This work presents a novel approach for enterprise-wide planning and scheduling problem in paper, pulp and printing industry. This is essentially a large dimensional mixed integer nonlinear optimization problem. In the current approach it has been systematically decomposed into the interconnected smaller sub-problems by using spatial and temporal considerations. Each of these sub-problems can be solved relatively easily and with less computational efforts using existing capabilities of the available solvers and personal computers.

         The proposed decomposition has a hierarchical structure, which strongly relies on the algorithmic approach with minimal use of heuristics and permits systematic analysis of the problem at various levels. The rolling horizon concept is used to deal with the dimensionality problem along with the changing future demands and possible disruptions. The resulting set of large scale sub-problems are solved using ILOG CPLEX 7.1 Solver. The efficacy of the proposed solution approach is evaluated by conducting studies on a large dimensional industrial problem (3200 orders, 5 paper machines and 3 month horizon, about 300,000 variables). The proposed solution scheme provides satisfactory solutions to the large scale industrial problem for multiple scenarios in a reasonable time frame (about 2 to 3 minutes on 2.4 GHz PIV PC). Thus, the proposed solution scheme can effectively deal with large dimensional enterprise-wise order allocation, inventory allocation, run formation, trimming and pattern sequencing problems in a coordinated fashion.

         This project was funded by Honeywell Technology Solution Laboratory. (HTSL), Bangalore and IIT Bombay team working on the project consisted of Prof Sachin Patwardhan (PI), Prof K P Madhavan (Consultant), Mangesh Kapadi and S A Munawar (project engineers). Also, HTSL, Bangalore and IIT Bombay are in the process of jointly filing two US patents based on the technology developed.