Production scheduling model

The production scheduling model has been presented in detail in Chapter 2 of this textbook, but is briefly presented in this section of the chapter for purposes of continuity and facilitation of understanding. For a detailed explanation of each of the production scheduling constraints, the reader is referred to Chapter 2. 

Zangwill, W. 1966a. A deterministic multi-period production scheduling model with backlogging. Management Science. 13(1) 105-119. 

Haijunkoski, I., Maravehas, C., Bongers, R, Castro, R, EngeU, S., Grossmann, I.E., Hooker, J., Mendez, C., Sand, G., and Wassick, J. (2013) Scope for industrial applications of production scheduling models and solution methods. Computers and Chemical Engineering 62(5), 161-193. 

If a simulation model is used as part of a MES to evaluate production schedules and support daily operation the presentation of simulation results quite often is integrated in the MES environment. The planner might not even see or know the simulation model itself. There might be a feature such as assess order schedule within the MES, which starts a simulation experiment. Details on this and on the other ways of application will be illustrated by the examples in the next section. 

Both the mixing process and the approximation of the product profiles establish nonconvex nonlinearities. The inclusion of these nonlinearities in the model leads to a relatively precise determination of the product profiles but do not affect the feasibility of the production schedules. A linear representation of both equations will decrease the precision of the objective but it will also eliminate the nonlinearities yielding a mixed-integer linear programming model which is expected to be less expensive to solve. 

The terms able to promise or available to promise (ATP) indicate whether a given customer, product, volume, date, or time request can be met for a potential order. ATP requests might be filled from inventory, unallocated planned production, or spare capacity (assuming additional production). When the production scheduler is content with the current plan, made up of firm orders and forecast orders, the forecast orders are removed but the planned production is left intact. This produces inventory profiles in the model that represent ATP from inventory and from unallocated planned production (Baker, 1993 Smith, 1998). 

The result of this work with the model clearly shows that there are worthwhile savings to be had from brine optimisation particularly as the cost of implementation will be small. The plant already has a system which optimises its production schedule and the brine optimisation results can be added to the associated program in the form of a simple look-up table. 

The tools for carrying out an analysis step are likely the most well developed as aids for design in virtually every discipline. In analysis, one proposes a model that can describe the behavior of whatever phenomenon is of interest. Such models can range from how an oil droplet might sit on top of a pool of water to the production scheduling of an entire company. They can range in level of detail the oil droplet could take the power of a supercomputer to solve, while the company model might exist on a personal computer. 

Eq.(l 1) is included to rectify capacity availability in the planning model. This correction is done based on the scheduling model task assignment (Wijt,)- Eq.(ll) should he merely apphed to those equipments which are production bottlenecks. Additionally, it is worth to mention that it must be checked that market demand is not actually the bottleneck process in the plarming period, where scheduhng is performed. 

Understanding the requirements of a GMP facility is critical to developing an accurate cost and schedule model for the new product. A process fit that appears simple for a fine chemical could require substantial renovations for an API. 

Pure simulation approaches are proposed by Pitty et al. (2008) and Adhitya and Srini-vasan (2010). Pitty et al. (2008) propose a discrete-event simulation model for a refinery supply chain. Operational decisions such as unloading schedules and production planning are made based on simple priority rules. Various configurations of the modelled SC are studied and compared to reveal optimization potentials. This approach explicitly considers some details of ship and pipeline transports. Adhitya and Srinivasan (2010) describe a discrete-event simulation model for an SC producing and distributing lubricant additives. Here, batch production is modelled. Again, operational production decisions are made by priority rules and a scenario analysis is conducted to evaluate the effects of other priority... 

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