MSGID: 1:317/3 6448a8ee   
   PID: hpt/lnx 1.9.0-cur 2019-01-08   
   TID: hpt/lnx 1.9.0-cur 2019-01-08   
    Computational 'short cuts' offer fast answers to complex supply chain   
   problems    
      
     Date:   
         April 25, 2023   
     Source:   
         North Carolina State University   
     Summary:   
         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   
         shows that efficient mathematical tools serve almost as well as more   
         computationally demanding optimization models for determining the   
         best places to locate elements in a supply chain, and can provide   
         businesses with the relevant information far more quickly.   
      
      
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   FULL STORY   
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   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 serve almost as   
   well as more computationally demanding optimization models for determining   
   the best places to locate elements in a supply chain, and can provide   
   businesses with the relevant information far 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 a Ph.D. student in NC State's Edward P. Fitts   
   Department of Industrial and Systems Engineering. "We looked 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 both buy the products and return them for recycling   
   or reuse. These multi-level supply chains are extremely complex, and the   
   location of every point in the supply chain has significant ramifications   
   in terms of cost, transportation time, and so on.   
      
   "While there are models that allow us to identify the exact optimal   
   solution for where each point in the supply chain should be located,   
   those models are computationally demanding. So we wanted to see how   
   well more computationally efficient tools might perform, and whether   
   they could be a suitable replacement for use in making supply chain   
   management decisions."  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 Grey Wolf   
   Optimizer (GWO) and the Whale Optimization Algorithm (WOA), against a   
   computational model capable of finding the exact optimal solution. The   
   researchers tested the heuristics against the exact optimization model   
   for 15 different problems, reflecting a range of multilevel supply   
   chain challenges.   
      
   The heuristics and the exact optimization model were all designed to find   
   the best sites for every point in a supply chain, and then determine the   
   cost of putting that supply chain in place. 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 their   
   best, the GWO was able to establish supply chain sites with costs that   
   were within 0.01% of the exact optimization model while the 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 drops   
   out unexpectedly -- a store closes, a manufacturing site is shut down   
   by flooding, etc. -- you need to act quickly to reestablish the supply   
   chain," says Sadeghi.   
      
   "If it's a complex supply chain -- and you don't have access to   
   a supercomputer -- there may be a significant advantage in using a   
   heuristic that can give you a very good answer about where to replace   
   a missing link within hours, rather than waiting days to run an exact   
   optimization model."  The researchers also found an unexpected advantage   
   to 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 up when some of the variables   
   changed. For example, if there was a slight shift in the location of a   
   node in a supply chain network created by a heuristic, there would be   
   a slight shift in the related cost. However, similar changes in supply   
   chain networks developed by the exact optimization model were more likely   
   to cause significant shifts in cost.   
      
   "Altogether, our findings here suggest there may be significant advantages   
   for supply chain managers in adopting the use of heuristics," says Rob   
   Handfield, who co-authored the study.   
      
   "We don't expect anyone to abandon the use of exact optimization   
   models for long-term planning, but at the very least heuristics may   
   be a useful way of testing the robustness of 'optimal' networks," says   
   Handfield, who is the Bank of America University Distinguished Professor   
   of Operations and Supply Chain Management in NC State's Poole College of   
   Management. "And heuristics may be particularly valuable for supply chain   
   managers who are forced to respond rapidly to unexpected disruptions in   
   their networks."   
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   ==========================================================================   
   Story Source: Materials provided by   
   North_Carolina_State_University. Original written by Matt Shipman. Note:   
   Content may be edited for style and length.   
      
      
   ==========================================================================   
   Journal Reference:   
      1. Amir Hossein Sadeghi, Erfan Amani Bani, Ali Fallahi, Robert   
      Handfield.   
      
         Grey Wolf Optimizer and Whale Optimization Algorithm for Stochastic   
         Inventory Management of Reusable Products in a Two-Level Supply   
         Chain.   
      
         IEEE Access, 2023; 1 DOI: 10.1109/ACCESS.2023.3269292   
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   Link to news story:   
   https://www.sciencedaily.com/releases/2023/04/230425111205.htm   
      
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