Abstract
With the advent of the era of big data (BD), people”s living standards and lifestyle have been greatly changed, and people's requirements for the service level of the service industry are becoming higher and higher. The personalized needs of customers and private customization have become the hot issues of current research. The service industry is the core enterprise of the service industry. Optimizing the service industry supply network and reasonably allocating the tasks are the focus of the research at home and abroad. Under the background of BD, this paper takes the optimization of service industry supply network as the research object and studies the task allocation optimization of service industry supply network based on the analysis of customers' personalized demand and user behavior. This paper optimizes the supply chain network of service industry based on genetic algorithm (GA), designs genetic operator, effectively avoids the premature of the algorithm, and improves the operation efficiency of the algorithm. The experimental results show that when m = 8 and n = 40, the average running time of the improved GA is 54.1 s. The network optimization running time of the algorithm used in this paper is very fast, and the stability is also higher.
Highlights
With the increasing competition in the world market, competition is no longer a competition between enterprises, but a competition between supply chains. e basis of competition between supply chains is the competition of supply chain networks [1,2,3]
Chain refers to a functional network chain structure that centers around the core enterprise, starting from supporting parts, making intermediate products and final products, and sending products to consumers by the sales network, connecting suppliers, manufacturers, distributors, and end users as a whole. e business philosophy of supply chain management is to seek the overall optimization of the supply chain from the perspective of consumers and through the cooperation between enterprises
This paper proposes an improved genetic operator algorithm to study the optimization of service supply chain network. is paper first introduces the basic concepts of big data (BD) and supply chain and improves the traditional genetic operator, aiming at the existing multilevel logistics network, which does not fully consider the customer’s potential interest demand
Summary
With the increasing competition in the world market, competition is no longer a competition between enterprises, but a competition between supply chains. e basis of competition between supply chains is the competition of supply chain networks [1,2,3]. Efficient supply chain network can reduce the operating costs of enterprises, and respond to market demand quickly, reduce operational risks, and improve the overall competitiveness. With the expansion of enterprise scale, the hub distribution center of enterprise supply chain network is expanding rapidly, seizing the market share, resulting in many hub locations design unreasonable [4]. Chain refers to a functional network chain structure that centers around the core enterprise, starting from supporting parts, making intermediate products and final products, and sending products to consumers by the sales network, connecting suppliers, manufacturers, distributors, and end users as a whole. Chain refers to the chain structure or network related to the production and distribution of raw material suppliers, producers, wholesalers, retailers, and end users [6]. Chain usually has the characteristics of complex, dynamic, and user-oriented demand
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