Adopting a make-to-order (MTO) production mode allows manufacturers to accommodate a wider variety of customer requirements without a prohibitive increase in inventory of finished products. Since MTO production involves a wide variety of process features, a resource plan is necessary to coordinate production of customer orders so that resources are used efficiently and customer order due dates are met.

This dissertation develops optimal and heuristic methods that embed characteristics of the just-in-time (JIT) philosophy to create resource plans for MTO environments. JIT is a well-known productivity concept in which jobs are attempted to be started near to and finished on their due dates in order to reduce work in process (WIP), inventory, lead time, and cost. In the JIT philosophy, an ideal plan for a single order would have zero queue time, zero earliness, and zero lateness. The methods remain cognizant of the ideal plan for an order as they make adjustments to the actual plan for each order that are necessary to accommodate resource constraints.

A new binary integer linear programming (BILP) model is formulated to solve resource planning problems in MTO environments. The objective function contains weighted costs for earliness, tardiness, lead time, and subcontractor capacity. The initial solution is generated using the ideal plan for each order. Extensive computational results show that this initialization method often reduces computational time such that the BILP model can reach the optimal solution within an acceptable amount of time when it otherwise could not.

Due to the extremely limited scalability of optimal methods, which renders them inappropriate for most realistic make-to-order environments, a heuristic method utilizing tabu search is developed to solve resource planning problems. This is a two-phase algorithm. Like the optimal method, the tabu search algorithm in the first phase also generates an initial solution using the ideal plan for each order, and it then creates a finite capacity plan. It furthermore remains cognizant of the ideal plan in the second phase as it searches for solutions that respect resource constraints but that have good performance in terms of order earliness, tardiness, and lead time. A benchmark study of the developed algorithm reveals that the tabu search algorithm provides better solutions in terms of problem scalability and solution quality than other methods including the BILP optimization approach and other heuristic approaches such as FIFO or EDD.