Simultaneous optimization scheduling with two agents on an unbounded serial-batching machine

This paper considers a class of simultaneous optimization scheduling with two competitive agents on an unbounded serial-batching machine. The cost function of each agent depends on the completion times of its jobs only. According to whether the jobs from different agents can be processed in a common batch, compatible model and incompatible model are investigated. For the incompatible model, we consider batch availability and item availability. For each problem, we provide a polynomial-time algorithm that can find all Pareto optimal schedules.

Hierarchical minimization of two maximum costs on a bounded serial-batching machine

This paper studies a hierarchical optimization problem of scheduling $n$ jobs on a serial-batching machine, in which two objective functions are maximum costs. By a hierarchical optimization problem, we mean the problem of optimizing the secondary criterion under the constraint that the primary criterion is optimized. A serial-batching machine is a machine that can handle up to $b$ jobs in a batch and jobs in a batch start and complete respectively at the same time and the processing time of a batch is equal to the sum of the processing times of jobs in the batch. When a new batch starts, a constant setup time $s$ occurs. We confine ourselves to the bounded model, where $b<n$. We present an $O(n^4)$-time algorithm for this hierarchical optimization problem. For the special case where two objective functions are maximum lateness, we give an $O(n^3\log n)$-time algorithm.