DEVELOPING METHODS TO SOLVE THE WORKFORCE ASSIGNMENT PROBLEM CONSIDERING WORKER HETEROGENEITY AND LEARNING AND FORGETTINGDEVELOPING METHODS TO SOLVE THE WORKFORCE ASSIGNMENT PROBLEM CONSIDERING WORKER HETEROGENEITY AND LEARNING AND FORGETTING

In this research we study how the assignment of a fully cross-trained workforce organized on a serial production line affect throughput. We focus on two serial production environments: dynamic worksharing on a production line, similar to bucket brigade systems and a fixed assignment serial-production line where workers work on a specific task during a given time period. For the dynamic assignment environment we concentrated on the impact of different assignment approaches and policies on the overall system performance. First, we studied two worker two station lines when incomplete dominance is possible as well as the effects of duplicating tooling at these lines. One focus of this research was to optimally solve the dynamic worksharing assignment problem and determine exact percentages of work performed by each worker under the assumptions presented. We developed a mixed integer programming formulation for n workers and m stations that models one-cycle balanced line behavior where workers exchange parts at exactly one position. This formulation is extended to incorporate multiple production lines. We also developed a two-cycle formulation that models a condition when workers exchange parts at exactly two positions in a periodic manner. We also determined throughput levels when workers productivity changes over time due to workers' learning and forgetting characteristics.A fixed worker assignment system considers a serial production setting in which work is passed from station to station with intermediate buffers between stations. We considered two models. The first model assumes that workers perform tasks based on their steady-state productivity rate. The second model assumes that workers' productivity rates vary based on their learning and forgetting characteristics. Heuristic methods were developed and implemented to solve these two models and to determine optimal throughput levels and optimal worker assignments. We were also able to demonstrate the importance of introducing learning and forgetting into these types of worker assignment problems. A final focus of this research was the comparison of the dynamic worksharing and fixed worker assignment environments