Multi-stage optimization configuration method for product value chain in uncertain environment

Abstract

The invention discloses a multi-stage optimal configuration method for a product value chain in an uncertain environment. Constructing a multi-cycle and multi-product product value chain network model containing 10 nodes; the product value chain network model is composed of nodes and edges, the edges represent discrete events, the discrete events refer to trigger behaviors from one node to another node, and the given probability of occurrence of each discrete event is set as an uncertainty parameter; according to the product value chain network model, an objective function and constraint conditions based on multi-stage random mixed integer linear programming are established, and then optimal configuration of the product value chain is obtained through optimization. According to the method, the design and planning problems of the product value chain under the environment that the number of raw materials and the use position demand level are uncertain are solved, the calculation complexity is lower, and it can be guaranteed that the optimization result has good accuracy.

Description

technical field [0001] The invention belongs to a product value chain optimization processing method in the field of industrial engineering, in particular to a multi-stage optimal configuration method of a product value chain mode in an uncertain environment. Background technique [0002] Due to the shortage of resources and the deterioration of the environment, governments, enterprises and academic institutions pay more and more attention to the management of product life cycle. As described by Papageorgiou (2009), there is growing interest in the design and planning problem of closed-loop value chains, which aim to collect and reuse end-of-life products and link environmental concerns to business opportunities related to value recovery of used products . Salema et al. (2010) proposed a multi-period, multi-product network model for simultaneous design and planning of value chains with reverse flows, where a two-tier network is considered in both forward and reverse directi...

AI Technical Summary

Problems solved by technology

In an uncertain environment, such as the quantity of raw materials and the demand level of the location of use are uncertain, there is currently no effective modeling method for modeling uncertainty

In addition, the current uncertainty modeling basically adopts a two-stage optimization model, which cannot be applied to the complex and diverse situations of various products in multiple time periods, and most of them do not allow decision correction in each time period

Therefore, in the actual production environment, the application value is limited, and it is impossible to maximize the number of recycled products, and the accuracy of the optimization result is low.

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