Fast genetic algorithm approaches to solving discrete-time mixed integer linear programming problems of capacity planning and scheduling of biopharmaceutical manufacture

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

doi:10.1016/j.compchemeng.2018.09.019

Cited by 33 publications

(15 citation statements)

References 17 publications

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“…Due to shared variables w in Equations (10) and (11), these equations cannot be solved separately. Therefore, two different sets of shared variables are introduced in model of TS and DS and reformulate Equations (10) and (11). Moreover, in traditional approach, TSO optimizes its objective and provide shared variables, T k called targets.…”

Section: Generalized Hierarchical Optimization Modelmentioning

confidence: 99%

“…This makes coordinator problem a mixed integer linear problem (MILP). As discussed in previous studies for large‐scale MILP, evolutionary algorithms perform better than traditional optimization techniques . Hence, coordinator will send targets (local bests) to TSO and DSOs if optimization algorithm does not provide global solution.…”

Section: Introductionmentioning

confidence: 99%

“…As discussed in previous studies for large-scale MILP, evolutionary algorithms perform better than traditional optimization techniques. 11 Hence, coordinator will send targets (local bests) to TSO and DSOs if optimization algorithm does not provide global solution. This leads to an error between local best targets and expected global best targets of coordinator.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

TSO and DSO with large‐scale distributed energy resources: A security constrained unit commitment coordinated solution

Nawaz

Wang

et al. 2019

Int Trans Electr Energ Syst

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Summary Modern power system requires optimally coordinated operation between transmission and distribution systems due to large‐scale integration of distributed energy resources (DERs). Commonly, transmission system operator (TSO) solves its own cost optimization problem and evaluates required targets for each distribution system operator (DSO). Thus, TSO problem becomes more complex and highly scaled with increase in number of connected distribution systems, which requires more computational time and resources. Therefore, this paper proposed a method to reduce computational burden on TSO and solve coordinated security constrained unit commitment (C‐SCUC) problem by involving a coordinator in upper level hierarchy. Coordinator is required to solve system‐wide problem to adjust targets for transmission system (TS) and all distribution systems (DS). Besides, TSO and DSO only need to solve their own cost optimization problems. In this paper, we have proposed analytical target cascading (ATC) along with hybridized particle swarm optimization with differential evolution (PSO‐DE) for solving increased complexity of the coordinated problem. ATC provides coordination between transmission and distribution sections of power system while hybrid PSO‐DE provides better accuracy of optimization in ATC layers. For evaluation of efficacy of proposed method, this paper has taken IEEE 118‐bus system as TS and IEEE 33‐bus system as DS. Results has depicted that proposed method could help TSO in reducing computational resources and improving computational time.

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Section: Generalized Hierarchical Optimization Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

TSO and DSO with large‐scale distributed energy resources: A security constrained unit commitment coordinated solution

Nawaz

Wang

et al. 2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

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“…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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“…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. Jankauskas et al (2018) developed fast genetic algorithm approaches for generating near optimal solutions to medium-and long-term biopharmaceutical planning problems formulated originally as discrete-time MILP models.…”

Section: Introductionmentioning

confidence: 99%

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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Fast genetic algorithm approaches to solving discrete-time mixed integer linear programming problems of capacity planning and scheduling of biopharmaceutical manufacture

Cited by 33 publications

References 17 publications

TSO and DSO with large‐scale distributed energy resources: A security constrained unit commitment coordinated solution

TSO and DSO with large‐scale distributed energy resources: A security constrained unit commitment coordinated solution

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

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

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