A Computationally Efficient Optimal Maintenance Scheduling Method

Maintenance Scheduling: Description, Status and Future Directions

“…The modern techniques include simulated annealing [5,47,71], genetic algorithms [20,21,56,78,85], heuristics [35,51,77], memetics [8], and hybrids [6,49,50]. Various optimisation studies have also been conducted, including by Billinton and colleague [9], Charles-Owaba [13], Christiaanse [15], El-Sharkh [28], Pan [69], Yamayee and associate [86], and Zunn and Quintana [91,92]. Models have been developed in different situations: proportional intensities [64], full history [43], warranted products [41], finite source [43], uncertainty [54], deregulated system [59], and planning [79].…”

Generational and Steady-State Genetic Algorithms for Generator Maintenance Scheduling Problems

“…The answer to the first question automatically provides the answer to the second. Several deterministic mathematical methods and heuristic techniques are reported in the literature for solving these problems [1,2,3]. General solution methods are based on integer programming, branch-and-bound techniques, dynamic programming, etc.…”

“…GAs with both generational and steady state approaches yield the optimum solution for small problems when appropriate GA parameters are chosen. Here we present the results of applying a steady state GA to a larger test problem comprising 21 units over a planning period of 52 weeks, which was loosely derived from the example presented in [2] with some simplifications and additional constraints. The data for the test problem is given in Table 1.…”

Water Wave Optimization Algorithm for solving Thermal Unit Maintenance Scheduling

“…Thus, it is a mixed integer problem. In early literature, mathematical methods like Integer Programming (IP) [3], Dynamic Programming (DP) [4,5] and Branch and bound approach (B&B) [6] have been used for solving TUMS problem. But these approaches are restricted only to solve small size systems due to "curse of dimensionality" [7].…”