DocumentCode :
3588851
Title :
Exploring Experts Decisions in Concrete Delivery Dispatching Systems Using Bayesian Network Learning Techniques
Author :
Maghrebi, Mojtaba ; Waller, S. Travis
Author_Institution :
Sch. of Civil & Environ. Eng., Univ. of New South Wales (UNSW), Sydney, NSW, Australia
fYear :
2014
Firstpage :
103
Lastpage :
108
Abstract :
Optimally solving large scale Ready Mixed Concrete Dispatching Problems (RMCDPs) in polynomial time is a crucial issue and, in the absence of automated solutions, experts are hired to handle resource allocation tasks in concrete dispatching centres. Therefore, in the Ready Mixed Concrete (RMC) industry, the performance of experts is accepted as the only practical solution, although there is no benchmark for assessing the quality of their decisions. This paper aims to discover the experts´ decisions in the RMC context by using Bayesian Network. Finding the optimum graph in Bayesian Network is NP-hard, therefore, this research uses a wide range of heuristic search algorithms (Hill Climbing, K2, Look Ahead Hill Climbing, Repeated Hill Climbing, Tabu Search, Simulated Annealing and Genetic Algorithm). A large scale dataset gathered from an active RMC was used for evaluating the proposed idea. Results show that Simulated Annealing search algorithm outperformed other search algorithms, although there is not a significant difference between them. However, interpreting the network obtained by Simulated Annealing involves much more effort than other networks with similar accuracy, such as K2.
Keywords :
belief networks; cement industry; computational complexity; decision making; dispatching; expert systems; learning (artificial intelligence); resource allocation; search problems; simulated annealing; Bayesian network learning technique; NP-hard problem; RMCDP; concrete delivery dispatching system; expert decision; optimum graph; polynomial time; ready mixed concrete dispatching problem; ready mixed concrete industry; resource allocation; simulated annealing search algorithm; Accuracy; Bayes methods; Concrete; Dispatching; Genetic algorithms; Search problems; Simulated annealing; Bayesian Network; Experts´ Decisions; Ready Mixed Concrete;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Artificial Intelligence, Modelling and Simulation (AIMS), 2014 2nd International Conference on
Print_ISBN :
978-1-4799-7599-0
Type :
conf
DOI :
10.1109/AIMS.2014.9
Filename :
7102443
Link To Document :
بازگشت