Scheduling of Single-Stage and Continuous Processes on Parallel Lines with Intermediate Due Dates

Lee, Kyu-Hwang; Heo, Soon-Ki; Lee, Ho-Kyung; Lee, In‐Beum

doi:10.1021/ie010097d

Cited by 22 publications

(34 citation statements)

References 8 publications

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“…Because of the batch or order oriented characteristics of the sequential processes, it is possible to define continuous variables directly to represent the timings of the batches without the use of time slots. This alternative direction has also been pursued to formulate continuous-time scheduling models for sequential processes, as reported in the work presented by Ku and Karimi (1988), Cerdá, Henning, and Grossmann (1997), Méndez et al (2000bMéndez et al ( , 2001), Moon, Park, and Lee (1996), Hui, Gupta, and van der Meulen (2000), Hui and Gupta (2001), Orçun, Altinel, and Hortasu (2001), and Lee, Heo, Lee, and Lee (2002).…”

Section: Sequential Processesmentioning

confidence: 99%

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

Floudas

Lin

2004

Computers & Chemical Engineering

593

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: Sequential Processesmentioning

confidence: 99%

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

Floudas

Lin

2004

Computers & Chemical Engineering

593

378

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“…Due to this special structure of the processing recipe, orders/batches/jobs are used to represent production and mass balances are often not taken into account explicitly. A significant amount of work has been dedicated to the development of MILP based approaches for this class of scheduling problems exploring the special structure of sequential processes, which can be classified into two main groups as methods based on time slots (for example, Pinto and Grossmann, 1995;Karimi and McDonald, 1997;Lamba and Karimi, 2002a;Bok and Park, 1998;Moon and Hrymak, 1999) and methods based on direct definition of sequences and/or timings of orders/batches (for example, Ku and Karimi, 1998;Cerdá, Henning, and Grossmann, 1997;Cerdá, 2000b, 2001;Moon, Park, and Lee, 1996;Hui, Gupta, and Meulen, 2000;Hui and Gupta, 2001;Orçun, Altinel, and Hortaçsu, 2001;Lee et al, 2002). Readers interested in this class of scheduling problems are directed to a recent review by Floudas and Lin (2004).…”

Section: Process Representationmentioning

confidence: 99%

Mixed Integer Linear Programming in Process Scheduling: Modeling, Algorithms, and Applications

2005

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This paper reviews the advances of mixed-integer linear programming (MILP) based approaches for the scheduling of chemical processing systems. We focus on the short-term scheduling of general network represented processes. First, the various mathematical models that have been proposed in the literature are classified mainly based on the time representation. Discrete-time and continuous-time models are presented along with their strengths and limitations. Several classes of approaches for improving the computational efficiency in the solution of MILP problems are discussed. Furthermore, a summary of computational experiences and applications is provided. The paper concludes with perspectives on future research directions for MILP based process scheduling technologies.Process scheduling has attracted an increasing amount of attention from both the academia and the industry in the past decade. The reason for this is twofold. First, it reflects the pressure faced by the chemical processing and manufacturing related industries to improve productivity and reduce costs. Second, it is driven by the substantial advances of related modeling and solution techniques, as well as the rapidly growing computational power. The problem of interest is to determine the most efficient way to produce a set of products in a time horizon given a set of limited resources and processing recipes. The activities to be scheduled usually take place in multiproduct and multipurpose plants, in which a wide variety of different products can be manufactured via the same recipe or different recipes by sharing limited resources, such as equipment, material, time, and utilities. These types of plants have long been employed to manufacture chemicals in small quantities which have high added-values or frequently changing process parameters or demands. Their inherent operational flexibility provides the platform for great benefits that can be obtained through good production schedules.Mathematical programming, especially Mixed Integer Linear Programming (MILP), because of its rigorousness, flexibility and extensive modeling capability, has become one of the most widely explored methods for process scheduling problems. Applications of MILP based scheduling methods range from the simplest single-stage *

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“…Ierapetritou et al 7 proposed a continuoustime (CT) formulation for batch and semi-continuous plants which can deal with intermediate due date and was based on predetermined time slots built on fixed event points and variable event points. Lee et al 8 addressed scheduling problems of single-stage and continuous multiproduct processes on parallel lines. The formulation predivided the scheduling horizon into multiple intervals by demand due date, and the assignment of several different tasks in each interval is to be determined.…”

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling of the Multigrade Parallel Distillation Column System with a Continuous-Time Formulation

Shao

Zhu

et al. 2019

Ind. Eng. Chem. Res.

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In the fine chemical industry, optimal scheduling of continuous processes involving multigrade and parallel units is an important problem. The existing scheduling formulations can be classified into continuous-time (CT) and discrete-time (DT) formulations. Most previous studies adopted DT approaches that are more convenient for establishing an optimization model. However, the DT formulation has two inherent shortcomings. One is that it often requires more integer variables than the CT formulation. The other is that the solution of a DT formulation is often only suboptimal, or even infeasible, for the original scheduling problem. This work proposes a CT formulation for optimal scheduling of multigrade parallel distillation processes. This formulation simultaneously addresses continuous demand satisfaction, sequence-dependent transitions, and run-length constraints. The performance of the proposed model is demonstrated through the solution of three different delivery scenarios and is shown to be better than that of a DT formulation in terms of both model scales and solution accuracy, from which one can infer the advantages of the CT formulation in dealing with time-related issues for process scheduling.

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Scheduling of Single-Stage and Continuous Processes on Parallel Lines with Intermediate Due Dates

Cited by 22 publications

References 8 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

Mixed Integer Linear Programming in Process Scheduling: Modeling, Algorithms, and Applications

Optimal Scheduling of the Multigrade Parallel Distillation Column System with a Continuous-Time Formulation

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