A two-stage robust model for express service network design with surging demand

Zhang, X.; Liu, X.

doi:10.1016/j.ejor.2021.06.031

Cited by 18 publications

(6 citation statements)

References 34 publications

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“…The inequalities (2)-( 4) are commodity flow balance constraints; for commodity k, the supply at the origin and the demand at the destination are d k , and the volumes flow out equal flow in at all other nodes. Constraint condition (5) ensures that the commodity flow on arc (i, j) cannot exceed the capacity of total vehicles on that arc.…”

Section: …… ……mentioning

confidence: 99%

“…Such problems are generally formulated as mixed-integer optimization models that employ integer variables in service selection and continuous variables in commodity flow. SND problems fall into the category of the network design problem, which is applied in many applications [5]. In SND formulations, the arcs of the service network represent services operated by transportation assets, and different commodities must be transported from their origins to destinations in time.…”

Section: Introductionmentioning

confidence: 99%

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An Iterative Backbone Algorithm for Service Network Design Problems

2022

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Service network design problems arise at airlines, trucking companies, and railroads wherever there is a need to determine cost-minimizing routes and schedules, given resource availability and service constraints. In recent years, the application of consolidation-based service network design in the express service has attracted lots of academic attention due to the rapid growth of the express industry. This paper studies the consolidation-based service network design problem, which jointly determines the commodity flow, vehicle dispatching, and fleet sizing. We propose a mixed-integer optimization model to address the problem and design an efficient iterative backbone algorithm to solve large-scale real-world problems. The numerical results of large-scale instances confirmed that the solution obtained by our proposed algorithm is better than that of the primal model, and the running time taken is less than half that of the general solution approach. The computational study confirmed the effectiveness and efficiency of the proposed algorithm.

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Section: …… ……mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Iterative Backbone Algorithm for Service Network Design Problems

2022

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“…The sub-problem considers the worst variation of the uncertain parameters to verify the rationality of the second-stage robust optimization results. Repeated iterations can ensure that the optimization results fall into local optimal solutions [25][26][27]. In [28], for the mixed integer programming in the subproblem, KKT conditions are added to the main problem, and then the duality is performed to obtain the subproblem, so as to avoid the MLIP problem of the subproblem.…”

Section: Introductionmentioning

confidence: 99%

Collaborative planning of data center and energy storage based on i-C&CG solving algorithm

2024

Journal of Electrical Systems

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With the rapid development of new power systems and advanced technologies such as artificial intelligence, the penetration rate of renewable energy is increasing, and the electricity consumption required for computing power calculation is surging. On the one hand, the uncertainty of wind power output has in-creasingly serious impact on the operation safety of power system. On the other hand, internet data center (IDC) needs to consume a lot of power to maintain the computing power system. Therefore, a two-stage robust collaborative programming model of IDC and BESS with life constraints is proposed herein. Plan storage capacity and data center server configuration with the goal of minimizing system operation and planning costs. An inexact column-and-constraint generation (i-C&CG) algorithm is proposed herein to solve the problem, and the IEEE-30 nodes system is simulated. The planning considering the life constraints of BESS are more reasonable, and i-C&CG algorithm can reduce the difficulty of solving two-stage robust planning problem of power system.

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“…Second, the logistics network is built based on the geographical location distribution of logistics enterprise headquarters and branches [4][5]. Third, regional logistics network structure is characterized based on the data such as road freight order [6][7][8][9], railway freight flow [10][11], sea freight flow [12], air freight flow [13][14] and express freight bill [15][16][17][18]. From the perspective of research scale, the existing research mainly focuses on the global scale [19][20], national scale [21][22], provincial scale [23], and the analysis and research granularity is relatively coarse.…”

Section: Introductionmentioning

confidence: 99%

Research on the Spatial-Time Evolution and Influence Mechanism of Logistics Network Structure in Zhengzhou Metropolitan Area

Liu

et al. 2023

IEEE Access

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Based on the modified gravity model and social network analysis method, the evolution characteristics and driving mechanism of logistics network spatial structure in Zhengzhou metropolitan area (ZMA ) from 2008 to 2019 are analyzed at the county (district) scale. The research finds that: (1) The distribution of logistics links in counties (districts) of ZMA has significant spatial heterogeneity, showing a spatial pattern of "sparse in the east and dense in the west", and the counties (districts) logistics links are concentrated in Zhengzhou. ( 2) The overall level of the logistics network density in ZMA is low, but it is fluctuating and rising. The overall realization is the logistics radiation effect. The core area of the logistics network layout is mainly concentrated in the main urban area of Zhengzhou and surrounding counties (districts), and the spatial scope is expanding and connecting into one piece. The contact density of counties (districts) in the core area is high, and the contact density between counties (districts) in the edge area is low. (3) The logistics connection intensity of counties (districts) in ZMA has a positive role in promoting the logistics connection intensity of surrounding areas, with obvious spatial correlation characteristics. (4) The upgrading of industrial structure (lnInd), the level of economic development (lnGDP), and the size of population (lnPop) are the main factors that promote the formation of the local logistics network structure. The upgrading of industrial structure (lnInd) and the level of economic development (lnGDP) restrain the logistics network structure and internal logistics network structure in the surrounding areas, while the population size (lnPop) and government intervention (lnGov) promote the formation of the external logistics network structure in the surrounding areas.INDEX TERMS Zhengzhou metropolitan area, logistics network structure, modified gravity model, Social network analysis, influential mechanism.

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A two-stage robust model for express service network design with surging demand

Cited by 18 publications

References 34 publications

An Iterative Backbone Algorithm for Service Network Design Problems

An Iterative Backbone Algorithm for Service Network Design Problems

Collaborative planning of data center and energy storage based on i-C&CG solving algorithm

Research on the Spatial-Time Evolution and Influence Mechanism of Logistics Network Structure in Zhengzhou Metropolitan Area

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