Modelling empty container repositioning logistics / Dong-Ping Song, Jingxin Dong.

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Modelling empty container repositioning logistics / Dong-Ping Song, Jingxin Dong.

9783030933838 (electronic bk.)
3030933830 (electronic bk.)
9783030933821
3030933822

Cham : Springer, [2022]

©2022

English

1 online resource : illustrations (some color)

10.1007/978-3-030-93383-8 doi

TS160 .S66 2022

658.7/87

The book takes the inventory control perspective to tackle empty container repositioning logistics problems in regional transportation systems by explicitly considering the features such as demand imbalance over space, dynamic operations over time, uncertainty in demand and transport, and container leasing phenomenon. The book has the following unique features. First, it provides a discussion of broad empty equipment logistics including empty freight vehicle redistribution, empty passenger vehicle redistribution, empty bike repositioning, empty container chassis repositioning, and empty container repositioning (ECR) problems. The similarity and unique characteristics of ECR compared to other empty equipment repositioning problems are explained. Second, we adopt the stochastic dynamic programming approach to tackle the ECR problems, which offers an algorithmic strategy to characterize the optimal policy and captures the sequential decision-making phenomenon in anticipation of uncertainties over time and space. Third, we are able to establish closed-form solutions and structural properties of the optimal ECR policies in relatively simple transportation systems. Such properties can then be utilized to construct threshold-type ECR policies for more complicated transportation systems. In fact, the threshold-type ECR policies resemble the well-known (s, S) and (s, Q) policies in inventory control theory. These policies have the advantages of being decentralized, easy to understand, easy to operate, quick response to random events, and minimal on-line computation and communication. Fourth, several sophisticated optimization techniques such as approximate dynamic programming, simulation-based meta-heuristics, stochastic approximation, perturbation analysis, and ordinal optimization methods are introduced to solve the complex stochastic optimization problems. The book will be of interest to researchers and professionals in logistics, transport, supply chain, and operations research.

Includes bibliographical references.

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Online resource; title from PDF title page (SpringerLink, viewed February 7, 2022).

Print version: 9783030933821

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