Spare parts supply chain network modeling based on a novel scale-free network and replenishment path optimization with Q learning
مدل سازی شبکه زنجیره تامین قطعات یدکی بر اساس یک شبکه جدید بدون مقیاس و بهینه سازی مسیر پر کردن با یادگیری Q-2021
The efficiency of inventory management determines the customers’ buying experience, so a supply chain network with a shorter replenishment time is needed. The supply chain network is hoped to be robust to the stock-out of some distributors in the network under random customer demands. At the same time, replenishment path optimization method with the objective of minimizing the replenishment time is required. After reviewing previous work done in the field of supply network topology, scale-free network is proven to be efficient when it was used to model supply network. In addition, multi-agent based collaborative replenishment model is smarter. But, there is rare research on multi-agent based collaborative replenishment in the supply chain modelled by scale-free network. In this study, we proposed a spare parts supply chain network model based on a novel scale- free network. In this network growth process, the connection probability function of connecting new distributor to the existing distributors in the network, is constructed considering the connection number (for an existing distributor, its connection number means the number of other distributors which have collaborative relationship with it) and inventory capacity of the existing distributors and the transit time between new distributor and existing distributors. The connection probability function is built from the standpoints of both new distributor and the existing distributors. Furthermore, different selection policies are discussed in the network growth process to improve the efficiency. Unlike other replenishment path optimization methods, Q learning takes the advantage of interacting with the environment to make a dynamic decision. So, Q learning is selected to optimize the replenishment path in supply chain network. In the experiment, network static and dynamic performance is analyzed using the indicators: degree distribution, clustering coefficient, centrality and response time. Experi- mental results showed that the replenishment time of supply chain network which are optimized by Q learning is reduced by approximately 40%. So, the shorter replenishment time of the supply chain network is verified.
Keywords: Spare parts supply chain network | Scale-free network | Q learning algorithm | Random customer demands
HEPart: A balanced hypergraph partitioning algorithm for big data applications
HEPart: یک الگوریتم پارتیشن بندی فوق العاده گرافیکی متعادل برای برنامه های داده بزرگ-2018
Minimizing the query cost among multi-hosts is important to data processing for big data applications. Hypergraph is good at modeling data and data relationships of complex networks, the typical big data applications, by representing multi-way relationships or interactions as hyperedges. Hypergraph parti tioning (HP) helps to partition the query loads on several hosts, enabling the horizontal scaling of large scale networks. Existing heuristic HP algorithms are generally vertex hypergraph partitioning, designed to minimize the number of cut hyperedges while satisfying the balance requirements of part weights regarding vertices. However, since workloads are mainly produced by group operations, minimizing query costs landing on hyperedges and balancing the workloads should be the objectives in horizontal scaling. We thus propose a heuristic hyperedge partitioning algorithm, HEPart. Specifically, HEPart directly partitions the hypergraph into K sub-hypergraphs with a minimum cutsize for vertices, while satisfying the balance constraint on hyperedge weights, based on the effective move of hyperedges. The performance of HEPart is evaluated using several complex network datasets modeled by undirected hypergraphs, under different cutsize metrics. The partitioning quality of HEPart is then compared with alternative hyperedge partitioners and vertex hypergraph partitioning algorithms. The experimental findings demonstrate the utility of HEPart (e.g. low cut cost while keeping load balancing as required, especially over scale-free networks).
Keywords: Hypergraph partitioning ، Hyperedge partitioning ، Load balancing ، Big data
The moderating impact of supply network topology on the effectiveness of risk management
تاثیر واسطه ای مکان شبکه تامین روی سودمندی مدیریت خطر-2018
While supply chain risk management offers a rich toolset for dealing with risk at the dyadic level, less attention has been given to the effectiveness of risk management in complex supply networks. We bridge this gap by building an agent based model to explore the relationship between topological characteristics of complex supply networks and their ability to recover through inventory mitigation and contingent rerouting. We simulate upstream supply networks, where each agent represents a supplier. Suppliers connectivity patterns are generated through random and preferential attachment models. Each supplier manages its inventory using an anchor-and-adjust ordering policy. We then randomly disrupt suppliers and observe how different topologies recover when risk management strategies are applied. Our results show that topology has a moderating effect on the effectiveness of risk management strategies. Scale-free supply networks generate lower costs, have higher fill-rates, and need less inventory to recover when exposed to random disruptions than random networks. Random networks need significantly more inventory distributed across the network to achieve the same fill rates as scale-free networks. Inventory mitigation improves fill-rate more than contingent rerouting regardless of network topology. Contingent rerouting is not effective for scale-free networks due to the low number of alternative suppliers, particularly for short-lasting disruptions. We also find that applying inventory mitigation to the most disrupted suppliers is only effective when the network is exposed to frequent disruptions; and not cost effective otherwise. Our work contributes to the emerging field of research on the relationship between complex supply network topology and resilience.
keywords: Supply chain risk management |Complex supply networks |Random networks |Scale-free networks |Inventory mitigation |Contingent rerouting |Agent-based modelling
Hybrid modeling and empirical analysis of automobile supply chain network
مدل سازی هیبرید و تجزیه و تحلیل تجربی شبکه زنجیره تامین خودرو-2017
Based on the connection mechanism of nodes which automatically select upstream and downstream agents, a simulation model for dynamic evolutionary process of consumer driven automobile supply chain is established by integrating ABM and discrete modeling in the GIS-based map. Firstly, the rationality is proved by analyzing the consistency of sales and changes in various agent parameters between the simulation model and a real automobile supply chain. Second, through complex network theory, hierarchical structures of the model and relationships of networks at different levels are analyzed to calculate various characteristic parameters such as mean distance, mean clustering coefficients, and degree distributions. By doing so, it verifies that the model is a typical scale-free network and small-world network. Finally, the motion law of this model is analyzed from the perspective of complex self-adaptive systems. The chaotic state of the simulation system is verified, which suggests that this system has typical nonlinear characteristics. This model not only macroscopically illustrates the dynamic evolution of complex networks of automobile supply chain but also microcosmically reflects the business process of each agent. Moreover, the model construction and simulation of the system by means of combining CAS theory and complex networks supplies a novel method for supply chain analysis, as well as theory bases and experience for supply chain analysis of auto companies.
Keywords: Complex network | Complex self-adaptive system | Automobile | Supply chain | Hybrid modeling |Empirical analysis
Gossip spread in social network Models
گسترش شایعات بی اساس در مدل های شبکه های اجتماعی-2017
Gossip almost inevitably arises in real social networks. In this article we investigate the relationship between the number of friends of a person and limits on how far gossip about that person can spread in the network. How far gossip travels in a network depends on two sets of factors: (a) factors determining gossip transmission from one person to the next and (b) factors determining network topology. For a simple model where gossip is spread among people who know the victim it is known that a standard scale-free network model produces a non-monotonic relationship between number of friends and expected relative spread of gossip, a pattern that is also observed in real networks (Lind et al., 2007). Here, we study gossip spread in two social network models (Toivonen et al., 2006; Vázquez, 2003) by exploring the parameter space of both models and fitting them to a real Facebook data set. Both models can produce the non-monotonic relationship of real networks more accurately than a standard scale-free model while also exhibiting more realistic variability in gossip spread. Of the two models, the one given in Vázquez (2003) best captures both the expected values and variability of gossip spread.
Keywords: Social network | Gossip | Spread | Variability
Hypergraph partitioning for social networks based on information entropy modularity
تقسیم بندی هایپرگراف برای شبکه های اجتماعی بر اساس مدولار بودن انتروپی اطلاعات-2017
A social network is a typical scale-free network with power-law degree distribution characteristics. It demonstrates several natural imbalanced clusters when it is abstracted as a graph, and expands quickly under its generative mechanism. Hypergraph is superior for modeling multi-user operations in social networks, and partitioning the hypergraph modeled social networks could ease the scaling problems. However, todays popular hypergraph partitioning tools are not sufficiently scalable; thus, unable to achieve high partitioning quality for naturally imbalanced datasets. Recently proposed hypergraph partitioner, hyperpart, replaces the balance constraint with an entropy constraint to achieve high-fidelity partitioning solutions, but it is not tailored for scale-free networks, like social networks. In order to achieve scalable and high quality partitioning results for hypergraph modeled social networks, we propose a partitioning method, EQHyperpart, which utilizes information-Entropy-based modularity Q value (EQ) to direct the hypergraph partitioning process. This EQ considers power-law degree distribution while describing the “natural” structure of scale-free networks. We then apply simulated annealing and introduce a new definition of hyperedge cut, micro cut, to avoid the local minima in convergence of partitioning, developing EQHyperpart into two specific partitioners, namely: EQHyperpart-SA and EQHyperpart-MC. Finally, we evaluate the utility of our proposed method using classical social network datasets, including Facebook dataset. Findings show that EQHyperpart partitioners are more scalable than competing approaches, achieving a tradeoff between modularity retaining and cut size minimizing under balance constraints, and an auto-tradeoff without balance constraints for hypergraph modeled social networks.
Keywords: Scale-free network | Social network partitioning | Hypergraph partitioning | Information entropy | Modularity