A new mixed-integer programming formulation for the maximally diverse grouping problem with attribute values

Abstract The paper presents a new mixed-integer programming formulation for the maximally diverse grouping problem (MDGP) with attribute values. MDGP is of assigning items to groups such that all are as heterogeneous possible. In version values, heterogeneity measured by sum pairwise absolute differences values assigned items, i.e. Manhattan metric. advantage objective function can be reformulated it linear instead quadratic like in standard formulation. We evaluate model comparison two different formulations from literature. Our leads substantially improved computation times and solves instances realistic sizes (for example assignment students seminars) up 70 three attributes, 50 five 30 ten attributes (near) optimality within half an hour.