General set-covering formulations (GSCFs) of labor tour scheduling problems have recently received substantial attention in the research literature. The most successful heuristic approaches to these problems have used the linear programming (LP) solution to the GSCF as a starting point and subsequently applied heuristic augmentation and improvement procedures to obtain feasible integer solutions. Integer programming (IP) methods eliminate the need for augmentation and improvement procedures, but have generally been considered intractable for large GSCFs. In this paper we present a sequential mixed-integer programming (SMIP) heuristic for discontinuous (< 24 hours/day) tour-scheduling problems which takes advantage of the structure of the GSCF. The new heuristic substantially outperformed two prominent LP-based methods across 432 full-time workforce test problems, yielding optimal solutions for 429 of the problems. For a set of 36 test problems associated with a mixed-workforce scheduling environment that allowed both full-time and part-time employees with varying levels of cost and productivity, the SMIP heuristic yielded solution costs that were significantly better than previously published costs obtained with competitive methods.
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