A quota-based integrated commercial fishery owns fishing trawlers, processing plants, and fish quotas. Such a fishery must decide how to schedule trawlers for fishing and landing, how to schedule processing of products, how to schedule labour for processing, and how to plan inventory of raw materials and products. This problem is of great economic significance to New Zealand, whose economy depends to a large extent on the fishery industry. To assist the fishery manager, we develop a mixed integer linear program (MILP) for optimal scheduling of fishing trawlers, production planning (processing) and labour allocation for a quota-based integrated fishery of New Zealand. The model decides when and where each trawler should go for fishing, how much fish each trawler should land, and how much product to produce in each period. Since the fishery is a private farm, its main objective will be profit maximization (or cost minimization if its demand is on contract). The government manages the conservation of fish through the quota allocation. In this thesis the objective of the fishery model is to maximise the total profit. We demonstrate our model with examples based on data from a major New Zealand fishery. We investigate ways to manage the uncertainties involved in trawler scheduling and production planning of the fishery. To manage end-of-planning-horizon effects in the fishery, we develop a simple safety stock approach. We also analyse the workability of a rolling horizon approach to solve the longer planning horizon models and to deal with the end-of-planning horizon effects. We investigate the effect of initial and final position of the trawlers on the profit. We also investigated many different challenging data sets to observe the impact on the effectiveness of our IFPM. The second objective of this thesis is to develop an efficient solution procedure for the MILP, named integrated fishery planning model (IFPM). The IFPM consists of a fishing subproblem, a processing subproblem, and complicating side constraints. We have tried techniques including LP relaxation, Lagrangean relaxation (LR), Dantzig-Wolfe decomposition (DWD) and decomposition-based pricing (DBP). We develop a new DBP method to solve the IFPM. It gives excellent computation times. We also develop a decomposition-based O'Neill pricing (DBONP) method to improve the solution obtained from DBP procedure. It improves the DBP solutions but takes longer time to solve the IFPM. Finally, we develop a simple and efficient reduced cost-based pricing (RCBP) method. It takes less time to solve the IFPM and yields excellent results. The initial formulations for several planning horizons are solved using the AMPL modelling language and CPLEX with branch and bound. Relevant results and computational difficulties are reported.