Minimizing mean weighted execution time loss on identical and uniform processors
Information Processing Letters
Minimizing the number of tardy job units under release time constraints
Discrete Applied Mathematics - Combinatorial Optimization
Single machine scheduling to minimize total late work
Operations Research
Genetic local search in combinatorial optimization
CO89 Selected papers of the conference on Combinatorial Optimization
Mathematics of Operations Research
Evolution based learning in a job shop scheduling environment
Computers and Operations Research - Special issue on genetic algorithms
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Local Search in Combinatorial Optimization
Artificial Neural Networks: An Introduction to ANN Theory and Practice
Revenue Management: Research Overview and Prospects
Transportation Science
Computers and Industrial Engineering
Open shop scheduling problems with late work criteria
Discrete Applied Mathematics
A note on the two machine job shop with the weighted late work criterion
Journal of Scheduling
Late work minimization in a small flexible manufacturing system
Computers and Industrial Engineering
Computers and Operations Research
Computers and Operations Research
A comparison of solution procedures for two-machine flow shop scheduling with late work criterion
Computers and Industrial Engineering
Approximation algorithms for scheduling a single machine to minimize total late work
Operations Research Letters
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The work concerns the permutation flow shop scheduling problem with release times and the late work criterion. The late work criterion estimates the quality of a solution with regard to the duration of the late parts of jobs, not taking into account the quantity of the delay for the fully late activities. Particular jobs consist of a sequence of tasks, which have to be executed in the same order on a set of dedicated machines. The execution of a job has to start after its release time and it should finish preferably before its due date. Since the problem is known to be NP-hard, we propose a genetic algorithm to solve this scheduling case. We describe the components of the method, which is based on an indirect solution representation as a sequence of priority dispatching rules. A sequence of rules is transformed to a schedule by the list scheduling approach. Then, we report results of computational experiments, which were preceded by the tuning process of the genetic algorithm. Tests were performed for randomly generated instances of different difficulty in terms of the distribution of release times and due dates over time, as well as the number of jobs and machines. We analyze the results of computational experiments disclosing a strong influence of the problem data on the efficiency of the proposed meta-heuristic algorithm.