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 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 Quantum Inspired General Variable Neighborhood Search (qGVNS) for Dynamic Garbage Collection

2017 ◽  
Author(s):  
Christos Papalitsas ◽  
Panayiotis Karakostas ◽  
Theodore Andronikos ◽  
Spyros Sioutas ◽  
Konstantinos Giannakis
Keyword(s):  
Variable Neighborhood Search ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Real Life ◽  
Neighborhood Search ◽  
Np Hard ◽  
Combinatorial Optimization Problems ◽  
Np Hard Problem ◽  
Gps System ◽  
Tools And Techniques

General Variable Neighborhood Search (GVNS) is a well known and widely used metaheuristic for efficiently solving many NP-hard combinatorial optimization problems. Quantum General Variable Neighborhood Search (qGVNS) is a novel, quantum inspired extension of the conventional GVNS. Its quantum nature derives from the fact that it takes advantage and incorporates tools and techniques from the field of quantum computation. Travelling Salesman Problem (TSP) is a well known NP-Hard problem which has broadly been used for modelling many real life routing cases. As a consequence, TSP can be used as a basis for modelling and finding routes for Geographical Systems (GPS). In this paper, we examine the potential use of this method for the GPS system of garbage trucks. Specifically, we provide a thorough presentation of our method accompanied with extensive computational results. The experimental data accumulated on a plethora of symmetric TSP instances (symmetric in order to faithfully simulate GPS problems), which are shown in a series of figures and tables, allow us to conclude that the novel qGVNS algorithm can provide an efficient solution for this type of geographical problems.

scholarly journals A modified Ant Colony Optimization algorithm to solve a dynamic traveling salesman problem: A case study with drones for wildlife surveillance

2018 ◽  
Vol 6 (3) ◽  
pp. 368-386 ◽  
Author(s):  
Sudipta Chowdhury ◽  
Mohammad Marufuzzaman ◽  
Huseyin Tunc ◽  
Linkan Bian ◽  
William Bullington
Keyword(s):  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Real Life ◽  
Dynamic Environments ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Neighborhood Search ◽  
Ant Colony Optimization Algorithm ◽  
Pheromone Trails

Abstract This study presents a novel Ant Colony Optimization (ACO) framework to solve a dynamic traveling salesman problem. To maintain diversity via transferring knowledge to the pheromone trails from previous environments, Adaptive Large Neighborhood Search (ALNS) based immigrant schemes have been developed and compared with existing ACO-based immigrant schemes available in the literature. Numerical results indicate that the proposed immigrant schemes can handle dynamic environments efficiently compared to other immigrant-based ACOs. Finally, a real life case study for wildlife surveillance (specifically, deer) by drones has been developed and solved using the proposed algorithm. Results indicate that the drone service capabilities can be significantly impacted when the dynamicity of deer are taken into consideration. Highlights Proposed a novel ACO-ALNS based metaheuristic. Four variants of the proposed metaheuristic is developed to investigate the efficiency of each of them. A real life case study mirroring the behavior of DTSP is developed.

From goods to solutions: how does the content of an offering affect network configuration?

2014 ◽  
Vol 44 (1/2) ◽  
pp. 132-154 ◽  
Author(s):  
Mehmet Chakkol ◽  
Mark Johnson ◽  
Jawwad Raja ◽  
Anna Raffoni
Keyword(s):  
Supply Network ◽  
Network Configuration ◽  
Structured Interviews ◽  
Supply Networks ◽  
Content Type ◽  
Goods And Services ◽  
Value Propositions ◽  
Operant Resources ◽  
Industrial Context

Purpose – This paper aims to adopt service-dominant logic (SDL) to empirically explore network configurations resulting from the provision of goods, goods and services, and solutions. Design/methodology/approach – This paper uses a single, in-depth, exploratory case study in a truck manufacturer and its supply network. An abductive approach is adopted. In total, 54 semi-structured interviews were conducted. Findings – Three value propositions are clearly discernible within the truck provider. These range from a truck to a “solution”. These propositions have different supply network configurations: dyadic, triadic and tetradic. The extent to which different network actors contribute to value co-creation varies across the offerings. Research limitations/implications – This paper is based on a single, in-depth case study developed in one industrial context. Whilst this represents an appropriate approach given the exploratory nature of the study, further empirical investigation is needed across different industries. Originality/value – This paper is one of the first to empirically examine supply networks using SDL. A rich understanding of the challenges faced by a truck manufacturer in providing different value propositions and the resulting network configurations are discussed. In so doing, evidence is provided of a more complex, tetradic network configuration for solutions, with varying degrees of interplay between actors in the flow of operand and operant resources to create value.

scholarly journals A supply network governance framework: a case study of the South Australian mining industry

2018 ◽  
Vol 11 (1) ◽  
pp. 55-78 ◽  
Author(s):  
Larissa Statsenko ◽  
Alex Gorod ◽  
Vernon Ireland
Keyword(s):  
Supply Chain ◽  
Conceptual Framework ◽  
Network Structure ◽  
Mining Industry ◽  
Network Governance ◽  
Supply Network ◽  
Supply Networks ◽  
Policy Makers ◽  
Content Type

Purpose The competitiveness of mining regions largely depends on the performance of the regional supply chains that provide services to mining companies. These local supply chains are often highly intertwined and represent a regional supply network for the industry. Individual companies often use supply chain strategies that are sub-optimal to overall supply network performance. To effectively respond to an uncertain business environment, policy-makers and supply chain participants would benefit by a governance framework that would allow to incentivise the formation of supply networks structures enabling effective operations. The purpose of this paper is to offer an empirically grounded conceptual framework based on Complex Adaptive Systems (CASs) governance principles, which links network governance mechanisms with supply network structure and operational performance to incentivise the formation of adaptive and resilient supply networks in the mining industry. Design/methodology/approach A mixed method research design and a case study of the South Australian mining sector were used to collect empirical data. Qualitative interviews and network analysis of the SA mining industry regional supply network structure were conducted. The relationships between network parameters were interpreted using CAS theory. Findings An empirically grounded conceptual framework based on CAS governance principles is developed. The case study revealed that supply chain strategies and governance mechanisms in the SA mining industry have led to the formation of a hierarchical, scale-free structure with insufficient horizontal connectivity which limits the adaptability, responsiveness and resilience of the regional supply network. Research limitations/implications The findings are drawn from a single case study. This limits generalisability of the findings and the proposed framework. Practical implications The proposed framework draws the attention of the policy-makers and supply chain participants towards the need for utilising CAS governance principles to facilitate the formation of adaptive, responsive and resilient regional supply networks in the mining industry. Originality value The proposed conceptual framework is an attempt to parameterise the governance of the regional supply networks in the mining industry.

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