|
The supply chain of both manufacturing and commercial enterprises comprises a highly distributed environment,
in which complex processes evolve in a network of companies (see Figure 1). Such processes
include materials procurement and storage, production of intermediate and final products, warehousing,
sales, customer service, and distribution. The role of the supply chain in a company’s competitiveness
is critical, since the supply chain affects directly customer satisfaction, inventory and distribution costs,
and responsiveness to the ever changing markets. This role becomes more critical in today’s distributed
manufacturing environment, in which companies focus on core competencies and outsource supportive
tasks, thus creating large supply networks. Within this environment, there are strong interactions of
multiple entities, processes, and data. For each process in isolation, it is usually feasible to identify those
decisions that are locally optimal, especially in a deterministic setting. However, decision making in
supply chain systems should consider intrinsic uncertainties, while coordinating the interests and goals
of the multitude of processes involved.
Computational Intelligence (CI) is a term corresponding to a new generation of algorithmic methodologies in artificial intelligence, which combines elements of learning, adaptation, evolution, and approximate (fuzzy) reasoning to create programs that can be considered intelligent.
Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods presents computational intelligence methods for addressing supply chain issues. Emphasis is given to techniques that provide effective solutions to complex supply chain problems and exhibit superior performance to other methods of operations research. |
|
