Multi-Objective Model for determining Optimal Buffer Size and Redundancy-Availability Allocation Simultaneously in Manufacturing Systems
Message:
Abstract:
Objective

This research was carried out with the aim of simultaneously examining the two categories of the most commonly encountered issues in the field of production and operations including the redundancy allocation and the buffers allocation. The study sought to optimize goals such as accessibility, system costs, and buffer capacity and for this purpose, variables such as the optimum capacity of buffers between machines, the number of high-reliability machines and their allocation, and the appropriate time schedule for maintenance and repair were investigated.

Methods

Considering the categorization of emergency and preventive failures for machinery, taking into account the cost of any failure for machinery, and considering the non-exponential and increasing distribution function for a variety of failures, it is very difficult to obtain and calculate mathematical functions related to the objectives of Availability and Cost explicitly. Therefore, a combination of simulation, experimental design, and neural network approach was used to estimate these two objective functions. In order to solve the proposed model, the NSGA-II algorithm was coded in MATLAB. Also, in order to analyze the efficiency of the suggested Algorithm, the MOPSO Algorithm was used and the Algorithms were compared with each other based on the performance measures of the algorithms.

Results

After applying the numerical example with the approach used, the results of the research indicate the validity of the proposed methodology for the problem under study.

Conclusion

Based on the set of solutions obtained from the algorithms used, different combinations of variables (including the number of machines per station, buffer capacity and duration of repairs) can be used to achieve the appropriate level of objectives.

Article Type:
Research/Original Article
Language:
Persian
Published:
Journal of Industrial Management, Volume:11 Issue:34, 2019
Pages:
427 - 460
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