代写assignment-物流智能路线设定计划

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26/11/2019

本文主要讲的是物流智能路线设定计划,第三方物流提供气候智能与BC省的食品和饮料部门合作,以实现这一点,以控制如图2所示的预计排放量。公司的时间表与非常具体的目标首次制定(气候智能,2014年)。一旦这样做,智能路由策略制定,基本上是一套智能工作习惯和实践,针对一些现有的公司司机(气候智能,2014)。公司的司机接受了培训,比如更换公司的卡车,减少空转的时间。本篇代写assignment文章由英国论文通AssignmentPass辅导网整理,供大家参考阅读。

A third- party logistics provide Climate Smart worked with the food and beverage sector in BC to achieve this for controlling projected emissions as presented in Figure 2. Companies’ timeline with very specific aims were first formulated (Climate Smart, 2014). Once this was done, smart routing strategies were formulated that were basically a set of smart working habits and practices aimed at some of the existing company drivers (Climate Smart, 2014). The company drivers were trained in practices like switching of the company truck with less time devoted to idling.
Refrigeration equipment in the case of delivery trucks was recognized and truckers were trained on how to spot leaks in the refrigeration system that could be used to control energy wastes (Climate Smart, 2014). Waste cooking oil from restaurants were made use of in the trucks, as retrofitting to use them as biodiesel was being analysed and researched into. This step was to ensure that none of the resources which went into the production and manufacturing was not wasted, and could actually be used as by-products as energy for delivery. The use of hybrid and electric delivery trucks were to present much cleaner fuel sourcing as well. The core of these intelligent route formulating and tracking systems was the use of a GPS calibrated specifically for the delivery vehicles that would ensure there is better carbon reduction (Climate Smart, 2014).
Swift has been used in the context of optimizing delivery and logistics. A SMART routing map of SWIFT is presented in Figure 3. Swift is basically a fast routing platform that can be helpful for improving restaurant delivery services. The optimization offered in the case of another non-restaurant logistics called Easy Deliveries (Swift, 2015). Easy Deliveries was a company that was aware of the benefits of short supply chains and delivery optimization and for that purpose used to calculate the delivery optimized route for its drivers and in doing so spent as much as 5 hours on manually applying driver routes. In assessing the route optimization, the company found that an average driver in fact has spent around 17 hours a day on the roads.

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