Integrated Production and Transportation Scheduling in E-Commerce Supply Chain with Carbon Emission Constraints

Carbon emission constraints and trading policies in e-commerce environments have brought huge challenges to the operation of supply chain enterprises. In order to ensure the good operation of the e-commerce supply chain in a low-carbon environment, a supply chain scheduling optimization method based on integration of production and transportation with carbon emission constraints is proposed; we use it to analyze the impact of centralized decision-making mode and decentralized decision-making mode on supply chain scheduling and establish a scheduling optimization model that aims at optimal carbon emissions and costs. A multilevel genetic algorithm was designed according to the characteristics of the model, and numerical examples are used to verify the effectiveness of the model and algorithm. The results show that the centralized decision-making mode plays the role of the carbon emission constraints to the greatest extent; the carbon emissions and the cost are smallest in the centralized decision-making mode. The decentralized decision-making mode leads to the overall cost preference of the supply chain due to separate decisions made by enterprises, and the carbon emissions in the supply chain are greater. Transportation experts, business managers and government departments are interesting for integrated production and transportation scheduling in e-commerce supply chain with carbon emission constraints. Further research should address integrated production and transportation scheduling in dual-channel low supply chains.

Supply Chain Scheduling Using Double Deep Time-Series Differential Neural Network

The purpose of supply chain scheduling is to be able to find an optimized plan and strategy so as to optimize the benefits of the entire supply chain. This paper proposes a method for processing tightly coordinated supply chain task scheduling problems based on an improved Double Deep Timing Differential Neural Network (DDTDN) algorithm. The Semi-Markov Decision Process (SMDP) modeling of the state characteristics and action characteristics of the supply chain scheduling problem is realized, so as to transform the task scheduling problem of the tightly coordinated supply chain into a multi-stage decision problem. The deep neural network model can help fit the state value function, and the unique reinforcement learning online evaluation mechanism can realize the selection of the best action strategy combination, and optimize it under the condition of only the stator processing time. Finally, the optimal action strategy group is obtained.

Fast Algorithms for Basic Supply Chain Scheduling Problems

A basic supply chain scheduling problem in which the orders released over time are to be delivered into the batches with unlimited capacity is considered. The delivery of each batch has a fixed cost D, whereas any order delivered after its release time yields an additional delay cost equal to the waiting time of that order in the system. The objective is to minimize the total delivery cost of the batches plus the total delay cost of the orders. A new algorithmic framework is proposed based on which fast algorithms for the solution of this problem are built. The framework can be extended to more general supply chain scheduling models and is based on a theoretical study of some useful properties of the offline version of the problem. An online scenario is considered as well, when at each assignment (order release) time the information on the next order released within the following T time units is known but no information on the orders that might be released after that time is known. For the online setting, it is shown that there is no benefit in waiting for more than D time units for incoming orders, i.e., potentially beneficial values for T are 0<T<D, and three linear-time algorithms are proposed, which are optimal for both the offline and the online cases when T≥D. For the case 0<T<D an important real-life scenario is studied. It addresses a typical situation when the same number of orders are released at each order release time and these times are evenly distributed within the scheduling horizon. An optimal algorithm which runs much faster than earlier known algorithms is proposed.