Optimisation frameworks for integrated planning with allocation of transportation resources for industrial gas supply chains

•Integrated production-distribution planning and transportation resource allocation.•An MILFP model with Dinkelbach and reformulation-linearisation methods.•Approach based on a multi-objective optimisation with the -constraint method.•Performance demonstration and comparison with industry-relevant c...

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Bibliographic Details
Published in:Computers & chemical engineering Vol. 164; p. 107897
Main Authors: Lee, Yena, Pinto, Jose M., Papageorgiou, Lazaros G.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.08.2022
Subjects:
Dinkelbach’s algorithm
Integrated supply chain planning
Mixed-integer linear fractional programming
Multi-objective optimisation
Reformulation-linearisation method
Transportation resource allocation
Dinkelbach’s algorithm
Reformulation-linearisation method
MILP
MILFP
IRP
Integrated supply chain planning
MINLP
Transportation resource allocation
MOO
Multi-objective optimisation
RTP
NLP
VMI
Mixed-integer linear fractional programming
VRP
LFP
ISSN:0098-1354
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Summary:•Integrated production-distribution planning and transportation resource allocation.•An MILFP model with Dinkelbach and reformulation-linearisation methods.•Approach based on a multi-objective optimisation with the -constraint method.•Performance demonstration and comparison with industry-relevant case studies. This work addresses the integrated optimisation of production-distribution planning and allocation of transportation resources for industrial gas supply chains. The production-distribution planning decisions include the production plan, purchasing plan for both a liquefied product and raw material from external suppliers, distribution plan by railcars and trucks, and demand allocation. In contrast, the allocating decisions of transportation resources involve the number of trucks and railcars at each plant, depot, and third-party supplier. First, we propose a mixed-integer nonlinear programming (MINLP) model, and then the MINLP model is reformulated as a mixed-integer linear fractional programming (MILFP) model. Furthermore, we present a multi-objective optimisation (MOO) model as an alternative approach. As solution strategies, we adopt Dinkelbachs algorithm and the reformulation-linearisation method for the MILFP model, whereas the ε-constraint method is used for the MOO model. Finally, industry-relevant case studies illustrate the applicability and performance of the proposed models and solution methods.
ISSN:0098-1354
DOI:10.1016/j.compchemeng.2022.107897