Spatial-Temporal Adaptive Optimal Allocation of Archival Tasks

Archival task allocation for Modern Canton Customs (1861-1949) is a heavy workload due to the massive quantities of data. An archival task allocation system is used to make the allocation process easier. However, traditional methods applied in the allocation system lead to lower efficiency and waste of human force because of the irrational hypotheses. This article presents two algorithms of allocating to spatial-temporal system users based on the heuristic rules, namely offline (ALGOOFF) and adaptive (ALGOAD) allocation algorithms, which significantly improve the performance of the archival task allocation system. With simulation data and authentic data, ALGOAD, by employing just 48 percent of the system users, can achieve the same accuracy rate as the commonly used circular policies. And the additional experiments with simulation data composed of the randomly selected system users verify the following conclusions: (1) the adaptive method is better than the offline task allocation method; (2) the adaptive algorithm can save more human force even when the skills of adaptive archive allocation system users and the difficulty of the archival translation tasks are varied; (3) the adaptive algorithm has continuability without affecting its performance; (4) the adaptive method saves resources even when the completion time the adaptive system users spend on archival translation tasks is different.

Abstract licence: CC BY-NC-ND

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