Model production planning

Goyal, Suresh K. A Simple Procedure for Price Break Models. Production Planning Control (1995) 6, 584-585. 

These plans create a basis for ensuring that work is carried out under controlled conditions, but the staff, equipment, materials, processes, and documentation must be up to the task before work commences. A model production process is illustrated in Figure 9.3. The shaded boxes indicate interfaces external to the production process. The variables are too numerous to illustrate the intermediate steps. 

Birewar and Grossmann (1990a) proposed a model for the simultaneous determination of the best production goals and the best allocation of tasks to the equipment units. They incorporated inventory costs into the objective function. To keep commitments that have been made at the stage of production planning, a penalty PNu) was incurred in the objective function for not meeting the commitments for product i in interval r as a linear function in terms of the shortfalls SFn) ... 

Another perspective for production simulation is automatic capacity utilization optimization of multi-product systems. As discussed, this task may be very difficult because of the many different variables and boundary conditions. In an environment integrating optimization and simulation, the optimizer systematically varies the important decision variables in an external loop while the simulation model carries out production planning with the specified variables in the internal loop (see Gunther and Yang [3]). The target function, for example total costs or lead times, can be selected as required. The result of optimization is a detailed proposal for the sequence of the placed orders. 

The modeling of the plant, the chemical processes, and the production plan can be performed entirely in a graphical manner using three editors, the recipe editor, the plant editor and the production plan editor. A fourth editor is available for the definition and update of a physical properties library. The physical properties library is required to parameterize the models of the plant and of the recipes for the calculation of state changes during the processing steps in simulation. 

The modeling of the recipes, the plant(s) and the production plans was done graphically using PPSiM. The individual steps of the modeling process are summarized in the following sections. 

The chemical process reflected by the basic recipes was modelled using the PPSiM-recipe editor. Each of the nine product groups was represented by one recipe for a nominal size of 100 kg. The outputs of the recipes were adjusted to 200 and 500 kg during production planning by the scaling factors of the control recipes. 

After a simulation-based design of an efficient pipeless plant had been performed, the existing standard multipurpose plant was modelled by a reference model and compared to the pipeless plant setup by determining the overall production time for different production plans. 

Different types of industries require different characteristics to be taken into account, because in model-based planning the real decision situation must be represented adequately, as the solution will otherwise not provide any benefit. Along the lines of Meyr and Stadtler [3], the characteristics of different supply chains can be classified into functional attributes (procurement type, production type, distribution type, and sales type) and structural attributes (topography of a supply chain, integration, and coordination). 

Suerie, C. (2005) Time Continuity in Discrete Time Models. New Approaches for Production Planning in Process Industries, Springer, Berlin. 

Global models since being more strategic oriented less addressing chemical supply chain specifics required for tactical production planning such as change-overs or recipes... 

Gupta/Maranas (2003) as one example for a demand uncertainty model present a demand and supply network planning model to minimize costs. Production decisions are made here and now and demand uncertainty is balanced with inventories independently incorporating penalties for safety stock and demand violations. Uncertain demand quantity is modeled as normally distributed random variables with mean and standard deviation. The philosophy to have one production plan separated from demand uncertainty can be transferred to the considered problem. Penalty costs for unsatisfied demand and normally distributed demand based on historical data... 

Lababidi et al. (2004) incorporate uncertainty and stochastic market prices and raw material costs for a petrochemical case. They modeled market price and raw material price uncertainty as given and analyzed the effects on production utilization. They initially observed that prices can have significant influence on production plans and utilization. 

Commodity-oriented models focus more on the market interfaces in sales and procurement and consider less complexity in the supply chain determining volumes and values in the value chain integration with production planning and chemical-specifics often not modeled... 

Jackson JR, Grossmann IE (2003) Temporal Decomposition Scheme for Nonlinear Multisite Production Planning and Distribution Models. Industrial Engineering Chemistry Research 42 3045-3055... 

Jackson, J.R. and Grossmann, I.E. (2003) Temporal decomposition scheme for nonlinear multisite production planning and distribution models. Industrial el Engineering Chemistry Research, 42, 3045. 

The model was then solved for the total refinery network and the PVC petrochemical complex. As shown in Table 5.4, the proposed model redesigned the refinery network and operating policies and also devised the optimal production plan for the PVC complex from all available process technologies. The model selected gas oil, an intermediate refinery stream, as the refinery feedstock to the petrochemical complex as opposed to the normally used light naphtha feedstock in industrial practice. In fact, this selection provided the optimal strategy as the light naphtha stream was used instead in the gasoline... 

Table 7.6 shows the solution of the refineries network using the SAA scheme with N = 2000 and N = 20000 where the proposed model required 790CPUs to converge to the optimal solution. In addition to the master production plan devised for each refinery, the solution proposed the amounts of each intermediate stream to be exchanged between the different processes in the refineries. The formulation considered the uncertainty in the imported crude oil prices, petroleum product prices and demand. The three refineries collaborate to satisfy a given local market demand where the model provides the production and blending level targets for the individual sites. The annual production cost across the facilities was found to be 6 650 868. 

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