scholarly journals Collaborative Distribution in the Soft Time Window of Agricultural-Means Supply Chain Based on Simulated Annealing-Genetic Algorithm

2020 ◽  
Vol 53 (6) ◽  
pp. 835-844
Author(s):  
Yanxin Zhu ◽  
Jiajing Wang ◽  
Meiyu Li
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Simulated Annealing ◽  
Customer Satisfaction ◽  
Optimization Model ◽  
Time Window ◽  
Cost Optimization ◽  
Simulated Annealing Genetic Algorithm ◽  
Appointment Time ◽  
The Cost

This paper mainly explores the collaborative distribution to multiple customers at the terminal of agricultural-means supply chain (AMSC). Firstly, a cost optimization model for collaborative distribution constrained by time window was constructed based on fuzzy appointment time function. Next, the proposed model was solved by simulated annealing-genetic algorithm (SA-GA). Through a case study, the cost optimization model constrained by customer satisfaction was compared with that not constrained by customer satisfaction. The results show that the cost optimization model constrained by customer satisfaction made the customers more satisfied without greatly elevating the distribution cost. The research results shed new light on the collaborative distribution of time-sensitive agricultural-means (AM) products, and the management of the AMSC.

Computational Intelligence in Cross Docking

2014 ◽  
Vol 2 (1) ◽  
pp. 1-8
Author(s):  
Bo Xing
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Simulated Annealing ◽  
Tabu Search ◽  
Computational Intelligence ◽  
Simple Concept ◽  
Simulated Annealing Genetic Algorithm ◽  
Cross Docking ◽  
Open Questions ◽  
Key Issues

Cross docking is a practice in logistics with the main operations of goods flow directly from receiving to the shipping docks without stopping or being put away into storage. It is a simple concept to talk about, but a challenging one to implement. So far, many different approaches have been followed in order to improve the efficiency of a cross docking system. However, as the complexity increases, the use of computational intelligence (CI) in those problems is becoming a unique tool of imperative value. In this paper, different CI methods, such as Tabu search, simulated annealing, genetic algorithm, and fuzzy logic. The key issues in implementing the proposed approaches are discussed, and finally the open questions are highlighted.

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