Acceptance strategies for maximizing agent profits in online scheduling

Abstract

In the global logistics market, agents need to decide upon whether to accept jobs offered sequentially. For each offer, an agent makes an immediate selection decision with little knowledge about future jobs; the goal is to maximize the profit. We study this online decision problem of acceptance of unit length jobs with time constraints, which involves online scheduling. We present theoretically optimal acceptance strategies for a fundamental case, and develop heuristic strategies in combination with an evolutionary algorithm for more general and complex cases. We show experimentally that in the fundamental case the performance of heuristic solutions is almost the same as that of theoretical solutions. In various settings, we compare the results achieved by our online solutions to those generated by the optimal offline solutions; the average-case performance ratios are about 1.1. We also analyze the impact of the ratio between the number of slots and the number of jobs on the difficulty of decisions and the performance of our solutions. © 2013 Springer-Verlag Berlin Heidelberg.