An exploratory study of computational challenges in industrial grouping problems

Grouping problems are hard combinatorial problems concerned with partitioning or grouping items into categories, based on a given set of decision criteria. Complex industrial problems such as home healthcare scheduling, vehicle routing problem, task assignment, and team formation fall into this class of problems. These grouping problems are characterized with complex features, posing several computational challenges to decision makers in various disciplines. This study is concerned with investigation of common challenges inherent in grouping problems across industry disciplines. Based on recent case studies in the literature, the paper investigates common challenges and complicating features in real-world grouping problems. These features are classified into model abstraction, presence of multiple constraints, fuzzy management goals, and computational complexity. Further analysis of the case examples revealed four types of the complicating features. Insights into the general grouping problem and the inadequacies of solution methods are presented. Suitable approaches are then suggested. Thus, the study recommends solution approaches that make use of multi-criteria, flexible, interactive approaches that incorporate fuzzy set theory, fuzzy logic, multi-criteria decision, and expert systems.