A novel approach to scheduling of zero-wait batch processes under processing time variations

Ryu, Jun-Hyung; Pistikopoulos, Efstratios N.

doi:10.1016/j.compchemeng.2006.05.006

Cited by 37 publications

(17 citation statements)

References 17 publications

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“…Raaymakers and Hoogeveen (2000) proposed a simulated annealing algorithm to solve a scheduling problem characterised as multiprocessor no-wait job shop problem with overlapping operations. Ryu and Pistikopoulos (2007) introduced parametric programming for solving this problem under uncertainty. Recent studies on scheduling of quality tests have been restricted to the pharmaceutical research and development pipeline.…”

Section: Literature Reviewmentioning

confidence: 99%

Scheduling with multiple tasks per job – the case of quality control laboratories in the pharmaceutical industry

Ruíz-Torres

Ablanedo-Rosas

Otero

2012

International Journal of Production Research

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This paper addresses a complex scheduling problem encountered in a major pharmaceutical industry setting. Specifically, the problem deals with assigning tasks to technicians as part of the quality control phase in order to minimise the total flowtime, and the number of jobs not meeting a required time window. The problem considers test batching, overlapping tests, and resource assignments constrained by test specific capability requirements. Furthermore, batching tasks of similar types is possible, but batch sizes are particular to each product-test type combination. This is a significant difference from previous literature in batching parallel machines. The particular problem described in the paper is highly relevant to the pharmaceutical industry and has not been previously addressed in the literature. Various approaches to solve this particular problem are described and compared via statistical analyses. Finally, the authors present a software prototype with implemented solution algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

Scheduling with multiple tasks per job – the case of quality control laboratories in the pharmaceutical industry

Ruíz-Torres

Ablanedo-Rosas

Otero

2012

International Journal of Production Research

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“…21 Multiparametric programming has already found applications in process scheduling under uncertainty. Ryu et al 22 and Ryu and Pistikopoulos 23 studied the special class of sequential processes with varying processing times and equipment availability. They derive a multiparametric (mp)-MILP scheduling problem that exhibits parameter dependent entries of the right-hand side (RHS) constraint vector.…”

Section: Introductionmentioning

confidence: 99%

Proactive scheduling of batch processes by a combined robust optimization and multiparametric programming approach

Wittmann-Hohlbein

Pistikopoulos

2013

AIChE Journal

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We address short-term batch process scheduling problems contaminated with uncertainty in the data. The mixed integer linear programming (MILP) scheduling model, based on the formulation of Ierapetritou and Floudas, Ind Eng Chem Res. 1998; 37(11):4341-4359, contains parameter dependencies at multiple locations, yielding a general multiparametric (mp) MILP problem. A proactive scheduling policy is obtained by solving the partially robust counterpart formulation. The counterpart model may remain a multiparametric problem, yet it is immunized against uncertainty in the entries of the constraint matrix and against all parameters whose values are not available at the time of decision making. We extend our previous work on the approximate solution of mp-MILP problems by embedding different uncertainty sets (box, ellipsoidal and budget parameter regulated uncertainty), and by incorporating information about the availability of uncertain data in the construction of the partially robust scheduling model. For any parameter realization, the corresponding schedule is then obtained through function evaluation. V C 2013 American Institute of Chemical Engineers AIChE J, 59: 2013 Keywords: process scheduling, mixed integer programming, multiparametric programming, robust optimization IntroductionThe area of scheduling of chemical and pharmaceutical processes has received significant attention in industry and academia with a number of excellent reviews summarizing the key contributions in this field. [1][2][3][4][5][6][7][8] Scheduling is likely to be subject to uncertainty attributed to endogenous factors such as varying processing times or production rates, as well as to exogenous factors arising from variations in the market demand, product prices, or time horizon, etc. A classification of sources of uncertainty in process operations is found in the work by Pistikopoulos. 9 The optimal scheduling policy for a chemical process based on nominal data may not be optimal or even feasible any more once a deviation from the nominal values has occurred. Proactive scheduling is motivated by the need to address uncertainty upfront in order to restrict disruptions and avoid rescheduling in response to disturbances. Provided knowledge about the probability distribution of the uncertain data is available, stochastic programming methods are widely used in proactive scheduling. In this category fall the works of Bonfill et al., 10 Vin and Ierapetritou, 11 and Balasubramanian and Grossmann 12 addressing demand uncertainty, as well as Bonfill et al., 13 Bonfill et al., 14 and Balasubramanian and Grossmann 15 addressing operational level related uncertainty such as processing time variability. Scenario based formulations suffer from an increased problem size with respect to a growing number of uncertain parameters involved. In the open literature, another approach to account for the presence of uncertainty in the model is to employ its robust counterpart formulation. The objective of robust optimization is to identify scheduling policies tha...

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“…The solution of multi-parametric scheduling problems has been previously discussed in [299] and [360]. In [183] the procedure for deriving and solving scheduling problems via multiparametric programming, using a state-space model representation [336] and a mp-MILP reformulation is presented.…”

Section: Process Scheduling Strategy (Pss)mentioning

confidence: 99%

“…Integration of scheduling and control: Similarly to MPC, multi-parametric programming approaches for scheduling have been presented in the past [202,203,299,300]. However, the recently developed representation of scheduling problems in a state-space model [335] has allowed for the applicability of the concepts of mp-MPC in scheduling [183].…”

Section: Recent Developmentsmentioning

confidence: 99%

Model-based multi-parametric programming strategies towards the integration of design, control and operational optimization

Diangelakis

Pistikopoulos

2017

Computer Aided Chemical Engineering

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A novel approach to scheduling of zero-wait batch processes under processing time variations

Cited by 37 publications

References 17 publications

Scheduling with multiple tasks per job – the case of quality control laboratories in the pharmaceutical industry

Scheduling with multiple tasks per job – the case of quality control laboratories in the pharmaceutical industry

Proactive scheduling of batch processes by a combined robust optimization and multiparametric programming approach

Model-based multi-parametric programming strategies towards the integration of design, control and operational optimization

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