Green logistics location-routing problem with eco-packages

Wang, Yong; Peng, Shouguo; Zhou, Xuesong; Mahmoudi, Monirehalsadat; Zhen, Lu

doi:10.1016/j.tre.2020.102118

Cited by 144 publications

(67 citation statements)

References 74 publications

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“…The model is shown below. In Eqs ( 12 ) to ( 14 ), G 1 and E 1 represent green innovation efficiency and the level of high-tech industries development, respectively; C represents the coupling degree; T represents the comprehensive reconciliation index of green innovation efficiency and the level of high-tech industries development; D represents the coordination degree; α , and β are the coefficients to be determined; and α + β = 1 mainly indicates the importance of each system [ 56 ]. In this paper, it is presumed that α = β = 0.5 [ 57 ].…”

Section: Methodsmentioning

confidence: 99%

Spatial analysis, coupling coordination, and efficiency evaluation of green innovation: A case study of the Yangtze River Economic Belt

2020

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Green innovation is an important driving force to promote the sustainable development of urban society and economy. This paper constructs an evaluation index system containing social undesirable outputs, and uses the Super-SBM model and the Malmquist-Luenberger index to evaluate green innovation efficiency in 42 cities along the Yangtze River Economic Belt from 2013 to 2017. Additionally, spatial autocorrelation analysis is used to study the spatial correlation of green innovation efficiency. Finally, the coupling coordination degree model is used to study the coupling coordination degree between green innovation efficiency and high-tech industries. The following results were obtained. (1) The average value of green innovation efficiency increased from 1.0446 to 1.0987, and the annual average growth rate of total factor productivity of green innovation was 1.1%. (2) Green innovation efficiency of the Yangtze River Economic Belt had a significant spatial positive correlation, but the types of agglomeration among cities in different sections of the Yangtze River were quite different. (3) The coupling coordination degree between green innovation efficiency and the development level of high-tech industries in the cities of the Yangtze River Economic Belt was in the basic coordination stage. Based on the research results, we suggest that cities in this belt further enhance the interactive relationship between green innovation and economic development and promote the coordinated development of economy and society.

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Section: Methodsmentioning

confidence: 99%

Spatial analysis, coupling coordination, and efficiency evaluation of green innovation: A case study of the Yangtze River Economic Belt

2020

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“…Another class of VRP that is more complex is the twoechelon, multicenter VRP and its extensions. Some examples of recent works on MCVRP are [32][33][34][35]. e collaborative MCVRP was studied in [32] where collaboration is made among centers by reassignment of customers to centers (through clustering) as well as transshipments among centers.…”

Section: Multiechelon Multicenter Vrp and Extensionsmentioning

confidence: 99%

“…e collaborative MCVRP was studied in [32] where collaboration is made among centers by reassignment of customers to centers (through clustering) as well as transshipments among centers. Wang et al [34] then extended the problem so that collaboration can be made between multiple periods. A twoechelon location routing problem with simultaneous pickup and delivery was investigated in [33].…”

Section: Multiechelon Multicenter Vrp and Extensionsmentioning

confidence: 99%

Vehicle Routing Problem with Transshipment: Mathematical Model and Algorithm

Leelertkij

Parthanadee

Buddhakulsomsiri

2021

Journal of Advanced Transportation

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This paper presents a new variant of vehicle routing problem with paired transshipment demands (VRPT) between retail stores (customers) in addition to the regular demand from depot to retail stores. The problem originates in a real distribution network of high-end retail department stores in Thailand. Transshipment demands arise for one-order-per-season expensive items, whose inventories at the depot may become shortage after the middle of a season, while they remain available at some retail stores. A transshipment demand is a request for items that need to be picked up from a specific store that has the items and delivered to the store that requests the items. The objective of solving the VRPT is to find delivery routes that can satisfy both regular demands and transshipment demands in the same routes without incurring too much additional transportation distance. A mixed integer linear programming model is formulated to represent the VRPT. Six small problem instances are used to test the model. A hybrid threshold accepting and neighborhood search heuristic is also developed to solve large problem instances of VRPT. The heuristic is further extended to include a forbidden list of transshipment demands that should not be included in the same routes. The purpose is to prevent incurring too much additional distance from satisfying transshipment demands. With the forbidden list, the problem becomes vehicle routing problem with optional transshipment demands (VRPOT). Computational testing shows promising results that indicate effectiveness of the proposed hybrid heuristics as well as the forbidden list.

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“…In general, these approaches can be classified into statistical methods [7] and machine learning methods [8,9]. Statistical methods generally can explain the mechanism between independent and dependent variables based on the rigorous mathematical formula [10,11]. From a methodological perspective, regression analysis and hazard-based methods are the two main statistical methods to analyze incident duration data.…”

Section: Introductionmentioning

confidence: 99%

Application of the Bayesian Model Averaging in Analyzing Freeway Traffic Incident Clearance Time for Emergency Management

Zou

Lin

Yang

et al. 2021

Journal of Advanced Transportation

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Identifying the influential factors in incident duration is important for traffic management agency to mitigate the impact of traffic incidents on freeway operation. Previous studies have proposed a variety of approaches to determine the significant factors for traffic incident clearance time. These methods commonly select a single “true” model among a majority of alternative models based on some model selection criteria. However, the conventional methods generally neglect the uncertainty related to the choice of models. This paper proposes a Bayesian Model Averaging (BMA) model to account for model uncertainty by averaging all plausible models using posterior probability as the weight. The BMA model is used to analyze the 2,584 freeway incident records obtained from I-5 corridor in Seattle, WA, USA. The results show that the BMA approach has the capability of interpreting the causal relationship between explanatory variables and clearance time. In addition, the BMA approach can provide better prediction performance than the Cox proportional hazards model and the accelerated failure time models. Overall, the findings in this study can be useful for traffic emergency management agency to apply an alternative methodology for predicting traffic incident clearance time when model uncertainty is considered.

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Green logistics location-routing problem with eco-packages

Cited by 144 publications

References 74 publications

Spatial analysis, coupling coordination, and efficiency evaluation of green innovation: A case study of the Yangtze River Economic Belt

Spatial analysis, coupling coordination, and efficiency evaluation of green innovation: A case study of the Yangtze River Economic Belt

Vehicle Routing Problem with Transshipment: Mathematical Model and Algorithm

Application of the Bayesian Model Averaging in Analyzing Freeway Traffic Incident Clearance Time for Emergency Management

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