Logistical network design with robustness and complexity considerations

In: International journal of physical distribution and logistics management, Band 37, Heft 3, S. 201-222

Abstract

PurposeThis paper aims to propose a logistical network design framework with robustness and complexity considerations.Design/methodology/approachThe paper defines robustness, complexity, and normalised efficiency of a logistical network. A mathematical model is then constructed based on the conceptual framework and applied to a hypothetical case study with varying robustness requirements. The mathematical model is formulated as an Mixed‐Integer Linear Programming problem. Furthermore, the paper introduces a graph‐theoretic view of the logistical network and presents its topological properties such as average path length, clustering coefficient, and degree distribution.FindingsThe results show that logistical network configurations can be obtained with desirable robustness levels whilst minimising cost. The relationships of robustness versus normalised efficiency and complexity are also presented. The results show that relationships between logistical network topological properties and robustness exist, as in other real world natural and man‐made complex networks.Practical implicationsLogistical network design is one of the earliest strategic decisions in supply chain management that supply chain managers have to make. Practitioners and researchers typically focus on optimising efficiency and/or responsiveness of logistical networks. It is argued that logistical network designers should also consider robustness and complexity as they are important characteristics of logistical network functionality. The logistical network design frame work successfully incorporates robustness and complexity into design considerations.Originality/valueThis paper newly introduces other important performance measures, robustness and complexity, into the logistical network design objective. The design framework is highly relevant and adds value to logistical network designers and managers.