A vibration damping optimization algorithm for solving a new multi-objective dynamic cell formation problem with workers training

- Developing a novel multi-objective mixed integer model in CMS.
- Considering time dependent workers training costs in the model.
- Presenting a new representation method and a new operator for the algorithm.
- Applying Multi-Objective Vibration Damping Optimization (MOVDO) to solve the model.
- Comparing the proposed algorithm (MOVDO) with NSGA-II and NRGA.

This paper presents a comprehensive multi-objective mixed integer mathematical programming model which considers cell formation and production planning problems simultaneously. This comprehensive model includes dynamic system reconfiguration, multi period production planning, operation sequence, alternative process plans for part types, machine and worker flexibility, duplicate machines, machine capacity, available time of workers and worker assignment. The aim of the proposed model is to minimize inter and intra-cell movement costs, machine and reconfiguration costs, setup costs, production planning costs (holding, backorder and subcontracting costs) and workers hiring, firing, training and salary costs, as well as minimizing summation of machines idle times as a second objective. Due to NP-hardness of the problem, a recent and efficient meta-heuristic algorithm namely multi-objective vibration damping optimization (MOVDO) is designed for finding Pareto-optimal frontier. In order to check the efficiency of the developed algorithm, it is compared with two salient multi-objective genetic algorithms named NSGAII and NRGA. Finally, by generating some test problems in small and large scales and using some multi-objective comparison metrics, the algorithms are compared and analyzed statistically.