Aliza R. Heching

Combined Pricing and Inventory Control Under Uncertainty

This paper addresses the simultaneous determination of pricing and inventory replenishment strategies in the face of demand uncertainty. More specifically, we analyze the following single item, periodic review model. Demands in consecutive periods are independent, but their distributions depend on the items price in accordance with general stochastic demand functions. The price charged in any given period can be specified dynamically as a function of the state of the system. A replenishment order may be placed at the beginning of some or all of the periods. Stockouts are fully backlogged. We address both finite and infinite horizon models, with the objective of maximizing total expected discounted profit or its time average value, assuming that prices can either be adjusted arbitrarily (upward or downward) or that they can only be decreased. We characterize the structure of an optimal combined pricing and inventory strategy for all of the above types of models. We also develop an efficient value iteration method to compute these optimal strategies. Finally, we report on an extensive numerical study that characterizes various qualitative properties of the optimal strategies and corresponding optimal profit values.

Modeling a complex global service delivery system

Enterprises and IT service providers are increasingly challenged with improving the quality of service while reducing the cost of service delivery. Effectively balancing dynamic customer workload, strict service level constraints, and diverse service personnel skills challenges the most experienced management teams. In this paper we describe a modeling framework for analyzing complex service delivery systems. The interaction among various key factors are included in the model to allow decision-making around staffing skill levels, scheduling, and service level constraints in system design. We demonstrate the applicability of the proposed approach in a large IT services delivery environment.

Closed loop performance management for service delivery systems

IT service delivery becomes an increasingly challenging business as customers demand improved quality of service while providers are driven to reduce the cost of delivery. While effective service delivery requires advances in many areas including workload management and workforce optimization, in this paper we focus on service request dispatching decision-making. Specifically, we propose a closed loop performance management solution that leverages feedback controllers to dynamically adjust the priority of service requests considering both (static) contractual service attainment targets and (dynamic) attainment levels achieved. We demonstrate the applicability of the proposed approach in a simulation testbed that models a large IT service delivery environment, and compare its performance with two open loop dispatching policies.

Elements of system design optimization in service quality management

Services Quality is an area of opportunity for IT service providers to innovate and deliver outstanding results to their customers. The objective is to minimize the variance of key quality indicators and deliver predictable capabilities. In this paper we adopt the Lean Sigma methodology from the manufacturing domain as a quality control framework and propose an optimized system model for managing predictability and reducing cost in the IT incident management process. The model establishes guidelines for receiving, classifying and distributing work in a service delivery organization. It defines metrics, controls and management objectives. Using simulation, we conduct an extensive numerical study to show how the model behaves under different operational scenarios that reflect a diverse skill base, the presence of service level objectives, and incoming work with varying levels of complexity. In addition, we provide managerial insight into what drives performance and illustrate the trade-offs between different service delivery designs.

Comment: Using Product Profiling to Illustrate Manufacturing-Marketing Misalignment

In teir July August 1998 article, Hill, Menda, and Dilts described Rumack Pharmaceutical Company (disguised name) as an example of misalignment between marketing and manufacturing. The problem was that marketing had stretched the product line beyond manufacturings capacity limits. The authors stated that operations managers took an action that would seem unwise or contrary to accepted practice in many of todays manufacturing organizations. Rumack decided to increase production lot sizes by an average of 100 percent [p. 61].The authors were respectful (appropriately, in view of Rumacks open door to their research) of the deliberations that resulted in this decision. Many readers, though, will probably wonder, as I do, about the correctness of the decision, considering the widely held impression that lot sizes should fall, not rise.

Scheduling Home Hospice Care with Logic-Based Benders Decomposition

We propose an exact optimization method for home hospice care staffing and scheduling, using logic-based Benders decomposition (LBBD). The objective is to match hospice care aides with patients and schedule visits to patient homes, so as to maximize the number of patients serviced by available staff, while meeting requirements of the patient plan of care and scheduling constraints imposed by the patients and the staff. The Benders master problem assigns aides to patients and days of the week and is solved by mixed integer programming (MIP). The routing and scheduling subproblem decouples by aide and day of the week and is solved by constraint programming. We report preliminary computational results for problem instances obtained from a major hospice care provider. We find that LBBD is superior to state-of-the-art MIP and solves problems of realistic size, if the aim is to conduct staff planning on a rolling basis while maintaining continuity of the care arrangement for patients currently receiving service.

Optimal capacity management and planning in services delivery centers

This paper considers human server systems of queues that arise within the information technology services industry. We develop a two-phase stochastic optimization solution approach to effectively and efficiently address the capacity management and planning processes of information technology services delivery centers. A large collection of numerical experiments of real-world human server system environments investigates various issues of both theoretical and practical interest, quantifying the significant benefits of our approach as well as evaluating the financial-performance trade-offs often encountered in practice.

Workload Management in Dynamic IT Service Delivery Organizations

Enterprises and service providers are increasingly looking to global service delivery as a means for containing costs while improving the quality of service delivery. However, it is often difficult to effectively manage the conflicting needs associated with dynamic customer workload, strict service level constraints, and efficient service personnel organization. In this paper we propose a dynamic approach for workload and personnel management, where organization of personnel is dynamically adjusted based upon differences between observed and target service level metrics. Our approach consists of constructing a dynamic service delivery organization and developing a feedback control mechanism for dynamic workload management. We demonstrate the effectiveness of the proposed approach in an IT incident management example designed based on a large service delivery environment handling more than ten thousand service requests over a period of six months.

A semiparametric method for clustering mixed data

Despite the existence of a large number of clustering algorithms, clustering remains a challenging problem. As large datasets become increasingly common in a number of different domains, it is often the case that clustering algorithms must be applied to heterogeneous sets of variables, creating an acute need for robust and scalable clustering methods for mixed continuous and categorical scale data. We show that current clustering methods for mixed-type data are generally unable to equitably balance the contribution of continuous and categorical variables without strong parametric assumptions. We develop KAMILA (KAy-means for MIxed LArge data), a clustering method that addresses this fundamental problem directly. We study theoretical aspects of our method and demonstrate its effectiveness in a series of Monte Carlo simulation studies and a set of real-world applications.

Robust Scheduling with Logic-Based Benders Decomposition

We study project scheduling at a large IT services delivery center in which there are unpredictable delays. We apply robust optimization to minimize tardiness while informing the customer of a reasonable worst-case completion time, based on empirically determined uncertainty sets. We introduce a new solution method based on logic-based Benders decomposition. We show that when the uncertainty set is polyhedral, the decomposition simplifies substantially, leading to a model of tractable size. Preliminary computational experience indicates that this approach is superior to a mixed integer programming model solved by state-of-the-art software.