Integrated Aggregate Production and Marketing Promotion Planning under Uncertainty: A Case Study

This paper provides a general model that optimizes the aggregate production plan and marketing promotion plan simultaneously. There are three types of marketing promotions under consideration which are temporary discount, temporary volume increment, and offering premium gift when some units are bought. The aggregate production plan mainly considers number of workers, overtime, and inventory level in each period. Two main sources of uncertainty, namely, demand and effects of promotions are considered. They are handled by triangular fuzzy numbers, which represent pessimistic, most-likely, and optimistic situations. The optimization model is a fuzzy multi-objective linear programming model. A case study in a real company is used to illustrate the effectiveness of the model. The results show that the model can find a good compromised solution that simultaneously maximize the profits under pessimistic, most-likely, and optimistic situation

Finite Capacity Scheduling of Make-Pack Production: Case Study of Adhesive Factory

This paper developed a Finite Capacity Scheduling (FCS) system for make-pack production based on a real case of an adhesive factory. The FCS determines production quantity of each machine to conform with resource capacities and due date of customer orders while minimizes related total cost. The total cost includes total production, inventory, and cleaning cost. A Mixed Integer Linear Programming (MILP) model is formulated and solved by LINGO software. The computational time is very long since the model has a lot of integer variables. Thus, the model is solved for a reasonable time and the best but not optimal solution is reported with the lower bound. This paper tries fixed horizon and rolling horizon scheduling methods. The fixed horizon plans for an entire horizon of 30 days. The rolling horizon plans for a sub-horizon of 10, 15, and 17 days. An overlapping of sub-horizons is applied to reduce end-of-horizon effect. Three scenarios (high, normal, and low) of demands are considered. The fixed horizon method is applied first to all scenarios of demand. If the best solution is far away from the lower bound, the rolling horizon method is applied. The results indicated that the rolling horizon method may significantly reduce the total cost with the same computational time. Moreover, the rolling horizon method is more applicable for a dynamic situation where customers frequently change orders. The proposed MILP model can generate reasonable solutions and they are useful for scheduling decision of make-pack production