Self-Selecting Priority Queues with Burr Distributed Waiting Costs

Service providers, in the presence of congestion and heterogeneity of customer waiting costs, often introduce a fee-based premier option using which the customers self-segment themselves. Examples of this practice are found in health care, amusement parks, government (consular services), and transportation. Using a single-server queuing system with customer waiting costs modeled as a Burr Distribution, we perform a detailed analysis to (i) determine the conditions (fees, cost structure, etc.) under which this strategy is profitable for the service provider, (ii) quantify the benefits accrued by the premier customers; and (iii) evaluate the resulting impact on the other customers. We show that such self-selecting priority systems can be pareto-improving in the sense that they are beneficial to everyone. These benefits are larger when the variance in the customer waiting costs is high and the system utilization is high. We use income data from the poorest and richest areas (identified by zipcode) in the United States along with the countrywide income distribution to illustrate our results. Numerical results indicate that planning for a 20–40% enrollment in the high-priority option is robust in ensuring that all the stakeholders benefit from the proposed strategy

Information Flows in Capacitated Supply Chains with Fixed Ordering Costs

Many organizations have only recently recognized that sharing information with other members in their supply chain can lead to signficant reduction in the total costs.Usually these information flows are incorporated into existing operating policies at the various parties.In this paper we argue that, in some cases, it may be necessary to change the way the supply chain is managed to make complete use of the information flows. We support this argument by analyzing a supply chain containing a capacitated supplier and a retailer facing i.i.d. demands. In addition there are fixed ordering costs between the retailer and the supplier.In this setting, we consider two models: (1) the retailer is using the optimal (s,S) policy and providing the supplier information about her inventory levels; and (2) the retailer, still sharing information on her inventory levels, orders in a period only if by the previous period the cumulative end-customer demand since she last ordered was greater than \delta . Thus, in Model 1, information sharing is used to supplement existing policies; while, in Model 2, we have redefined operating policies to make better use of the information flows. We will show, via a detailed computational study, that the total supply chain costs of Model 2 are 10.4% lower, on the average, than that of Model 1. We noticed that this reduction in costs is higher at higher capacities, higher supplier penalty costs, lower retailer penalty costs, moderate values of set-up cost, and at lower end-customer demand variances.supply chain management, information sharing, inventory control

Analyzing Online Reviews: New Tools for Evaluating Visitor Experiences

CaRDI Research & Policy Brief Issue 6

Optimal periodic flexible policies for two-stage serial supply chains

In a two-stage serial supply chain, a periodic flexible policy (PF policy) allows the retailer to receive fixed orders that may depend on demand history in one period of the ordering cycle and order freely in other periods. Existing literature has shown that certain PF policies can significantly reduce the inefficiency in a decentralized supply chain. However, these works have mostly defined and implemented ad-hoc periodic flexible policies and have not attempted to identify the optimal periodic flexible policies. In this paper, we characterize the structure of the optimal PF policy using calculus of variations. In particular, we show that under the optimal PF policy, the retailer receives shipments either according to a state dependent capacitated policy or a state dependent order up to policy. Furthermore, we can approximate the retailer’s optimal restricted ordering function by a piecewise linear function and show numerically that this approximation is near optimal.Accepted versio

Managing a Customer Following a Target Reverting Policy

We consider a stochastic, capacitated production-inventory model in which the customer provides information about the expected timing of future orders to the supplier. We allow for randomness in customer order arrivals as well as the quantity demanded, but work under the assumption that the customer is making every effort to follow the schedule provided. We term this as a target reverting policy. This gives rise to an interesting nonstationary inventory control model at the supplier. After characterizing the optimal policy, we develop solution procedures to compute the optimal parameters. An extensive computational study provides insights into the behavior of this model at optimality. Further, comparing the cost of the optimal policy to the cost of simple policies that either ignore the customer's information or the capacity constraint, we are able to provide insights as to when these simplifications could be costly.Supply Chain, Capacitated Production-Inventory Model, Optimal Policy, Simulation-Based Optimization

Retailer policy, uncertainty reduction, and supply chain performance

We investigate how the retailer's inventory policy affects the total cost of a serial supply chain. When the retailer uses the locally optimal (s,S) policy, there is randomness in order time and order quantity to the supplier whereas the supplier sees randomness only in order quantity for the suboptimal (R,T) policy and only in order time for another suboptimal (Q,r) policy. Using an extensive computational study, we find that the suboptimal policies perform better from the total supply chain perspective. The benefit of policy changes is magnified when the retailer costs are low, when the supplier costs are high, and when there is information sharing.Supply chain management Inventory policy Uncertainty reduction Information sharing