New Classes of Lower Bounds for Bin Packing Problems
Proceedings of the 6th International IPCO Conference on Integer Programming and Combinatorial Optimization
Performance of Various Computers Using Standard Linear Equations Software
Performance of Various Computers Using Standard Linear Equations Software
A Genetic Algorithm for Hybrid Flow-shop Scheduling with Multiprocessor Tasks
Journal of Scheduling
Computers and Operations Research
Bin packing with items uniformly distributed over intervals [a,b]
SFCS '83 Proceedings of the 24th Annual Symposium on Foundations of Computer Science
Depth-bounded discrepancy search
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Computers and Operations Research
Parallel genetic algorithm for a flow-shop problem with multiprocessor tasks
ICCS'03 Proceedings of the 2003 international conference on Computational science: PartIII
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Improved limited discrepancy search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Solving two-stage hybrid flow shop using climbing depth-bounded discrepancy search
Computers and Industrial Engineering
Improving CP-based local branching via sliced neighborhood search
Proceedings of the 2011 ACM Symposium on Applied Computing
Fast lifting procedures for the bin packing problem
Discrete Optimization
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In this paper, we investigate the problem of minimizing makespan in a multistage hybrid flow-shop scheduling with multiprocessor tasks. To generate high-quality approximate solutions to this challenging NP-hard problem, we propose a discrepancy search heuristic that is based on the new concept of adjacent discrepancies. Moreover, we describe a new lower bound based on the concept of dual feasible functions. The proposed lower and upper bounds are assessed through computational experiments conducted on 300 benchmark instances with up to 100 jobs and 8 stages. For these instances, we provide evidence that the proposed bounds consistently outperform the best existing ones. In particular, the proposed heuristic successfully improved the best known solution of 75 benchmark instances.