In this work, we address a variety of problems with applications to ethanol production from biomass', agile manufacturing' and mass customization' domains. Our motivation stems from the potential use of biomass as an alternative to non-renewable fuels, the prevalence of flexible manufacturing systems', and the popularity of `mass customization' in today's highly competitive markets. Production scheduling and design and optimization of logistics network mark the underlying topics of our work. In particular, we address three problems, Biomass Logistics Problem, Hybrid Flow Shop Scheduling Problem, and Stochastic Demand Assembly Job Scheduling Problem.

The Biomass Logistics Problem is a strategic cost analysis for setup and operation of a biomass supply chain network that is aimed at the production of ethanol from switchgrass. We discuss the structural components and operations for such a network. We incorporate real-life GIS data of a geographical region in a model that captures this problem. Consequently, we develop and demonstrate the effectiveness of a `Nested Benders' based algorithm for an efficient solution to this problem.

The Hybrid Flow Shop Scheduling Problem concerns with production scheduling of a lot over a two-stage hybrid flow shop configuration of machines, and is often encountered in flexible manufacturing systems'. We incorporate the use of lot-streaming' in order to minimize the makespan value. Although a general case of this problem is NP-hard, we develop a pseudo-polynomial time algorithm for a special case of this problem when the sublot sizes are treated to be continuous. The case of discrete sublot sizes is also discussed for which we develop a branch-and-bound-based method and experimentally demonstrate its effectiveness in obtaining a near-optimal solution.

The Stochastic Demand Assembly Job Scheduling Problem deals with the scheduling of a set of products in a production setting where manufacturers seek to fulfill multiple objectives such as `economy of scale' together with achieving the flexibility to produce a variety of products for their customers while minimizing delivery lead times. We design a novel methodology that is geared towards these objectives and propose a Lagrangian relaxation-based algorithm for efficient computation.