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2133. Cooperation and competition in two-agent scheduling with parallel batching
Invited abstract in session WC-60: Machine Learning in Machine Scheduling, stream Project Management and Scheduling.
Wednesday, 12:30-14:00Room: S09 (building: 101)
Authors (first author is the speaker)
| 1. | Renjie Yu
|
| Business School, The University of Sydney | |
| 2. | Daniel Oron
|
| Business Analytics, The University of Sydney |
Abstract
We study single-machine scheduling problems involving two competing agents sharing a batching machine. This machine can handle at most one job from each agent simultaneously. Our focus is on two due date-related scheduling criteria: the number of late jobs and the total late work. We investigate two variations for each problem: minimizing the combined objectives of both agents, and minimizing the objective of one agent while imposing an upper bound on the other agent's objective. All four versions of the problem are demonstrated to be strongly NP-hard. To address this complexity, we devise efficient Mixed-Integer Linear Programming (MILP) formulations, and employ constructive algorithms and Tabu Search for approximate solutions. A comprehensive numerical analysis validates the effectiveness of the approximation algorithms.
Keywords
- Scheduling
- Programming, Dynamic
- Complexity and Approximation
Status: accepted
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