Production planning is a complex problem that is typically decomposed into decisions carried out at different control levels. The various methods used for production planning often assume a static environment, therefore, the plans developed may not be feasible when shop floor events change dynamically. In such an operating environment, a system simulation model updated with real-time data can be used to validate a proposed plan. In this paper, we propose a framework to evaluate and validate the feasibility of high-level production plans using a simulation model at a lower level thereby providing a base for improving the upper level plan. The idea is demonstrated with an assembly plant where the aggregate plan is evaluated using discrete event simulation (DES) of shop floor operations with resources allocated according to constraints imposed by the aggregate plan. We also discuss standardized integration interfaces required between simulations and production planning tools.
Managing a manufacturing system to meet production objectives in face of dynamic events and changing priorities is a major challenge. Simulation models are often used to evaluate and generate production plans and schedules to achieve those objectives. Indeed, the vision of smart manufacturing systems includes that simulation will be pervasive and integrated throughout the multiple layers of operation and decision-making (AIChE 2012). However, such models specifically for production planning, are not yet ubiquitous through the multiple control levels.
Production plans affect many other functions in the organization because they are the basis for acquiring raw materials and establishing resource requirements such as manpower, tooling, and machine capacity. Bitran et al. (1989) identified three decision levels into which the production planning problem can be decomposed: aggregate, scheduling, and dispatching. Aggregate planning relies on nominal production rates to determine capacity requirements during each period over the planning horizon. Typically, optimization formulations are applied to this problem. Scheduling determines production quantities for each product family during a period within capacity already set by the aggregate plan. Shop floor control determines actual resource and routing of production lots, precise timing, and dispatching procedures. Methods such as dynamic programming, expert systems, priority rules, and heuristics have been applied to scheduling and control problems.
Decomposing production planning into sub-problems simplifies the process of deriving a workable problem that is solved at each level. However, as noted by White (2012), changes in factors such as product mix, equipment status, and staffing imply that originally derived plans may not be accomplished by prevailing shop floor capacity. Tools such as simulations evaluate the impact of decisions and feedback within different production planning levels. Existing production systems such as CONWIP (CONstant Work In Process) help control work-in-progress but would need look-ahead capability and feedback that includes simulation output to an aggregate level (Spearman 1990).
Simulation result feedback for integrated production planning.
In this paper we propose a framework to evaluate feasibility of high-level production plans using a simulation model of the system at a lower level. The results of simulation model from the look-ahead results are used iteratively to generate a new upper level plan. The idea is demonstrated in an assembly plant where the aggregate plan is evaluated using discrete event simulation (DES) of shop floor operations with the schedule of resources are allocated to production activities according to constraint imposed by the aggregate plan. Simulation is used in three contexts: (1) as a surrogate for a real life plant, (2) as a look-ahead evaluation of plans that uses shop floor status, and (3) as feedback to generate a better plan by improving on a previous plan.
The tools used in this demonstration, as with most commercial systems, are often not interoperable. Therefore, interfaces to integrate simulation and scheduling tools are major focuses of this paper. The rest of the paper is organized as follows. Section 2 gives an overview of related work and shows how current work differs from previous research. Section 3 discusses relevant standardization needs. Section 4 presents the proposed framework. Section 5 describes a case study used to demonstrate the framework. Section 6 presents the final discussion and conclusion.