A hybrid spanning tree-based genetic/simulated annealing algorithm for a closed-loop logistics network design problem

Yadegari, Ehsan; Zandieh, M.; Najmi, Hesamaddin

doi:10.1504/ijads.2015.074612

Cited by 9 publications

(8 citation statements)

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“…For the integrated problem of forward/ reverse SCND, 14 test problem samples are used from small to large sizes based on a steel company in Iran. Some other data are generated based on papers in the literature: Wang and Hsu [14] and Yadegari et al [16,20]. In this part, the CSA's performance is compared to the performance of two of the algorithms in the literature that used the spanning tree representation method to solve the NP-hard problem.…”

Section: Computational Resultsmentioning

confidence: 99%

“…In this part, the CSA's performance is compared to the performance of two of the algorithms in the literature that used the spanning tree representation method to solve the NP-hard problem. The first algorithm is GA developed in [14] and the second is the SA developed in [16]. The three mentioned meta-heuristics are compared for efficiency and effectiveness.…”

Section: Computational Resultsmentioning

confidence: 99%

“…The proposed CDM model is an extension of the model provided in [14,16] in which their model only considered one objective function related to the total cost of shpping and developing facilities. However, the provided model in this paper simultaneously considers the cost and environmental effect of the SC.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…Fixed locations of suppliers According to the aforementioned symbols, the bi-objective mixed-integer linear programming is proposed for the green forward / reverse SCND to minimize costs and emission rate as follows (the first objective and the constraints can be found in [14,16]): Emission Rate…”

Section: Sets: Imentioning

confidence: 99%

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Collaborative Decision-Making Model of Green Supply Chain: Cloud-Based Metaheuristics

Yadegari

Delorme

2021

IFIP Advances in Information and Communication Technology

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The inter-organizational collaborative supply chain (SC) network involves the collaboration of various firms and decision-makers to increase the whole efficiency of an SC network. There is often a conflict between operations and environmental managers in how to design a supply network to simultaneously reduce greenhouse gas emissions and logistics costs. In this paper, a twodimensional collaborative decision-making (CDM) model for a SC network is developed. The proposed network is assumed to deliver the final product to customers in the forward flow from suppliers through manufacturers and distribution centers (DCs). Simultaneously, collecting recycled products from customers and entering them into a recovery cycle is examined. Mathematical modeling of this problem is going to minimize both the total costs and the environmental negative effects. To effectively manage the conflict, Pareto solutions for the bi-objective model are provided. Moreover, a cloud-based simulated annealing algorithm (CSA) has been applied for the first time in this area. We have compared its performance with the genetic algorithm (GA) and the simulated annealing (SA) algorithm of the literature.

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Section: Computational Resultsmentioning

confidence: 99%

Section: Computational Resultsmentioning

confidence: 99%

Section: Mathematical Modelmentioning

confidence: 99%

Section: Sets: Imentioning

confidence: 99%

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Collaborative Decision-Making Model of Green Supply Chain: Cloud-Based Metaheuristics

Yadegari

Delorme

2021

IFIP Advances in Information and Communication Technology

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“…The model presents an NP-hard problem that is not possible to solve through traditional methods in acceptable time, particularly in large size. In this regard, many researchers have been developed heuristics and metaheuristics to cope with this NP-hard [6,[9][10][11][12][13][14]. Although as the network presented in this study is not a case-based network, developing an efficient solution methodology to tackle this NP hard problem is still a critical need in this area [15].…”

Section: Introductionmentioning

confidence: 99%

A Comparison between Memetic Algorithm and Genetic Algorithm for an Integrated Logistics Network with Flexible Delivery Path

Behmanesh

Pannek

2021

SN Oper. Res. Forum

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The distribution/allocation problem is known as one of the most comprehensive strategic decision. In real-world cases, it is impossible to solve a distribution/allocation problem in traditional ways with acceptable time. Hence researchers develop efficient non-traditional techniques for the large-term operation of the whole supply chain. These techniques provide near optimal solutions particularly for large-scale test problems. This paper presents an integrated supply chain model which is flexible in the delivery path. As the solution methodology, we apply a memetic algorithm with a neighborhood search mechanism and novelty in population presentation method called “extended random path direct encoding method.” To illustrate the performance of the proposed memetic algorithm, LINGO optimization software serves as comparison basis for small size problems. In large-size cases that we are dealing with in real world, a classical genetic algorithm as the second metaheuristic algorithm is considered to compare the results and show the efficiency of the memetic algorithm.

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Cloud-based solution approach for a large size logistics network planning

et al. 2023

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A hybrid spanning tree-based genetic/simulated annealing algorithm for a closed-loop logistics network design problem

Cited by 9 publications

References 0 publications

Collaborative Decision-Making Model of Green Supply Chain: Cloud-Based Metaheuristics

Collaborative Decision-Making Model of Green Supply Chain: Cloud-Based Metaheuristics

A Comparison between Memetic Algorithm and Genetic Algorithm for an Integrated Logistics Network with Flexible Delivery Path

Cloud-based solution approach for a large size logistics network planning

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