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Heuristics and Metaheuristics for Solving Scheduling Problems
Abstract
Manufacturing scheduling plays a very important function in successful operation of the production planning and control department of an organization. It also offers a great theoretical challenge to the researchers because of its combinatorial nature. Earlier, researchers emphasized classical optimization methods such as linear programming and branch-and-bound method to solve scheduling problems. However, these methods have the limitation of tackling only small-sized scheduling problems because of the consumption of high computational (CPU) time. As a result, heuristics as well as various efficient optimization methods based on the evolutionary computing paradigm such as genetic algorithms, simulated annealing, and artificial immune system have been applied to scheduling problems for obtaining near optimal solutions. These computational tools are currently being utilized successfully in various engineering and management fields. We briefly discuss the overview of these emerging heuristics and metaheuristics and their applications to the scheduling problems. Given the rise in attention by the researchers, more emphasis has been given to explore artificial immune system in details
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