Paper Info
Title
Genetic programming for order acceptance and scheduling
Abstract
This paper focuses on order acceptance and scheduling (OAS) problem, where both acceptance and sequencing decisions have to be handled simultaneously. Because of its complexity, designing effective heuristics or meta-heuristics for OAS is challenging. This paper will investigate how genetic programming (GP) can be used to deal with OAS. The goal of this paper is to develop new GP frameworks to evolve high-performance scheduling rules/heuristics for OAS. The new frameworks are developed based on two key aspects: (1) separating acceptance and sequencing decisions, and (2) enhancing the quality of scheduling rules by embedding heuristic search mechanisms. The experimental results show that separating decisions is not trivial and can easily lead to overfitting issues. Meanwhile, embedding heuristic ideas into the scheduling rules can help search for better solutions for OAS.
Year
DOI
Venue
2013
10.1109/CEC.2013.6557677
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
genetic algorithms,order processing,scheduling,search problems,GP frameworks,OAS problem,acceptance decisions,embedding heuristic search mechanisms,genetic programming,high-performance scheduling heuristics,high-performance scheduling rules,meta-heuristics,order acceptance and scheduling problem,overfitting issues,scheduling rules quality,sequencing decisions,Beam Search,Genetic Programming,Iterative Dispatching Rules,Order Acceptance and Scheduling
Mathematical optimization,Heuristic,Single-machine scheduling,Computer science,Beam search,Genetic programming,Heuristics,Schedule,Artificial intelligence,Machine learning,Genetic algorithm,Metaheuristic
Conference
ISBN
Citations 
PageRank 
978-1-4799-0452-5
7
0.50
References 
Authors
10
4
Name
Order
Citations
PageRank
John Park1233.46
Su Nguyen234823.67
Mengjie Zhang33777300.33
Mark Johnston416513.77