Rule mining for scheduling cross training with a heterogeneous workforce

Abstract

This paper presents an association rule mining based framework for workforce scheduling to assist managers with robust real-time assignment decisions. We assume heterogeneous individual learning and forgetting behaviours, in which worker productivity changes dynamically. We explore a parallel production system that meets a specified production requirement over a fixed time horizon with the minimum workforce resources based on the number of worker-periods assigned. Three managerial policies are considered including: setting a maximum allowable individual cross-training level, balancing workload among workers and an unconstrained policy. We propose the use of several schedule attributes to quantify key aspects of optimised schedules that may, in turn, aid in determining robust assignment rules and the development of better cross-training policies. Current results indicate that the proposed approach is effective at identifying important rules, many of which add to our knowledge of useful workforce scheduling strategies.

Keywords:

• association rules
• data mining
• cross-training
• learning
• forgetting
• workforce scheduling
• worker heterogeneity
• productivity
• workforce resources