Abstract
In this paper, multi-objective optimization of energy-aware multi-product failure-prone manufacturing system is explored. The purpose is to determine the best sequence of jobs, optimal production rate and optimum preventive maintenance time for simultaneous optimization of three criterions of total weighted quadratic earliness and tardiness, system reliability and energy-consumption cost. Considering the uncertainties of the problem such as stochastically machine breakdown and maintenance, stochastic processing times as well as NP-hard nature of the problem, it is not possible to propose an analytical solution to this problem. Therefore, two novel algorithms by combining (1) simulation and NSGA-II/PSO and (2) simulation and NSGA-II/GA are proposed for solving this problem. A set of Pareto optimal solutions was obtained via this algorithm. Results show that the both methods converge to a same optimal solution, but the rate of convergence with NSGA-II/PSO is faster than NSGA-II/GA. The algorithms are evaluated by solving small-, medium- and large-scale problems. To the best of our knowledge, multi-product failure-prone manufacturing systems by considering sequence of jobs have not been explored in any paper and for the first time a new hedging point policy is presented for the mentioned problem.
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Abbreviations
- FPMS:
-
Failure-prone manufacturing system
- HPP:
-
Hedging point policy
- HJB:
-
Hamilton–Jacobi–Bellman
- NSGA-II/PSO:
-
Hybrid algorithm based on NSGA-II and PSO
- NSGA-II/GA:
-
Hybrid algorithm based on NSGA-II and GA
- n :
-
Number of jobs to be processed at time zero
- p j :
-
Processing time of job j
- d 1j–d 2j :
-
Time window due date of job j
- C j :
-
Completion time of job j
- T j :
-
Tardiness of job j
- E j :
-
Earliness of job j
- W j :
-
The tardiness penalty of job j
- h j :
-
The earliness penalty of job j
- s j :
-
Production speed for job j
- ξ(t) :
-
State of machine
- \(T_{\text{f}}^{i}\) :
-
Finish time of preventive maintenance i
- \(T_{\text{s}}^{i + 1}\) :
-
Start time of preventive maintenance i + 1
- U(t) :
-
Production rate of machine
- R(t) :
-
Reliability of system in time t
- f(t):
-
Probability density function of machine breakdown
- TEC:
-
Total energy cost
- α :
-
Weibull scale parameter
- β :
-
Weibull shape parameter
- S/N :
-
Signal-to-noise ratio
- L :
-
Number of objectives
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The authors would like to thank the reviewers and the editor for their constructive suggestions and comments.
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Amelian, S.S., Sajadi, S.M., Navabakhsh, M. et al. Multi-objective optimization of stochastic failure-prone manufacturing system with consideration of energy consumption and job sequences. Int. J. Environ. Sci. Technol. 16, 3389–3402 (2019). https://doi.org/10.1007/s13762-018-1742-7
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DOI: https://doi.org/10.1007/s13762-018-1742-7