Solving the integrated raw material procurement and lot-sizing problem with the ai...
Integrated lot sizing and blending problems under demand uncertainty
Methods for solving the integrated procurement and lot-sizing problem with perisha...
| Full text | |
| Author(s): |
Cunha, Artur Lovato
[1]
;
Santos, Maristela Oliveira
[1]
;
Morabito, Reinaldo
[2]
;
Barbosa-Povoa, Ana
[3]
Total Authors: 4
|
| Affiliation: | [1] Univ Sao Paulo, Inst Ciencias Matemt & Comp, BR-13560970 Sao Carlos, SP - Brazil
[2] Univ Fed Sao Carlos, Dept Engn Prod, BR-13565905 Sao Carlos, SP - Brazil
[3] Univ Tecn Lisboa, Inst Super Tecn, Ctr Estudos Gestao, P-1049101 Lisbon - Portugal
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | European Journal of Operational Research; v. 269, n. 3, p. 923-938, SEP 16 2018. |
| Web of Science Citations: | 2 |
| Abstract | |
This paper addresses the integration of two relevant problems within industry: raw material purchasing and production planning. The raw material purchasing problem considers the presence of several suppliers, each one with its own cost of materials and discount rates. The production planning problem considers batch production, in a system with a multistage product structure, multiple production tasks, and multipurpose storage tank restrictions. In this context the main decisions to be taken involve the choice of tasks to produce each one of the products, when they should be performed to meet all demands while considering production and multipurpose storage units capacity availability and the raw materials different discount rates. The main objective is then to guarantee demand at reduce purchasing and operating costs where the choice of less expensive tasks is at stake. Exploring discount levels, avoiding setup costs and reducing inventories are considered. A mixed integer programming (MIP) model is developed to integrate the two mentioned problems. Additionally, a subsequent model is also developed, which represents purchasing and production decisions independently, as it is often the case in real companies. The later model allow us to evaluate the efficiency of the proposed integrated model. Computational experiments were performed on CPLEX 12.6 optimization package and the results clearly show the advantages of the integrated model. (C) 2018 Elsevier B.V. All rights reserved. (AU) | |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |