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Dear you, this is The LearningYard Academy. Today Xiaobian brings you "How much to learn (13):”Express packaging algorithm ”, welcome your visit.
近年来，随着电商行业的迅猛崛起，快递数量日趋增长，每年由快递包装带来的污染呈指数倍数增长，这就给物流公司带来一个难题：如何对快递进行绿色包装呢？本文介绍通过算法进行快递打包。
In recent years, with the rapid rise of the e-commerce industry, the number of express delivery is increasing, and the pollution caused by express packaging has increased exponentially every year, which has brought a problem to logistics companies: how to green package express delivery? This article describes the express packaging through algorithms.
“智能算法”是指在工程实践中，经常会接触到一些比较“新颖”的算法或理论，比如模拟退火，遗传算法，禁忌搜索，神经网络，天牛须搜索算法，麻雀搜索算法等。这些算法或理论都有一些共同的特性（比如模拟自然过程。它们在解决一些复杂的工程问题时大有用武之地。
"Intelligent algorithm" refers to the fact that in engineering practice, you will often be exposed to some relatively "novel" algorithms or theories, such as simulated annealing, genetic algorithm, taboo search, neural network, beetle whisker search algorithm, sparrow search algorithm, etc. These algorithms or theories all have some common features (such as simulating natural processes). They are useful in solving some complex engineering problems.
我们主要采用的是遗传算法。遗传算法（Genetic Algorithms，简称 GA）是一种基于自然选择原理和自然遗传机制的搜索（寻优）算法，它是模拟自然界中的生命进化机制，在人工系统中实现特定目 标的优化。遗传算法的实质是通过群体搜索技术，根据适者生存的原则逐代进化，最终得到优解或准优解。它必须做以下操作：初始群体的产生、求每一个体的适应度、 根据适者生存的原则选择优良个体、被选出的优良个体两两配对，通过随机交叉其染色 体的基因并随机变异某些染色体的基因后生成下一代群体，按此方法使群体逐代进化， 直到满足进化终止条件。
We mainly use genetic algorithms. Genetic Algorithms (GA) is a search (optimization) algorithm based on the principle of natural selection and natural genetic mechanism, which simulates the evolutionary mechanism of life in nature and achieves the optimization of specific goals in artificial systems. The essence of genetic algorithm is to evolve generation by generation according to the principle of survival of the fittest through population search technology, and finally obtain an optimal solution or quasi-optimal solution. It must do the following: the generation of the initial population, the fitness of each individual, the selection of excellent individuals according to the principle of survival of the fittest, the pairing of selected excellent individuals, the generation of the next generation population is generated by randomly crossing the genes of its chromosomes and randomly mutating the genes of certain chromosomes, and the population evolves generation by generation in this way until the conditions for the end of evolution are met.
最为明显的遗传算法就是大自然物竟天择、适者生存。在大自然中，无论是植物还是动物，都遵循这个原则。遗传算法为进化算法，该计算模型模拟达尔文生物进化论的自然选择和遗传学机理的生物进化过程，是一类借鉴生物界的进化规律如适者生存，优胜劣汰遗传机制而演化来的随机化搜索方法，最初于1975 年由美国 Michigan 大学 J.Holland 教授首先提出来，在此之后 GA 这个名称广为人知，J.Holland教授所提出的原始版本的 GA，通常就是简单遗传算法（SGA）。 遗传算法有一个突出的特点，它能够解决一些非常抽象的问题。即能够直接操作结构对象，不需要有准确的数学模型表示，所以不用考虑问题能否求导、是否连续；能够隐去具体的内部操作，即有很好的隐并行性；能够从全局范围搜索最优解；它是概率化的，但是又能够自我适应，自我指导，不需要额外定制规则。
Genetic algorithm is an evolutionary algorithm, the computational model simulates the biological evolution process of natural selection and genetic mechanism of Darwinian biological evolution, is a kind of randomized search method that draws on the evolutionary laws of the biological world such as survival of the fittest, survival of the fittest genetic mechanism and evolution, originally proposed in 1975 by Professor J. Holland of Michigan University in the United States, after which the name GA was widely known, the original version of GA proposed by Professor J. Holland, It is usually the Simple Genetic Algorithm (SGA). Genetic algorithms have a prominent feature that they are able to solve some very abstract problems. That is, it can directly manipulate structural objects, and does not need to have an accurate mathematical model representation, so there is no need to consider whether the problem can be derived and whether it is continuous; Can hide specific internal operations, that is, there is good hidden parallelism; Ability to search for optimal solutions from a global scope; It's probabilistic, yet self-adapting, self-directing, and doesn't require additional custom rules.
目前在我国已有相关企业提出方案，菜鸟网络的算法专家，通过大数据和大规模优化技术，推出了一套“智能打包算法技术”，相比粗放的人工包装，至少可节省5%以上的包装耗材。
这套算法通俗来讲，就是可以利用算法优化，帮助仓库用更小的箱子装下所有的货品。在订单生成的那一瞬间，系统会自动计算出这个订单需要多大的箱子，几个箱子来装，找到最省材料的包装方法。
At present, relevant enterprises in China have proposed solutions, and the algorithm experts of Cainiao Network have launched a set of "intelligent packaging algorithm technology" through big data and large-scale optimization technology, which can save at least more than 5% of packaging consumables compared with extensive manual packaging.
In layman's terms, this algorithm can use algorithm optimization to help warehouses hold all goods in smaller boxes. At the moment of order generation, the system will automatically calculate how big boxes and several boxes are needed for this order, and find the most material-saving packaging method.
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