Monte Carlo optimization, simulation, and sensitivity of queueing networks
Monte Carlo optimization, simulation, and sensitivity of queueing networks
Future Generation Computer Systems
Ant Colony Optimization
Ant colony optimization theory: a survey
Theoretical Computer Science
A fast tabu search algorithm for the group shop scheduling problem
Advances in Engineering Software
Ant colony optimization for FOP shop scheduling: a case study on different pheromone representations
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The hyper-cube framework for ant colony optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Optimal makespan scheduling with given bounds of processing times
Mathematical and Computer Modelling: An International Journal
Solving a stochastic single machine problem with initial idle time and quadratic objective
Computers and Operations Research
Scheduling algorithms for a semiconductor probing facility
Computers and Operations Research
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This paper deals with a stochastic group shop scheduling problem. The group shop scheduling problem is a general formulation that includes the other shop scheduling problems such as the flow shop, the job shop and the open shop scheduling problems. Both the release date of each job and the processing time of each job on each machine are random variables with known distributions. The objective is to find a job schedule which minimizes the expected makespan. First, the problem is formulated in a form of stochastic programming and then a lower bound on the expected makespan is proposed which may be used as a measure for evaluating the performance of a solution without simulating. To solve the stochastic problem efficiently, a simulation optimization approach is developed that is a hybrid of an ant colony optimization algorithm and a heuristic algorithm to generate good solutions and a discrete event simulation model to evaluate the expected makespan. The proposed approach is tested on instances where the random variables are normally, exponentially or uniformly distributed and gives promising results.