Modeling And Optimization Of Non-Profit Hospital Call Centers With Service Blending

This dissertation focuses on the operations problems in non-profit hospital call centers with inbound and outbound calls service blending. First, the routing policy for inbound and outbound calls is considered. The objective is to improve the system utilization under constraints of service quality and operators\u27 quantity. A collection of practical staffing assignment methods, separating and mixing staffing policy are evaluated. Erlang C queuing model is used to decide the minimum number of operators required by inbound calls. Theoretical analysis and numerical experiments illustrate that through dynamically assigning the inbound and outbound calls to operators under optimal threshold policy, mixing staffing policy is efficient to balance the system utilization and service quality. Numerical experiments based on real-life data demonstrate how this method can be applied in practice. Second, we study the staffing shift planning problem based on the inbound and outbound calls routing policies. A mathematical programming model is developed, based on a hospital call center with one kind of inbound calls and multiple kinds of outbound calls. The objective is to minimize the staffing numbers, by deciding the shift setting and workload allocation. The inbound calls service level and staffing utilization are taken into consideration in the constraints. Numerical experiments based on actual operational data are included. Results show that the model is effective to optimize the shift planning and hence reduce the call centers\u27 cost. Third, we model the staffing shift planning problem for a hospital call center with two kinds of service lines. Each kind of service is delivered through both inbound calls and outbound calls. The inbound calls can be transferred between these two service lines. A mathematical programming model is developed. The objective is to minimize the staffing cost, by deciding the shift setting and workload allocation. The inbound calls service level and staffing utilization are taken into consideration in the constraints. Numerical experiments are carried out based on actual operational data. Results show that the model is effective to reduce the call centers\u27 labor cost

Near-Optimal Nonmyopic Contact Center Planning using Dual Decomposition

We address the problem of minimizing staffing cost in a contact center subject to service level requirements over multiple weeks. We handle both the capacity planning and agent schedule generation aspect of this problem. Our work incorporates two unique business requirements. First, we develop techniques that can provide near-optimal staffing for 247 contact centers over long term, upto eight weeks, rather than planning myopically on a week-on-week basis. Second, our approach is usable in an online interactive setting in which staffing managers using our system expect high quality plans within a short time period. Results on large real world and synthetic instances show that our Lagrangian relaxation based technique can achieve a solution within 94% of optimal on an average, for eight week problems within ten minutes, whereas a generic integer programming solver can only achieve a solution within 80% of optimal. Our approach is also deployed in live business environment and reduces headcount by a decile over techniques used previously by our client business units

Mathematical Models and Solution Approach for Staff Scheduling with Cross-Training at Call Centers

Call centers face demand that varies throughout the week across multiple service categories and typically employ non-standard workforce schedules to meet this demand. In call centers, cross-training provides a buffer against fluctuation of demand between categories and is widely used. Full cross-training, however, is financially impractical in most cases, which has created a challenging problem in how to optimize a cross-trained workforce, i.e., a) what categories should be cross-trained, b) what portion of the workforce should be cross-trained, and c) how to schedule their weekly assignments. This problem is motivated by the need of a Fortune 50 company\u27s technology support center to schedule its workforce with multiple service categories. To solve this problem to its fullest extent, a mixed integer programming model that addresses staff assignment composition, shift scheduling, days off assignment, and break assignment across multi-skilled agents is proposed. The model is gigantic in size with thousands of general integer variables and is hard to solve. To improve computational efficiency, a two-phase sequential optimization approach is developed. The first phase is to find the optimal composition of the workforce to decide what categories should be cross-trained and when they should be deployed; the second phase is a staff scheduling model to find the size of the workforce with their skill sets and their shifts and weekly tours. The two-phase approach is an order of magnitude faster than the original model and is able to obtain better solutions orders of magnitude faster. Experimental results with real data from the company clearly demonstrate the significance of cross-training; even partial limited cross-training, where 30% - 40% of the workforce is cross-trained with limited (two out of nine) skills per agent, results in considerable performance improvements. The model, when tested in the strategic analysis of the staff composition, suggested an estimated savings of 4% - 9% on staffing cost with an improved service level. Compared with other flexibility options such as part-time shifts, experiment results seem to suggest that cross-training could be a more effective approach to hedge against demand fluctuations when multiple service categories are involved

Machine learning applications in operations management and digital marketing

In this dissertation, I study how machine learning can be used to solve prominent problems in operations management and digital marketing. The primary motivation is to show that the application of machine learning can solve problems in ways that existing approaches cannot. In its entirety, this dissertation is a study of four problems—two in operations management and two in digital marketing—and develops solutions to these problems via data-driven approaches by leveraging machine learning. These four problems are distinct, and are presented in the form of individual self-containing essays. Each essay is the result of collaborations with industry partners and is of academic and practical importance. In some cases, the solutions presented in this dissertation outperform existing state-of-the-art methods, and in other cases, it presents a solution when no reasonable alternatives are available. The problems are: consumer debt collection (Chapter 3), contact center staffing and scheduling (Chapter 4), digital marketing attribution (Chapter 5), and probabilistic device matching (Chapters 6 and 7). An introduction of the thesis is presented in Chapter 1 and some basic machine learning concepts are described in Chapter 2

Constrained Optimization in Simulation:A Novel Approach

This paper presents a novel heuristic for constrained optimization of random computer simula-tion models, in which one of the simulation outputs is selected as the objective to be minimized while the other outputs need to satisfy prespecified target values. Besides the simulation out-puts, the simulation inputs must meet prespecified constraints including the constraint that the inputs be integer. The proposed heuristic combines (i) experimental design to specify the simu-lation input combinations, (ii) Kriging (also called spatial correlation modeling) to analyze the global simulation input/output data that result from this experimental design, and (iii) integer nonlinear programming to estimate the optimal solution from the Kriging metamodels. The heuristic is applied to an (s, S) inventory system and a realistic call-center simulation model, and compared with the popular commercial heuristic OptQuest embedded in the ARENA ver-sions 11 and 12. These two applications show that the novel heuristic outperforms OptQuest in terms of search speed (it moves faster towards high-quality solutions) and consistency of the solution quality

Establishing agent staffing levels in queueing systems with cross-trained and specialized agents

The determination of the right number of servers in a multi-server queueing system is one of the most important problems in applied queueing theory. The problem becomes more complex in a system that consists of both cross-trained and specialized servers. Such queueing systems are readily found in the call centres (also called contact centres) of financial institutions, telemarketing companies and other organizations that provide services to customers in multiple languages. They are also found in computer network systems where some servers are dedicated and others are flexible enough to handle various clients' requests. Over-staffing of these systems causes increased labour costs for the underutilized pool of agents on duty, while under-staffing results in reduced revenue from lost customers and an increase in queue times. The efficient design and analysis of these systems helps management in making better staffing decisions. This thesis aims to develop models for establishing agent staffing levels in organizations with cross-trained and specialized staff with a view to minimizing cost and maintaining a desirable customer satisfaction. The work investigates the effect of various traffic loads on the number of agents required and the cost. It also considers how using specialized agents, flexible agents and a combination of both categories of agents affects the system. It uses a contact centre that has agents with monolingual, bilingual and trilingual (English, French and Spanish) capabilities to do the study