Short-term Scheduling Of Batch Processes Research Articles

Feasibility Analysis in Batch Process: A Machine Learning Approach

Scheduling is a major issue in process operations, and it is essential for maximizing production output. Short-term scheduling is used in batch operations to allocate a set of restricted resources across time to manufacture one or more items according to a batch recipe. For such processes, a feasible design space can be described by the decision-maker as per the process constraints and objectives. A machine learning approach using support vector machines is implemented for the classification of the feasible region from a data set. An algebraic equation is obtained using support vectors (data points participating in creating classification boundary), which could be incorporated into the mathematical model. The equation will lead to attaining several objectives in the finite obtained region. In this paper, kernel-based support vector classification is tailored to a continuous-time formulation for the short-term scheduling of batch processes. First, using a classification SVC over a data set, it was observed that the mathematical model is infeasible for a set of production combination of two different products. Using the proposed methodology, a design space is presented graphically for feasible production targets. In the second case, the study is applied to perform optimization operations to target feasible region within a specified limit as a choice of decision-maker.

Genetic algorithms for batch sizing and production scheduling

This paper proposes three genetic algorithms with different types of crossovers dedicated to solve the problem of short-term scheduling of batch processes. The genetic algorithms suggested are able to determine the amount of batches produced, their production sequence, machinery assignments, and a different batch size for each batch. This is a more complex variation of the batch scheduling problem where genetic algorithms are usually limited to decide the batch production sequence. A manufacturing case study is used to test the performance when minimizing the makespan, in order to compare the proposed genetic algorithms against a simulated annealing implementation, a totally random search, and a simplified genetic algorithm with no crossover (to test the crossovers’ efficiency). The results show that one of the crossovers proposed has the fastest convergence in all scenarios, finding the best solutions in short searches. This crossover is slightly outperformed by the simulated annealing only in extensive searches with more iterations. The other two crossovers suggested have a good performance in certain scenarios, but show poor results in others.

A novel network-based continuous-time representation for process scheduling: Part II. General framework

In the first part of this series of papers we presented a new network-based continuous-time representation for the short-term scheduling of batch processes, which overcomes numerous shortcomings of existing approaches. In this second part, we discuss how this representation can be extended to address aspects such as: (i) preventive maintenance activities on unary resources (e.g., processing and storage units) that were planned ahead of time; (ii) resource-constrained changeover activities on processing and shared storage units; (iii) non-instantaneous resource-constrained material transfer activities; (iv) intermediate deliveries of raw materials and shipments of finished products at predefined times; and (v) scenarios where part of the schedule is fixed because it has been programmed in the previous scheduling horizon. The proposed integrated framework can be used to address a wide variety of process scheduling problems, many of which are intractable with existing tools.

Production Scheduling of a Large-Scale Industrial Batch Plant. I. Short-Term and Medium-Term Scheduling

The medium-term production scheduling of a multipurpose, multiproduct industrial batch plant is modeled using a novel mathematical framework. The methodology consists of the decomposition of the whole scheduling period into successive short horizons of a few days. A decomposition model is implemented to determine each short horizon and the corresponding products to be included. Then, a novel continuous-time formulation for short-term scheduling of batch processes with multiple intermediate due dates is applied to each short horizon selected, leading to a large-scale mixed-integer linear programming (MILP) problem. The scheduling model includes over 80 pieces of equipment and can take into account the processing recipes of hundreds of different products. Several characteristics of the production plant are incorporated into the scheduling model and actual plant data are used to model all parameters. Two different modes of operation are presented along with representative production schedules to demonstrate ...

Short-Term Scheduling of Batch Processes. A Comparative Study of Different Approaches

The approaches to scheduling problem formulation in chemical engineering can be broadly classified into two categories, namely, the standard recipe approach (SRA) and the overall optimization approach (OOA). In the SRA, first the recipes are standardized either empirically or via single-batch optimization (SBO) and then the production scheduling problem is formulated on the basis of these standardized recipes. However, the standardization of recipes removes degrees of freedom from the system, and because of this, the solutions obtained with this approach can be suboptimal, whereas in the OOA, the process dynamics are directly included in the scheduling problem formulation instead of the standardized recipes. This restores the additional degrees of freedom of the system, and therefore this approach can yield a better solution. However, direct inclusion of the process dynamics in the scheduling problem formulation results in a mixed-integer dynamic optimization (MIDO) problem, the solution of which can be a...

Continuous-Time Optimization Approach for Medium-Range Production Scheduling of a Multiproduct Batch Plant

The medium-range production scheduling problem of a multiproduct batch plant is studied. The methodology consists of a decomposition of the whole scheduling period to successive short horizons. A mathematical model is proposed to determine each short horizon and the products to be included. Then a novel continuous-time formulation for short-term scheduling of batch processes with multiple intermediate due dates is applied to each time horizon selected, leading to a large-scale mixed-integer linear programming (MILP) problem. Special structures of the problem are further exploited to improve the computational performance. An integrated graphical user interface implementing the proposed optimization framework is presented. The effectiveness of the proposed approach is illustrated with a large-scale industrial case study that features the production of 35 different products according to a basic three-stage recipe and its variations by sharing 10 pieces of equipment.

Effective Continuous-Time Formulation for Short-Term Scheduling. 2. Continuous and Semicontinuous Processes

Part 1 presented a novel continuous-time mathematical formulation for the short-term scheduling of batch processes. Most production networks, however, involve batch and continuous processes. On the basis of the same principles, this paper extends the proposed formulation to describe continuous processes. Two industrial case studies from fast moving consumer goods manufacturing are presented to illustrate the capability of the proposed formulation to describe plants with both batch and continuous processes, to incorporate cleanup requirements, and to consider storage requirements and limitations. It is demonstrated that the proposed approach outperforms all previously proposed continuous-time models for the short-term scheduling of continuous processes.

An extended dispatching rule approach in an on–line scheduling framework for batch process management

SUMMARY Short-term scheduling of batch processes in an on-line scheduling framework is a difficult problem because of the complexity and dynamic behaviour of batch processes. In this paper, an on-line scheduling framework for batch process management and a dispatching–simulation mechanism for short-term scheduling in the framework are presented. Moreover, an extended dispatching rule approach, which applies different dispatching rule combinations in the mechanism, and a search algorithm to find an appropriate dispatching rule combination with reasonable computational effort, are proposed. The effectiveness of the proposed approach and the search algorithm are demonstrated on an example multiproduct batch process model.