A new lot sizing and scheduling heuristic for multi-site biopharmaceutical production

被引:7
|
作者
Oyebolu, Folarin B. [1 ]
de Jeude, Jeroen van Lidth [2 ]
Siganporia, Cyrus [3 ]
Farid, Suzanne S. [3 ]
Allmendinger, Richard [4 ]
Branke, Juergen [1 ]
机构
[1] Univ Warwick, Warwick Business Sch, Coventry CV4 7AL, W Midlands, England
[2] Univ Warwick, Ctr Complex Sci, Coventry CV4 7AL, W Midlands, England
[3] UCL, Dept Biochem Engn, London WC1E 7JE, England
[4] Univ Manchester, Alliance Manchester Business Sch, Manchester M13 9SS, Lancs, England
基金
英国工程与自然科学研究理事会;
关键词
Evolutionary algorithm; Heuristics; Scheduling; Biopharmaceutical manufacture; Capacity planning; Construction heuristic; SEQUENCE-DEPENDENT SETUPS; GENETIC ALGORITHMS; META-HEURISTICS; OPTIMIZATION;
D O I
10.1007/s10732-017-9338-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Biopharmaceutical manufacturing requires high investments and long-term production planning. For large biopharmaceutical companies, planning typically involves multiple products and several production facilities. Production is usually done in batches with a substantial set-up cost and time for switching between products. The goal is to satisfy demand while minimising manufacturing, set-up and inventory costs. The resulting production planning problem is thus a variant of the capacitated lot-sizing and scheduling problem, and a complex combinatorial optimisation problem. Inspired by genetic algorithm approaches to job shop scheduling, this paper proposes a tailored construction heuristic that schedules demands of multiple products sequentially across several facilities to build a multi-year production plan (solution). The sequence in which the construction heuristic schedules the different demands is optimised by a genetic algorithm. We demonstrate the effectiveness of the approach on a biopharmaceutical lot sizing problem and compare it with a mathematical programming model from the literature. We show that the genetic algorithm can outperform the mathematical programming model for certain scenarios because the discretisation of time in mathematical programming artificially restricts the solution space.
引用
收藏
页码:231 / 256
页数:26
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