Incentive effects of common and separate queues with multiple servers: The principal-agent perspective

A two-server service network has been studied by Gilbert and Weng [13] fromthe principal-agent perspective. In the model, services are rendered by twoindependent facilities coordinated by an agency. The agency must devise astrategy to allocate customers to the facilities and determine the compensation.A common queue allocation scheme and separate queue allocation scheme are thencompared. It has been shown that the separate queue system gives morecompetition incentives to the independent facilities and induces a higherservice capacity. The main aim of this paper is to extend the results of thetwo-server queueing model to the case of multiple-server queueing model. Ouranalysis shows that in the case of multiple servers the separate queueallocation scheme creates more competition incentives for servers to increasetheir service capacities. In particular, when there are not severe diseconomiesassociated with increasing service capacity, the separate queue allocationscheme gives a lower expected sojourn time in equilibrium. © 2009 IEEE.published_or_final_versionProceedings of the 39th International Conference on Computers and Industrial Engineering (CIE39), Troyes, France, 6-8 July 2009, p. 1249-125

Design and control of agile automated CONWIP production lines

In this article, we study the design and control of manufacturing cells with a mix of manual and automated equipment, operating under a CONWIP pull protocol, and staffed by a single agile (cross-trained) worker. For a three-station line with one automated station, we fully characterize the structure of the optimal control policy for the worker and show that it is a static priority policy. Using analytical models and extensive simulation experiments, we also evaluate the effectiveness of practical heuristic control policies and provide managerial insights on automation configuration design of the line. This characterization of the worker control policy enables us to develop managerial insights into the design issues of how best to locate and concentrate automation in the line. Finally, we show that, in addition to ease of control and greater design flexibility, the CONWIP protocol also offers higher efficiency and robustness than does the push protocol. © 2008 Wiley Periodicals, Inc. Naval Research Logistics 2009Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/61525/1/20325_ftp.pd

Maximizing throughput in zero-buffer tandem lines with dedicated and flexible servers

Abstract For tandem queues with no buffer spaces and both dedicated and flexible servers, we study how flexible servers should be assigned to maximize the throughput. When there is one flexible server and two stations each with a dedicated server, we completely characterize the optimal policy. We use the insights gained from applying the Policy Iteration algorithm on systems with three, four, and five stations to devise heuristics for systems of arbitrary size. These heuristics are verified by numerical analysis. We also discuss the throughput improvement, when for a given server assignment, dedicated servers are changed to flexible servers

Server Assignment Policies for Maximizing the Steady-State Throughput of Finite Queueing Systems

For a system of finite queues, we study how servers should be assigned dynamically to stations in order to obtain optimal (or near-optimal) long-run average throughput. We assume that travel times between different service facilities are negligible, that each server can work on only one job at a time, and that several servers can work together on one job. We show that when the service rates depend only on either the server or the station (and not both), then all nonidling server assignment policies are optimal. Moreover, for a Markovian system with two stations in tandem and two servers, we show that the optimal policy assigns one server to each station unless that station is blocked or starved (in which case the server helps at the other station), and we specify the criterion used for assigning servers to stations. Finally, we propose a simple server assignment policy for tandem systems in which the number of stations equals the number of servers, and we present numerical results that show that our policy appears to yield near-optimal throughput under general conditions.Markov Decision Processes, Markovian Queueing Systems, Tandem Queues, Finite Buffers, Mobile and Cooperating Servers, Preemptive Service, Manufacturing Blocking

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.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47647/1/11134_2005_Article_2441.pd

Optimal Design and Control of Finite-Population Queueing Systems

We consider a service system with a finite population of customers (or jobs) and a service resource with finite capacity. We model this finite-population queueing system by a closed queueing network with two stages. The first stage, which represents the arrivals of customers for service, consists of an automated station with ample capacity. The second stage, which represents the service for customers, consists of multiple service stations which share the finite service resource. We consider both discrete and continuous service resources. We are interested in static or dynamic allocation of the service resource to the service stations in the second stage in order to optimize a given system measure. Specifically, a static allocation refers to a design problem, while a dynamic allocation refers to a control problem. In this thesis, we study both. For control problems, we specify a parallel-series structure for service stations. We first consider dynamically allocating a single flexible server under both preemptive and non-preemptive policies. We characterize the optimal policies of dynamically scheduling this single server in order to maximize the long-run average throughput of the system. In the special case of a series system, we show that the optimal policy is a sequential policy where each customer is served by the single server sequentially from the first station until the last one. For a parallel system, we show that there exists an optimal policy which gives the highest priority to the station that has the largest service rate. We also propose an index policy heuristic for the general parallel-series system and compare its performance as opposed to the optimal policy by a numerical study. Finally, we study dynamically allocating a finite amount of continuous service resource for the parallel system. For design problems, we consider allocating a finite amount of service resource which is continuously divisible and can be used at any of the service stations. Suppose that service times at a service station are exponentially distributed and their mean is a strictly increasing and concave function of the allocated service resource. We characterize the optimal allocation of the continuous resource in order to maximize the long-run average throughput of the system. We first show that the system throughput is non-decreasing in the number of customers. Then, we study the optimization problem in three cases depending on the population size of customers in the system. First, when there is a single customer, we show that the optimal allocation is given by a set of optimality equations. Secondly, when the number of customers approaches infinity, we show that the optimal allocation approaches to a limit. Finally, for any finite number of customers, we show that the system throughput is bounded up by a limit. Moreover, under a certain condition, we show that the system throughput function is Schur-concave.Doctor of Philosoph

Enabling flexibility through strategic management of complex engineering systems

”Flexibility is a highly desired attribute of many systems operating in changing or uncertain conditions. It is a common theme in complex systems to identify where flexibility is generated within a system and how to model the processes needed to maintain and sustain flexibility. The key research question that is addressed is: how do we create a new definition of workforce flexibility within a human-technology-artificial intelligence environment? Workforce flexibility is the management of organizational labor capacities and capabilities in operational environments using a broad and diffuse set of tools and approaches to mitigate system imbalances caused by uncertainties or changes. We establish a baseline reference for managers to use in choosing flexibility methods for specific applications and we determine the scope and effectiveness of these traditional flexibility methods. The unique contributions of this research are: a) a new definition of workforce flexibility for a human-technology work environment versus traditional definitions; b) using a system of systems (SoS) approach to create and sustain that flexibility; and c) applying a coordinating strategy for optimal workforce flexibility within the human- technology framework. This dissertation research fills the gap of how we can model flexibility using SoS engineering to show where flexibility emerges and what strategies a manager can use to manage flexibility within this technology construct”--Abstract, page iii

Simulation Optimization for Manufacturing System Design

A manufacturing system characterized by its stochastic nature, is defined by both qualitative and quantitative variables. Often there exists a situation when a performance measure such as throughput, work-in-process or cycle time of the system needs to be optimized with respect to some decision variables. It is generally convenient to express a manufacturing system in the form of an analytical model, to get the solutions as quickly as possible. However, as the complexity of the system increases, it gets more and more difficult to accommodate that complexity into the analytical model due to the uncertainty involved. In such situations, we resort to simulation modeling as an effective alternative.Equipment selection forms a separate class of problems in the domain of manufacturing systems. It assumes a high significance for capital-intensive industry, especially the semiconductor industry whose equipment cost comprises a significant amount of the total budget spent. For semiconductor wafer fabs that incorporate complex product flows of multiple product families, a reduction in the cycle time through the choice of appropriate equipment could result in significant profits. This thesis focuses on the equipment selection problem, which selects tools for the workstations with a choice of different tool types at each workstation. The objective is to minimize the average cycle time of a wafer lot in a semiconductor fab, subject to throughput and budget constraints. To solve the problem, we implement five simulation-based algorithms and an analytical algorithm. The simulation-based algorithms include the hill climbing algorithm, two gradient-based algorithms biggest leap and safer leap, and two versions of the nested partitions algorithm. We compare the performance of the simulation-based algorithms against that of the analytical algorithm and discuss the advantages of prior knowledge of the problem structure for the selection of a suitable algorithm

Investigations into Flexible Operational Paradigms to Mitigate Variability.

The work of this dissertation is concerned with the study of the effectiveness of paradigms of production flexibility to either improve system performance or mitigate system risk. A brief introduction to the concept of operational flexibility is provided in Chapter I. In Chapter II, we consider a cross-trained workforce on a serial production line, and we introduce a new strategy of worker cross training called a “fixed task zone chain” (FTZC) as a special type of zone based cross training. This new approach seeks to maximize the performance of a production line, in the same fashion as a standard two skill chain, but with a significant reduction in the number of skills that must be cross trained. This allows a firm to maintain nearly the same levels of throughput, but at a fraction of the cross-training and implementation costs. Chapter III targets our study of operational flexibility on the field of supply chain management. A flexible supply chain design is useful to mitigate the effect of stochastic supplier disruptions on operations and, especially, financial cash flows. The work of this chapter develops mitigation strategies for a firm to use in sourcing from flexible suppliers and demonstrates the conditions under which flexibility in the firm’s supply chain is necessary. Finally, to assist with the understanding of the flexibility paradigms, we have designed an instrument to promote the understanding of flexibility with respect to cross-training as well as to assist in its implementation. That is, we develop a hands on, active learning experience placing participants “on the job” in a serial production line of cross-trained workers where participants can: 1) learn basic concepts of operations management, production control, and workforce agility; 2) understand system responsiveness and what can be done to improve it; 3) generate creative thinking and discussion on the value of flexibility; and 4) experience first-hand foundational factory physics concepts like cycle time, throughput, and Work In Process (WIP).Ph.D.Industrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64773/1/dpwillia_1.pd