Batching and scheduling jobs on batch and discrete processors
Operations Research
Efficient algorithms for scheduling semiconductor burn-in operations
Operations Research
A genetic alorithm for multiple objective sequencing problems in mixed model assembly lines
Computers and Operations Research
Minimizing the makespan on a batch machine with non-identical job sizes: an exact procedure
Computers and Operations Research
A genetic algorithm to minimize maximum lateness on a batch processing machine
Computers and Operations Research
The Batch Loading and Scheduling Problem
Operations Research
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Computers and Operations Research
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Minimizing makespan on a single batching machine with release times and non-identical job sizes
Operations Research Letters
Hi-index | 0.00 |
This paper addresses the problem of scheduling jobs with non-identical sizes on a single batch processing machine. A batch processing machine is one which can process multiple jobs simultaneously as a batch as long as the total size of jobs being processed does not exceed the machine capacity. The batch processing time is equal to the longest processing time among all jobs in the batch. For the simultaneous minimization of the bi-criteria of makespan and maximum tardiness, we propose two different multi-objective genetic algorithms based on different representation schemes. While the first algorithm do search via generating sequences of jobs using genetic operators and then batching jobs keeping their order in the sequence, the second algorithm uses the idea of generating batches of jobs directly using genetic operators and ensures feasibility through using heuristic procedures. The type of representation used in the second algorithm allows introducing heuristics with the ability of biasing the search towards each objective and also allows hybridization with a local search heuristic that gives the ability of finding Pareto-optimal or locally efficient Pareto-solutions. Computational results show that the non-dominated solutions obtained by the latter algorithm are very superior in closeness to the true Pareto-optimal solutions and to keep diversity in the obtained Pareto-set, as the problem size increases.