scholarly journals Fixed interval scheduling with third‐party machines

Networks ◽  
2020 ◽  
Author(s):  
Ilia Fridman ◽  
Mikhail Y. Kovalyov ◽  
Erwin Pesch ◽  
Andrew Ryzhikov
Keyword(s):  
Fixed Interval ◽  
Third Party ◽  
Interval Scheduling ◽  
Fixed Interval Scheduling

GENETIC ALGORITHMS AND REINFORCEMENT LEARNING FOR THE TACTICAL FIXED INTERVAL SCHEDULING PROBLEM

2001 ◽  
Vol 10 (01n02) ◽  
pp. 23-38 ◽  
Author(s):  
EUGENE SANTOS ◽  
XIAOMIN ZHONG
Keyword(s):  
Reinforcement Learning ◽  
Optimization Problems ◽  
Structural Characteristics ◽  
Fixed Interval ◽  
Learning System ◽  
Scheduling Problem ◽  
Interval Scheduling ◽  
Minimum Number ◽  
Identical Machine ◽  
Fixed Interval Scheduling

Discrete optimization problems are usually NP hard. The structural characteristics of these problems significantly dictate the solution landscape. In this paper, we explore a structure-based approach to solving these kinds of problems. We use a reinforcement learning system to adaptively learn the structural characteristics of the problem, hereby decomposing the problem into several subproblems. Based on these structural characteristics, we develop a Genetic Algorithm by using structural operations to recombine these subproblems together to solve the problem. The reinforcement learning system directs the GA. We test our algorithm on the Tactical Fixed Interval Scheduling Problem(TFISP) which is the problem of determining the minimum number of parallel non-identical machine such that a feasible schedule exists for a given set of jobs. This work continues our work in exploiting structure for optimization.

Sign in / Sign up

Export Citation Format

Share Document