An efficient and user-friendly optimization framework for batch process scheduling

Tometzki, Thomas; Neymann, Tobias Claus; Steimel, Jochen; Subbiah, Subanatarajan; Engell, Sebastian

doi:10.3182/20100908-3-pt-3007.00007

Cited by 1 publication

(3 citation statements)

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“…The first case study is a multiproduct pipeless plant demonstrator which was developed at the Process Dynamics and Operations Group . In traditional multiproduct batch plants, the connections between the units are realized by fixed pipes and the transport of the materials between the units is performed by pumping or draining them through the pipes.…”

Section: Resultsmentioning

confidence: 99%

“…The first case study is a multiproduct pipeless plant demonstrator which was developed at the Process Dynamics and Operations Group. 54 In traditional multiproduct batch plants, the connections between the units are realized by fixed pipes and the transport of the materials between the units is performed by pumping or draining them through the pipes. In contrast to classical multiproduct batch plants, in a pipeless plant, the materials stay in the vessels and are transported using a movable container to the stationary processing units where they are processed.…”

Section: Resultsmentioning

confidence: 99%

“…The formal representation of the scheduling problem as a RTN serves as input to the modeling framework of TAOpt. The graphical formulation of the scheduling problem as RTN can be constructed using the RTN model builder introduced in ref . Unlike the approach of mathematical programming where the problem formulation has to be created by the users from the RTN representation and where experience and knowledge about the effect of using different possible model constructs on the performance of the solvers is required, in the tool TAOpt, the TA models of the recipes, resources, and other problem specific constraints are generated automatically from the RTN network that represents the scheduling problem.…”

Section: Timed Automata Optimizer - Taoptmentioning

confidence: 99%

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An Intuitive and Efficient Approach to Process Scheduling with Sequence-Dependent Changeovers Using Timed Automata Models

Subbiah

Schoppmeyer

Engell

2011

Ind. Eng. Chem. Res.

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In the process industries where multiple products have to be produced in the batch mode, the optimal assignment of the operations to the available resources and their sequencing can contribute considerably to economic success. Among the several methods proposed to model and solve batch scheduling problems, techniques based on a reachability analysis of timed automata (TA) models have gained attention recently. The appeal of the approach is the modular, intuitive, and straightforward graphical modeling of complex scheduling problems, and an efficient solution technique based upon reachability algorithms. In this contribution, we present an introduction to the TA-based approach to scheduling and specifically address the problem of batch scheduling with sequence-dependent setup and changeover times. In the TA-based approach, the resources, recipes, and additional timing constraints are modeled independently as sets of (priced) timed automata. The sets of individual automata are synchronized by means of synchronization labels and are composed by parallel composition to form a global automaton. A cost-optimal symbolic reachability analysis is performed on the composed automaton to derive schedules with the objective of minimizing makespan. The TA models of the recipes are extended here to include setup times as well as sequence-dependent changeovers. The performance of the approach to model and to solve real-world scheduling problems with sequence-dependent changeovers is demonstrated for two different case studies. A comparative study on the TA-based approach with various MILP formulations is performed on a famous case study from the literature, and the results are discussed.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Timed Automata Optimizer - Taoptmentioning

confidence: 99%

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