Simulation of workflow balancing in assembly shopfloor operations

Abstract

Purpose

To provide a new model to solve the assembly‐planning problem of a textile machine in a shopfloor which can help researchers and practitioners.

Design/methodology/approach

The assembly planning of a textile machine (repetitive manufacturing system) involves the allocation of operations to cross‐trained operators. Workflow is defined as the workloads assigned to the operators. Operators with smaller workloads are selected to be assigned new operations from the list of unscheduled operations. Three different scheduling strategies – random, shortest processing time, and longest processing time – are adopted for the selection of operations to be assigned to operators. Different combinations of these strategies are considered for the selection of both preceding and succeeding operations. A computer simulation program has been coded on an IBM/PC‐compatible system in the C++ language to study the performance of real data from the shopfloor.

Findings

The relative percentage of imbalance is adopted for evaluating the performance of these heuristics. The RL, SL and LL produced well balanced workload schedules with lesser RPI values for all operators other than heuristics.

Research limitations/implications

Non‐traditional approaches like genetic algorithms can be applied to determine the robustness of the results obtained by this research.

Practical implications

The experiments on simulated and real data clearly indicate that the order of succeeding operations determines the balanced workflows to the assembly of operations among the operators.

Originality/value

The allocation of assembly operations to the operators is modeled into a parallel machine‐scheduling problem with precedence constraints using the objective of minimizing the workflow among the operators.

Citation

Rajakumar, S., Arunachalam, V.P. and Selladurai, V. (2005), "Simulation of workflow balancing in assembly shopfloor operations", Journal of Manufacturing Technology Management, Vol. 16 No. 3, pp. 265-281. https://doi.org/10.1108/17410380510583590