Hyun Soo Ahn

Pricing and Manufacturing Decisions When Demand Is a Function of Prices in Multiple Periods

In most deterministic manufacturing decision models, demand is either known or induced by pricing decisions in the period that the demand is experienced. However, in more realistic market scenarios consumers make purchase decisions with respect to price, not only in the current period, but also in past and future periods. We model a joint manufacturing/pricing decision problem, accounting for that portion of demand realized in each period that is induced by the interaction of pricing decisions in the current period and in previous periods. We formulate a mathematical programming model and develop solution techniques. We identify structural properties of our models and develop closed-form solutions and effective heuristics for various special cases of our models. Finally, we conduct extensive computational experiments to quantify the effectiveness of our heuristics and to develop managerial insights.

OPTIMAL CONTROL OF A TWO-STAGE TANDEM QUEUING SYSTEM WITH FLEXIBLE SERVERS

We consider the optimal control of two parallel servers in a two-stage tandem queuing system with two flexible servers. New jobs arrive at station 1, after which a series of two operations must be performed before they leave the system. Holding costs are incurred at rate h1 per unit time for each job at station 1 and at rate h2 per unit time for each job at station 2.The system is considered under two scenarios; the collaborative case and the noncollaborative case. In the prior, the servers can collaborate to work on the same job, whereas in the latter, each server can work on a unique job although they can work on separate jobs at the same station. We provide simple conditions under which it is optimal to allocate both servers to station 1 or 2 in the collaborative case. In the noncollaborative case, we show that the same condition as in the collaborative case guarantees the existence of an optimal policy that is exhaustive at station 1. However, the condition for exhaustive service at station 2 to be optimal does not carry over. This case is examined via a numerical study.

On identifying and estimating the cycle time of product development process

A timely introduction of a new product has become invaluable to the firm since the competitors are capable of introducing new or similar products once the driving technology becomes available. In order to generate a high profit from a new product, managers in other departments, such as marketing and production, have to plan ahead of time so that a seamless series of operations can be executed from product development to mass production. Needless to say, a competitive edge is given to the firm with better knowledge on product development process. Such knowledge, nonetheless, is not easy to acquire since a typical product development process is a complex network of many relationships among activities, which we call patterns. In addition to its complex topology, the product development process is often uncertain, iterative, and evolving over time; therefore, even studying individual islands of relationships (patterns) is challenging. Although there were some existing models that shed lights on some of these patterns, very little has been done to systematically analyze the product development process as a whole. In this paper, we develop analytical models that capture essential properties, including uncertainty, iteration and evolution, and estimate the cycle time of each pattern. With our proposed models, the cycle time of a set of patterns (or the whole product development process) can be effectively estimated. As demonstrated in a case study, our model provides valuable insights on how product development process progresses over time, while the corresponding time estimate can help managers to set appropriate manufacturing and marketing strategies.

Dynamic Pricing of Limited Inventories When Customers Negotiate

Although take-it-or-leave-it pricing is the main mode of operation for many retailers, a number of retailers discreetly allow price negotiation when some haggle-prone customers ask for a bargain. At these retailers, the posted price, which itself is subject to dynamic adjustments in response to the pace of sales during the selling season, serves two important roles: (i) it is the take-it-or-leave-it price to many customers who do not bargain, and (ii) it is the price from which haggle-prone customers negotiate down. To effectively measure the benefit of dynamic pricing and negotiation in such a retail environment, one must take into account the interactions among inventory, dynamic pricing, and negotiation. The outcome of the negotiation (and the final price a customer pays) depends on the inventory level, the remaining selling season, the retailers bargaining power, and the posted price. We model the retailers dynamic pricing problem as a dynamic program, where the revenues from both negotiation and posted pricing are embedded in each period. We characterize the optimal posted price and the resulting negotiation outcome as a function of inventory and time. We also show that negotiation is an effective tool to achieve price discrimination, particularly when the inventory level is high and/or the remaining selling season is short, even when implementing negotiation is costly.

OPTIMAL CONTROL OF A FLEXIBLE SERVER

We consider the dynamic scheduling of a multiclass queueing system with two servers, one dedicated (server 1) and one flexible (server 2), with no arrivals. Server 1 is dedicated to processing type-1 jobs while server 2 is primarily responsible for processing type-2 jobs but can also aid server 1 with its work. We address when it is optimal for server 2 to aid server 1 with type-1 jobs rather than process type-2 jobs. The objective is to minimize the total holding costs incurred until all jobs in the system are processed and leave the system. We show that the optimal policy can exhibit one of three possible structures: (i) an exhaustive policy for type-2 jobs, (ii) a nonincreasing switching curve in the number of type-1 jobs and (iii) a nondecreasing switching curve in the number of type-1 jobs. We characterize the necessary and sufficient conditions under which each policy will be optimal. We also explore the use of the optimal policy for the problem with no arrivals as a heuristic for the problem with dynamic arrivals.

Production and distribution policy in a two-stage stochastic push-pull supply chain

We consider a model of a two-stage push-pull production-distribution supply chain. The orders arrive at the final stage according to a Poisson process. Two separate operations, which take place at different locations with exponential service times, are required to convert the raw materials into finished goods. When the first operation is completed the intermediate inventory is held at the first stage and then transported to the second stage where the items are produced to order. The objective is to minimize the average sum of the production, transportation, and holding costs. We consider the optimal policy for a version of this model. Our experimental analysis demonstrates that this optimal policy is counter-intuitive. We develop a heuristic based on a deterministic version of this model, and computationally test the heuristic.

Staffing decisions for heterogeneous workers with turnover

In this paper we consider a firm that employs heterogeneous workers to meet demand for its product or service. Workers differ in their skills, speed, and/or quality, and they randomly leave, or turn over. Each period the firm must decide how many workers of each type to hire or fire in order to meet randomly changing demand forecasts at minimal expense. When the number of workers of each type can by continuously varied, the operational cost is jointly convex in the number of workers of each type, hiring and firing costs are linear, and a random fraction of workers of each type leave in each period, the optimal policy has a simple hire- up-to/fire-down-to structure. However, under the more realistic assumption that the number of workers of each type is discrete, the optimal policy is much more difficult to characterize, and depends on the particular notion of discrete convexity used for the cost function. We explore several different notions of discrete convexity and their impact on structural results for the optimal policy.

Dynamic load balancing with flexible workers

We study the problem of dynamically allocating flexible workers to stations in tandem or serial manufacturing systems. Workers are trained to do a subset of consecutive tasks. We show that the optimal policy is often LBFS (last buffer first-served) or FBFS (first buffer first-served). These results generalize earlier results on the optimality of the pick-and-run, expedite, and bucket brigade-type policies. We also show that, for exponential processing times and general manufacturing networks, the optimal policy will tend to have several workers assigned to the same station.

Rationing Capacity in Advance Selling to Signal Quality

We consider a seller who can sell her product over two periods, advance and spot. The seller has private information about the product quality, which is unknown to customers in advance and publicly revealed in spot. The question we consider is whether the seller has an incentive to signal quality in advance and, if so, how she can convey a credible signal of product quality. We characterize the sellers signaling strategy and find that rationing of capacity in the advance period is an effective tool of signaling product quality. We find that the high-quality seller can distinguish herself by allocating less capacity than the low-quality seller in the advance period. We show that this signaling mechanism exists whenever advance selling would be optimal for both the high-quality and low-quality sellers if quality were known by the consumers. Interestingly, the sellers ability to ration rationing flexibility sometimes disadvantages the seller; this effect is independent of product quality. This paper was accepted by Yossi Aviv, operations management.

On the Introduction of an Agile, Temporary Workforce into a Tandem Queueing System

We consider a two-station tandem queueing system where customers arrive according to a Poisson process and must receive service at both stations before leaving the system. Neither queue is equipped with dedicated servers. Instead, we consider three scenarios for the fluctuations of workforce level. In the first, a decision-maker can increase and decrease the capacity as is deemed appropriate; the unrestricted case. In the other two cases, workers arrive randomly and can be rejected or allocated to either station. In one case the number of workers can then be reduced (the controlled capacity reduction case). In the other they leave randomly (the uncontrolled capacity reduction case). All servers are capable of working collaboratively on a single job and can work at either station as long as they remain in the system. We show in each scenario that all workers should be allocated to one queue or the other (never split between queues) and that they should serve exhaustively at one of the queues depending on the direction of an inequality. This extends previous studies on flexible systems to the case where the capacity varies over time. We then show in the unrestricted case that the optimal number of workers to have in the system is non-decreasing in the number of customers in either queue.