Flexible Servers in Understaffed Tandem Lines

We study the dynamic assignment of cross‐trained servers to stations in understaffed lines with finite buffers. Our objective is to maximize the production rate. We identify optimal server assignment policies for systems with three stations, two servers, different flexibility structures, and either deterministic service times and arbitrary buffers or exponential service times and small buffers. We use these policies to develop server assignment heuristics for Markovian systems with larger buffer sizes that appear to yield near‐optimal throughput. In the deterministic setting, we prove that the best possible production rate with full server flexibility and infinite buffers can be attained with partial flexibility and zero buffers, and we identify the critical skills required to achieve this goal. We then present numerical results showing that these critical skills, employed with an effective server assignment policy, also yield near‐optimal throughput in the Markovian setting, even for small buffer sizes. Thus, our results suggest that partial flexibility is sufficient for near‐optimal performance, and that flexibility structures that are effective for deterministic and infinite‐buffered systems are also likely to perform well for finite‐buffered stochastic systems.     

Approximate dynamic programming for the aeromedical evacuation dispatching problem: Value function approximation utilizing multiple level aggregation

Sequential resource allocation decision-making for the military medical evacuation of wartime casualties consists of identifying which available aeromedical evacuation (MEDEVAC) assets to dispatch in response to each casualty event. These sequential decisions are complicated due to uncertainty in casualty demand (i.e., severity, number, and location) and service times. In this research, we present a Markov decision process model solved using a hierarchical aggregation value function approximation scheme within an approximate policy iteration algorithmic framework. The model seeks to optimize this sequential resource allocation decision under uncertainty of how to best dispatch MEDEVAC assets to calls for service. The policies determined via our approximate dynamic programming (ADP) approach are compared to optimal military MEDEVAC dispatching policies for two small-scale problem instances and are compared to a closest-available MEDEVAC dispatching policy that is typically implemented in practice for a large-scale problem instance. Results indicate that our proposed approximation scheme provides high-quality, scalable dispatching policies that are more easily employed by military medical planners in the field. The identified ADP policies attain 99.8% and 99.5% optimal for the 6- and 12-zone problem instances investigated, as well as 9.6%, 9.2%, and 12.4% improvement over the closest-MEDEVAC policy for the 6-, 12-, and 34-zone problem instances investigated.     

Sequential and Simultaneous Decision Making for Optimizing Health Care Resource Flexibilities

Health care administrators commonly employ two types of resource flexibilities (demand upgrades and staffing flexibility) to efficiently coordinate two critical internal resources, nursing staff and beds, and an external resource (contract nurses) to satisfy stochastic patient demand. Under demand upgrades, when beds are unavailable for patients in a less acute unit, patients are upgraded to a more acute unit if space is available in that unit. Under staffing flexibility, nurses cross‐trained to work in more than one unit are used in addition to dedicated and contract nurses. Resource decisions (beds and staffing) can be made at a single point in time (simultaneous decision making) or at different points in time (sequential decision making). In this article, we address the following questions: for each flexibility configuration, under sequential and simultaneous decision making, what is the optimal resource level required to meet stochastic demand at minimum cost? Is one type of flexibility (e.g., demand upgrades) better than the other type of flexibility (e.g., staffing flexibility)? We use two‐stage stochastic programming to find optimal resource levels for two nonhomogeneous hospital units that face stochastic demand following a continuous, general distribution. We conduct a full‐factorial numerical experiment and find that the benefit of using staffing flexibility on average is greater than the benefit of using demand upgrades. However, the two types of flexibilities have a positive interaction effect and they complement each other. The type of flexibility and decision timing has an independent effect on system performance (capacity and staffing costs). The benefits of cross‐training can be largely realized even if beds and staffing levels have been determined prior to the establishment of a cross‐training initiative.     

Static and Dynamic Pricing of Excess Capacity in a Make‐to‐Order Environment

We consider a make‐to‐order manufacturer that serves two customer classes: core customers who pay a fixed negotiated price, and “fill‐in” customers who make submittal decisions based on the current price set by the firm. Using a Markovian queueing model, we determine how much the firm can gain by explicitly accounting for the status of its production facility in making pricing decisions. Specifically, we examine three pricing policies: (1) static, state‐independent pricing, (2) constant pricing up to a cutoff state, and (3) general state‐dependent pricing. We determine properties of each policy, and illustrate numerically the financial gains that the firm can achieve by following each policy as compared with simpler policies. Our main result is that constant pricing up to a cutoff state can dramatically outperform a state‐independent policy, while at the same time achieving most of the increase in revenue achievable from general state‐dependent pricing. Thus, we find that constant pricing up to a cutoff state presents an attractive tradeoff between ease of implementation and revenue gain. When the costs of policy design and implementation are taken into account, this simple heuristic may actually out‐perform general state‐dependent pricing in some settings.     

Pricing,Allocation, and Overbooking in Dynamic Service Network Competition When Demand Is Uncertain

We study the problem of combined pricing, resource allocation, and overbooking by service providers involved in dynamic noncooperative oligopolistic competition on a network that represents the relationships of the providers to one another and to their customers when service demand is uncertain. We propose, analyze, and compute solutions for a model that is more general than other models reported in the revenue management literature to date. In particular, previous models typically consider only three or four of five key revenue management features that we have purposely built into our model: (1) pricing, (2) resource allocation, (3) dynamic competition, (4) an explicit network, and (5) uncertain demand. Illustrative realizations of the abstract problem we study are those of airline revenue management and service provision by companies facing resource constraints. Under fairly general regularity conditions, we prove existence and uniqueness of a pure strategy Nash equilibrium for dynamic oligopolistic service network competition described by our model. We also show, for an appropriate notion of regularity, that competition leads to the underpricing of network services, a finding numerically illustrated by an example of intermediate size. Our proposed algorithm can be implemented using well‐known off‐the‐shelf commercial software.     

Flexibility Structure and Capacity Design with Human Resource Considerations

Most service systems consist of multidepartmental structures with multiskill agents that can deal with several types of service requests. The design of flexibility in terms of agents' skill sets and assignments of requests is a critical issue for such systems. The objective of this study was to identify preferred flexibility structures when demand is random and capacity is finite. We compare structures recommended by the flexibility literature to structures we observe in practice within call centers. To enable a comparison of flexibility structures under optimal capacity, the capacity optimization problem for this setting is formulated as a two‐stage stochastic optimization problem. A simulation‐based optimization procedure for this problem using sample‐path gradient estimation is proposed and tested, and used in the subsequent comparison of the flexibility structures being studied. The analysis illustrates under what conditions on demand, cost, and human resource considerations, the structures found in practice are preferred.     

Intelligent Procedures for Intra‐Day Updating of Call Center Agent Schedules

For nearly all call centers, agent schedules are typically created several days or weeks before the time that agents report to work. After schedules are created, call center resource managers receive additional information that can affect forecasted workload and resource availability. In particular, there is significant evidence, both among practitioners and in the research literature, suggesting that actual call arrival volumes early in a scheduling period (typically an individual day or week) can provide valuable information about the call arrival pattern later in the same scheduling period. In this paper, we develop a flexible and powerful heuristic framework for managers to make intra‐day resource adjustment decisions that take into account updated call forecasts, updated agent requirements, existing agent schedules, agents' schedule flexibility, and associated incremental labor costs. We demonstrate the value of this methodology in managing the trade‐off between labor costs and service levels to best meet variable rates of demand for service, using data from an actual call center.     

Hierarchical Multi‐skill Resource Assignment in the Telecommunications Industry

We formulate a discrete time Markov decision process for a resource assignment problem for multi‐skilled resources with a hierarchical skill structure to minimize the average penalty and waiting costs for jobs with different waiting costs and uncertain service times. In contrast to most queueing models, our application leads to service times that are known before the job is actually served but only after it is accepted and assigned to a server. We formulate the corresponding Markov decision process, which is intractable for problems of realistic size due to the curse of dimensionality. Using an affine approximation of the bias function, we develop a simple linear program that yields a lower bound for the minimum average costs. We suggest how the solution of the linear program can be used in a simple heuristic and illustrate its performance in numerical examples and a case study.     

Static-dynamic uncertainty strategy for a single-item stochastic inventory control problem

We consider a single-stage inventory system facing non-stationary stochastic demand of the customers in a finite planning horizon. Motivated by the practice, the replenishment times need to be determined and frozen once and for all at the beginning of the horizon while decisions on the exact replenishment quantities can be deferred until the replenishment time. This operating scheme is refereed to as a “static-dynamic uncertainty” strategy in the literature [3]. We consider dynamic fixed-ordering and linear end-of-period holding costs, as well as dynamic penalty costs, or service levels. We prove that the optimal ordering policy is a base stock policy for both penalty cost and service level constrained models. Since an exponential exhaustive search based on dynamic programming yields the optimal ordering periods and the associated base stock levels, it is not possible to compute the optimal policy parameters for longer planning horizons. Thus, we develop two heuristics. Numerical experiments show that both heuristics perform well in terms of solution quality and scale-up efficiently; hence, any practically relevant large instance can be solved in reasonable time. Finally, we discuss how our results and heuristics can be extended to handle capacity limitations and minimum order quantity considerations.     

基于EMSR方法的订单接受策略研究

选择性的订单接受策略对于按订单生产的制造型企业具有重要意义。本文在单一资源的情况下,采用收益管理中常用的期望边际座位收益EMSR-a和EMSR-b方法,分别得出了利润最大化的订单接受策略。然后,把这两种策略运用到一个算例,与FCFS策略进行比较。数据结果显示在企业利润方面,这两种策略都明显优于FCFS,利润均提高20%以上。     

Designing Efficient Infrastructural Investment and Asset Transfer Mechanisms in Humanitarian Supply Chains

We analyze the efficacy of different asset transfer mechanisms and provide policy recommendations for the design of humanitarian supply chains. As a part of their preparedness effort, humanitarian organizations often make decisions on resource investments ex ante because doing so allows for rapid response if an adverse event occurs. However, programs typically operate under funding constraints and donor earmarks with autonomous decision‐making authority resting with the local entities, which makes the design of efficient humanitarian supply chains a challenging problem. We formulate this problem in an agency setting with two independent aid programs, where different asset transfer mechanisms are considered and where investments in resources are of two types: primary resources that are needed for providing the aid and infrastructural investments that improve the operation of the aid program in using the primary resources. The primary resources are acquired from earmarked donations. We show that allowing aid programs the flexibility of transferring primary resources improves the efficiency of the system by yielding greater social welfare than when this flexibility does not exist. More importantly, we show that a central entity that can acquire primary resources from one program and sell them to the other program can further improve system efficiency by providing a mechanism that facilitates the transfer of primary resources and eliminates losses from gaming. This outcome is achieved without depriving the individual aid programs of their decision‐making autonomy while maintaining the constraints under which they operate. We find that outcomes with centralized resource transfer but decentralized infrastructural investments by the aid programs are the same as with a completely centralized system (where both resource transfer and infrastructural investments are centralized).     

Information‐Sensitive Replenishment when Inventory Records Are Inaccurate

Inspired by recent empirical work on inventory record inaccuracy, we consider a periodic review inventory system with imperfect inventory records and unobserved lost sales. Record inaccuracies are assumed to arrive via an error process that perturbs physical inventory but is unobserved by the inventory manager. The inventory manager maintains a probability distribution around the physical inventory level that he updates based on sales observations using Bayes Theorem. The focus of this study is on understanding, approximating, and evaluating optimal forward‐looking replenishment in this environment. By analyzing one‐ and two‐period versions of the problem, we demonstrate several mechanisms by which the error process and associated record inaccuracy can impact optimal replenishment. Record inaccuracy generally brings an incentive for a myopic manager to increase stock to buffer the added uncertainty. On the other hand, a forward‐looking manager will stock less than a myopic manager, in part to improve information content for future decisions. Using an approximate partially observed dynamic programming policy and associated bound, we numerically corroborate our analytical findings and measure the effectiveness of an intelligent myopic heuristic. We find that the myopic heuristic is likely sufficiently good in practical settings targeting high service levels.     

Optimal Policies for a Two‐Product Inventory System under a Flexible Substitution Scheme

We study a two‐product inventory model that allows substitution. Both products can be used to supply demand over a selling season of N periods, with a one‐time replenishment opportunity at the beginning of the season. A substitution could be offered even when the demanded product is available. The substitution rule is flexible in the sense that the seller can choose whether or not to offer substitution and at what price or discount level, and the customer may or may not accept the offer, with the acceptance probability being a decreasing function of the substitution price. The decisions are the replenishment quantities at the beginning of the season, and the dynamic substitution‐pricing policy in each period of the season. Using a stochastic dynamic programming approach, we present a complete solution to the problem. Furthermore, we show that the objective function is concave and submodular in the inventory levels—structural properties that facilitate the solution procedure and help identify threshold policies for the optimal substitution/pricing decisions. Finally, with a state transformation, we also show that the objective function is ‐concave, which allows us to derive similar structural properties of the optimal policy for multiple‐season problems.     

Cellular Bucket Brigades on U‐Lines with Discrete Work Stations

It is challenging to maximize and maintain productivity of a U‐line with discrete stations under the impact of variability. This is because maximizing productivity requires assigning workers to suitable tasks and maintaining productivity requires sufficient flexibility in task assignment to absorb the impact of variability. To achieve this goal, we propose an operating protocol to coordinate workers on the U‐line. Under the protocol the system can be configured such that its productivity is maximized. Workers are allowed to dynamically share work so that the system can effectively absorb the impact of variability. Analysis based on a deterministic model shows that the system always converges to a fixed point or a period‐2 orbit. We identify a sufficient condition for the system to converge to the fixed point. Increasing the number of stations improves productivity only under certain circumstances. The improvement is most significant when the number of stations in each stage increases from one to two, but further dividing the U‐line into more stations has diminishing return. Simulations based on random work velocities suggest that our approach significantly outperforms an optimized, static work allocation policy if variability in velocity is large.     

Cross‐training Decisions in Field Services with Three Job Types and Server–Job Mismatch*

To be cost‐effective, field service managers must balance the high cost of machine downtime with the high cost of cross‐training technicians in multiple skills. We study a field service system with three job types requiring three different skills. Each server has a primary skill, the cost of which is considered sunk, and up to two secondary skills, which is a managerial decision. We model two important characteristics that distinguish field services: server–job mismatch and the ratio of travel time to service time. We use a queueing framework and simulation to study three cross‐training decisions: the number of servers cross‐trained in secondary skills, the number of secondary skills each server should have, and the efficiency in each secondary skill. We find that complete cross‐training is cost‐effective in some field service situations. Typically, efficiency in secondary skills must be close to 100%, but when the probability of mismatch is high and the ratio of travel time to service time is high, efficiency in secondary skills must be less than 100%.     

A Comparison of Due Date Setting,Resource Assignment,and Job Preemption Heuristics for the Multiproject Scheduling Problem*

This research examines a heuristic, rule-based approach for setting due dates in a multiproject, multijob, or assembly shop. Due date estimation is a challenging problem because the operating environment is capacitated, involves the allocation of multiple resources, and allows for the preemption of resources from one project or job to another. The dynamic, continuous arrival of new jobs or orders frequently results in the preemption of resources through the application of managerially determined priority policies. These preemption policies have a significant impact on the ultimate completion time of a job or a project. A three-factor, full-factorial computer simulation experiment is used to assess the relative effectiveness of combinations of four due date setting heuristics, five resource assignment heuristics, and three resource preemption heuristics. Recommendations are made for the selection of due date and resource assignment heuristic combinations under the three preemption policies examined.     

Capacity Allocation and Pricing Strategies for New Wireless Services

Indoor cell phone users often suffer poor connectivity. One promising solution to this issue, femtocell technology, has been rapidly developed and deployed over the past few years. One of the biggest challenges facing femtocell deployment is the lack of a clear business model. This study investigates the economic incentive for cellular operators (also called macrocell operators) to enable femtocell service by leasing spectrum resources to independent femtocell operators. We model the interactions between a macrocell operator, a femtocell operator, and end‐users as a three‐stage dynamic game, and derive the equilibrium pricing and capacity allocation decisions. We show that when spectrum resources are very limited, the macrocell operator has more incentive to lease spectrum to the femtocell operator, as femtocell services can help cover more users and improve the utilization efficiency of the limited spectrum resource. However, when the total spectrum resource is large, femtocell service offers significant competition to macrocell service and, as a result, the macrocell operator has less incentive to enable femtocell service. We also show the impact of the additional operational costs and limited coverage of femtocell service on equilibrium decisions, consumer surplus, and social welfare.     

Focusing on Customer Time in Field Service: A Normative Approach

Although customer convenience should be rightfully considered a central element in field services, the customer experience suggests that service enterprises rarely take the customer's preferred time into account in making operational and scheduling decisions. In this paper we present the results of our exploratory research into two interrelated topics: the explicit inclusion of customer time in nonemergency field service delivery decisions and the analysis of trade‐off between the customer's convenience and field service provider's cost. Based on prior research in service quality we identify and illustrate two time‐based performance metrics that are particularly appropriate for assessing service quality in nonemergency field services: performance and conformance quality. To determine vehicle routes, we develop a hybrid heuristic derived from the existing and proven heuristic methods. A numerical example closely patterned after real‐life data is generated and used within a computational experiment to investigate alternate policies for promise time windows. Our experiment shows that over a reasonable range of customer cost parameters the policy of shorter promise time windows reduces the combined total cost incurred by the provider and the customers and should be considered a preferred policy by the field service provider. Managerial implications of this result are discussed.     

Supplier Selection for Framework Agreements in Humanitarian Relief

In this study, we consider the supplier selection problem of a relief organization that wants to establish framework agreements (FAs) with a number of suppliers to ensure quick and cost‐effective procurement of relief supplies in responding to sudden‐onset disasters. Motivated by the FAs in relief practice, we focus on a quantity flexibility contract in which the relief organization commits to purchase a minimum total quantity from each framework supplier over a fixed agreement horizon, and, in return, the suppliers reserve capacity for the organization and promise to deliver items according to pre‐specified agreement terms. Due to the uncertainties in demand locations and amounts, it may be challenging for relief organizations to assess candidate suppliers and the offered agreement terms. We use a scenario‐based approach to represent demand uncertainty and develop a stochastic programming model that selects framework suppliers to minimize expected procurement and agreement costs while meeting service requirements. We perform numerical experiments to understand the implications of agreement terms in different settings. The results show that supplier selection decisions and costs are generally more sensitive to the changes in agreement terms in settings with high‐impact disasters. Finally, we illustrate the applicability of our model on a case study.