Subodha Kumar

Short-term and long-term competition between providers of shrink-wrap software and software as a service

Software as a service (SaaS) has moved quickly from a peripheral idea to a mainstream phenomenon. By bundling a software product with delivery and maintenance service, SaaS providers can effectively differentiate their products with traditional shrink-wrap software (SWS). This research uses a game theoretical approach to examine short- and long-term competition between SaaS and SWS providers. We analyze the factors that affect equilibrium outcomes, including user implementation costs, SaaS providers operation efficiency, and quality improvement over time. Bundling software with service lowers software implementation cost for users, and our results suggest that it increases equilibrium prices. In providing software services, SaaS providers have to incur significant operation cost. In the long run, service operation cost may significantly affect SaaS firms ability to improve its software quality.

Lot streaming and scheduling heuristics for m -machine no-wait flowshops

Abstract The objective of this paper is to minimize makespan in m -machine no-wait flowshops with multiple products requiring lot streaming. A ‘product’ here implies many identical items. ‘Lot streaming’ creates sublots to move the completed portion of a production lot to downstream machines so that machine operations can be overlapped. For the single product case with fixed number of sublots we obtain optimal continuous-sized sublots and then use a heuristic to find integer-sized sublots. For the multi-product continuous-sized sublots case we show that the optimal sequencing of products may be attained by solving a traveling salesman problem. We then construct another heuristic to yield integer-sized sublots. Finally, we evaluate the use of genetic algorithmic meta-heuristics for the interacting decision phases in simultaneous lot streaming and sequencing. We conclude that while GA may deliver makespans comparable in quality to those given by heuristic methods that cleverly exploit problem features particular to lot streaming, GA loses out in computational efficiency. On the other hand, GA can optimize the number of sublots for each product — a task for which neither an analytical nor a heuristic method presently exists.

Scheduling advertisements on a web page to maximize revenue

Many web sites (e.g. Hotmail, Yahoo) provide free services to the users while generating revenues from advertising. Advertising revenue is, therefore, critical for these sites. This in turn raises the question, how should advertisements at a web site be scheduled in a planning horizon to maximize revenue. Advertisements on the web are specified by geometry and display frequency and both of these factors need to be considered in developing a solution to the advertisement scheduling problem. Since this problem belongs to the class of NP-hard problems, we first develop a heuristic called LSMF to solve the problem. This heuristic is then combined with a genetic algorithm (GA) to develop a hybrid GA. The hybrid GA solution is first compared with the upper bound obtained by running CPLEX for the integer programming formulation of the problem. It is then also compared with an existing algorithm proposed in the literature. Our computational results show that the hybrid GA performs exceptionally well in the sense that it provides optimal or near optimal solution for a wide range of problem instances of realistic sizes and the improvements over existing algorithm are substantial. Finally we present a case study to illustrate how revenue could be significantly increased with a small improvement in the advertisement schedule. It is the first such study in this setup.

Dynamic Pricing and Advertising for Web Content Providers

The accumulated evidence indicates that pure revenue models, such as free-access models and pure subscription fee-based models, are not sufficient to support the survival of online information sellers. Hence, hybrid models based on a combination of subscription fees and advertising revenues are replacing the pure revenue models. In response to increasing interest in hybrid models, we study the problem of dynamic pricing of web content on a site where revenue is generated from subscription fee as well as advertisements. We use the optimal control theory to solve the problem and obtain the subscription fee and the advertisement level over time. We first consider the case when the subscription fee can vary over time, but the advertisement level stays the same. Then we extend it by optimizing both the subscription fee and the advertisement level dynamically. We also present several analytical and numerical results that provide important managerial insights.

Selling or Advertising: Strategies for Providing Digital Media Online

Media and network companies are increasingly providing digital media online. We develop a model to examine optimal strategies for media providers to utilize the online channel to distribute digital media. We examine a number of options for media providers. Our results suggest that media companies should sell programs online when content quality is relative high and online access cost is low. When online access cost is relative high, media providers could use the advertising strategy. Overall, companies are better off providing both pricing and advertising options to consumers. We derive the optimal price and advertising level, and analyze the factors that affect the price and advertising decisions. We find that as advertisement revenue rate increases, advertising level should be kept low. In addition, media companies should set online price and advertising level with consideration of the traditional channel in order to avoid channel cannibalization. We also analyze the advertising level in the traditional channel. Our results suggest that as digital video recorder technologies provide more convenience to consumers, media companies should increase, rather than decrease, revenues from advertising.

Optimization Models for Assessing the Peak Capacity Utilization of Intelligent Transportation Systems

With limited economic and physical resources, it is not feasible to continually expand transportation infrastructure to adequately support the rapid growth in its usage. This is especially true for traffic coordination systems where the expansion of road infrastructure has not been able to keep pace with the increasing number of vehicles, thereby resulting in congestion and delays. Hence, in addition to striving for the construction of new roads, it is imperative to develop new intelligent transportation management and coordination systems. The effectiveness of a new technique can be evaluated by comparing it with the optimal capacity utilization. If this comparison indicates that substantial improvements are possible, then the cost of developing and deploying an intelligent traffic system can be justified. Moreover, developing an optimization model can also help in capacity planning. For instance, at a given level of demand, if the optimal solution worsens significantly, this implies that no amount of intelligent strategies can handle this demand, and expanding the infrastructure would be the only alternative. In this paper, we demonstrate these concepts through a case study of scheduling vehicles on a grid of intersecting roads. We develop two optimization models namely, the mixed integer programming model and the space–time network flow model, and show that the latter model is substantially more effective. Moreover, we prove that the problem is strongly NP-hard and develop two polynomial-time heuristics. The heuristic solutions are then compared with the optimal capacity utilization obtained using the space–time network model. We also present important managerial implications.

A Comparison of Pair Versus Solo Programming Under Different Objectives: An Analytical Approach

This study compares the performances of pair development (an approach in which a pair of developers jointly work on the same piece of code), solo development, and mixed development under two separate objectives: effort minimization and time minimization. To this end, we develop analytical models to optimize module-developer assignments in each of these approaches. These models are shown to be strongly NP-hard and solved using a genetic algorithm. The solo and pair development approaches are compared for a variety of problem instances to highlight project characteristics that favor one of the two practices. We also propose a simple criterion that can reliably recommend the appropriate approach for a given problem instance. Typically, for efficient knowledge sharing between developers or for highly connected systems, the pair programming approach is preferable. Also, the pair approach is better at leveraging expertise by pairing experts with less skilled partners. Solo programming is usually desirable if the system is large or the effort needed either to form a pair or to code efficiently in pairs is high. Solo programming is also appropriate for projects with a tight deadline, whereas the reverse is true for projects with a lenient deadline. The mixed approach (i.e., an approach where both the solo and pair practices are used in the same project) is only indicated when the system consists of groups of modules that are sufficiently different from one another.

Minimizing cycle time in large robotic cells

We solve scheduling problems for a Texas-based company called FSI International Inc. that designs and manufactures robotic cells for semiconductor wafer fabrication companies. A robotic cell contains several processing stations served by a robot, repetitively producing a set of identical parts. Processing constraints define the cell to be an m-stage flowshop, i.e., each part is processed in it through an identical sequence of m processing stages. The robot acts as a material handling device transferring parts between stages. The objective is to find a robot moves sequence that minimizes the average time to produce a part. We study three robotic cell configurations that are used by FSI and other designers of robotic cells. It is shown that a well known cyclic solution, referred to as downhill permutation achieves optimal solutions for practically relevant problem instances of the simplest robotic cell configuration. For other configurations, we define a new class of cyclic schedule of robot moves called Least Common Multiple (LCM) cycles that are simple and easy to understand and implement. Metaheuristic algorithms are then devised to find the best LCM-cycle, where each cyclic schedule is evaluated through linear programming. These algorithms are considerably superior to the heuristic currently being used at FSI and improvements could amount to several million dollars per year for FSIs clients. We also establish theoretical lower bounds on the average time to produce a part. Subsequently, through extensive computational experiments, we show that the proposed metaheuristic solutions are very close to optimal solutions.

Advertising Strategies in Electronic Retailing: A Differential Games Approach

We consider advertising problems under an information technology (IT) capacity constraint encountered by electronic retailers in a duopolistic setting. There is a considerable amount of literature on advertising games between firms, yet introducing an IT capacity constraint fundamentally changes this problem. In the presence of information processing constraints, although advertising may still cause a customer to switch, it may not result in a sale, i.e., the customer may be lost by both firms. This situation could occur when customers have a limited tolerance for processing delays and leave the website of a firm because of slow response. In such situations, attracting more traffic to a firms site (by increasing advertising expenditure) may not generate enough additional revenue to warrant this expenditure. We use a differential game formulation to obtain closed-form solutions for the advertising effort over time in the presence of IT capacity constraints. Based on these solutions, we present several useful managerial insights.

Scheduling Web Advertisements: A Note on the Minspace Problem

Free services to internet users are commonly available on many web sites e.g., Hotmail and Yahoo. For such sites, revenue generated from advertisements (hereafter also called “ads”) placed on the web pages is critical for survival. An effective way to schedule ads on web pages to optimize certain performance measures is an important problem that these sites need to address.In this note, we report improved approximation algorithms for the following problem: ads from a set of n ads A = {Ai,...,An} are to be placed on a web page during a planning horizon that is divided into N time intervals. In each time interval, ads are shown in a rectangular space called a slot. An ad Ai is specified by its size si and frequency wi and is to be scheduled in exactly wi slots. We are required to find a schedule that minimizes the maximum fullness among all the slots, where the fullness of a slot is the sum of the sizes of ads scheduled in that slot. Our results include (i) the first online algorithm with a performance bound of