We study a single-product setting in which a firm can source from two suppliers, one that is unreliable and another that is reliable but more expensive. Suppliers are capacity constrained, but the reliable supplier may possess volume flexibility. We prove that in the special case in which the reliable supplier has no flexibility and the unreliable supplier has infinite capacity, a risk-neutral firm will pursue a single disruption-management strategy: mitigation by carrying inventory, mitigation by single-sourcing from the reliable supplier, or passive acceptance. We find that a suppliers percentage uptime and the nature of the disruptions (frequent but short versus rare but long) are key determinants of the optimal strategy. For a given percentage uptime, sourcing mitigation is increasingly favored over inventory mitigation as disruptions become less frequent but longer. Further, we show that a mixed mitigation strategy (partial sourcing from the reliable supplier and carrying inventory) can be optimal if the unreliable supplier has finite capacity or if the firm is risk averse.

Contingent rerouting is a possible tactic if the reliable supplier can ramp up its processing capacity, that is, if it has volume flexibility. We find that contingent rerouting is often a component of the optimal disruption-management strategy, and that it can significantly reduce the firms costs. For a given percentage uptime, mitigation rather than contingent rerouting tends to be optimal if disruptions are rare.

https://pubsonline.informs.org/doi/suppl/10.1287/mnsc.1060.0515/suppl_file/mnsc.1060.0515-sm-tomlin_companion_5_06.pdf

On the Value of Mitigation and Contingency Strategies for Managing Supply Chain Disruption Risks

Telephone call centers are an integral part of many businesses, and their economic role is significant and growing. They are also fascinating sociotechnical systems in which the behavior of customers and employees is closely intertwined with physical performance measures. In these environments traditional operational models are of great value--and at the same time fundamentally limited--in their ability to characterize system performance.We review the state of research on telephone call centers. We begin with a tutorial on how call centers function and proceed to survey academic research devoted to the management of their operations. We then outline important problems that have not been addressed and identify promising directions for future research.

/pdf/commissioned-paper-telephone-call-centers-tutorial-review-3kz951v5q4.pdf

Commissioned Paper: Telephone Call Centers: Tutorial, Review, and Research Prospects

Preface. 1. Introduction. 2. Observable Queues. 3. Unobservable Queues. 4. Priorities. 5. Reneging and Jockeying. 6. Schedules and Retrials. 7. Competition Among Servers. 8. Service Rate Decisions. Index.

/pdf/to-queue-or-not-to-queue-equilibrium-behavior-in-queueing-5c9jnzn4em.pdf

To Queue or Not to Queue: Equilibrium Behavior in Queueing Systems

We consider dynamic routing and power allocation for a wireless network with time varying channels. The network consists of power constrained nodes which transmit over wireless links with adaptive transmission rates. Packets randomly enter the system at each node and wait in output queues to be transmitted through the network to their destinations. We establish the capacity region of all rate matrices (/spl lambda//sub ij/) that the system can stably support - where (/spl lambda//sub ij/) represents the rate of traffic originating at node i and destined for node j. A joint routing and power allocation policy is developed which stabilizes the system and provides bounded average delay guarantees whenever the input rates are within this capacity region. Such performance holds for general arrival and channel state processes, even if these processes are unknown to the network controller. We then apply this control algorithm to an ad-hoc wireless network where channel variations are due to user mobility, and compare its performance with the Grossglauser-Tse (2001) relay model.

/pdf/dynamic-power-allocation-and-routing-for-time-varying-c9rwdekcfv.pdf

Dynamic power allocation and routing for time varying wireless networks

We consider dynamic routing and power allocation for a wireless network with time-varying channels. The network consists of power constrained nodes that transmit over wireless links with adaptive transmission rates. Packets randomly enter the system at each node and wait in output queues to be transmitted through the network to their destinations. We establish the capacity region of all rate matrices (/spl lambda//sub ij/) that the system can stably support-where /spl lambda//sub ij/ represents the rate of traffic originating at node i and destined for node j. A joint routing and power allocation policy is developed that stabilizes the system and provides bounded average delay guarantees whenever the input rates are within this capacity region. Such performance holds for general arrival and channel state processes, even if these processes are unknown to the network controller. We then apply this control algorithm to an ad hoc wireless network, where channel variations are due to user mobility. Centralized and decentralized implementations are compared, and the stability region of the decentralized algorithm is shown to contain that of the mobile relay strategy developed by Grossglauser and Tse (2002).

Dynamic power allocation and routing for time-varying wireless networks

This article reviews the literature on strategic capacity management concerned with determining the sizes, types, and timing of capacity investments and adjustments under uncertainty. Specific attention is given to recent developments to incorporate multiple decision makers, multiple capacity types, hedging and risk aversion. Capacity is a measure of processing abilities and limitations and is represented as a vector of stocks of various processing resources, while investment is the change of capacity and includes expansion and contraction. After discussing general issues in capacity investment problems, the article reviews models of capacity investment under uncertainty in three settings: The first reviews optimal capacity investment by single and multiple risk-neutral decision makers in a stationary environment where capacity remains constant. Allowing for multiple capacity types, the associated optimal capacity portfolio specifies the amounts and locations of safety capacity in a processing network. Its key feature is that it is unbalanced, i.e., regardless of how uncertainties are realized, one typically will never fully utilize all capacities. The second setting reviews the adjustment of capacity over time and the structure of optimal investment dynamics. The article ends by reviewing how to incorporate risk-aversion in capacity investment and contrasts hedging strategies involving financial versus operational means.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2213357_code560268.pdf?abstractid=2213357&mirid=3

Capacity Management, Investment, and Hedging: Review and Recent Developments

This article studies optimal investment in flexible manufacturing capacity as a function of product prices (margins), investment costs and multivariate demand uncertainty. We consider a two-product firm that has the option to invest in product-dedicated resources and / or in a flexible resource that can produce either product, but has to make its investment decision before demands are observed. The flexible resource provides the firm with a hedge against demand uncertainty, but at a higher investment cost than the dedicated resources. Our analysis highlights the important role of price (margin) and cost mix differentials, which, in addition to the correlation between product demands, significantly affect the investment decision and the value of flexibility. Contrary to the intuition also prevalent in the academic literature, we show that it can be advantageous to invest in flexible resources even with perfectly positively correlated product demands.

Investment Strategies for Flexible Resources

We consider a general single-server multiclass queueing system that incurs a delay cost Ck(Tk) for each class k job that resides Tk units of time in the system. This paper derives a scheduling policy that minimizes the total cumulative delay cost when the system operates during a finite time horizon. Denote the marginal delay cost function and the (possibly non-stationary) average processing time of class k by ck = C'k and 1/uk, respectively, and let ak(t) be the "age" or time that the oldest class k job has been waiting at time t. We call the scheduling policy that at time t serves the oldest waiting job of that class k with the highest index uk(t)ck(ak(t)), the generalized cu rule. As a dynamic priority rule that depends on very little data, the generalized cu rule is attractive to implement. We show that, with nondecreasing convex delay costs, the generalized cu rule is asymptotically optimal if the system operates in heavy traffic and give explicit expressions for the associated performance characteristics: the delay (throughput time) process and the minimum cumulative delay cost. The optimality result is robust in that it holds for a countable number of classes and several homogeneous servers in a nonstationary, deterministic or stochastic environment where arrival and service processes can be general and interdependent.

Dynamic Scheduling with Convex Delay Costs: The Generalized CU Rule

This paper studies how judicious resource allocation in networks mitigates risk. Theory is presented for general utility functions and mean-variance formulations and is illustrated with networks featuring resource diversification, flexibility (e.g., inventory substitution), and sharing (commonality). In contrast to single-resource settings, risk-averse newsvendors may invest more in networks than risk-neutral newsvendors: some resources and even total spending may exceed risk-neutral levels. With normally distributed demand, risk-averse newsvendors change resource levels roughly proportionally to demand variance while risk-neutral agents adjust only proportionally to standard deviation. Two effects explain this operational hedge and suggest rules of thumb for strategic placement of safety capacity and inventory in networks. (1) Risk pooling suggests re-balancing capacity toward inexpensive resources that serve lower profit variance markets. This highlights the role of profit variance (instead of demand variance) in risk-averse network investment. (2) Ex-post revenue maximization suggests re-balancing capacity toward flexible but away from shared capacity when markets differ in profitability. Capacity imbalance and allocation flexibility thus mitigate profit risk and truly are operational hedges.

Risk Mitigation in Newsvendor Networks: Resource Diversification, Flexibility, Sharing, and Hedging

Commonality strategies assemble different products from at least one common component and one other product-specific component. The distinguishing feature of commonality, i.e., the presence of dedicated components to be assembled with a common component, is shown to be mathematically inconsequential in the sense that the unified commonality problem for two products can be reduced to an equivalent substitution flexibility problem without those dedicated components. This significant simplification provides the first general, closed-form condition for commonality adoption and identifies its value drivers. Commonality is optimal even for perfectly correlated demands if products have sufficiently different margins. This introduces the "revenue maximization option" of commonality as a second benefit that is independent of the traditional risk pooling benefit. "Pure commonality" strategies are never optimal unless complexity costs are introduced. Dual sourcing, externalities and operational hedging features of commonality are discussed.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2213307_code560268.pdf?abstractid=2213307&mirid=1&type=2

A Jan

Papers

Capacity Management, Investment, and Hedging: Review and Recent Developments

Investment Strategies for Flexible Resources

Dynamic Scheduling with Convex Delay Costs: The Generalized CU Rule

Risk Mitigation in Newsvendor Networks: Resource Diversification, Flexibility, Sharing, and Hedging

Note - Commonality Strategies: Value Drivers and Equivalence with Flexible Capacity and Inventory Substitution