An integrated location-routing problem with post-disaster relief distribution

Xiao-wen, Wei; Qiu, Huaxin; Wang, Dujuan; Duan, Jiahui; Wang, Yanzhang; Cheng, T. C. E.

doi:10.1016/j.cie.2020.106632

Cited by 50 publications

(41 citation statements)

References 27 publications

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“…Moreover, 2E-LRP is a prominent NP-hard problem [45][46][47]. e 2E-LRP optimization is usually solved by establishing a mathematical model [44,48,49].…”

Section: Literature Reviewmentioning

confidence: 99%

Two-Echelon Location-Routing Problem with Time Windows and Transportation Resource Sharing

Wang

Sun

Guan

et al. 2021

Journal of Advanced Transportation

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In this work, a two-echelon location-routing problem with time windows and transportation resource sharing (2E-LRPTWTRS) is solved by selecting facility locations and optimizing two-echelon vehicle routes. The optimal solutions improve the efficiency of a logistics network based on the geographical distribution and service time windows of logistics facilities and customers. Furthermore, resource utilization is maximized by enabling resource sharing strategies within and among different logistics facilities simultaneously. The 2E-LRPTWTRS is formulated as a biobjective optimization model, and obtaining the smallest number of required delivery vehicles and the minimum total operating cost are the two objective functions. A two-stage hybrid algorithm composed of k-means clustering and extended multiobjective particle swarm optimization algorithm is proposed for 2E-LRPTWTRS optimization. A self-adaptive mechanism of flight parameters is introduced and adopted during the iterative process to balance the evolution of particles and improve the efficiency of the two-stage hybrid algorithm. Moreover, 20 small-scale instances are used for an algorithm comparison with multiobjective genetic algorithm and nondominated sorting genetic algorithm-II, and the solutions demonstrate the superiority of the proposed algorithm in optimizing logistics networks. The proposed optimization model and hybrid algorithm are tested by employing a real-world case of 2E-LRPTWTRS in Chongqing, China, and the optimization results verify the positive role of the developed model and algorithm in improving logistics efficiency, reducing operating cost, and saving transportation resources in the operations of two-echelon logistics networks.

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“…Moreover, 2E-LRP is a prominent NP-hard problem [45][46][47]. e 2E-LRP optimization is usually solved by establishing a mathematical model [44,48,49].…”

Section: Literature Reviewmentioning

confidence: 99%

Two-Echelon Location-Routing Problem with Time Windows and Transportation Resource Sharing

Wang

Sun

Guan

et al. 2021

Journal of Advanced Transportation

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“…Reducing the time it takes the relief aids to reach the damaged area and reducing the costs A location-routing problem with post-disaster relief distribution 2020 Wang et al [32] hybrid genetic algorithm…”

Section: Maximizing the Prize And Minimizing Co2 Emissionmentioning

confidence: 99%

Proposing a model for a resilient supply chain: A meta-heuristic algorithm

Shafaghizadeh

Ebrahimnejad

Mehrdad

et al. 2021

IJE

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“…PSO and IGSA are two population-based metaheuristics. PSO has been triumphantly extended to deal with varied complex optimization problems [8,55,56] including DLBPs [40]. IGSA has been employed to solve DLBPs [40].…”

Section: Experimental Studymentioning

confidence: 99%

An enhanced group teaching optimization algorithm for multi-product disassembly line balancing problems

Liang

Gao

et al. 2021

Complex Intell. Syst.

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Big data have been widely studied by numerous scholars and enterprises due to its great power in making highly reliable decisions for various complex systems. Remanufacturing systems have recently received much attention, because they play significant roles in end-of-life product recovery, environment protection and resource conservation. Disassembly is treated as a critical step in remanufacturing systems. In practice, it is difficult to know the accurate data of end-of-life products such as disassembly time because of their various usage processes, leading to the great difficulty of making effective and reliable decisions. Thus, it is necessary to model the disassembly process with stochastic programming method where the past collected data are fitted into stochastic distributions of parameters by applying big data technology. Additionally, designing and applying highly efficient intelligent optimization algorithms to handle a variety of complex problems in the disassembly process are urgently needed. To achieve the global optimization of disassembling multiple products simultaneously, this work studies a stochastic multi-product disassembly line balancing problem with maximal disassembly profit while meeting disassembly time requirements. Moreover, a chance-constrained programming model is correspondingly formulated, and then, an enhanced group teaching optimization algorithm incorporating a stochastic simulation method is developed by considering this model’s features. Via performing simulation experiments on real-life cases and comparing it with five popularly known approaches, we verify the excellent performance of the designed method in solving the studied problem.

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An integrated location-routing problem with post-disaster relief distribution

Cited by 50 publications

References 27 publications

Two-Echelon Location-Routing Problem with Time Windows and Transportation Resource Sharing

Two-Echelon Location-Routing Problem with Time Windows and Transportation Resource Sharing

Proposing a model for a resilient supply chain: A meta-heuristic algorithm

An enhanced group teaching optimization algorithm for multi-product disassembly line balancing problems

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