Recent advances in hybrid priority-based genetic algorithms for logistics and SCM network design

Gen, Mitsuo; Lin, Lin; Yun, YoungSu; Inoue, Hisaki

doi:10.1016/j.cie.2018.08.025

Cited by 43 publications

(19 citation statements)

References 61 publications

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“…to dynamic conditions that might affect daily operations (e.g., continuous changes in availability of raw materials, transport costs, or customers’ demands, among others). Consequently, solving methods need to be able to address all these characteristics of real-world SCNs together with the fact that, in most cases, SCNs constitute large-scale complex systems [46] . Modeling these concerns means: (i) considering uncertainty to represent operational and/or disruption risks (inherent in supply chain resiliency research) coherently with real-world features; (ii) optimizing the supply chain design according to suitable criteria, such as costs, profit, environmental impact, or resilience; and (iii) using time-efficient methods when designing and assessing resilient supply chains considering their complexity.…”

Section: Insights and Future Research Directionsmentioning

confidence: 99%

Simulation-optimization methods for designing and assessing resilient supply chain networks under uncertainty scenarios: A review

Tordecilla

Juan

Montoya‐Torres

et al. 2021

Simulation Modelling Practice and Theory

101

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The design of supply chain networks (SCNs) aims at determining the number, location, and capacity of production facilities, as well as the allocation of markets (customers) and suppliers to one or more of these facilities. This paper reviews the existing literature on the use of simulation-optimization methods in the design of resilient SCNs. From this review, we classify some of the many works in the topic according to factors such as their methodology, the approach they use to deal with uncertainty and risk, etc. The paper also identifies several research opportunities, such as the inclusion of multiple criteria (e.g., monetary, environmental, and social dimensions) during the design-optimization process and the convenience of considering hybrid approaches combining metaheuristic algorithms, simulation, and machine learning methods to account for uncertainty and dynamic conditions, respectively.

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Section: Insights and Future Research Directionsmentioning

confidence: 99%

Simulation-optimization methods for designing and assessing resilient supply chain networks under uncertainty scenarios: A review

Tordecilla

Juan

Montoya‐Torres

et al. 2021

Simulation Modelling Practice and Theory

101

View full text Add to dashboard Cite

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“…Most complicated network problems are known to be NP-complete [16,28,29], and the SCLSC design problem proposed in this paper is no exception. Existing works in the literature [4,[29][30][31] have shown that meta-heuristics-based approaches such as GA, CS, and Tabu search (TS) have been adapted to solve complicated network problems efficiently. However, there exist many situations in which most conventional single-based meta-heuristics approaches do not perform particularly well [28,31,32].…”

Section: Proposed Hga Approachmentioning

confidence: 99%

“…However, there exist many situations in which most conventional single-based meta-heuristics approaches do not perform particularly well [28,31,32]. To address this gap, various hybrid approaches have been developed that use GA or other similar approaches [29,[31][32][33][34][35][36][37].…”

Section: Proposed Hga Approachmentioning

confidence: 99%

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Sustainable Closed-Loop Supply Chain Design Problem: A Hybrid Genetic Algorithm Approach

2020

Self Cite

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In this paper, we propose a solution to the sustainable closed-loop supply chain (SCLSC) design problem. Three factors (economic, environmental, and social) are considered for the problem and the three following requirements are addressed while satisfying associated constraint conditions: (i) minimizing the total cost; (ii) minimizing the total amount of CO2 emission during production and transportation of products; (iii) maximizing the social influence. Further, to ensure the efficient distribution of products through the SCLSC network, three types of distribution channels (normal delivery, direct delivery, and direct shipment) are considered, enabling a reformulation of the problem as a multi-objective optimization problem that can be solved using Pareto optimal solutions. A mathematical formulation is proposed for the problem, and it is solved using a hybrid genetic algorithm (pro-HGA) approach. The performance of the pro-HGA approach is compared with those of other conventional approaches at varying scales, and the performances of the SCLSC design problems with and without three types of distribution channels are also compared. Finally, we prove that the pro-HGA approach outperforms its competitors, and that the SCLSC design problem with three types of distribution channels is more efficient than that with a single distribution channel.

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“…Research by Huang and Wang (2009) showed in order to reduce the risk of uncertainty in the process of network optimization, the optimal robust solution of hub-spoke network is obtained under the condition of multiple possibilities of demand and cost, and an optimization method based on multi-objective optimization genetic algorithm is proposed. Research by Gen, Lin, Yun and Inoue (2018) showed the latest progress of genetic algorithm based on hybrid priority in solving multilevel logistics or supply chain management network problems. Introduced: (1) sugar cane supply chain management network model, (2) multi-objective supply chain network model, (3) flexible multistage logistics network model, (4) multi-objective reverse logistics network model.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Genetic algorithm for the cargo shunting cooperation between two hub-and-spoke logistics networks

Huang

2019

JIEM

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Purpose: The overstocked goods flow in the hub of hub-and-spoke logistics network should be disposed of in time, to reduce delay loss and improve the utilization rate of logistics network resources. The problem we need to solve is to let logistics network cooperate by sharing network resources to shunt goods from one hub-and-spoke network to another hub-and-spoke network.Design/methodology/approach: This paper proposes the hub shunting cooperation between two hub-and-spoke networks. Firstly, a hybrid integer programming model was established to describe the problem, and then a multi-layer genetic algorithm was designed to solve it and two hub-and-spoke networks are expressed by different gene segments encoded by genes. The network data of two third-party logistics companies in southern and northern China are used for example analysis at the last step. Findings: The hub-and-spoke networks of the two companies were constructed simultaneously. The transfer cost coefficient between two networks and the volume of cargo flow in the network have an impact on the computation of hubs that needed to be shunt and the corresponding cooperation hubs in the other network.Originality/value: Previous researches on hub-and-spoke logistics network focus on one logistics network, while we study the cooperation and interaction between two hub-and-spoke networks. It shows that two hub-and-spoke network can cooperate across the network to shunt the goods in the hub and improve the operation efficiency of the logistics network.

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Recent advances in hybrid priority-based genetic algorithms for logistics and SCM network design

Cited by 43 publications

References 61 publications

Simulation-optimization methods for designing and assessing resilient supply chain networks under uncertainty scenarios: A review

Simulation-optimization methods for designing and assessing resilient supply chain networks under uncertainty scenarios: A review

Sustainable Closed-Loop Supply Chain Design Problem: A Hybrid Genetic Algorithm Approach

Genetic algorithm for the cargo shunting cooperation between two hub-and-spoke logistics networks

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