This thesis addresses two distinct problems in facility design and scheduling for manufacturing firms operating in volatile environments and producing multiple batches of products. In regards to the facility layout problem, a new comprehensive mathematical model that integrates layout configuration and production planning in the design of dynamic distributed layouts is formulated. The model incorporates a number of important manufacturing attributes such as demand fluctuation, system reconfiguration, lot splitting, work load balancing, alternative routings, machine capability and tooling requirements. In addition, the model allows the optimization of several cost elements in an integrated manner. These include material handling, machine relocation, setup, inventory carrying, in-house production and subcontracting costs. With respect to the scheduling problem, a mathematical formulation for scheduling of manufacturing systems with distributed layouts is developed. The objective of scheduling model is the minimization of the weighted sum of makespan and total traveling distance by the products. Thus on one hand, the problem is to find a schedule of operations on machines (the sequence and starting times of the various operations) which minimizes the overall finishing time or makespan. On the other hand, the problem is to find assignment of jobs to the machines such that total distance traveled by parts is minimized.

Optimal solutions for the proposed mathematical models can only be found for small size problems due to NP-complexity. To solve both models for larger-size problems, two hybrid metaheuristics for solving the facility design model and a genetic algorithm for the scheduling model have been developed. All proposed algorithms are thoroughly examined with an emphasis on solution convergence, solution quality and algorithm robustness. For both cases, we provide numerical results to support various managerial insights. In particular in facility design problem, we draw a managerial insight as to how high product variety and high volatility in the production environment can be accommodated without harm to operational efficiency or cost. Similarly in the scheduling study, we show that linking scheduling and material handling performance can contribute to the development of accurate models to obtain a schedule that can also greatly enhance system performance.