Improved three-index unit-specific event-based model for short-term scheduling of batch plants

Vooradi, Ramsagar; Shaik, Munawar A.

doi:10.1016/j.compchemeng.2012.03.014

Cited by 32 publications

(79 citation statements)

References 18 publications

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“…This feature enables the utilization of even fewer intervals in order to achieve the optimal solution. Many such multi‐grid models have been proposed in the literature, either in the form of unit‐specific event‐based models, or in the form of unit slot‐based models . A comprehensive computational comparison of scheduling models across all of the existing categories has been conducted by Shaik et al for the deterministic case.…”

Section: Process Scheduling Models For Robust Optimizationmentioning

confidence: 99%

Theoretical and computational comparison of continuous‐time process scheduling models for adjustable robust optimization

Lappas

Gounaris

2018

AIChE Journal

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Coping with uncertainty in system parameters is a prominent hurdle when scheduling multi-purpose batch plants. In this context, our previously introduced multi-stage adjustable robust optimization (ARO) framework has been shown to obtain more profitable solutions, while maintaining the same level of immunity against risk, as compared to traditional robust optimization approaches. This paper investigates the amenability of existing deterministic continuous-time scheduling models to serve as the basis of this ARO framework. A comprehensive computational study is conducted that compares the numerical tractability of various models across a suite of literature benchmark instances and a wide range of uncertainty sets. This study also provides, for the first time in the open literature, robust optimal solutions to process scheduling instances that involve uncertainty in production yields. slot-based models always require N -1 slots (where N represents the optimal number of event points for a global model) to achieve the optimal solution. To avoid confusion, we will adhere to the terminology of event points, that is, when we report that an instance was solved with N event points, the SK05 model specifically was solved with N -1 slots.

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Section: Process Scheduling Models For Robust Optimizationmentioning

confidence: 99%

Theoretical and computational comparison of continuous‐time process scheduling models for adjustable robust optimization

Lappas

Gounaris

2018

AIChE Journal

View full text Add to dashboard Cite

show abstract

“…The proposed deterministic scheduling model is formulated based on the model framework proposed by Vooradi and Shaik (V&S model) [26]. For the reason of computation time, ∆n is set to be zero.…”

Section: Deterministic Scheduling Modelmentioning

confidence: 99%

“…Coefficient c ui and maximum supply amount of utility U max ui are fuzzy numbers, as given by constraints (25) and (26). The two fuzzy numbers are described by triangular possibility distributions.…”

Section: Uncertainty Descriptionmentioning

confidence: 99%

“…So, utility disturbances usually occur. In fuzzy model, the parameter U max ui is defined as a fuzzy number to describe the uncertain utility supply as given by constraint (26).…”

Section: Different Tasks In Different Unitsmentioning

confidence: 99%

“…Some assumptions in this paper are given as follows: (i) the switching time between different tasks is neglected [26]; (ii) raw materials are sufficient and supplied at the beginning of the scheduling horizon [27]; (iii) intermediates and final products are only from producing [28][29][30].…”

Section: Problem Statementioning

confidence: 99%

See 2 more Smart Citations

Uncertain Production Scheduling Based on Fuzzy Theory Considering Utility and Production Rate

et al. 2017

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Abstract:Handling uncertainty in an appropriate manner during the real operation of a cyber-physical system (CPS) is critical. Uncertain production scheduling as a part of CPS uncertainty issues should attract more attention. In this paper, a Mixed Integer Nonlinear Programming (MINLP) uncertain model for batch process is formulated based on a unit-specific event-based continuous-time modeling method. Utility uncertainty and uncertain relationship between production rate and utility supply are described by fuzzy theory. The uncertain scheduling model is converted into deterministic model by mathematical method. Through one numerical example, the accuracy and practicability of the proposed model is proved. Fuzzy scheduling model can supply valuable decision options for enterprise managers to make decision more accurate and practical. The impact and selection of some key parameters of fuzzy scheduling model are elaborated.

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Multi‐stage adjustable robust optimization for process scheduling under uncertainty

2016

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Variations in parameters such as processing times, yields, and availability of materials and utilities can have a detrimental effect in the optimality and/or feasibility of an otherwise "optimal" production schedule. In this paper, we propose a multi-stage adjustable robust optimization approach to alleviate the risk from such operational uncertainties during scheduling decisions. We derive a novel robust counterpart of a deterministic scheduling model, and we show how to obey the observability and non-anticipativity restrictions that are necessary for the resulting solution policy to be implementable in practice. We also develop decision-dependent uncertainty sets in order to model the endogenous uncertainty that is inherently present in process scheduling applications. A computational study reveals that, given a chosen level of robustness, adjusting decisions to past parameter realizations leads to significant improvements, both in terms of worst-case objective as well as objective in expectation, compared to the traditional robust scheduling approaches.

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Improved three-index unit-specific event-based model for short-term scheduling of batch plants

Cited by 32 publications

References 18 publications

Theoretical and computational comparison of continuous‐time process scheduling models for adjustable robust optimization

Theoretical and computational comparison of continuous‐time process scheduling models for adjustable robust optimization

Uncertain Production Scheduling Based on Fuzzy Theory Considering Utility and Production Rate

Multi‐stage adjustable robust optimization for process scheduling under uncertainty

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