Multiprocessor Scheduling with Rejection
SIAM Journal on Discrete Mathematics
Preemptive multiprocessor scheduling with rejection
Theoretical Computer Science
A Simulated Annealing Approach to Bicriteria Scheduling Problems on a Single Machine
Journal of Heuristics
Online scheduling with machine cost and rejection
Discrete Applied Mathematics
Theoretical Computer Science
Bounded single-machine parallel-batch scheduling with release dates and rejection
Computers and Operations Research
A PTAS for parallel batch scheduling with rejection and dynamic job arrivals
Theoretical Computer Science
A simulated annealing approach to a bi-criteria sequencing problem in a two-stage supply chain
Computers and Industrial Engineering
On several scheduling problems with rejection or discretely compressible processing times
TAMC'06 Proceedings of the Third international conference on Theory and Applications of Models of Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA
IEEE Transactions on Evolutionary Computation
Dominance-Based Multiobjective Simulated Annealing
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On-line scheduling of unit time jobs with rejection: minimizing the total completion time
Operations Research Letters
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In this paper, the authors consider a single machine scheduling problem with rejection. In traditional research, it is assumed all jobs must be processed. However, in the real-world situation, certain jobs can be rejected. In this study, the jobs can be either accepted and scheduled or be rejected at the cost of a penalty. Two objective functions are considered simultaneously: (1) minimization of the sum of weighted completion times for the accepted jobs, and (2) minimization of the sum of penalties for the rejected jobs. The authors apply two-phase method (TPM), which is a general technique to solve bi-objective combinatorial optimization problems, to find all supported and non-supported solutions for small-sized problems. The authors present a mathematical model for implementing both phases. On the other hand, three different bi-objective simulated annealing algorithms have also been developed to find a good estimation of Pareto-optimal solutions for large-sized problems. Finally the authors discuss the results obtained from each of these algorithms.