COMPLEXITY FACTORS AND INTUITION-BASED METHODS FOR FACILITY NETWORK DESIGN

Logistics managers frequently utilize decision support systems (DSS) t o make facility network design decisions. Many DSS do not provide opti mization capabilities, but instead rely on scenario evaluation as a me ans for developing solutions, We experimentally assessed the performan ces of decision makers, including experienced managers, who used four variants of a scenario evaluation-based DSS to solve realistically siz ed network design problems of varying complexities. Complexity factors included DSS attributes, problem size, network types, and demand disp ersion patterns. Decision makers' performances were assessed relative to optimal solutions, Overall, the decision makers generated relativel y high-quality solutions using the DSS variants. The type of design pr oblem solved did not significantly impact problem-solving performance. However, performance degraded and variability in solution quality esc alated as problem size was increased. The availability of incremental solution cost improvement cues in the DSS significantly improved solut ion quality and reduced performance variability. Iconic graphic enhanc ements to the DSS did not consistently affect performance. However, si gnificant interactions existed among the effects of DSS graphics capab ilities, DSS information cues, and problem attributes.