Improvement heuristic for the flow-shop scheduling problem: An adaptive-learning approach

Adaptive Production Scheduling and Control in One-Of-A-Kind Production

“…However, there is a discrepancy of optimality of heuristics in the literature, because the optimality is evaluated by the deviation from the best known upper bounds that are under continuous improvement. For example, the deviation of 3.33% is for NEH and 9.96% for CDS in Ruiz and Maroto (2005), but 3.56% for NEH and 10.22% for CDS in Agarwal et al (2006), and 3.59% for NEH and 11.28% for CDS in our case study. In the table, the column "Opt."…”

“…However, for computational complexity, they take much longer time. For example, an adaptive learning approach (ALA) heuristic is in phase 3 for Fm/prmu/C max problems (Agarwal et al, 2006). The deviation of ALA heuristic is only 1.74% for Taillard's benchmarks (Taillard, 1993), much better than 3.56% of NEH heuristic.…”

“…However, for the largest instance in Taillard's benchmarks, i.e. 500 jobs and 20 machines, it takes more than 19 hours for ALA heuristic to get a solution, more than 20 hours for Simulated Annealing, and more than 30 hours for Tabu search (Agarwal et al, 2006). Even for the recent IG_RS heuristic, it takes 300 seconds to get a solution to a 500-job 20-machine instance.…”

Multi-Mode Resource-Constrained Project-Scheduling Problem With Renewable Resources: New Solution Approaches

“…Using this adaptive search approach, the gaps are reduced significantly. This approach was first applied in Agarwal et al (2006) for the flow-shop scheduling problem.…”

“…Constraint set (1) ensures that each activity is executed only once, set (2) ensures that the precedence constraints are satisfied and set (3) ensures that the resource constraints are satisfied. For large N the problem is difficult to solve optimally and therefore heuristics are used.…”