Distributionally robust inventory routing problem to maximize the service level under limited budget

Liu, Ming; Liu, Xin; Zheng, Feifeng; Chu, Chengbin

doi:10.1016/j.tre.2019.04.005

Cited by 18 publications

(12 citation statements)

References 56 publications

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“…(2) The distribution vehicles start from the cigarette distribution center and return to the distribution center after the retail merchants of the route. (The order in which the distribution vehicles pass through the retailers is called the route) 5 Wireless Communications and Mobile Computing (7) The distance between the customer node and the other customer node and between the customer node and the distribution center is the arc distance between them (8) The distribution personnel carry out the distribution within normal working hours (8 hour working system) (9) The delivery vehicles should be delivered within the time window agreed by customers; otherwise, they will be punished (10) The delivery vehicles are delivered strictly according to the order quantity 6…”

Section: Model Hypothesismentioning

confidence: 99%

“…In the above model, Equation ( 6) represents the optimal objective function of cigarette route optimization, in which the first term represents the total distance cost of vehicles, the second represents the total fuel consumption cost of the vehicles, the third represents the total fixed cost of the vehicles, and the fourth represents the penalty cost. Equation (7) indicates that each vehicle can only belong to one depot (distribution center). Equation (8) indicates that the carrying capacity of the vehicle passing through the customer node cannot be greater than the maximum carrying capacity of the vehicle.…”

Section: Model Hypothesismentioning

confidence: 99%

“…Schmid et al investigated mode and user-type effects in the value of travel time savings (VTTS) using a pooled RP/SP Mixed Logit modeling approach for mode, route, and destination choice data [6]. Meanwhile, Liu et al proposed a novel distributed robust chance constrained formulation and developed the sample average approximation method and a model-based hierarchical approach to handle the stochastic inventory routing problem of modules [7]. Then, Zhen et al developed a hybrid particle swarm optimization algorithm and a hybrid genetic algorithm to solve the multijourney vehicle routing problem [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimization Model and Algorithm of Cigarette Distribution Route Based on Cluster Analysis

Xiao

Wang

et al. 2022

Wireless Communications and Mobile Computing

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Cigarette distribution is an important part of the tobacco logistics supply chain, and it will affect the distribution cost, efficiency, and service quality. In this paper，we choose a two-stage optimization method to analyze the cigarette distribution route across the Administrative Region in China. First, we use a K-means clustering algorithm to generate an initial center. Then, we optimize the clustering region with the vehicle load and workload as constraints. Finally, we establish the cigarette distribution route optimization model by taking the lowest distribution cost as the objective function. In the model calculation, we not only select the adaptive genetic algorithm as the solution algorithm but also use the scanning algorithm to generate the initial population ,design adaptive crossover and mutation probability, and also design inverse operator to correct the error, and we wish to improve the solution performance of the algorithm by this way.In addition, taking the cigarette distribution of Chongqing Tobacco Logistics as an example, this paper analyzes the number of vehicles, average loading rate, and total distribution distance and distribution cost, and finds that the cross-regional joint distribution route is better than independent distribution. And we also find that using clustering analysis and the genetic algorithm could reduce the amount of computation and solve the problem effectively.

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Section: Model Hypothesismentioning

confidence: 99%

Section: Model Hypothesismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimization Model and Algorithm of Cigarette Distribution Route Based on Cluster Analysis

Xiao

Wang

et al. 2022

Wireless Communications and Mobile Computing

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“…This motivates the development of distributionally robust optimization (DRO), which could avoid the over‐conservatism of RO and the requirement of precise probability distributions in SP (Liu et al., 2021). The DRO method is particularly novel and has become an attractive method to solve uncertain optimization problems (Liu et al., 2019; Jia et al., 2020; Huang et al., 2020; Yang et al., 2021). For example, Liu et al.…”

Section: Literature Reviewmentioning

confidence: 99%

Distributional robustness and lateral transshipment for disaster relief logistics planning under demand ambiguity

Wang

Yang

et al. 2022

Int Trans Operational Res

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This paper considers facility location, inventory pre‐positioning and vehicle routing as strategic and operational decisions corresponding to preparedness and response phases in disaster relief logistics planning. For balancing surpluses and shortages, an effective lateral transshipment strategy is proposed to evenly distribute the relief resources between warehouses after the disaster occurs. To handle ambiguity in the probability distribution of demand, we develop a risk‐averse two‐stage distributionally robust optimization (DRO) model for the disaster relief logistics planning problem, which specifies the worst‐case mean‐conditional value‐at‐risk (CVaR) as a risk measure. For computationally tractability, we transform the robust counterpart into its equivalent linear mixed‐integer programming model under the discrepancy‐based ambiguity set centered at the nominal (empirical) distributions on the observed demand from the historical data. We verify the effectiveness of the proposed DRO model and the value of lateral transshipment strategy by an illustrative small‐scale example. The numerical results show that the proposed DRO model has advantage on avoiding over‐conservative solutions compared to the classic robust optimization model. We also illustrate the applicability of the proposed DRO model by a real‐world case study of hurricanes in the southeastern United States. The computational results demonstrate that the proposed DRO model has superior out‐of‐sample performance and can mitigate the adverse effects of Optimizers' Curse compared with the traditional stochastic programming model.

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“…23 In other words, the DRO approach is stable with regards to the underlying probability distributions, and shows better performance in out-of-sample simulations than SP and RO. 24,25 From the perspective of ambiguity set construction, DRO approaches covered in literature can be classified as moment-based 26,27 and Wassersteindistance based. 24,25,28 Ambiguity sets constructed via the Wasserstein metric exhibit favorable asymptotic and finite sample guarantees.…”

mentioning

confidence: 99%

Distributionally robust optimization for the closed‐loop supply chain design under uncertainty

Zhang

Yuan

2022

AIChE Journal

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The closed-loop supply chain network (CLSCN) contains reverse flows that collect products from customers and recycle or remanufacture usable parts. The CLSCN design problem is becoming more and more prominent under the context of Sustainable Development and Circular Economy. Parameters associated with a CLSCN including customer demands, transportation costs, or disposal rates are usually subject to uncertainty. Furthermore, natural or man-made disruptions may cause part of the CLSCN to malfunction. We herein propose a hybrid stochastic and distributionally robust optimization (DRO) approach to hedge against discrete disruption scenarios and uncertain customer demands. We also tailor a Benders decomposition-based algorithm to efficiently solve the resulting large-scale mixed integer linear programming reformulations. Computational experiments demonstrate that the proposed algorithm can outperform commercial solvers such as CPLEX, and the DRO approach can produce solutions with low average costs and low variance in out-of-sample tests.

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Distributionally robust inventory routing problem to maximize the service level under limited budget

Cited by 18 publications

References 56 publications

Optimization Model and Algorithm of Cigarette Distribution Route Based on Cluster Analysis

Optimization Model and Algorithm of Cigarette Distribution Route Based on Cluster Analysis

Distributional robustness and lateral transshipment for disaster relief logistics planning under demand ambiguity

Distributionally robust optimization for the closed‐loop supply chain design under uncertainty

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