Ebook: Adaptive Search and the Management of Logistic Systems: Base Models for Learning Agents

Global competition and growing costumer expectations force indus­ trial enterprises to reorganize their business processes and to support cost-effective customer services. Realizing the potential savings to be gained by exacting customer-delivery processes, logistics is currently sub­ ject to incisive changes. This upheaval aims at making competitive ad­ vantage from logistic services instead of viewing them simply as business necessity. With respect to this focus logistics management comprises the process of planning, implementing, and controlling the efficient, effective flow and storage of goods and services, and related information from point of origin to point of consumption for the purpose of conforming customer requirements I . This definition implies a holistic view on the logistic network, where the actors are suppliers, manufacturers, stock keepers, shipping agents, distributors, retailers and finally consumers. The flow of goods along the supply chain considers raw-materials, work-in-process parts, intermedi­ ate and finished products, and possibly waste. The prevailing manage­ ment of logistics operation is driven by aggregated forecasting of these material flows. Modern logistics management propagates a disaggregated view of the material flow in order to meet the precise requirements at the interface between actors in the supply chain. Replacing aggregated information by detailed values establishes the prerequisites for an integrated process planning which goes for the shift from anticipatory towards response­ based logistic81. Smaller units of goods are considered at shorter periods for planning, implementing and controlling the material flow. From Icf. the Council of Logistics Management (1995).

---

The management problems addressed in Adaptive Search and theManagement of Logistic Systems stem from the recent changes in the logistics sector. Global competition and increasing consumer expectations are forcing industrial enterprises to reorganize their business processes to support better and more cost-efficient services. Quick and precise customer delivery processes have been recognized as the key to significant savings and growth for a company. In seeking solutions to the related problems of planning and control in today's logistic activities, the book is divided into two parts. Part I lays down the fundamentals of evolutionary adaptive systems before an architectural model for adaptive agents is developed. Part II is devoted to applications of these adaptive agents. By focusing on elementary yet complex problems, it is hoped to glean sufficient details of the real world.
In its examination of the logistics network, the book employs a simple yet powerful framework of evolutionary search, allowing efficient support for reactive problem solving by the actors in the logistics network. Following modern terminology, we refer to evolutionary algorithms as learning or adaptive agents. Different from other software artifacts, which are also designated as agents in the current literature, these adaptive agents do not communicate directly by exchanging messages. Instead, while exploring the space of logistic operation, they communicate by mutually changing details of the common environment they perceive.

---

The management problems addressed in Adaptive Search and theManagement of Logistic Systems stem from the recent changes in the logistics sector. Global competition and increasing consumer expectations are forcing industrial enterprises to reorganize their business processes to support better and more cost-efficient services. Quick and precise customer delivery processes have been recognized as the key to significant savings and growth for a company. In seeking solutions to the related problems of planning and control in today's logistic activities, the book is divided into two parts. Part I lays down the fundamentals of evolutionary adaptive systems before an architectural model for adaptive agents is developed. Part II is devoted to applications of these adaptive agents. By focusing on elementary yet complex problems, it is hoped to glean sufficient details of the real world.
In its examination of the logistics network, the book employs a simple yet powerful framework of evolutionary search, allowing efficient support for reactive problem solving by the actors in the logistics network. Following modern terminology, we refer to evolutionary algorithms as learning or adaptive agents. Different from other software artifacts, which are also designated as agents in the current literature, these adaptive agents do not communicate directly by exchanging messages. Instead, while exploring the space of logistic operation, they communicate by mutually changing details of the common environment they perceive.
Content:
Front Matter....Pages i-xiii
Front Matter....Pages 1-1
From Artificial to Computational Intelligence....Pages 3-20
Principles of Systems....Pages 21-45
Genetic Algorithms....Pages 47-59
Adaptation to Static Environments....Pages 61-84
Adaptive Agents....Pages 85-105
Front Matter....Pages 107-107
Problem Representation in Logistics Systems....Pages 109-130
Adaptive Scheduling....Pages 131-153
Towards Real World Scheduling Systems....Pages 155-177
Adaptive Agents at Work....Pages 179-199
Epilogue....Pages 201-201
Back Matter....Pages 203-219

---

The management problems addressed in Adaptive Search and theManagement of Logistic Systems stem from the recent changes in the logistics sector. Global competition and increasing consumer expectations are forcing industrial enterprises to reorganize their business processes to support better and more cost-efficient services. Quick and precise customer delivery processes have been recognized as the key to significant savings and growth for a company. In seeking solutions to the related problems of planning and control in today's logistic activities, the book is divided into two parts. Part I lays down the fundamentals of evolutionary adaptive systems before an architectural model for adaptive agents is developed. Part II is devoted to applications of these adaptive agents. By focusing on elementary yet complex problems, it is hoped to glean sufficient details of the real world.
In its examination of the logistics network, the book employs a simple yet powerful framework of evolutionary search, allowing efficient support for reactive problem solving by the actors in the logistics network. Following modern terminology, we refer to evolutionary algorithms as learning or adaptive agents. Different from other software artifacts, which are also designated as agents in the current literature, these adaptive agents do not communicate directly by exchanging messages. Instead, while exploring the space of logistic operation, they communicate by mutually changing details of the common environment they perceive.
Content:
Front Matter....Pages i-xiii
Front Matter....Pages 1-1
From Artificial to Computational Intelligence....Pages 3-20
Principles of Systems....Pages 21-45
Genetic Algorithms....Pages 47-59
Adaptation to Static Environments....Pages 61-84
Adaptive Agents....Pages 85-105
Front Matter....Pages 107-107
Problem Representation in Logistics Systems....Pages 109-130
Adaptive Scheduling....Pages 131-153
Towards Real World Scheduling Systems....Pages 155-177
Adaptive Agents at Work....Pages 179-199
Epilogue....Pages 201-201
Back Matter....Pages 203-219
....