Integrated network design and scheduling problems : optimization algorithms and applications

Abstract

We consider the new class of integrated network design and scheduling (INDS) problems. These problems focus on selecting and scheduling operations that will change the characteristics of a network, while being specifically concerned with the performance of the network over time. Motivating applications of INDS problems include infrastructure restoration after an extreme event and building humanitarian distribution supply chains. While similar models have been proposed, no one has performed an extensive review of INDS problems from their complexity, network and scheduling characteristics, information, and solution methods.

We validate INDS problems as an appropriate model for many real life situations, including restoration of infrastructures after a large-scale disruptive event. We justify the use of a novel dispatching rule framework that can be easily customized to many types of complex INDS problems by demonstrating that it achieves near-optimal solutions rapidly.

We extend INDS problems to incorporate release dates which represent the earliest an operation can be performed on a network component. INDS problems with flexible release dates are then examined through the introduction of specialized machine(s) that can perform work to move the release date of a component earlier in time. An online optimization setting is explored with INDS problems, where the release date of a component is not known.

We present computational analysis based on case studies on realistic data sets representing the power, waste water, and emergency supply chain infrastructures of coastal New Hanover County, North Carolina, the power and telecommunications networks of lower Manhattan, New York, and the power network of a realistic artificial community CLARC County. These tests demonstrate the importance of a dispatching rule to arrive at near-optimal solutions during real-time decision making activities.

We examine INDS problems under a parallel identical machine scheduling environment where the performance of the network is evaluated by solving classic network optimization problems. We prove that all considered INDS problems are NP-Hard. We propose a novel heuristic dispatching rule algorithm that selects and schedules sets of arcs based on their interactions in the network. These interactions are measured by examining network optimality conditions.