Significant uncertainty associated with Chinese urban logistics, caused by random vehicle operational restrictions due to severe weather (e.g., smog) in addiction to normal traffic variation makes the tactical product...
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Significant uncertainty associated with Chinese urban logistics, caused by random vehicle operational restrictions due to severe weather (e.g., smog) in addiction to normal traffic variation makes the tactical production and distribution planning decisions quite challenging. In this paper, we propose a two-stage stochastic integer programming model for an optimal production distribution capacity planning problem under the aforementioned uncertainties. We aim to minimize both procurement spending and the expected operational cost under logistic uncertainty. Given the computational burden of solving the resultant stochastic integer program for real-world instances, we develop an improved stochasticbranch-and-bound (SBB) algorithm embedding with Tabu search method. We conduct the numerical study to verify the superiority of the proposed algorithm. We also offer managerial insights to practitioners and policy recommendations to municipal governments based on the numerical study results. (C) 2018 The Authors. Published by Elsevier Ltd.
Significant uncertainty associated with Chinese urban logistics, caused by random vehicle operational restrictions due to severe weather (e.g., smog) in addiction to normal traffic variation makes the tactical product...
详细信息
Significant uncertainty associated with Chinese urban logistics, caused by random vehicle operational restrictions due to severe weather (e.g., smog) in addiction to normal traffic variation makes the tactical production and distribution planning decisions quite challenging. In this paper, we propose a two-stage stochastic integer programming model for an optimal production distribution capacity planning problem under the aforementioned uncertainties. We aim to minimize both procurement spending and the expected operational cost under logistic uncertainty. Given the computational burden of solving the resultant stochastic integer program for real-world instances, we develop an improved stochasticbranch-and-bound (SBB) algorithm embedding with Tabu search method. We conduct the numerical study to verify the superiority of the proposed algorithm. We also offer managerial insights to practitioners and policy recommendations to municipal governments based on the numerical study results.
In this paper we present a solution methodology based on the stochasticbranch and boundalgorithm to find optimal, or close to optimal, solutions to the stochastic airport runway scheduling problem. The objective of ...
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In this paper we present a solution methodology based on the stochasticbranch and boundalgorithm to find optimal, or close to optimal, solutions to the stochastic airport runway scheduling problem. The objective of the scheduling problem is to find a sequence of aircraft operations on one or several runways that minimizes the total makespan, given uncertain aircraft availability at the runway. Enhancements to the general stochasticbranch and boundalgorithm are proposed and we give the specific details pertaining to runway scheduling. We show how the algorithm can be terminated early with solutions that are close to optimal, and investigate the impact of the uncertainty level. The computational experiment indicates that the sequences obtained using the stochasticbranch and boundalgorithm have, on average, 5-7% shorter makespans than sequences obtained using deterministic sequencing models. In addition, the proposed algorithm is able to solve instances with 14 aircraft using less than 1 min of computation time. (C) 2014 Elsevier Ltd. All rights reserved.
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