In the aftermath of a large disaster, the routing of vehicles carrying critical supplies can greatly impact the arrival times to those in need. Since it is critical that the deliveries are both fast and fair to those being served, it is not clear that the classic cost-minimizing routing problems properly reflect the priorities relevant in disaster relief. In this paper, we take the first steps in developing new methodologies for these problems. We focus specifically on two alternative objective functions for the TSP and VRP: one that minimizes the maximum arrival time (minmax) and one that minimizes the average arrival time (minavg). To demonstrate the potential impact of using these new objective functions, we bound the worst case performance of optimal TSP solutions with respect to these new variants and extend these bounds to include multiple vehicles and vehicle capacity. Similarly, we examine the potential increase in routing costs that result from using these alternate objectives. We present solution approaches for these two variants of the TSP and VRP which are based on well known insertion and local search techniques. These are used in a series of computational experiments to help identify the types of instances where TSP and VRP solutions can be significantly different from optimal minmax and minavg solutions.

Many companies with consumer direct service models, especially grocery delivery services, have found that home delivery poses an enormous logistical challenge due to the unpredictability of demand coupled with strict delivery windows and low profit margin products. These systems have proven difficult to manage effectively and could benefit from new technology, particularly to manage the interaction between order capture and promise and order delivery. In this paper, we define routing and scheduling problems that capture important features of this emerging business model and propose algorithms, based on insertion heuristics, for their solution. The emphasis is on profit maximization. The vendor has to decide which requests to accept and in which time slot to guarantee delivery, for those that are accepted. Computational experiments demonstrate the importance of an integrated approach to order capture and promise and order delivery and the quality and value of the proposed algorithms. 3 Literature Research is emerging that analyzes routing strategies for unattended home deliveries where time slots are not of concern. In (Punakivi 2000), the routing options studied include the use of fixed routes and the optimal sequencing of the deliveries on routes as soon as all deliveries are known. The results demonstrate the importance of optimization in CD with savings from optimal routing

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M any e-tailers providing attended home delivery, especially e-grocers, offer narrow delivery time slots to ensure satisfactory customer service. The choice of delivery time slots has to balance marketing and operational considerations, which results in a complex planning problem. We study the problem of selecting the set of time slots to offer in each of the zip codes in a service region. The selection needs to facilitate cost-effective delivery routes, but also needs to ensure an acceptable level of service to the customer. We present a fully automated approach that is capable of producing high-quality delivery time slot offerings in a short amount of time. Computational experiments reveal the value of this approach and the impact of the environment on the underlying trade-offs.

Background: Faculty are encouraged to use a variety of teaching/learning strategies to engage nursing students. While simulation and games are now common, there were no reports in the nursing literature using an "escape room" concept. Escape rooms use an entertainment approach as teams engage in critical thinking to solve puzzles and find clues to escape a room. In the classroom setting, this concept is modified to solve a mystery by finding various objects through a series of puzzles to locate clues. Some of these games involve finding numerical clues to open locks on a box, such as a toolbox. The purpose of this study was to describe the use of a toolbox gaming strategy based on an escape room concept to help students learn about cardiovascular medications in a pharmacology course. Methods: This pilot study employed a descriptive qualitative method to investigate an approach to pharmacology education. The sample consisted of first semester nursing students. Results: Student responses to criteria-based questions resulted in three themes: engaging, teamwork, and frustration, related to using a toolbox scenario strategy as a pathway to learning. Conclusions: This descriptive study yielded mixed results from the students who were frustrated by time constraints but engaged in the learning experience. Lessons are offered for future improvements.

I n this paper, we present a solution approach for the inventory-routing problem. The inventory-routing problem is a variation of the vehicle-routing problem that arises in situations where a vendor has the ability to make decisions about the timing and sizing of deliveries, as well as the routing, with the restriction that customers are not allowed to run out of product. We develop a two-phase approach based on decomposing the set of decisions: A delivery schedule is created first, followed by the construction of a set of delivery routes. The first phase utilizes integer programming, whereas the second phase employs routing and scheduling heuristics. Our focus is on creating a solution methodology appropriate for large-scale real-life instances. Computational experiments demonstrating the effectiveness of our approach are presented.

Insertion heuristics have proven to be popular methods for solving a variety of vehicle routing and scheduling problems. In this paper, we focus on the impact of incorporating complicating constraints on the efficiency of insertion heuristics. The basic insertion heuristic for the standard vehicle routing problem has a time complexity of O(n3). However, straightforward implementations of handling complicating constraints lead to an undesirable time complexity of O(n4). We demonstrate that with careful implementation it is possible, in most cases, to maintain the O(n3) complexity or, in a few cases, increase the time complexity to O(n3 log n). The complicating constraints we consider in this paper are time windows, shift time limits, variable delivery quantities, fixed and variable delivery times, and multiple routes per vehicle. Little attention has been given to some of these complexities (with time windows being the notable exception), which are common in practice and have a significant impact on the feasibility of a schedule as well as the efficiency of insertion heuristics.

Same-day delivery for online purchases is a recent trend in online retail. We introduce a multi-vehicle dynamic pickup and delivery problem with time constraints that incorporates key features associated with same-day delivery logistics. To make better informed decisions, our solution approach incorporates information about future requests into routing decisions. We also introduce an analytical result that identifies when it is beneficial for vehicles to wait at the depot. We present a wide range of computational experiments that demonstrate the value of our approach. The results show that more requests can be filled when time windows are evenly spread throughout the day compared to when many requests' time windows occur late in the day. However, the anticipation of future requests is most valuable when many requests' time windows occur late in the day. As a result of increased flexibility, experiments also demonstrate that the value of anticipating the future decreases when the number of vehicles or the arrival rate of requests increases. The online appendix is available at https://doi.org/10.1287/trsc.2016.0732 .