No abstract

No abstract

No abstract

We study project scheduling in a competitive setting taking the perspective of a project manager with an adversary, using a Stackelberg game format. The project manager seeks to limit the adversary's opportunity to react to the project and therefore wants to manage the project in a way that keeps the adversary “in the dark” as long as possible while completing the project on time. We formulate and illustrate a new form of project management problem for secret projects where the project manager uses a combination of deception, task scheduling, and crashing to minimize the time between when the adversary initiates a response to the project to when the project is completed. We propose a novel mixed-integer linear programming formulation for the problem and determine characteristics of optimal schedules in this context. Using a detailed example of nuclear weapons development, we illustrate the interconnectedness of the deception, task scheduling, and crashing, and how these influence adversary behavior.

We model a system that consists of a stream of customers processed through three steps by two resources. The first resource, an investigator, handles the first step, in which she collects information from the customer and decides what work will be done in the second step by the second resource, the back office. In the third step, the investigator returns to the customer armed with the additional information or analysis done by the back office and provides the customer with a conclusion, solution, or diagnosis. The investigator has to prioritize either seeing a new customer or completing the work with a customer already in the system. While serving one customer, the investigator may be interrupted by requests from the other customers in the system. Our main objective is to understand the impact of the investigator's choices on system throughput. In addition, we are interested in the occupancy of the system (and thus the flow time of customers). We create a stylized queueing model to examine the investigator's decisions and show that, when interruptions are not an issue, the investigator should prioritize new customers to maximize throughput, keeping the system as full as possible. If customers who have been in the system for a long time generate interruptions and thus additional work for the investigator, we show that it is asymptotically optimal for the investigator to keep the system occupancy low and prioritize discharging customers. Our conclusions are based on a model of a re-entrant queue with dedicated servers serving multiple stations, with two novel features: a buffer that is shared between stations, and jobs in the system generating additional work for the servers. This paper was accepted by Assaf Zeevi, stochastic models and simulation.

In durable goods markets, such as those for automobiles or computers, the coexistence of selling and leasing is common as is the existence of both corporate and individual consumers. Leases to corporate consumers affect the price of used goods on the second‐hand market which in turn affect the buying and leasing behavior of individual consumers. The setting of prices (or volumes) for sale and lease to individual and corporate consumers is a complicated problem for manufacturers. We consider a manufacturer who concurrently sells and leases a finitely durable good to both individual and corporate consumers. The interaction between the manufacturer and consumers is modeled as a dynamic sequential game, where each player seeks to maximize its own payoff over an infinite horizon. We study how the corporate channel substitutability of new goods and used goods and transaction costs in the second‐hand market affect the manufacturer's pricing decisions, consumer behavior, and social welfare in the retail market. Making a number of simplifying assumptions, including two‐period lifetime for the finitely durable goods, we consider Markov Perfect Equilibrium as the solution concept. We show that the manufacturer can maximize her profit by segmenting consumers according to their willingness to pay. Selling and leasing are the mechanisms used for price discrimination in the retail market. We show that as she leases a larger share of her production to the corporate consumer, (1) the manufacturer does not necessarily have to adjust the optimal selling price of new goods to individual consumers, and the volume of sales of new goods to individual consumers can stay the same; (2) the manufacturer does increase the retail lease price, and the number of individual leases decreases; (3) the net supply of used goods on the market increases, leading to a lower market price for used goods; and (4) more individual consumers are able to participate in the market, and their collective welfare or net utility improves. We also show that as production costs increase the manufacturer increases prices, reducing volumes across all channels. When transaction costs increase, the manufacturer reduces leasing in both corporate and retail channels.

We consider the work flow in a medical teaching facility, examining the process that involves an initial patient exam by a resident physician, a subsequent conference between the resident and the attending physician, and the attending physician's visit with the patient. We create an analytical model of a tandem queue with finite buffer space to analyze the impact of different work prioritization policies on the throughput and the flow time of patients in the facility—measures that influence both the facility's finances and patients' satisfaction. We derive throughput-optimal policies and show that these policies involve dynamic batching. This finding is interesting because our model does not include any setup times, and setup times normally imply batching; rather it is the uncertain service times and the requirement for simultaneous service in the conference step that make batching optimal. The optimal dynamic batching policy is complex, so we consider a simpler static batching policy. We show that, in systems with limited buffer space, large batches can sometimes degrade efficiency by simultaneously increasing flow time and decreasing throughput. However, in general, both flow time and throughput increase with batch size. Flow time increases at a faster rate than throughput, so hospital management may want to consider what batch size is optimal given the value it places on the two measures.

Manufacturers and distributors of expensive implants and other medical supplies often require buyers to sign non‐disclosure agreements treating all information concerning negotiated prices as trade secrets. Such agreements make it difficult for hospitals to obtain accurate pricing benchmarks. To save on procurement costs and to obtain pricing information, most hospitals in the United States join group purchasing organizations (GPOs). GPOs are believed to lower procurement costs by aggregating hospitals’ demand. Whether GPOs indeed add value to the healthcare supply chain and produce actual savings for hospitals are debated policy issues, as evidenced by the ongoing discussions on the topic in the US Congress. Some hospitals procure using GPO contracts, and some try to improve on prices available via GPO contracts, negotiating custom contracts directly with the GPO vendors. Using a game‐theoretic model, we prove that GPOs that operate independently and allow for custom contracting limit the benefit of demand aggregation to smaller hospitals only. The larger hospitals gain primarily from using the GPO as an infomediary to obtain critical pricing information benchmarks. Our results further explain why the introduction of custom contracting lowers the value of access to this pricing information for the hospitals, and how the savings through custom contracting can be misleading. We reveal how GPO vendors can exploit information asymmetry about their prices and earn even higher profits, and why, contrary to the industry's belief, the resulting savings are never higher for any hospital, not even for the larger ones when the GPOs allow custom contracting.