TY - JOUR
T1 - A global optimization for sustainable multi-domain global manufacturing
AU - Kristianto, Yohanes
AU - Gunasekaran, Angappa
N1 - Funding Information:
The authors are most grateful to the two anonymous reviewers, who provided helpful comments on the presentation of this paper. This research is supported by the Academy of Finland under Contract agreement and decision no. 269693
Publisher Copyright:
© 2015 Elsevier Ltd
PY - 2018/1
Y1 - 2018/1
N2 - A multi-period and domain nonlinear optimization model is developed in this article. The model incorporates the design of forward–reverse manufacturing networks topology, product platform and operation capacity planning. The model takes into account the lead times and costs for each period of planning and is formulated as mixed integer nonlinear programming (MINLP). A two stages branch and bound (B–B) with cutting planes and under-estimators is proposed, which exploits the problem structure by solving problem relaxation at the first stage upper bound (UB) and generates cutting planes and under-estimators at the second stage lower bound (LB). The application in a three-echelon forward–reverse global manufacturing network shows that the proposed algorithm is capable of efficiently handling large scale and non-convex problem formulation in order to achieve a global optimum. Some important results from the model are presented in terms of their impacts on the sustainability of global manufacturing.
AB - A multi-period and domain nonlinear optimization model is developed in this article. The model incorporates the design of forward–reverse manufacturing networks topology, product platform and operation capacity planning. The model takes into account the lead times and costs for each period of planning and is formulated as mixed integer nonlinear programming (MINLP). A two stages branch and bound (B–B) with cutting planes and under-estimators is proposed, which exploits the problem structure by solving problem relaxation at the first stage upper bound (UB) and generates cutting planes and under-estimators at the second stage lower bound (LB). The application in a three-echelon forward–reverse global manufacturing network shows that the proposed algorithm is capable of efficiently handling large scale and non-convex problem formulation in order to achieve a global optimum. Some important results from the model are presented in terms of their impacts on the sustainability of global manufacturing.
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U2 - 10.1016/j.cor.2015.12.001
DO - 10.1016/j.cor.2015.12.001
M3 - Article
AN - SCOPUS:84983134519
SN - 0305-0548
VL - 89
SP - 307
EP - 323
JO - Computers and Operations Research
JF - Computers and Operations Research
ER -