Lot sizing and scheduling are two important issues in the field of production planning. Despite the fact that these issues are dependent to each other, in most researches they analyzes separately and hierarchically. Considering the relationship, the general lot sizing and scheduling problem (GLSP), consider these two issues as one problem. Basic consumption in most researches in the field of lot sizing and scheduling and specifically in GLSP models, is that companies should respond to all predetermined demands, but in a business with goal of maximizing benefit, fulfilling all demand maybe is not the best answer. In this thesis profit maximization general lot sing and scheduling problem with demand choice flexibility (PGLSP) is studied. This problem is an extension of GLSP, by adding demand choice flexibility. In other words, amount of demand accepted in each period, lot sizing and scheduling are problems which considered simultaneously in this problem. Accepted demand is between upper bound and lower bound of it in each period. With regard to this consumption the In this thesis four models are represented for the problem and their efficiency is analyzed in different groups of problems. These models were different in the way of lot sizing and scheduling. The first and the second models use micro periods for products sequencing. These micro periods do not exist in third and forth models and these models consider sequencing problem like a TSP model. Lots in the first and third models are determined by the unit of products which produce in each period, while in the second and forth models they separated by the period which they use. Changes needed in each of presented models by adding minimum lot size constraint and without existing triangular inequality between setup times are also considered. Seven heuristic algorithms are presented and compare on the basis of rolling horizon and fix and relax methods. While these algorithms reduce the solving time of the problem, their answers have a very good quality. Keywords: Lot sizing, Scheduling, Profit maximization, Demand choice flexibility
