Multi-objective hybrid harmony search-simulated annealing for location-inventory-routing problem in supply chain network design of reverse logistics with CO2 emission

Misni, Farahanim; Lee, Lai Soon; Jaini, Nor Izzati

doi:10.1088/1742-6596/1988/1/012054

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…According to this demand, relevant inventory and transportation costs are obtained, making the model easier to establish and solve. Among the literature reviewed, there are 37 papers concerning deterministic needs [5,37,39,41,[43][44][45][46]49,50,59,63,65,[69][70][71][72][76][77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94][95].…”

Section: Classification Based On Demand Data Typesmentioning

confidence: 99%

Logistics Center Location-Inventory-Routing Problem Optimization: A Systematic Review Using PRISMA Method

et al. 2022

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A traditional logistics decision model mainly studies the location decision of logistics distribution centers, storage inventory management, vehicle scheduling, and transportation routes. The logistics location-inventory-routing problem (LIRP) is an integrated optimization of the three problems—a comprehensive optimization problem for the whole logistics system. This review paper uses the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) method to review the literature on LIRP systematically. A total of 112 LIRP-related studies published between 2010 and 2021 are reviewed and classified based on 10 abstract and citation databases. The classification includes four aspects: problem characteristics, demand data types, model-based solutions, and application fields. From this systematic review, a few observations are recorded. First, the most popular problems among researchers are the multi-period multi-product problem, the multi-echelon single-link problem, and the multi-depot multi-retailer problem. Based on the objective function, the minimization of total supply chain cost is the primary concern of the LIRP literature. Researchers also favor other problem characteristics such as multi-objective programming, inventory control replenishment policy, and a homogeneous fleet of vehicles. We found that stochastic data are a common factor in an uncertain environment and have broad coverage. When dealing with the LIRP, heuristic and metaheuristic algorithms are the most widely used solution methodologies in the literature. In the application field of LIRP, the perishable products logistics network is mentioned in most applications. Finally, we discuss and emphasize the challenges of and recommendations for future work. This paper provides a systematic review of the literature on LIRP based on the PRISMA method, which contributes vital support and valuable information for researchers interested in LIRP.

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Section: Classification Based On Demand Data Typesmentioning

confidence: 99%

Logistics Center Location-Inventory-Routing Problem Optimization: A Systematic Review Using PRISMA Method

et al. 2022

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“…In [9], the researchers proposed an integrated Multi-Objective Hybrid Harmony Search-Simulated Annealing (MOHS-SA) algorithm to find the trade-off between the total cost of operating facilities, and the cost of CO2 emissions. The research presented a number of alternatives to warehouse locations, but it lacked any possibility of self-learning or determining the optimal location for these warehouses.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Framework for Locating Physical Internet Hubs using Latitude and Longitude Classification

Helmi¹,

Emam²,

Abdelsalam³

2022

IJACSA

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This article proposes framework for determining the optimal or near optimal locations of physical internet hubs using data mining and deep learning algorithms. The framework extracts latitude and longitude coordinates from various data types as data acquisition phase. These coordinates has been extracted from RIFD, online maps, GPS, and GSM data. These coordinates has been class labeled according to decision maker's preferences using k-mean, density based algorithm (DB Scan and hierarchical clustering analysis algorithms. The proposed algorithm uses haversine distance matrix to calculate the distance between each coordinates rather than the Euclidian distance matrix. The haversine matrix provides more accurate distance surface of a sphere. The framework uses the class labeled data after the clustering phase as input for the classification phase. The classification has been performed using decision tree, random forest, Bayesian, gradient decent, neural network, convolutional neural network and recurrent neural network. The classified coordinates has been evaluated for each algorithms. It has been found that CNN, RNN outperformed the other classification algorithms with accuracy 97.6% and 97.9% respectively.

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“…According to the findings, TLBO performance is better than that of PSO, and VBO is better than TLBO and Jaya algorithms. Also, another study [20] aims to find the cost model of Multi-Objective Hybrid Harmony Search-Simulated Annealing (MOHS-SA) and Economic Production Quantity (EPQ). They proposed a hybrid of PSO and HS, called PSOHS for determining and estimating the kinetic parameter of essential amino acids, primarily aspartate metabolisms, in Arabidopsis thaliana.…”

Section: Related Workmentioning

confidence: 99%

Using metaheuristics to improve the placement of multi-controllers in software-defined networking enabled clouds

Radam¹,

Al-Janabi²,

Jasim³

2022

PEN

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SDN is a model that separates the control and the data levels in an arrangement to enhance capability to program and configure the network in a more agile and efficient manner. Multiple controller modules have been used in the SDN engineering to empower programmable and adaptable configurations such as improving scalability and reliability. The distance and time calculations and other performance measures have to be considered in solving the Multi-Controller Position Problem (MCPP). This paper investigates the use of metaheuristic algorithms to build an MCPP mathematical model. Both the symmetric Harmony Search (HS) modelling and the Particle Swarm Optimization (PSO) algorithm are considered in this respect. Thus, our hybrid approach is proposed and known as Harmony Search with Particle Swarm Optimization (HSPSO) is applied and we compared the extracted results with the state-of-the-art techniques in the previous literature. Besides the development of the mathematical model, a simulation study has been done considering the relevant parameters including the link distance description and the access time between the SDN entities. The console simulation uses NetBeans with CloudsimSDN procedure files in the SDN-based cloud environment.

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Multi-objective hybrid harmony search-simulated annealing for location-inventory-routing problem in supply chain network design of reverse logistics with CO2 emission

Cited by 5 publications

References 23 publications

Logistics Center Location-Inventory-Routing Problem Optimization: A Systematic Review Using PRISMA Method

Logistics Center Location-Inventory-Routing Problem Optimization: A Systematic Review Using PRISMA Method

Deep Learning Framework for Locating Physical Internet Hubs using Latitude and Longitude Classification

Using metaheuristics to improve the placement of multi-controllers in software-defined networking enabled clouds

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