Highlights:
Cellular Manufacturing systems cover a wide range of industries.
Inflation rate can impose financial harms on cellular manufacturing systems.
The over-allocation of workers, which usually happens in dynamic systems, causes reduction of the system performance.
The proposed algorithm in this research can successfully schedule cellular systems to reduce system costs.
Goal:
The main aim is to determine the best trade-off values between in-house manufacturing and outsourcing, and track the impact of uncertain costs on gained schedules. To be more comprehensive, the performance of human resources is restricted and the partial demands are considered uncertain.
Design / Methodology / Approach:
In this paper a new method for minimizing human resource costs, including operating, salary, hiring, firing, and outsourcing in a dynamic cellular manufacturing system is presented where all system costs are uncertain during manufacturing periods and can be affected by inflation rate. For this purpose, a multi-period scheduling model that is flexible enough to use in real industries has been proposed. To solve the proposed model, a hybrid Ant Colony Optimization and the Tabu Search algorithm (ACTS) are proposed and the outcomes are compared with a Branch-and-Bound based algorithm.
Results:
Our findings showed that the inflation rate has significant effect on multi-period system planning. Moreover, utilizing system capability by the operator, for promoting and using temporary workers, can effectively reduce system costs. It is also found that workers’ performance has significant effect on total system costs.
Limitations of the investigation:
This research covers the cellular manufacturing systems.
Practical implications:
The algorithm is applied for 17 series of dataset that are found in the literature. The proposed algorithm can be easily applied in real industries.
Originality / Value:
The authors confirm that the current research and its results are original and have not been published before. The proposed algorithm is useful to schedule cellular manufacturing systems and analyse various production conditions.
Evaluating impact of market changes on increasing cell-load variation in dynamic cellular manufacturing systems using a hybrid Tabu search and simulated annealing algorithms
