literature in order to generate simulated datasets that drive the training process of proposed ANN framework.

Manufacturing cell formation with flexible processing capabilities and worker assignment: Comparison of constraint programming and integer programming approaches

“…A genetic algorithm was employed to solve the problem. Worker flexibility is also studied by Murali et al, 20 Hamedi et al, 16 and Nikoofarid and Aalaei 21 in VCMSs. Goldengorin et al 5 proposed a flexible CF model based on the p-median problem to adapt real-life constraints such as capacities and operational sequences.…”

“…If machine j is assigned to cell k, (Y j = = k) returns to 1 and 0 otherwise. Similarly, constraint (18) shows the upper limit Upm for the total number of machines that can be assigned to cell k. Constraints (19) and (20) state that each machine and worker should be assigned to only one cell. Constraints (21) and (22) state that duplicate of a machine and a worker can be assigned to a cell if necessary.…”

Cross-Trained Worker Assignment Problem in Cellular Manufacturing System Using Swarm Intelligence Metaheuristics

“…However, their study had only focused on worker assignment for a single cell, although multiple cells exist in the CMS. Murali et al [30] developed an artificial neural network model for assigning workforce in virtual cells under dual resource constrained context where the number of available workers is less than total number of available machines. Egilmez et al [31] developed a fourphased hierarchical methodology to address the stochastic skill-based manpower allocation problem.…”

Effect of training algorithms on neural networks aided pavement diagnosis

“…Multilayer perceptrons (MLPs), frequently referred as multi-layer feedforward neural networks, consist of an input layer, one or more hidden layer, and an output layer and they have numerous applications in the engineering domain (Murali et al, 2010). Learning in a MLP is an unconstrained optimization problem, which is subject to the minimization of a global error function depending on the synaptic weights of the network.…”