Applying Evolutionary Algorithm to The Distribution Planning for Chilled Foods Considering Product Quality

碩士 === 國立交通大學 === 運輸與物流管理學系 === 102 === As consumer increasing concerns on healthy eating, food safety has emerged as one of the most critical issues in resent years, and the standard of food quality has been raised as well. Chilled foods are perishable and temperature-sensitive. Physical, chemical...

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Bibliographic Details
Main Authors: Chin, Cheng-Lin, 覃震霖
Other Authors: Chen, Mu-Chen
Format: Others
Language:zh-TW
Published: 2014
Online Access:http://ndltd.ncl.edu.tw/handle/59029575378454396843
Description
Summary:碩士 === 國立交通大學 === 運輸與物流管理學系 === 102 === As consumer increasing concerns on healthy eating, food safety has emerged as one of the most critical issues in resent years, and the standard of food quality has been raised as well. Chilled foods are perishable and temperature-sensitive. Physical, chemical and biological changes occur after food production or processing, and it leads to quality deterioration. Other studies indicate that the quality degradation of meat and fish follow the first-order reactions, meaning that the quality of meat and fish decrease rapidly, and it’s more difficult to estimate. The quality deterioration of meat is mainly caused by microbial growth, and the temperature is the most important factor that influences the growth rate. Therefore, it’s effective to suppress microbial growth by practicing temperature control during the distribution. However, quality deterioration still occurs in chilled meat because of the cold-tolerant microbe, which continues breeding in low-temperature environment, making the product reach the end of shelf life, and causing the failure of meeting the demand of customers. In view of this, the objective of this study is to develop the distribution planning that considering the quality deterioration during the distribution for chilled foods, which is a mathematical model that combines shelf life forecasting and Vehicle Routing Problem with Time-Window (VRPTW). In order to solve this NP-hard problem, this study also modifies a new metaheuristic called Biogeography-Based Optimization (BBO) to solve it, and validate the effectiveness of the proposed BBO based algorithms by Solomon’s benchmark instances. In addition, BBO and Genetic Algorithm (GA) are both Evolutionary Algorithm, therefore, this study also presents the analysis and comparison of computational result between BBO and GA.