Supply chain networks can be incredibly complex, with multiple manufacturing and distribution points, and the location of each node in those networks has a significant effect on everything from profitability to product cost to environmental impact. New research from North Carolina State University shows that efficient mathematical tools do almost as well as the most computationally demanding optimization models in determining the best places to locate items in a supply chain, and can provide companies with relevant information much more quickly.
“Our work focuses on supply chains that improve economic and environmental performance by embracing sustainability,” says Amir Sadeghi, first author of the study and Ph.D. student in NC State’s Edward P. Fitts Department of Industrial and Systems Engineering. “We look at supply chains where elements of their products can be reused, such as printing technologies that reuse printer cartridges. These supply chains involve multiple manufacturing facilities, as well as many more distribution sites where consumers can purchase the products and return them for recycling or reuse.These multi-tiered supply chains are extremely complex, and the location of each point in the supply chain has significant ramifications in terms of cost, transportation time, etc.
“While there are models that allow us to identify the exact optimal solution for the location of each point in the supply chain, those models are computationally demanding. So we wanted to see how well more computationally efficient tools could work and whether they could be a suitable replacement for use in supply chain management decision making”.
Specifically, the researchers wanted to test the performance of two well-established heuristics, which are algorithm “shortcuts” capable of providing a good, but not necessarily optimal, answer to a complex problem quickly. They compared these two heuristics, which are called the Gray Wolf Optimizer (GWO) and the Whale Optimization Algorithm (WOA), with a computational model capable of finding the exact optimal solution. The researchers tested the heuristics with the exact optimization model for 15 different problems, reflecting a variety of multi-tiered supply chain challenges.
The exact optimization model and heuristics were designed to find the best sites for each point in a supply chain and then determine the cost of implementing that supply chain. All three tools account for many variables that influence cost, such as transportation distance and real estate and construction costs.
The researchers were surprised at how well the heuristics worked. There was some variability in the performance of the heuristics, depending on the specific supply chain challenge used in each test. However, at its peak, GWO was able to establish supply chain sites with costs that were within 0.01% of the exact optimization model, while WOA’s costs were within 0.07% of the exact optimization model. . And, on average, the heuristics were able to provide their solutions in about half the time of the exact optimization model.
“If you have an established supply chain and one of your nodes goes offline unexpectedly (a store closes, a manufacturing site shuts down due to flooding, etc.), you need to act quickly to restore the supply chain,” Sadeghi says. . “If you’re dealing with a complex supply chain, and you don’t have access to a supercomputer, there can be a significant advantage in using a heuristic that can give you a very good answer on where to replace a missing link in a matter of hours, rather than than waiting days to run an exact optimization model”.
The researchers also found an unexpected advantage in the heuristics: they were more robust than the exact optimization model. In practical terms, that means that the answers provided by the heuristics were more likely to hold when some of the variables changed. For example, if there were a slight change in the location of a node in a supply chain network created by a heuristic, there would be a slight change in the related cost. However, similar changes to supply chain networks developed by the exact optimization model were more likely to cause significant changes in costs.
“Taken together, our findings here suggest that there may be significant advantages for supply chain managers to adopting the use of heuristics,” says Rob Handfield, co-author of the study.
“We don’t expect anyone to abandon the use of exact optimization models for long-term planning, but at least heuristics can be a useful way to test the robustness of ‘optimal’ networks,” says Handfield, who is the Bank Professor. Distinguished in Operations and Supply Chain Management from the University of America at NC State’s Poole College of Management. “And heuristics can be particularly valuable to supply chain managers who are forced to respond quickly to unexpected outages in their networks.”
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