Heuristics and policies for online pickup and delivery problems

Master ThesisIn the last few decades, increased attention has been dedicated to a speci c subclass of Vehicle Routing Problems due to its signi cant importance in several transportation areas such as taxi companies, courier companies, transportation of people, organ transportation, etc. These problems are characterized by their dynamicity as the demands are, in general, unknown in advance and the corresponding locations are paired. This thesis addresses a version of such Dynamic Pickup and Delivery Problems, motivated by a problem arisen in an Australian courier company, which operates in Sydney, Melbourne and Brisbane, where almost every day more than a thousand transportation orders arrive and need to be accommodated. The rm has a eet of almost two hundred vehicles of various types, mostly operating within the city areas. Thus, whenever new orders arrive at the system the dispatchers face a complex decision regarding the allocation of the new customers within the distribution routes (already existing or new) taking into account a complex multi-level objective function. The thesis thus focuses on the process of learning simple dispatch heuristics, and lays the foundations of a recommendation system able to rank such heuristics. We implemented eight of these, observing di erent characteristics of the current eet and orders. It incorporates an arti cial neural network that is trained on two hundred days of past data, and is supervised by schedules produced by an oracle, Indigo, which is a system able to produce suboptimal solutions to problem instances. The system opens the possibility for many dispatch policies to be implemented that are based on this rule ranking, and helps dispatchers to manage the vehicles of the eet. It also provides results for the human resources required each single day and within the di erent periods of the day. We complement the quite promising results obtained with a discussion on future additions and improvements such as channel eet management, tra c consideration, and learning hyper-heuristics to control simple rule sequences.The thesis work was partially supported by the National ICT Australia according to the Visitor Research Agreement contract between NICTA and Martin Damyanov Aleksandro

A Quality Improvement Project to Improve Family Recognition of Medical Team Member Roles

OBJECTIVE: Previous studies have shown that inpatients and families in academic settings have a limited ability to recall either their medical team members or the roles of those members. This is an important issue for patient and family satisfaction as well as patient safety. The objective of this study was to increase families’ recognition of medical team members’ roles. METHODS: We established a multidisciplinary quality improvement leadership team, measured family recognition of medical team members and their roles, and conducted 2 PDSA (Plan-Do-Study-Act) cycles. The first intervention was standardization of the content and delivery of our verbal team introductions to ensure inclusion of essential elements and family engagement. The second intervention was addition of an informational white board in each patient room. The prospective study included 105 families in the preintervention phase, 103 post-PDSA cycle 1, and 92 post-PDSA cycle 2. RESULTS: After conduction of 2 PDSA cycles, the recognition of the attending role increased from 49% to 87% (P = .000), the resident role from 39% to 73% (P = .000), and the medical student from 75% to 89% (P = .038). CONCLUSIONS: The multidisciplinary quality improvement model was effective in improving family recognition of the roles of attending physicians, resident physicians, and medical students. Consistent attention to engaging the families and explaining our roles as well as providing informational white boards are effective interventions to facilitate this process