Scope for industrial applications of production scheduling models and solution methods

Harjunkoski, Iiro; Maravelias, Christos T.; Bongers, Peter; Castro, Pedro M.; Engell, Sebastian; Grossmann, Ignacio E.; Hooker, John; Méndez, Carlos A.; Sand, Guido; Wassick, John M.

doi:10.1016/j.compchemeng.2013.12.001

Cited by 442 publications

(272 citation statements)

References 176 publications

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“…Scheduling plays an important role in all industrial production facilities [1]. Contingent on the scale of operation, optimization based scheduling methods can even achieve multi-million dollars increase in profits [2].…”

Section: Introductionmentioning

confidence: 99%

A General State-Space Formulation for Online Scheduling

Gupta

Maravelias

2017

Processes

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Abstract:We present a generalized state-space model formulation particularly motivated by an online scheduling perspective, which allows modeling (1) task-delays and unit breakdowns; (2) fractional delays and unit downtimes, when using discrete-time grid; (3) variable batch-sizes; (4) robust scheduling through the use of conservative yield estimates and processing times; (5) feedback on task-yield estimates before the task finishes; (6) task termination during its execution; (7) post-production storage of material in unit; and (8) unit capacity degradation and maintenance. Through these proposed generalizations, we enable a natural way to handle routinely encountered disturbances and a rich set of corresponding counter-decisions. Thereby, greatly simplifying and extending the possible application of mathematical programming based online scheduling solutions to diverse application settings. Finally, we demonstrate the effectiveness of this model on a case study from the field of bio-manufacturing.

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Section: Introductionmentioning

confidence: 99%

A General State-Space Formulation for Online Scheduling

Gupta

Maravelias

2017

Processes

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“…Transitions arise naturally in scheduling problems and have been modeled and discussed in a number of PSE papers (Karamarkar and Schrage, 1985;Sahinidis and Grossmann, 1991;Kondili et al, 1993;Lee at al., 1996;Wolsey, 1997;Kelly and Zyngier, 2007;Liu et al, 2010;Harjunkoski et al, 2014). Two contributions are worth bringing up in more details; Kondili et al (1993), using the discrete time representation, introduced a mechanism for identifying transitions that relies on the evaluation of any two time-periods, as opposed to the traditional form in which only consecutive time-periods are evaluated (Lee at al., 1996).…”

Section: Studied Approachesmentioning

confidence: 99%

“…In such an aggressive environment, optimization of the plan and schedule is a valuable differentiator that creates nontrivial cost reduction opportunities. Historically, oil industry scheduling has normally been considered for subsystems of the refinery due to the high degree of complexity involved in addressing the problem globally, although efforts treating more than one subsystem of the refinery simultaneously can be identified (Gothe-Lundgren et al, 2002, Simão et al, 2007Luo and Rong, 2007;Shah et al, 2009, Harjunkoski et al, 2014, Sha and Ierapetritou, 2015Gao et al, 2015). Jia and Ierapetritou (2004) decompose the refinery scheduling problem into three subsystems: (i) crude oil unloading, mixing and inventory control; (ii) production unit scheduling, and (iii) product blending and distribution.…”

Section: Introductionmentioning

confidence: 99%

A novel transition identification mechanism for the diesel blending and distribution scheduling problem using the discrete time representation with two time-scales granularity

Dimas

Murata

Neiro

2017

Braz. J. Chem. Eng.

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-Transitions between tasks arise in many different scheduling problems. Sometimes transitions are undesired because they incur costs; sometimes they are undesired because they require setup time, and sometimes both. In one way or the other, frequently, transitions need to be identified and penalized in order for their frequency to be minimized. The present work is concerned with the study of alternative optimization formulations to address transitions with the blending and distribution scheduling of oil derivatives. Our study starts by revisiting a model proposed in the literature that was built considering a very short time horizon (24 h). Next, improvements concerning the transition constraints are evaluated and a new approach is proposed with the purpose of extending model applicability to cases where longer time horizons are of interest. The new proposed mechanism of evaluating transitions relies on aggregating the detailed discrete time scale (hours) to a higher and less detailed level (days). Transitions are then evaluated on the lower level of aggregation with the benefit of reducing the number of required constraints. It must also be emphasized that the proposed model is built on the basis of a set of heuristics that have direct impact on solution and solution time. Results attained for a four-day time horizon demonstrate cost savings on the order of 32% when compared with four sequenced schedules of a one-day time horizon each. Savings are mainly obtained as a consequence of the reduction of the predicted number of transitions.

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“…The resulting models are difficult to solve, thereby motivating intensive research in this area. Harjunkoski et al (2014) and Méndez et al (2006) present comprehensive reviews of the models and applications of process scheduling. We focus on analyzing the general characteristics of modeling frameworks to identify the key elements for multilevel optimization.…”

Section: Process Schedulingmentioning

confidence: 99%

Perspectives in multilevel decision-making in the process industry

Brunaud¹,

Grossmann²

2017

Front. Eng

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Decisions in supply chains are hierarchically organized. Strategic decisions involve the long-term planning of the structure of the supply chain network. Tactical decisions are mid-term plans to allocate the production and distribution of materials, while operational decisions are related to the daily planning of the execution of manufacturing operations. These planning processes are conducted independently with minimal exchange of information between them. Achieving a better coordination between these processes allows companies to capture benefits that are currently out of their reach and improve the communication among their functional areas. We propose a network representation for the multilevel decision structure and analyze the components that are involved in finding integrated solutions that maximize the sum of the benefits of all nodes of the decision network. Although such task is very challenging, significant research progress has been made in each component of this structure. An overview of strategic models, mid-term planning models, and scheduling models is presented to address the solution of each node in the decision network. Coordination mechanisms for converging the integrated solutions are also analyzed, including solving large-scale models, multiobjective optimization, bi-level programming, and decomposition. We conclude by summarizing the challenges that hinder the full integration of multilevel decision making in supply chain management.

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Scope for industrial applications of production scheduling models and solution methods

Abstract: who inspired us all and shared his valuable experiences in the border of academia and industry and cannot be with us today to see the final result of this joint effort.

Cited by 442 publications

References 176 publications

A General State-Space Formulation for Online Scheduling

A General State-Space Formulation for Online Scheduling

A novel transition identification mechanism for the diesel blending and distribution scheduling problem using the discrete time representation with two time-scales granularity

Perspectives in multilevel decision-making in the process industry

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