A global optimization for sustainable multi-domain global manufacturing
Introduction
Global manufacturing is an alternative solution to parent enterprises that enables them to reduce costs, increase revenues and improve reliability. However, substantial geographical distances in these global situations not only increase transportation costs, but complicate decision making due to inventory cost trade-offs that are the result of increased lead-times in the supply chain. A lack of infrastructures in developing countries diminishes the effectiveness of business processes and also potentially raises environmental problems such as carbon dioxide emissions into the atmosphere, as well as water contamination that are related to the capacity of proper waste reuse or the disposal thereof [13]. From time to time, the rate of consumption of non-recyclable raw materials continues to increase; in fact, at some levels such consumption happens to be higher than its replenishment. Therefore, a global environmentally responsible solution should be implemented as countries׳ environmental initiatives. However, environmental initiatives in different countries provide different levels of potential success, which can affect said countries׳ economic indicators [47].
In terms of problem complexity, environmentally conscious global manufacturing is more difficult to manage than local manufacturing [9], [27]. In addition to economic dynamics across the globe, which includes the variability and uncertainty of currency exchange rates, economic and political instability [9], network topology is a crucial factor for reducing carbon foot-prints and at the same time, improves manufacturing competitiveness. Internally, such variability and uncertainty is likely to affect the entire decision making process, including those pertaining to product prices, delivery lot size, order interval and capacity investment. However, managing the mentioned risks will give a global manufacturing company a competitive advantage.
The research theme of this article is that of developing a dynamic and stochastic model of global manufacturing. The model aims to complement previous contributions related to integrated multi-echelon supply chain design with inventories under endogenous and exogenous uncertainties [45] by comprehensively approaching product design, manufacturing and logistics as a whole, rather than separate parts. The practical contribution of the model is illustrated by exhibiting the following properties: 1) multi-domain; 2) dynamic (multi-period); 3) has several sources of endogenous and exogenous uncertainties; 4) large scale and involves thousands of variables and decision making parameters. The solution methods available for these types of problems are still at relatively early stages of development [5] and their capabilities still limited, due to their potentially significant computational expenses [35]. Therefore, the solution method that combines branch and reduce technique [38] and interior point method [42] in order to handle a large scale model is the theoretical contribution made by this article. The linear assumptions of design, quality and product pricing are the gap between the current model and practice.
Section snippets
Challenges for decision support systems of sustainable global manufacturing
During the past decade, global manufacturing research trends are moving from optimization models of economic operations [12], [17], [48] to strategic alliances in product development and sustainable manufacturing (Table 1). This shift in trends, which moved from a cost advantage to improved access to customers, has been adopted by the Supply Chain Operations Reference (SCOR) model, which defines performance as a function of reliability, responsiveness, flexibility, cost and assets [37].
Problem statement
This section is divided into five sub-sections: 1) problem environment; 2) objective of the model; 3) assumptions of the model; 4) constraints used in the model; 5) decision variables. Several international features are included in the model, as per Villegas and Ouenniche [41].
MINLP model
The unification of product and manufacturing design can be achieved under the umbrella of generic product and manufacturing flows (GPM). As shown in Fig. 1, GPM instantiation manifests the integration of product configuration and manufacturing networks. At a certain node of particular manufacturing operations, there are a certain number of generic product structures (GPS). A GPS can be specified within a range of design parameters (DPs), which can be viewed as product customization and as a
Solution method
This section outlines the solution strategy and its details for solving the global manufacturing problem. The solution method is a general method for non-convex MINLP. Therefore, all used notations are different to those in the previous section, but the specific global manufacturing problem is re-used.
Results and discussion
To further demonstrate the potential of the algorithm, preliminary results with a large-scale problem are presented in Fig. 1. Another global optimization algorithm (branch and reduce) is used to compare the performance of both the current and existing methods. In both cases, the termination criterion is UB-LB <=10−12.
Conclusion
An algebraic method was implemented to develop decision support systems (DSS) for sustainable global manufacturing. The sustainability of global manufacturing networks was shown in a multi-period setting of the model, which involves product remanufacturing. The model was formulated as a non-convex MINLP that involved discrete variables of topology selection, production capacity planning, transportation routings and product platform design. A new solution algorithm based on the branch and bound
Acknowledgements
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
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