Modern heuristic techniques for combinatorial problems
A genetic algorithm for flowshop sequencing
Computers and Operations Research - Special issue on genetic algorithms
Scheduling parallel manufacturing cells with resource flexibility
Management Science
Scheduling two parallel semiautomatic machines to minimize machine interference
Computers and Operations Research
Computers and Operations Research
One-operator–two-machine flowshop scheduling with setup and dismounting times
Computers and Operations Research
Parallel machine scheduling with a common server
Discrete Applied Mathematics
A heuristic algorithm for minimizing mean flow time with unit setups
Information Processing Letters
Scheduling parallel machines with a single server: some solvable cases and heuristics
Computers and Operations Research
Scheduling problems for parallel dedicated machines under multiple resource constraints
Discrete Applied Mathematics - International symposium on combinatorial optimisation
Initialization strategies and diversity in evolutionary timetabling
Evolutionary Computation
On-line scheduling of two parallel machines with a single server
Computers and Operations Research
Scheduling two parallel machines with a single server: the general case
Computers and Operations Research
Parallel machine scheduling problems with a single server
Mathematical and Computer Modelling: An International Journal
Scheduling with multiple servers
Automation and Remote Control
Computers and Operations Research
Genetic algorithm for rotary machine scheduling with dependent processing times
Journal of Intelligent Manufacturing
Hi-index | 0.00 |
This paper addresses a scheduling problem on parallel dedicated machines in which the setup times are sequence-dependent and the setup operations are performed by a single server. The objective of the problem is to minimize the makespan of the system. The problem is formulated as an integer program and the lower bounds are constructed. A special case of the problem is presented and solved in polynomial time. For the general cases, a hybrid genetic algorithm is developed to solve the problem. The algorithm is tested by both randomly generated data sets and real-world data sets from a printing industry. The computational results show that the algorithm is efficient and effective for both types of data sets.