Enhancing production efficiency through integer linear programming-based production planning

Abstract

The management of a manufacturing plant's primary duties include the efficient planning, scheduling, and synchronization of all production activities. As a result, the management of the plant must design the manufacturing process to minimize the overall production cost, taking into account the resources that cannot be compromised. In this study, Shree Pashupati Biscuit Industries Pvt. Ltd. is chosen, and an integer linear programming (ILP) model is developed to predict the total number of batches that the facility should make each month from each product in order to meet the monthly demand with the resources at hand. The goal is to reduce the plant's monthly production costs. After a month of collecting the necessary data from the manufacturing facility, the goal function and restrictions were developed. In order to ensure that no consumer is dissatisfied, the management has placed a high priority on meeting demand. Any workable solution discovered by the model must meet the demand in accordance with the managerial need. Demand constraint is therefore seen as a harsh limitation. According to the monthly demand, the management is compelled to alter the labor and machine requirements more regularly. Therefore, labor and machine hour restrictions are regarded as mild restrictions. The simulated ILP model was constructed as an Excel spreadsheet model, and it was then solved using Excel Solver, which applies the simplex approach and takes into account the model's requirement for integers. Until a workable solution is identified, the total number of hours of labor and machine availability can be altered within a specific range. The number of batches to be produced from each product and the accompanying minimal monthly cost are determined by the solved model. This production strategy uses both the physical and human resources in the best possible way by preventing the manufacturing of surplus biscuits. The solution can also be used to calculate the required extra hours, machine and labor idle times, and additional overtime costs, which are then added to the monthly production costs.