Bin packing using semi-ordinal data
Operations Research Letters
Ordinal On-Line Scheduling on Two Uniform Machines
COCOON '00 Proceedings of the 6th Annual International Conference on Computing and Combinatorics
Semi-online scheduling with machine cost
Journal of Computer Science and Technology
Journal of Global Optimization
Optimal on-line algorithms for the uniform machine scheduling problem with ordinal data
Information and Computation
Optimal preemptive semi-online scheduling on two uniform processors
Information Processing Letters
Optimal semi-online preemptive algorithms for machine covering on two uniform machines
Theoretical Computer Science
Semi-online scheduling jobs with tightly-grouped processing times on three identical machines
Discrete Applied Mathematics - Special issue: Max-algebra
Exploiting incomplete information to manage multiprocessor tasks with variable arrival rates
Computers and Operations Research
List scheduling for jobs with arbitrary release times and similar lengths
Journal of Scheduling
Semi-online scheduling jobs with tightly-grouped processing times on three identical machines
Discrete Applied Mathematics
Optimal on-line algorithms for the uniform machine scheduling problem with ordinal data
Information and Computation
Optimal preemptive semi-online scheduling on two uniform processors
Information Processing Letters
Semi on-line algorithms for the partition problem
Operations Research Letters
Bin packing using semi-ordinal data
Operations Research Letters
Semi-online scheduling with decreasing job sizes
Operations Research Letters
Semi-on-line scheduling with ordinal data on two uniform machines
Operations Research Letters
Semi-on-line problems on two identical machines with combined partial information
Operations Research Letters
Semi-online scheduling for jobs with release times
Journal of Combinatorial Optimization
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The minimization of maximum completion time for scheduling n jobs on m identical parallel machines is an NP-hard problem for which many excellent heuristic algorithms have been developed. In this paper, the problem is investigated under the assumption that only limited information about the jobs is available. Specifically, processing times are not known for the jobs; rather, the ordering of the jobs by processing time is known. For the cases of two and three parallel machines, algorithms which cannot be improved upon with respect to worst case performance ratio are developed. For the case of four parallel machines, an algorithm which is near optimal with respect to worst case performance ratio is developed. For arbitrary m, an algorithm which produces solutions whose value is at most five-thirds times the optimal value is presented. Finally, it is shown that as the number of machines gets arbitrarily large, the best possible ordinal algorithm has worst case performance ratio of at least 32.