Batch scheduling

Intelligence in Numerical Computing improving Batch Scheduling Algorithms through Explanation-Based Learning... 

Realff, M.J., Machine Learning for the Improvement of Combinatorial Optimization Algorithms A Case Study in Batch Scheduling. Ph.D Thesis, MIT., Cambridge, MA, 1992. 

Engineered Mixed-Integer Programming in Chemical Batch Scheduling"... 

It is the objective of this paper to provide a comprehensive review of the state-of-the art of short-term batch scheduling. Our aim is to provide answers to the questions posed in the above paragraph. The paper is organized as follows. We first present a classification for scheduling problems of batch processes, as well as of the features that characterize the optimization models for scheduling. We then discuss representative MILP optimization approaches for general network and sequential batch plants, focusing on discrete and continuous-time models. Computational... 

Classification of Batch Scheduling Problems 165 (1) Process topology... 

Having presented the general features of typical batch scheduling problems we introduce a roadmap that describes the main features of current optimization... 

In addition to the time representation and material balances, scheduling models are based on different concepts or basic ideas that arrange the events of the schedule over time with the main purpose of guaranteeing that the maximum capacity of the shared resources is never exceeded. As can be seen in Figure 8.5 and Table 8.1, we classified these concepts into five different types of event representations, which have been broadly utilized to develop a variety of mathematical formulations for the batch scheduling problem. Although some event representations are more... 

Having introduced a general road map for classifying problems and models for batch scheduling we present a brief review on the specific models that have been proposed in the literature (for model details see Mendez et al. [6]). ... 

A simpler and general discrete time scheduling formulation can also be derived by means of the Resource Task Network concept proposed by Pantelides [10], The major advantage of the RTN formulation over the STN counterpart arises in some problems involving many identical pieces of equipment. In these cases, the RTN formulation introduces a single binary variable instead of the multiple variables used by the STN model. The RTN-based model also covers all the features at the column on discrete time in Table 8.1. In order to deal with different types of resources in a uniform way, this approach requires only three different classes of constraints in terms ofthree types of variables defining the task allocation, the batch size, and the resource availability. Briefly, this model reduces the batch scheduling problem to a simple resource balance problem carried out in each predefined time period. 

In most chemical batch scheduling problems the underlying data is not exactly known at the time the schedule has to be generated. Typical sources of uncertainties are (1) failures of reactors, equipment, and resources, (2) varying processing times, (3) varying product qualities, and (4) varying customer s demands. 

The vast majority of model based chemical batch scheduling approaches ignore the uncertainty by assuming the data to be certainly known. In contrast, uncertainty conscious scheduling approaches do not ignore the uncertainties. They can be classified according to two approaches [1,2] ... 

This algorithm has been successfully applied to chemical batch scheduling problems [13-17]. 

Fora recent survey of reactive and stochastic chemical batch scheduling approaches, the reader is referred to Floudas and Lin [2], For a survey of the different types of probabilistic mathematical models that explicitly take uncertainties into account, see Sahinidis [12]. For detailed information about stochastic programming and its applications, the reader is referred to the books of Birge and Louveaux [9], Ruszczynski and Shapiro [10], or Wallace and Ziemba [26]. 

Clostermann, E. (2005) Empirical analysis of scenario decomposition in chemical batch scheduling. Diplomarbeit, Universitat Duisburg-Essen. 

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