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- W2017451208 abstract "Most of the products imported from Mexico to U.S. can be classified as perishable. The United States Trade Representative has reported that the overall importations from Mexico to U.S. were equivalent to $16.4 billion during 2012. Due to the geographical location of both countries most of the transportation of products across the U.S. - Mexico border is road modal transportation. The inspection of trucks at the border entry points can take a long and unpredictable time. For perishable products, these inspections have a very important effect on their shelf life once they arrive to their destination. This paper presents a novel model that combines a metaheuristic with a simulation. The proposed method was coded in Matlab <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>®</sup> . This model helps in the selection of the amount of product that will be sent through each of the available routes. Random length of disruptive inspection time and availability of servers at the entry points are very important for the Supply Chain (SC) that includes the transportation system of perishables from Mexico to U.S. The proposed Simulation-based Optimization Model (SimOpt) minimizes transportation time and transportation cost of agricultural products traded across the U.S. - Mexico border to build a resilient supply chain. This SimOpt model considers realistic continuous probability distributions for the inspection time. This variability also accounts for the availability of inspection servers and lanes in the points of entry. The solution procedure finds solutions for a weighted (time and freight cost) objective function. The results of a case study are presented." @default.
- W2017451208 created "2016-06-24" @default.
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- W2017451208 date "2014-12-01" @default.
- W2017451208 modified "2023-10-17" @default.
- W2017451208 title "A preliminary simulated annealing for resilience supply chains" @default.
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- W2017451208 doi "https://doi.org/10.1109/cipls.2014.7007167" @default.
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