4.6 Article

Taking advantage of order consolidation in simultaneous batching and scheduling of multiproduct batch plants

Journal

COMPUTERS & CHEMICAL ENGINEERING
Volume 156, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compchemeng.2021.107564

Keywords

multiproduct batch plants; batching; production scheduling; order consolidation; MILP model

Funding

  1. Argentine Agency for Scientific and Technological Promotion (ANPCyT)
  2. Universidad Tecnologica Nacional (UTN) from Argentina [PICT-2017-4004, SIUTIFE0005233TC, SIUTIFE0 005246TC]

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This study examines the impact of order consolidation on optimizing production schemes and improving production efficiency in multiproduct multistage batch plants. By using a discrete-time mixed-integer linear programming model, simultaneous solutions to batching and scheduling problems were achieved, showing that order consolidation can reduce batch requirements and improve the utilization efficiency of plant production capacity.
The simultaneous solution of batching and scheduling problems in multiproduct multistage batch plants remains being a challenge for both achieving computationally efficient models and considering all possi-ble production scenarios. When multiple orders for each product are addressed, they are treated individ-ually, which results in solutions that can be improved if all orders are consolidated in a total demand for that product. Thus, fewer batches should be required to satisfy the demand, leading to a more efficient use of the plant production capacity. This work analyzes the advantages and consequences of adopt-ing order consolidation when it is supported by the production scheme and the product characteristics. A discrete-time mixed-integer linear formulation (MILP) is presented for the simultaneous solution of batching and scheduling problems, in which order consolidation of each product is admitted. Through several examples, results are assessed and compared with those obtained when the orders are processed individually. (c) 2021 Elsevier Ltd. All rights reserved.

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