Abstract
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.
Highlights
Designing Appropriate Facilities is vital for scheduling manufacturing systems engineering
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
Our findings showed that the inflation rate has significant effect on multi-period system planning
Summary
Designing Appropriate Facilities is vital for scheduling manufacturing systems engineering. Norman et al (2002) focused on assigning workers in CMS when the aim was maximizing system profit and Ertay and Ruan (2005) developed the idea of determining the number of operators to optimize the number of output products For this purpose, using weighted input data, a data envelopment analysis (DEA) was applied. Li et al (2012) focused on minimizing average salary while maximizing average of satisfaction For this purpose they developed a multi-objective mixed integer programming method to determine the number of cross-trained labors and tasks that must be assigned to the labors in flexible assembly cell layout. Another contribution of their research was considering worker satisfaction and task redundancy levels. A new mixed integer mathematical model is addressed by considering uncertain costs to find the best combination of worker allocation and outsourcing in the presence of the mentioned condition
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