Turing Award lecture on computational complexity and the nature of computer science
Communications of the ACM
On the Structure of Polynomial Time Reducibility
Journal of the ACM (JACM)
Scheduling a batch processing machine with incompatible job families
Computers and Industrial Engineering
A genetic algorithm to minimize maximum lateness on a batch processing machine
Computers and Operations Research
Bicriterion scheduling with equal processing times on a batch processing machine
Computers and Operations Research
Flowshop scheduling problem with a batching machine and task compatibilities
Computers and Operations Research
Scheduling hybrid flowshop with parallel batching machines and compatibilities
Computers and Operations Research
Minimizing makespan in a flow shop with two batch-processing machines using simulated annealing
Robotics and Computer-Integrated Manufacturing
A DP algorithm for minimizing makespan and total completion time on a series-batching machine
Information Processing Letters
Scheduling jobs with release dates on parallel batch processing machines
Discrete Applied Mathematics
A hybrid Particle Swarm Optimization - Simplex algorithm (PSOS) for structural damage identification
Advances in Engineering Software
SNPD '09 Proceedings of the 2009 10th ACIS International Conference on Software Engineering, Artificial Intelligences, Networking and Parallel/Distributed Computing
Computers and Operations Research
Short Communication: Modeling of asphalt concrete via simulated annealing
Advances in Engineering Software
NP-complete scheduling problems
Journal of Computer and System Sciences
Tabu search heuristic for two-machine flowshop with batch processing machines
Computers and Industrial Engineering
A poly-hybrid PSO optimization method with intelligent parameter adjustment
Advances in Engineering Software
Advances in Engineering Software
Discovering approximate expressions of GPS geometric dilution of precision using genetic programming
Advances in Engineering Software
Advances in Engineering Software
Minimizing makespan in a two-machine flowshop scheduling with batching and release time
Mathematical and Computer Modelling: An International Journal
Mixed integer formulation to minimize makespan in a flow shop with batch processing machines
Mathematical and Computer Modelling: An International Journal
Adaptive learning algorithm of self-organizing teams
Expert Systems with Applications: An International Journal
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This paper presents a general kind of flow shop scheduling problem in a manufacturing supply chain where a group of jobs can be processed on a machine simultaneously. Examples of such environment occur in chemical processes, semiconductor industries, electronics manufacturing, wafer fabrication, and pharmaceutical industries, etc. In this problem not only should the sequence of jobs be determined but also the formation of batches is considered as a new variable in the model. The problem under investigation is NP-hard for cost of total earliness; total tardiness and makespan as objectives. During recent years, the nature-inspired computational intelligent algorithms are successfully employed for achieving the optimum design of supply chain structures. Hence, three effective computational intelligence algorithms including a hybrid genetic algorithm (HGA), a hybrid simulated annealing (HSA) and an improved discrete particle swarm optimization (PSO) algorithm are developed and analyzed for solving the batch processing machine scheduling problem addressed in current paper. Furthermore, an adaptive learning approach which is inspired by the training weights in artificial neural network (ANN) environment is embedded into the algorithms so as to enhance the quality of solutions. An extensive simulation experiments is conducted and the performance of algorithms is compared with the traditional genetic algorithm, particle swarm optimization, some well known dispatching rules such as STPT, LTPT, SBMPT, LBMPT, EDD, MST and also with the powerful commercial solver LINGO. The attained results show the appropriate performance of our algorithms.