Abstract
This paper considers the problem of scheduling a given number of jobs on a specified number of identical parallel robots with unequal release dates and precedence constraints in order to minimize mean tardiness. This problem is strongly NP-hard. The author proposes a hybrid intelligent solution system, which uses Genetic Algorithms and Simulated Annealing (GA+SA). A genetic algorithm, as is well known, is an efficient tool for the solution of combinatorial optimization problems. Solutions for problems of different scales are found using genetic algorithms, simulated annealing and a Hybrid Intelligent Solution System (HISS). Computational results of empirical experiments show that the Hybrid Intelligent Solution System (HISS) is successful with regards to solution quality and computational time.
Highlights
Due to the growing cost of raw materials, labour, energy and an increasingly competitive environment, manufacturers must produce cheaper and high quality products
The results show that genetic algorithms (GA) gives better results than Simulated Annealing (SA) in large‐size problems
GA, SA and Hybrid Intelligent Solution System (HISS) have been applied to different sizes of parallel robot problems
Summary
Due to the growing cost of raw materials, labour, energy and an increasingly competitive environment, manufacturers must produce cheaper and high quality products. Biskup et al (2008) considered the problem of scheduling a given number of jobs on a specified number of identical parallel machines to minimize total tardiness [27]. In order to solve a type of identical and non‐identical parallel machine scheduling problem in order to minimize the total weighted completion time, a genetic algorithm with a new extended representation encoding was proposed by Zhou et al (2007) [28]. The job with n‐number of precedence constraints and unequal release dates is tasked with minimizing mean tardiness on m‐number of parallel robots using genetic algorithms. This paper deals with the problem of scheduling a given number of jobs on a specified number of identical parallel robots with unequal release dates and precedence constraints so as to minimize mean tardiness.
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