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Books > Business & Economics > Business & management > Management & management techniques > Management decision making
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Modelling Empty Container Repositioning Logistics (Hardcover, 1st ed. 2022)
Loot Price: R3,660
Discovery Miles 36 600
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Modelling Empty Container Repositioning Logistics (Hardcover, 1st ed. 2022)
Expected to ship within 10 - 15 working days
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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.
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