Continuous-Time Heuristic Model for Medium-Term Capacity Planning of a Multi-Suite, Multi-Product Biopharmaceutical Facility

Jankauskas, Karolis; Papageorgiou, Lazaros G.; Farid, Suzanne S.

doi:10.1016/b978-0-444-63965-3.50219-1

Cited by 5 publications

(7 citation statements)

References 4 publications

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“…This paper is an extension of the earlier work (Jankauskas et al, 2017) which presented a variable-length chromosome structure and a set of new genetic operators to automatically determine the optimal order, number, and length of production campaigns for a single-objective biopharmaceutical capacity planning and scheduling problem. In this section, the key components of the GA are described.…”

Section: Methodsmentioning

confidence: 99%

“…Siganporia et al (2014) proposed a large-scale discrete-time MILP model to optimise long-term capacity plans for a portfolio of biopharmaceutical products, with either batch or perfusion bioprocesses, across multiple facilities to meet quarterly demand. Jankauskas et al (2017) developed a variable-length GA-based optimisation approach for medium-term capacity planning of a multi-product, multisuite biopharmaceutical facility using a continuous-time representation. Taking inspiration from GA-based approaches to job-shop scheduling, Oyebolu et al (2017) proposed a problem-tailored construction heuristic for scheduling demands of multiple products sequentially across several facilities to generate a long-term manufacturing schedule.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, the deterministic single-objective variable-length GA presented in Jankauskas et al (2017) is extended with multi-objective and Monte Carlo simulation components for the generation of medium-term production schedules that are robust to the variations in product demand. For the sake of brevity, the integrated Monte Carlo simulation and multi-objective GA approach will be referred to as the stochastic GA while the multi-objective GA without the embedded Monte Carlo simulation will be referred to as the deterministic GA.…”

Section: Introductionmentioning

confidence: 99%

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Multi-objective biopharma capacity planning under uncertainty using a flexible genetic algorithm approach

Jankauskas

Farid

2019

Computers & Chemical Engineering

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This paper presents a flexible genetic algorithm optimisation approach for multiobjective biopharmaceutical planning problems under uncertainty. The optimisation approach combines a continuous-time heuristic model of a biopharmaceutical manufacturing process, a variable-length multi-objective genetic algorithm, and Graphics Processing Unit (GPU)-accelerated Monte Carlo simulation. The proposed approach accounts for constraints and features such as rolling product sequencedependent changeovers, multiple intermediate demand due dates, product QC/QA release times, and pressure to meet uncertain product demand on time. An industrially-relevant case study is used to illustrate the functionality of the approach. The case study focused on optimisation of conflicting objectives, production throughput and product inventory levels, for a multi-product biopharmaceutical facility over a 3-year period with uncertain product demand. The advantages of the multiobjective GA with the embedded Monte Carlo simulation were demonstrated by comparison with a deterministic GA tested with Monte Carlo simulation postoptimisation.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-objective biopharma capacity planning under uncertainty using a flexible genetic algorithm approach

Jankauskas

Farid

2019

Computers & Chemical Engineering

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“…First, Vieira et al (2016) solved a set of example problems based on a Resource Task Network (RTN) continuous-time single-grid formulation focusing on addressing specific operational characteristics of bioprocesses. Jankauskas et al (2017) then used a continuous-time model optimised by a GA which is underpinned by a dynamic chromosome structure (i.e., vector of decision vari-ables) that is allowed to vary in length. Also, Gatica et al (2003) and Levis and Papageorgiou (2004) present a mathematical programming approach for the capacity planning problem with a focus on longterm planning and capacity investment decisions under uncertainty of clinical trials rather than scheduling.…”

Section: Literature Surveymentioning

confidence: 99%

“…Math-ematical programming models under uncertainty have used methods such as chance-constrained programming (Lakhdar et al, 2006) or dealt with uncertainty through scenario analysis (Siganporia et al, 2014). Recently there have been genetic algorithm (GA) (Holland, 1975) approaches to production planning predominantly for batch processes such as work done in studies by Oyebolu et al (2017) and Jankauskas et al (2017Jankauskas et al ( , 2019. Dynamic and stochastic simulation models for perfusion culture have focused on capturing the impact of failures and variability on cost of goods rather than on optimal scheduling or planning (Pollock et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Dynamic scheduling of multi-product continuous biopharmaceutical facilities: A hyper-heuristic framework

Oyebolu

Allmendinger

Farid

et al. 2019

Computers & Chemical Engineering

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The biopharmaceutical industry is increasingly interested in moving from batch to semi-continuous manufacturing processes. These continuous bioprocesses are more failure-prone and process failure is more consequential. In addition, the probability of failure is dependent on process run time which generally is determined independent of scheduling considerations. This work presents a discrete-event simulation of continuous bioprocesses in a scheduling environment. Dynamic scheduling policies are investigated to make operational decisions in a multiproduct manufacturing facility and react to process failure events and uncertain demand. First, different scheduling policies are adapted from the stochastic lot sizing literature and a novel look-ahead scheduling policy is proposed. Then, policy parameters (including process run time) are tuned using evolutionary algorithms. Our results demonstrate that the tuned policies perform much better than a policy that estimates policy parameters based on service level considerations and a policy based on a fixed cyclical sequence.

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Analysis of commonly used scheduling models for multistage biopharmaceutical processes

2021

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Several works have been reported in the literature over the past two decades to schedule a multiproduct facility in the biopharmaceutical industry. The present work attempts to analyze a few commonly used scheduling models, based on different time representations, for midterm planning or long‐term scheduling of multistage, multiproduct biopharmaceutical facilities for multiperiod demand. Several model inconsistencies/limitations in the published literature and in the reported Gantt charts, such as (i) real‐time storage violation, (ii) early product delivery, (iii) inadequate mapping of upstream and downstream tasks, (iv) no initial setup time, and (v) incomplete sequencing/modelling of storage tasks, thus (iv) overestimating the reported objective values in their results, are identified. Accordingly, one of the unit‐specific‐event‐based literature models is improved in this work to address these limitations. An improved model is proposed with enhanced/new features such as modified material balances, proper sequencing of storage based on storage bypassing allowed for intermediates and bypassing not allowed for products, modified shelf‐life constraints, initial setup time constraints, and updated bounds on storage, giving better results compared to the published literature models.

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Continuous-Time Heuristic Model for Medium-Term Capacity Planning of a Multi-Suite, Multi-Product Biopharmaceutical Facility

Cited by 5 publications

References 4 publications

Multi-objective biopharma capacity planning under uncertainty using a flexible genetic algorithm approach

Multi-objective biopharma capacity planning under uncertainty using a flexible genetic algorithm approach

Dynamic scheduling of multi-product continuous biopharmaceutical facilities: A hyper-heuristic framework

Analysis of commonly used scheduling models for multistage biopharmaceutical processes

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