23 Production Systems and Information EngineeringVolume 2 (2004), pp. 23-36PROJECT-ORIENTED APPROACH TO PRODUCTIONPLANNING AND SCHEDULING IN MAKE-TO-ORDERMANUFACTURINGPÉTER E GRI,A NDRÁS K OVÁCS,A NDRÁS MÁRKUS,JÓZSEF VÁNCZAComputer and Automation Research InstituteHungarian Academy of SciencesH-1111 Budapest, Kende u 13-17HUNGARY{egri,akovacs,markus,vancza}@sztaki.hu[Received December 2004 and accepted January 2005]Abstract. In this paper, we present a unified framework for production planningand scheduling in make-to-order manufacturing. Both planning and schedulingproblems are captured as resource-constrained project scheduling problems. Theactual models and solution techniques are different at the two levels. Hence, wealso suggest an aggregation method that provides the connection betweenplanning and scheduling. The viability of the approach is demonstrated by someexperimental results on large-scale industrial problem instances.Keywords: Production planning, scheduling, aggregation1.I NTRODUCTIONProduction planning and scheduling (PPS) match future production load and capacities by determining the flow of materials and the use of resources, over various horizons and on different levels of detail. Albeit planning and scheduling problems have their own, specific timescale, resource and activity model granularity as well as optimization criteria, the two levels of PPS are strongly interdependent. On the one hand, planning guarantees on the long term the observance of high level temporal and resource capacity constraints and thus sets the goals as well as the resource and temporal constraints for scheduling. On the other hand, scheduling is responsible for unfolding a production plan into executable schedules; i.e., to detailed resource assignments and operation sequences. No scheduling strategy can improve much on an inadequate plan, whereas a bad scheduling strategy that wastes resources may inhibit the fulfillment of a good plan. All this makes PPS extremely complex and hard to solve. At the same time, PPS calls for efficient decision support methods and intuitive, flexible models with fast, reliable solution techniques that scale-up well even to large problem instances. Hence, even if production planning and scheduling problems
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