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Books > Computing & IT > Applications of computing > Artificial intelligence
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Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management (Hardcover, 2009 ed.)
Loot Price: R4,378
Discovery Miles 43 780
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Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management (Hardcover, 2009 ed.)
Series: Studies in Computational Intelligence, 144
Expected to ship within 10 - 15 working days
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Logistics and supply chain management deal with managing the ?ow of
goods or services within a company, from suppliers to customers,
and along a supply chain where companies act as suppliers as well
as customers. As transportation is at the heart of logistics, the
design of tra?c and transportation networks combined with the
routing of vehicles and goods on the networks are important and
demanding planning tasks. The in?uence of transport, logistics, and
s- ply chain management on the modern economy and society has been
growing steadily over the last few decades. The worldwide division
of labor, the conn- tion of distributed production centers, and the
increased mobility of individuals lead to an increased demand for
e?cient solutions to logistics and supply chain management
problems. On the company level, e?cient and e?ective logistics and
supply chain management are of critical importance for a company's
s- cessanditscompetitiveadvantage.
Properperformanceofthelogisticsfunctions can contribute both to
lower costs and to enhanced customer service. Computational
Intelligence (CI) describes a set of methods and tools that often
mimic biological or physical principles to solve problems that have
been di?cult to solve by classical mathematics. CI embodies neural
networks, fuzzy logic, evolutionary computation, local search, and
machine learning approaches. Researchersthat workinthis areaoften
comefromcomputer science, operations research, or mathematics, as
well as from many di?erent engineering disciplines. Popular and
successful CI methods for optimization and planning problems are
heuristic optimization approaches such as evolutionary algorithms,
local search methods, and other types of guided search methods.
General
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