Designing of Container Feeder Service Networks Under Unstable Demand Conditions

The COVID-19 pandemic has greatly magnified supply challenges in all industries, and virus waves continue to cause an extraordinary amount of variation in both the demand for and the availability of necessary products. This uncertainty has also forced many organizations including container liner shipping to redesign their supply chain. Feeder services from hub ports are essential chain of shipping networks. This chapter addresses the design of feeder networks under consideration of demand fluctuations over the year. For this purpose, a perturbation-based variable neighbourhood search approach is developed in order to determine the feeder ship fleet size and mix, the fleet deployment, service routes, and voyage schedules to minimize operational costs. In the case study investigation, the authors consider the feeder network design problem faced by a feeder shipping company as a sample application. The performance of alternate network configurations is compared under dynamic demand conditions. Numerical results highlight the advantage of dynamic and flexible design of feeder service networks.

Local Search with a SAT Oracle for Combinatorial Optimization

NP-hard combinatorial optimization problems are pivotal in science and business. There exists a variety of approaches for solving such problems, but for problems with complex constraints and objective functions, local search algorithms scale the best. Such algorithms usually assume that finding a non-optimal solution with no other requirements is easy. However, what if it is NP-hard? In such case, a SAT solver can be used for finding the initial solution, but how can one continue solving the optimization problem? We offer a generic methodology, called Local Search with SAT Oracle (), to solve such problems. facilitates implementation of advanced local search methods, such as variable neighbourhood search, hill climbing and iterated local search, while using a SAT solver as an oracle. We have successfully applied our approach to solve a critical industrial problem of cell placement and productized our solution at Intel.

A Cluster First-Route Second Solution Approach for the Multi-Period Home Healthcare Routing and Scheduling Problem

In the home healthcare routing and scheduling problem (HHCRSP), nurses are allocated to a variety of services demanded by clients during a planning horizon. The properties of this problem resemble vehicle routing and nurse scheduling. To propose an efficient solution, the authors consider various issues such as multi-depot, travelling time, time windows, synchronisation, the qualification levels, and other features of nurses and clients. In addition, the continuity of care and work overload should not be ignored in this perspective. First, the authors developed a model in which the continuity of care is redefined by considering connected (synchronous) jobs and the work overload is formulated considering nurse-to-patient staffing ratio. Second, a two-stage solution approach based on a cluster-assign algorithm and variable neighbourhood search (VNS) and variable neighbourhood descent (VND) algorithms are tested on a series of large-scale instances. Computational results present the relations and trade-offs among the aforementioned issues.