Single routing network model of mobile grocery considering Inventory management with service time dependent demand
Mobile grocery trucks or mobile palengke has become a nationwide initiative to provide people with basic necessities and prime commodities during a time when the country is in lockdown due to the pandemic. However, there is a need for extensive research in order to determine the most efficient way t...
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| Main Authors: | , , |
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| 格式: | text |
| 語言: | English |
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Animo Repository
2021
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| 在線閱讀: | https://animorepository.dlsu.edu.ph/etdb_induseng/6 https://animorepository.dlsu.edu.ph/context/etdb_induseng/article/1005/viewcontent/aggabao2.pdf |
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| 總結: | Mobile grocery trucks or mobile palengke has become a nationwide initiative to provide people with basic necessities and prime commodities during a time when the country is in lockdown due to the pandemic. However, there is a need for extensive research in order to determine the most efficient way to operate these vehicles since most vehicle routing problems do not integrate certain aspects of a mobile grocery truck such as handling multiple inventory, practicing service time per customer, prioritization of customers and a demand which is dependent on the service time. Studies regarding vehicle routing problems are combined with multiple-product inventory management in order to formulate a mixed-integer nonlinear programming model considering customer prioritization. This model would determine the optimum route to the different customers which would maximize profit given time and capacity constraints. The model would also determine how long the vehicle would operate for a customer given the minimum and maximum service time in order to maximize profit. In addition, it would also identify the optimal amount per product to be brought per trip of the mobile grocery truck given the capacity of the vehicle. In order to analyze how the model would behave given different inputs, different scenarios were applied such as changes in travel time, changes in capacity, change in penalty cost for unserved customers, and exclusion of penalty cost for overtime. From the different scenarios, In addition, removing penalty costs from the equation greatly increases profit as well as reducing the travel time. Changing the capacity of the vehicle does little to the overall profit. The scenario where customer prioritization changes how the system’s decision making behaves. The researchers have also envisioned a sample interface that could be used to utilize the model in an easier way. |
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