The abstract MAC layer

A diversity of possible communication assumptions complicates the study of algorithms and lower bounds for radio networks. We address this problem by defining an Abstract MAC Layer. This service provides reliable local broadcast communication, with timing guarantees stated in terms of a collection of abstract delay functions applied to the relevant contention. Algorithm designers can analyze their algorithms in terms of these functions, independently of specific channel behavior. Concrete implementations of the Abstract MAC Layer over basic radio network models generate concrete definitions for these delay functions, automatically adapting bounds proven for the abstract service to bounds for the specific radio network under consideration. To illustrate this approach, we use the Abstract MAC Layer to study the new problem of Multi-Message Broadcast, a generalization of standard single-message broadcast, in which any number of messages arrive at any processes at any times. We present and analyze two algorithms for Multi-Message Broadcast in static networks: a simple greedy algorithm and one that uses regional leaders. We then indicate how these results can be extended to mobile networks.Cisco Systems, Inc.Lehman Brothers (1993-2008)CUNY (A New MAC-Layer Paradigm for Mobile Ad-Hoc Networks)National Science Foundation (U.S.) (NSF Award Number CCF-0726514)National Science Foundation (U.S.) (NSF Award Number CNS-0715397

A Two-Tier Urban Delivery Network with Robot-based Deliveries

In this paper, we investigate a two-tier delivery network with robots operating on the second tier. We determine the optimal number of local robot hubs as well as the optimal number of robots to service all customers and compare the resulting operational cost to conventional truck-based deliveries. Based on the well-known p-median problem, we present mixed-integer programs that consider the limited range of robots due to battery size. Compared to conventional truck-based deliveries, robot-based deliveries can save about 70% of operational cost and even more, up to 90%, for a scenario with customer time windows

Flexible Time Window Management for Attended Home Deliveries

In the competitive world of online retail, customers can choose from a selection of delivery time windows on a retailer's website. Creating a set of suitable and cost-efficient delivery time windows is challenging, since customers want short time windows, but short time windows can increase delivery costs significantly. Furthermore, the acceptance of a request in a particular short time window can greatly restrict the ability to accommodate future requests. In this paper, we present customer acceptance mechanisms that enable flexible time window management in the booking of time-window based attended home deliveries. We build tentative delivery routes and check which time windows are feasible for each new customer request. We offer the feasible long delivery time windows as a standard and let our approaches decide when to offer short time windows. Our approaches differ in the comprehensiveness of information they consider with regard to customer characteristics as well as detailed characteristics of the evolving route plan. We perform a computational study to investigate the approaches' ability to offer short time windows and still allow for a large number of customers to be served. We consider various demand scenarios, partially derived from real order data from a German online supermarket

Flexible dynamic time window pricing for attended home deliveriesy

In the challenging environment of attended home deliveries, pricing of different delivery options can play a crucial role to ensure profitability and service quality of retailers. To differentiate between standard and premium delivery options, many retailers include time windows of various lengths and fees within their offer sets. Customers want short delivery time windows, but expect low delivery fees. However, longer time windows can help to maintain flexibility and profitability for the retailer. We present flexible dynamic time window pricing policies that measure the impact of short time windows on the underlying route plan during the booking process and set delivery fees accordingly. Our goal is to nudge customers to choose time windows that do not overly restrict the flexibility of route plans. To this end, we introduce three dynamic pricing policies that consider temporal and/or spatial routing and customer characteristics. We consider customer behavior through a nested logit model, which is able to mimic customer choice for time windows of multiple lengths. We perform a computational study considering realistic travel and demand data to investigate the effectiveness of flexible dynamic time window pricing. Our pricing policies are able to outperform static pricing policies that reflect current business practice