Esma Senturk Gel

Performance Opportunity for Workforce Agility in Collaborative and Noncollaborative Work Systems

To gain insight into the potential logistical benefits of worker cross-training and agile workforce policies, we study simple models of flexible workers in serial production systems. The primary control issue is how to assign workers to jobs/stations over time. Under assumptions of complete worker flexibility and collaborative work, we prove that a simple expedite policy minimizes along each sample path the cycle time (delay) for each job. Therefore, the expedite policy also minimizes work in process and maximizes throughput along every sample path. We also compute the performance improvement opportunity achievable using flexible workers as opposed to the optimal static allocation of workers. This enables us to examine the factors that make workforce agility a potentially attractive strategy. We also consider the intuitive analog of the expedite policy for the noncollaborative work environment, which we call the pick-and-run policy; however, we demonstrate by counterexample that it is not always optimal. Finally, we extend some of our insights from the demand-constrained environment to a capacity-constrained environment operating under a CONstant WIP (CONWIP) protocol.

Heuristics for workforce planning with worker differences

This study considers decisions in workforce management assuming individual workers are inherently different as measured by general cognitive ability (GCA). A mixed integer programming (MIP) model that determines different staffing decisions (i.e., hire, cross-train, and fire) in order to minimize workforce related costs over multiple periods is described. Solving the MIP for a large problem instance size is computationally burdensome. In this paper, two linear programming (LP) based heuristics and a solution space partition approach are presented to reduce the computational time. A genetic algorithm was also implemented as an alternative method to obtain better solutions and for comparison to the heuristics proposed. The heuristics were applied to realistic manufacturing systems with a large number of machine groups. Experimental results shows that performance of the LP based heuristics performance are surprisingly good and indicate that the heuristics can solve large problem instances effectively with reasonable computational effort.

Using an optimized queueing network model to support wafer fab design

We develop an Optimized Queueing Network (OQNet) capacity planning tool for supporting the design of new and reconfigured semiconductor fabrication facilities that makes use of queueing network approximations and an optimization routine. The basic problem addressed by this tool is to minimize the facility cost required to meet specified volume and cycle time targets. Features common to semiconductor environments, such as batch processes, re-entrant flows, multiple product classes, and machine setups, are incorporated into the model. Comparisons with simulation show that the queueing and other approximations are reasonably accurate. Tests of the optimization routine demonstrate that it can find good solutions quickly.

Modelling inherent worker differences for workforce planning

Most of the literature in the area of workforce planning assumes that workers are identical. This paper considers fundamental decisions in workforce management assuming that workers are inherently different. General Cognitive Ability (GCA) is used as the measure for individual differences. A mixed integer programming model is developed to determine the amount of hiring, firing, and cross-training for each GCA level to minimize total costs, which include training costs, salary costs, firing costs and missed production costs over multiple time periods. Two sets of experiments were developed: (1) to show that the model can be applied to realistic manufacturing systems with large numbers of machine groups, and (2) to study the parameters that affect workforce decisions. Our results indicate that worker differences should be considered when planning and managing the workforce.

Interactive evolutionary multi-objective optimization for quasi-concave preference functions

We present a new hybrid approach to interactive evolutionary multi-objective optimization that uses a partial preference order to act as the fitness function in a customized genetic algorithm. We periodically send solutions to the decision maker (DM) for her evaluation and use the resulting preference information to form preference cones consisting of inferior solutions. The cones allow us to implicitly rank solutions that the DM has not considered. This technique avoids assuming an exact form for the preference function, but does assume that the preference function is quasi-concave. This paper describes the genetic algorithm and demonstrates its performance on the multi-objective knapsack problem.

Factors affecting opportunity of worksharing as a dynamic line balancing mechanism

We consider the problem of optimal worksharing between two adjacent workers each of whom processes a fixed task in addition to their shared task(s). We use a Markov Decision Process (MDP) model to compute optimal policies and provide a benchmark for evaluating threshold policy heuristics. Our approach differs from previous studies of dynamic line balancing in that we focus on system architecture factors that affect the performance improvement opportunity possible through worksharing relative to a traditional static worker allocations, as well as practical heuristics for worksharing. We find that three such factors are significant whether we use an optimal or a heuristic control policy: ability to preempt the shared task, granularity of the shared task and overall variability of the task times. Understanding the role of these factors in a given production environment provides a means for determining where and how worksharing can have significant logistical benefits.

Bucket brigades with worker learning

Abstract The dynamics and throughput of a bucket brigade production system is studied when workers’ speeds increase due to learning. It is shown that, if the rules of the bucket brigade system allow a re-ordering of its workers then the bucket brigade production system is very robust and will typically rebalance to a self-organizing optimal production arrangement. As workers learn only those parts of the production line that they work on, the stationary velocity distribution for the workers of a stable bucket brigade is non-uniform over the production line. Hence, depending on the initial placement of the workers, there are many different stationary velocity distributions. It is shown that all the stationary distributions lead to the same throughput.

Quantitative Comparison of Approximate Solution Sets for Bi-criteria Optimization Problems*

We present the Integrated Preference Functional (IPF) for comparing the quality of proposed sets of near-pareto-optimal solutions to bi-criteria optimization problems. Evaluating the quality of such solution sets is one of the key issues in developing and comparing heuristics for multiple objective combinatorial optimization problems. The IPF is a set functional that, given a weight density function provided by a decision maker and a discrete set of solutions for a particular problem, assigns a numerical value to that solution set. This value can be used to compare the quality of different sets of solutions, and therefore provides a robust, quantitative approach for comparing different heuristic, a posteriori solution procedures for difficult multiple objective optimization problems. We provide specific examples of decision maker preference functions and illustrate the calculation of the resulting IPF for specific solution sets and a simple family of combined objectives.

Web server QoS models: applying scheduling rules from production planning

Most web servers, in practical use, use a queuing policy based on the Best Effort model, which employs the first-in-first-out (FIFO) scheduling rule to prioritize web requests in a single queue. This model does not provide Quality of Service (QoS). In the Differentiated Services (DiffServ) model, separate queues are introduced to differentiate QoS for separate web requests with different priorities. This paper presents web server QoS models that use a single queue, along with scheduling rules from production planning in the manufacturing domain, to differentiate QoS for classes of web service requests with different priorities. These scheduling rules are Weighted Shortest Processing Time (WSPT), Apparent Tardiness Cost (ATC), and Earliest Due Date. We conduct simulation experiments and compare the QoS performance of these scheduling rules with the FIFO scheme used in the basic Best Effort model with only one queue, and the basic DiffServ model with two separate queues. Simulation results demonstrate better QoS performance using WSPT and ATC, especially when requested services exceed the capacity of a web server.

Bucket brigades revisited: Are they always effective?

Previous work on the dynamics of bucket brigades has focused on systems in which workers can be ordered with respect to their speeds and where this ordering does not change throughout the line. While this assumption is valid in most environments, it may not be satisfied in some. We consider such environments and explore the conditions under which bucket brigades continue to be effective (compared to a traditional static allocation) with respect to self-balancing behavior and throughput performance. A two worker bucket brigade is studied where one worker is faster than the other over some part of the production line and slower over another part of the line. We analyze the dynamics and throughput of the bucket brigade in two environments with passing and blocking. We present the dynamics of the system in each region of the parameter space and provide insights and operating principles for the implementation and management of the bucket brigades under various scenarios.