Planning and Scheduling Continuous Operations in the Process Industries

Herrmann, Sascha; Schwindt, Christoph

doi:10.1007/978-3-7908-1921-2_14

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Relative to their batch counterparts, continuous processes have drawn less attention in the literature (Shaik et al , 2009). However, a number of modeling approaches have been developed over the years including linear programming (Bertoletti, 1960), differential and integral calculus (Odi and Karimi, 1988), mixed integer and global optimization (Kallrath, 2002, 2005), discrete‐ and continuous‐time formulations (Floudas and Lin, 2004; Neumann et al , 2005), heuristic hierarchical decomposition (Herrmann and Schwindt, 2007), and stochastic process analysis (Tan and Gershwin, 2009). Yet, none of the reviewed works explore the essential features of the formulated research problems.…”

Section: Methodology and Literature Reviewmentioning

confidence: 99%

Decision support system for continuous production

Bakhrankova

2010

Industrial Management &Amp; Data Systems

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PurposeThe purpose of this paper is to develop energy optimizer (ENEO) – a model‐based decision support system (DSS) for an existing European chemical plant with a multi‐stage continuous production process. The system comprises two modules – energy cost minimization and joined energy cost minimization and output maximization. Following the description of the researched production, the paper presents a gist of the underlying formulations. Then, it tests the DSS on real data instances with a focus on its configuration, practical implications and implementation challenges.Design/methodology/approachThe design of the planning tool is consistent with that of the model‐based DSS and based on the existing information systems. The defined research problems are explored with the use of quantitative methods – the operations research methodology.FindingsThe findings show that ENEO reflects the essence of the researched production process and can provide benefits in practical business operations.Research limitations/implicationsBoth the proposed system configuration and the formulated models lay a foundation to further research within the described industrial setting.Practical implicationsThe system can be utilized in daily operations to provide substantial cost savings, improved capacity utilization and reactivity.Originality/valueThis paper contributes to research by bridging the gap between theory and practice. On the one hand, it describes an unexplored problem and its subsequent solution embodied in the DSS. On the other hand, it emphasizes the importance of applying the operations research methodology to the real‐world issues. Therefore, this work is valuable to both academics and practitioners.

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Section: Methodology and Literature Reviewmentioning

confidence: 99%

Decision support system for continuous production

Bakhrankova

2010

Industrial Management &Amp; Data Systems

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“…Hence Belloli et al, [27] used a nonlinear gain scheduling controller to maximize accuracy and precision gravimetric feeders are the best available solution, which measure the weight of the hopper and the feeder. Jing-hua Hao et al, [28] dealt a hybrid approach for solving the bottleneck stage scheduling problem (BSP) using the surrogate modelling technique. Sascha Herrmann and Christoph Schwindt [29] considered the continuous process case of the short-term production planning of multi-product continuous plants in the process industries.…”

Section: Critical Machines Based Schedulingmentioning

confidence: 99%

Critical Machine Based Scheduling -A Review

Vivek

Saravanan²,

Chandrasekaran

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. This article aims to identify the natural occurrence of the critical machines in scheduling. The exciting scheduling in the real time manufacturing environment is focused on considering equal weight-age of all the machines, but very few researchers were considered the real time constraint(s) like processor/ machine/ workstation availability, etc.,. This article explores the gap between the theory and practices by identifying the critical machine in scheduling and helps the researcher to find the suitable problem in their case study environment. Through the literature survey, it is evident that, in scheduling the occurrence of the critical machine is in nature. The critical machine is found in various names and gives a various range of weight-age based on the particular manufacturing environment and it plays a vital role in scheduling which includes one or more circumstances of occurrence in the production environment. Very few researchers were reported that in manufacturing environment, the critical machine occurrence is in nature, but most of the researchers were focused to optimize the manufacturing environment by only reducing the cycle time. In realtime manufacturing environment, the scheduling of critical machine(s) was keenly monitored and some weight-age was considered.

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Planning and Scheduling Continuous Operations in the Process Industries

Cited by 2 publications

References 12 publications

Decision support system for continuous production

Decision support system for continuous production

Critical Machine Based Scheduling -A Review

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