Lee, Y;
Pinto, JM;
Papageorgiou, LG;
(2022)
Optimisation frameworks for integrated planning with allocation of transportation resources for industrial gas supply chains.
Computers and Chemical Engineering
, 164
, Article 107897. 10.1016/j.compchemeng.2022.107897.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Optimisation frameworks for integrated planning with allocation of transportation resources for industrial gas supply chains |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.compchemeng.2022.107897 |
| Publisher version: | https://doi.org/10.1016/j.compchemeng.2022.107897 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Mixed-integer linear fractional programming, Dinkelbach’s algorithm, Reformulation-linearisation method, Multi-objective optimisation, Integrated supply chain planning, Transportation resource allocation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10159933 |
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