Assortment optimization using an attraction model in an omnichannel environment

Making assortment decisions is becoming an increasingly difficult task for many retailers worldwide as they implement omnichannel initiatives. Discrete choice modeling lies at the core of this challenge, yet existing models do not sufficiently account for the complex shopping behavior of customers in an omnichannel environment. In this paper, we introduce a discrete choice model called the multichannel attraction model (MAM). A key feature of the MAM is that it specifically accounts for both the product substitution behavior of customers within each channel and the switching behavior between channels. We formulate the corresponding assortment optimization problem as a mixed integer linear program and provide a computationally efficient heuristic method that can be readily used for obtaining high-quality solutions in large-scale omnichannel environments. We also present three different methods to estimate the MAM parameters based on aggregate sales transaction data. Finally, we describe general effects of the implementation of widely-used omnichannel initiatives on the MAM parameters, and carry out numerical experiments to explore the structure of optimal assortments, thereby gaining new insights into omnichannel assortment optimization. Our work provides the analytical framework for future studies to assess the impact of different omnichannel initiatives

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Technology requirements for communication satellites in the 1980's

The key technology requirements are defined for meeting the forecasted demands for communication satellite services in the 1985 to 1995 time frame. Evaluation is made of needs for services and technical and functional requirements for providing services. The future growth capabilities of the terrestrial telephone network, cable television, and satellite networks are forecasted. The impact of spacecraft technology and booster performance and costs upon communication satellite costs are analyzed. Systems analysis techniques are used to determine functional requirements and the sensitivities of technology improvements for reducing the costs of meeting requirements. Recommended development plans and funding levels are presented, as well as the possible cost saving for communications satellites in the post 1985 era

Multichannel optical access networks : design and resource management

At present there is a strong worldwide push towards bringing fiber closer to individual homes and businesses. The next evolutionary step is the cost-effective all-optical integration of fiber-based access and metro networks. STARGATE [1] is an all-optical access-metro architecture which does not rely on costly active devices, e.g., Optical Cross-Connects (OXCs) or Fixed Wavelength Converters (FWCs), and allow low-cost PON technologies to follow low-cost Ethernet technologies from EPON access into metro networks, resulting in significantly reduced cost and complexity. It makes use of an overlay island of transparency with optical bypassing capabilities. In this thesis we first propose Optical Network Unit (ONU) architectures, and discuss several technical challenges, which allow STARGATE EPONs (SG-EPONs) to evolve in a pay-as-you-grow manner while providing backward compatibility with legacy infrastructure and protecting previous investment. Second, and considering all the hardware constraints, we present the corresponding dynamic bandwidth allocation algorithm for effective resource management in these networks and investigate their performances (delay, throughput) through simulation experiments. We further investigate the problem of transmission grant scheduling in multichannel optical access networks using a scheduling theoretic approach. We show that the problem can be modeled as an Open Shop and we formulate the joint scheduling and wavelength assignment problem as a Mixed Integer Linear Program (MJLP) whose objective is to reduce the length of a scheduling period. Since the problem is known to be NP-hard, we introduce a Tabu Search based heuristic for solving the joint problem. Different other heuristics are also considered and their performances are compared with those of Tabu and MILP. Results indicate that by appropriately scheduling transmission grants and assigning wavelengths, substantial and consistent improvements may be obtained in the network performance. For example, Tabu shows a reduction of up to 29% in the schedule length with substantial reduction in channel idle gaps yielding to both higher channel utilization and lower queuing delays. Additionally, when the number of channels in the network is not small, the benefits of performing appropriate wavelength assignment, together with transmission scheduling, are observed and discussed. We further perform a packet-level simulation on the considered network to study the benefits of efficient grant scheduling; significant improvements are shown both in terms of system utilization and packet queuing delays

Low Latency Audio Processing

PhDLatency in the live audio processing chain has become a concern for audio engineers and system designers because significant delays can be perceived and may affect synchronisation of signals, limit interactivity, degrade sound quality and cause acoustic feedback. In recent years, latency problems have become more severe since audio processing has become digitised, high-resolution ADCs and DACs are used, complex processing is performed, and data communication networks are used for audio signal transmission in conjunction with other traffic types. In many live audio applications, latency thresholds are bounded by human perceptions. The applications such as music ensembles and live monitoring require low delay and predictable latency. Current digital audio systems either have difficulties to achieve or have to trade-off latency with other important audio processing functionalities. This thesis investigated the fundamental causes of the latency in a modern digital audio processing system: group delay, buffering delay, and physical propagation delay and their associated system components. By studying the time-critical path of a general audio system, we focus on three main functional blocks that have the significant impact on overall latency; the high-resolution digital filters in sigma-delta based ADC/DAC, the operating system to process low latency audio streams, and the audio networking to transmit audio with flexibility and convergence. In this work, we formed new theory and methods to reduce latency and accurately predict latency for group delay. We proposed new scheduling algorithms for the operating system that is suitable for low latency audio processing. We designed a new system architecture and new protocols to produce deterministic networking components that can contribute the overall timing assurance and predictability of live audio processing. The results are validated by simulations and experimental tests. Also, this bottom-up approach is aligned with the methodology that could solve the timing problem of general cyber-physical systems that require the integration of communication, software and human interactions