scholarly journals Enhanced Variable Neighborhood Search-Based Recovery Supplier Selection for Post-Disruption Supply Networks

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 670
Author(s):  
Yuting Chen ◽  
Ping Lou ◽  
Wen Jiang
Keyword(s):  
Supplier Selection ◽  
Large Scale ◽  
Network Performance ◽  
Variable Neighborhood Search ◽  
Performance Metrics ◽  
Recovery Process ◽  
Neighborhood Search ◽  
Supply Network ◽  
Supply Networks ◽  
Selection Of

With the increasing reliance on global sourcing and the growth in the likelihood of disruptive incidents, today’s supply networks are more prone to unexpected natural and man-made disruptive events. In order to alleviate the losses caused by these disruptive events, when a large-scale event disrupts multiple suppliers simultaneously, a single or several critical suppliers should be selected from the disrupted ones to assist them to recover their production as soon as possible. The selection of these recovery suppliers is of great importance in the recovery process of the entire supply network. Thus, this paper proposes a recovery supplier selection method from the view of the supply network structure. Firstly, a tripartite graph-based supply model is proposed to depict a two-stage supply network, which consists of multiple manufacturers and suppliers as well as the diverse product supply-demand interdependence connecting them. To measure the impacts caused by supplier disruptions and to evaluate the effectiveness of recovery supplier decisions, two supply network performance metrics reflecting product supply availability are also given. Then, the recovery supplier selection problem is described as a combinatorial optimization problem. To solve this problem, a heuristic algorithm, with enhanced variable neighborhood search (EVNS) is designed based on the general framework of a variable neighborhood search. Finally, experiments based on a real-world supply network are conducted. The experimental results indicate that the proposed method is applicable and effective.

scholarly journals Adaptive Variable Neighborhood Search-Based Supply Network Reconfiguration for Robustness Enhancement

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Ping Lou ◽  
Yuting Chen ◽  
Song Gao
Keyword(s):  
Structural Design ◽  
Variable Neighborhood Search ◽  
Real Life ◽  
Tohoku Earthquake ◽  
Neighborhood Search ◽  
Network Reconfiguration ◽  
Supply Network ◽  
Supply Networks ◽  
Robustness Metrics

Robustness of a supply network highly depends on its structure. Although structural design methods have been proposed to create supply networks with optimal robustness, a real-life supply network can be quite different from these optimal structural designs. Meanwhile, real cases such as Thailand floods and Tohoku earthquake demonstrate the vulnerability of supply networks in real life. Obviously, it is urgent to enhance the robustness of existing real-life supply networks. Thus, in this paper, a supply network reconfiguration method based on adaptive variable neighborhood search (AVNS) is proposed to enhance the structural robustness of supply networks facing both random and target disruptions. Firstly, a supply network model considering the heterogeneous roles of entities is introduced. Based on the model, two robustness metrics, Rr and Rt, are proposed to describe the tolerance of supply networks facing random and target disruptions, respectively. Then, the problem of reconfiguration-based supply network robustness enhancement is described. To solve the problem effectively and efficiently, a new heuristic based on general variable neighborhood search, namely, AVNS, is proposed. Finally, a case study based on three real-life supply networks is presented to verify the applicability and effectiveness of the proposed robustness enhancing method.

scholarly journals Self-Adjusting Variable Neighborhood Search Algorithm for Near-Optimal k-Means Clustering

Computation ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 90
Author(s):  
Lev Kazakovtsev ◽  
Ivan Rozhnov ◽  
Aleksey Popov ◽  
Elena Tovbis
Keyword(s):  
Large Scale ◽  
Variable Neighborhood Search ◽  
Graphics Processing Unit ◽  
Search Algorithm ◽  
Fixed Time ◽  
Exact Algorithms ◽  
Neighborhood Search ◽  
Data Sets ◽  
Processing Unit ◽  
Online Computation

The k-means problem is one of the most popular models in cluster analysis that minimizes the sum of the squared distances from clustered objects to the sought cluster centers (centroids). The simplicity of its algorithmic implementation encourages researchers to apply it in a variety of engineering and scientific branches. Nevertheless, the problem is proven to be NP-hard which makes exact algorithms inapplicable for large scale problems, and the simplest and most popular algorithms result in very poor values of the squared distances sum. If a problem must be solved within a limited time with the maximum accuracy, which would be difficult to improve using known methods without increasing computational costs, the variable neighborhood search (VNS) algorithms, which search in randomized neighborhoods formed by the application of greedy agglomerative procedures, are competitive. In this article, we investigate the influence of the most important parameter of such neighborhoods on the computational efficiency and propose a new VNS-based algorithm (solver), implemented on the graphics processing unit (GPU), which adjusts this parameter. Benchmarking on data sets composed of up to millions of objects demonstrates the advantage of the new algorithm in comparison with known local search algorithms, within a fixed time, allowing for online computation.

Sustainable supply network management

2019 ◽  
Vol 68 (6) ◽  
pp. 1164-1190 ◽  
Author(s):  
Leonardo Marques
Keyword(s):  
Network Management ◽  
Large Scale ◽  
Diffusion Processes ◽  
Knowledge Diffusion ◽  
Future Research ◽  
Special Focus ◽  
Supply Network ◽  
Sustainable Supply Chain ◽  
Supply Networks ◽  
Content Type

Purpose The purpose of this paper is to scrutinise how the sustainable supply chain management (SSCM) literature has discussed knowledge dynamics across the extended supply network, particularly in the contemporary context of fragmented, globally dispersed supply networks. Design/methodology/approach A systematic approach to reviewing the literature is applied, covering 20 years, starting with 267 references, and narrowing down to 88 articles specifically addressing knowledge diffusion processes across the extended supply network. Findings This study shows that vertical ties limited to direct suppliers or third-party monitoring of global suppliers are both insufficient. Lack of co-opetition is an impediment to knowledge diffusion. And the debate of whether or not global dispersion is an impediment to knowledge diffusion seems inconclusive. More importantly, there is a lack of network-level studies mapping the diversity of actors in supply networks. Research limitations/implications First, future SSCM research should shift from an operational focus to strategic knowledge diffusion. Second, the scope of SSCM should expand from linear buyer–supplier relationships to multi-tier and multilateral studies. Special focus should be placed on the literature on social network to support processes that look at the drivers of effective large-scale, global diffusion of sustainability. Originality/value This review contends that it is paramount to set a new research direction captured in a new definition of “sustainable supply network management”. Future research should overcome the barriers of data collection at the network level in order to contribute to the field’s current challenges, which clearly lies in globally dispersed and complex supply network, not dyads or linear chains.

scholarly journals Routing Based Multi-Agent System for Network Reliability in the Smart Microgrid

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2992
Author(s):  
Niharika Singh ◽  
Irraivan Elamvazuthi ◽  
Perumal Nallagownden ◽  
Gobbi Ramasamy ◽  
Ajay Jangra
Keyword(s):  
Large Scale ◽  
Network Performance ◽  
Performance Metrics ◽  
Network Reliability ◽  
Operating Conditions ◽  
Multi Agent System ◽  
Agent System ◽  
Agent Based ◽  
Multi Agent ◽  
System Operating

Microgrids help to achieve power balance and energy allocation optimality for the defined load networks. One of the major challenges associated with microgrids is the design and implementation of a suitable communication-control architecture that can coordinate actions with system operating conditions. In this paper, the focus is to enhance the intelligence of microgrid networks using a multi-agent system while validation is carried out using network performance metrics i.e., delay, throughput, jitter, and queuing. Network performance is analyzed for the small, medium and large scale microgrid using Institute of Electrical and Electronics Engineers (IEEE) test systems. In this paper, multi-agent-based Bellman routing (MABR) is proposed where the Bellman–Ford algorithm serves the system operating conditions to command the actions of multiple agents installed over the overlay microgrid network. The proposed agent-based routing focuses on calculating the shortest path to a given destination to improve network quality and communication reliability. The algorithm is defined for the distributed nature of the microgrid for an ideal communication network and for two cases of fault injected to the network. From this model, up to 35%–43.3% improvement was achieved in the network delay performance based on the Constant Bit Rate (CBR) traffic model for microgrids.

scholarly journals Solving balanced multi-weighted attribute set partitioning problem with variable neighborhood search

Filomat ◽  
2019 ◽  
Vol 33 (9) ◽  
pp. 2875-2891
Author(s):  
Dusan Dzamic ◽  
Bojana Cendic ◽  
Miroslav Maric ◽  
Aleksandar Djenic
Keyword(s):  
Local Search ◽  
Large Scale ◽  
Variable Neighborhood Search ◽  
Heuristic Method ◽  
Set Partitioning ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Computationally Efficient ◽  
Local Search Procedure ◽  
Problem Instances

This paper considers the Balanced Multi-Weighted Attribute Set Partitioning (BMWASP) problem which requires finding a partition of a given set of objects with multiple weighted attributes into a certain number of groups so that each attribute is evenly distributed amongst the groups. Our approach is to define an appropriate criterion allowing to compare the degree of deviation from the ?perfect balance? for different partitions and then produce the partition that minimizes this criterion. We have proposed a mathematical model for the BMWASP and its mixed-integer linear reformulation. We evaluated its efficiency through a set of computational experiments. To solve instances of larger problem dimensions, we have developed a heuristic method based on a Variable Neighborhood Search (VNS). A local search procedure with efficient fast swap-based local search is implemented in the proposed VNS-based approach. Presented computational results show that the proposed VNS is computationally efficient and quickly reaches all optimal solutions for smaller dimension instances obtained by exact solver and provide high-quality solutions on large-scale problem instances in short CPU times.

scholarly journals Resilience in interorganizational networks: dealing with day-to-day disruptions in critical infrastructures

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mitchell J. van den Adel ◽  
Thomas A. de Vries ◽  
Dirk Pieter van Donk
Keyword(s):  
Information Exchange ◽  
Large Scale ◽  
Critical Infrastructures ◽  
Supply Network ◽  
Interorganizational Network ◽  
Supply Networks ◽  
Content Type ◽  
Essential Services ◽  
Boundary Information ◽  
Practical Implications

Purpose Critical infrastructures (CIs) for essential services such as water supply and electricity delivery are notoriously vulnerable to disruptions. While extant literature offers important insights into the resilience of CIs following large-scale disasters, our understanding of CI resilience to the more typical disruptions that affect CIs on a day-to-day basis remains limited. The present study investigates how the interorganizational (supply) network that uses and manages the CI can mitigate the adverse consequences of day-to-day disruptions. Design/methodology/approach Longitudinal archival data on 277 day-to-day disruptions within the Dutch national railway CI were collected and analyzed using generalized estimating equations. Findings The empirical results largely support the study’s predictions that day-to-day disruptions have greater adverse effects if they co-occur or are relatively unprecedented. The findings further show that the involved interorganizational network can enhance CI resilience to these disruptions, in particular, by increasing the overall level of cross-boundary information exchange between organizations inside the network. Practical implications This study helps managers to make well-informed choices regarding the target and intensity of their cross-boundary information-exchange efforts when dealing with day-to-day disruptions affecting their CI. The findings illustrate the importance of targeting cross-boundary information exchange at the complete interorganizational network responsible for the CI and to increase the intensity of such efforts when CI disruptions co-occur and/or are unprecedented. Originality/value This study contributes to our academic understanding of how network-level processes (i.e. cross-boundary information exchange) can be managed to ensure interorganizational (supply) networks’ resilience to day-to-day disruptions in a CI context. Subsequent research may draw from the conceptual framework advanced in the present study for examining additional supply network-level processes that can influence the effectiveness of entire supply networks. As such, the present research may assist scholars to move beyond a simple dyadic context and toward examining complete supply networks

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