Abstract
This work focuses on the study of robust no-wait flow shop scheduling problem (R-NWFSP) under the interval-valued fuzzy processing time, which aims to minimize the makespan within an upper bound on total completion time. As the uncertainty of actual job processing times may cause significant differences in processing costs, a R-NWFSP model whose objective function involves interval-valued fuzzy sets (IVFSs) is proposed, and an improved SAA is designed for its efficient solution. Firstly, based on the credibility measure, chance constrained programming (CCP) is utilized to make the deterministic transformation of constraints. The uncertain NWFSP is transformed into an equivalent deterministic linear programming model. Then, in order to tackle the deterministic model efficiently, a simulated annealing algorithm (SAA) is specially designed. A powerful neighborhood search method and new acceptance criterion are applied to find better solutions. Numerical computations demonstrate the high efficiency of the SAA. In addition, a sensitivity analysis convincingly shows that the applicability of the proposed model and its solution strategy under interval-valued fuzzy sets.
Highlights
Antonio Espuña CamarasaThe no-wait flow shop scheduling problem (NWFSP) is one of the most active studies in the current production scheduling field
This work focuses on the study of robust no-wait flow shop scheduling problem (R-NWFSP) under interval-valued fuzzy processing time to deal with uncertainty in practice
It is obvious that the computational results obtained by the simulated annealing algorithm (SAA) is the smallest compared with the three corresponding comparison approaches, which fully proves that SAA has better performance
Summary
The no-wait flow shop scheduling problem (NWFSP) is one of the most active studies in the current production scheduling field. The ‘optional’ schedules obtained by deterministic NWFSP may be poor [19], which motivates many people to study the production process with uncertainties In this case, some researchers have concentrated on the scheduling problem with uncertain data, and introduced the fuzzy sets theory proposed by. Kundu et al [25] presented a transformation method to solve the uncertain linear programming problem under interval-valued fuzzy variables These studies demonstrate a promising direction for using IVFSs to study the scheduling problems with uncertainties. This work focuses on the study of robust no-wait flow shop scheduling problem (R-NWFSP) under interval-valued fuzzy processing time to deal with uncertainty in practice. To consider the uncertainties of this problem, a R-NWFSP model whose objective function involves interval-valued fuzzy sets (IVFSs) will be proposed, and an algorithm will be designed to solve our deterministic model with high efficiency.
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