Sequencing with earliness and tardiness penalties: a review
Operations Research
Minimizing total tardiness on one machine is NP-hard
Mathematics of Operations Research
A practical use of Jackson's preemptive schedule for solving the job shop problem
Annals of Operations Research
Scheduling Computer and Manufacturing Processes
Scheduling Computer and Manufacturing Processes
Exploring relaxation induced neighborhoods to improve MIP solutions
Mathematical Programming: Series A and B
Production Scheduling by Reachability Analysis - A Case Study
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 2 - Volume 03
Edge Finding for Cumulative Scheduling
INFORMS Journal on Computing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Models and strategies for variants of the job shop scheduling problem
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Journal of Intelligent Manufacturing
Hi-index | 0.00 |
This paper deals with an application of constraint programming in production scheduling with earliness and tardiness penalties that reflects the scheduling part of the Just-In-Time inventory strategy. Two scheduling problems are studied, an industrial case study problem of lacquer production scheduling, and also the job-shop scheduling problem with earliness/tardiness costs. The paper presents two algorithms that help the constraint programming solver to find solutions of these complex problems. The first algorithm, called the cost directed initialization, performs a greedy initialization of the search tree. The second one, called the time reversing transformation and designed for lacquer production scheduling, reformulates the problem to be more easily searchable when the default search or the cost directed initialization is used. The conducted experiments, using case study instances and randomly generated problem instances, show that our algorithms outperform generic approaches, and on average give better results than other nontrivial algorithms.