A novel branch and bound algorithm for scheduling flowshop plants with uncertain processing times

Balasubramanian, J.; Grossmann, Ignacio E.

doi:10.1016/s0098-1354(01)00735-9

Cited by 86 publications

(48 citation statements)

References 29 publications

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“…They introduced several metrics to evaluate the robustness of a schedule and proposed a multiperiod programming model using extreme points of the demand range as scenarios to generate a single sequence of tasks with the minimal average makespan over all scenarios. Balasubramanian and Grossmann (2002) proposed a multiperiod MILP model for scheduling multistage flowshop plants with uncertain processing times described by discrete or continuous (using discretization schemes) probability distributions. The objective is to minimize expected makespan and a special branch and bound algorithm was used based on lower bounding by an aggregated probability model.…”

Section: Stochastic Schedulingmentioning

confidence: 99%

Continuous-time versus discrete-time approaches for scheduling of chemical processes: a review

Floudas

Lin

2004

Computers & Chemical Engineering

585

378

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An overview of developments in the scheduling of multiproduct/multipurpose batch and continuous processes is presented. Existing approaches are classified based on the time representation and important characteristics of chemical processes that pose challenges to the scheduling problem are discussed. In contrast to the discrete-time approaches, various continuous-time models have been proposed in the literature and their strengths and limitations are examined. Computational studies and applications are presented. The important issues of incorporating scheduling at the design stage and scheduling under uncertainty are also reviewed.

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Section: Stochastic Schedulingmentioning

confidence: 99%

Continuous-time versus discrete-time approaches for scheduling of chemical processes: a review

Floudas

Lin

2004

Computers & Chemical Engineering

585

378

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“…Furthermore, it is complicated by the fact that the nature of the uncertainties can be quite different in the various levels of the decision making (e.g., strategic planning vs. short term scheduling). Most of the research thus far has focused on operational uncertainty, such as quality, inventory management and handling uncertain processing time (e.g., Zipkin, 2000, Montgomery, 2000, Balasubramanian and Grossmann, 2002. Much less work has focused on uncertainty at the tactical level, for instance, production planning with uncertain demand (Gupta and Maranas, 2003;Balasubramanian and Grossmann, 2004).…”

Section: Uncertainty Challengementioning

confidence: 99%

Enterprise‐wide optimization: A new frontier in process systems engineering

2005

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“…Besides several publications on several approaches to solve scheduling problems in the process industry (cf., [62], [106], [117], [12]), there are also industrial success stories. Successful applications of stochastic optimization to production scheduling problems in the chemical process industry are reported, for instance, by Sand et al [105] and Engell et al [33].…”

Section: Scheduling Under Uncertainty -Industrial Casestudiesmentioning

confidence: 99%

Solving Planning and Design Problems in the Process Industry Using Mixed Integer and Global Optimization

Kallrath

2005

Ann Oper Res

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This contribution gives an overview on the state-of-the-art and recent advances in mixed integer optimization to solve planning and design problems in the process industry. In some case studies specific aspects are stressed and the typical difficulties of real world problems are addressed.Mixed integer linear optimization is widely used to solve supply chain planning problems. Some of the complicating features such as origin tracing and shelf life constraints are discussed in more detail. If properly done the planning models can also be used to do product and customer portfolio analysis.We also stress the importance of multi-criteria optimization and correct modeling for optimization under uncertainty. Stochastic programming for continuous LP problems is now part of most optimization packages, and there is encouraging progress in the field of stochastic MILP and robust MILP.Process and network design problems often lead to nonconvex mixed integer nonlinear programming models. If the time to compute the solution is not bounded, there are already a commercial solvers available which can compute the global optima of such problems within hours. If time is more restricted, then tailored solution techniques are required.

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A novel branch and bound algorithm for scheduling flowshop plants with uncertain processing times

Cited by 86 publications

References 29 publications

Continuous-time versus discrete-time approaches for scheduling of chemical processes: a review

Continuous-time versus discrete-time approaches for scheduling of chemical processes: a review

Enterprise‐wide optimization: A new frontier in process systems engineering

Solving Planning and Design Problems in the Process Industry Using Mixed Integer and Global Optimization

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