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Introduces the concept of service‐oriented agile logistics and presents a generic apparatus for the design of such systems. An analysis of future communication‐based logistics leads to the logistic control and engineering system SERVICES. The logistic system is conceived as a “society” of interacting “self‐responsible intelligent service‐producing actors”, where services or functions are taken as the system‐base. This means that, instead of working with process‐modules, the development of a service‐oriented information‐system primarily works with service‐modules that program the operational interaction between client, service‐producer and possibly sub‐contracted service‐producers. From this the supporting execution‐control in the context of the service‐producing units can be deduced. A case study of a high‐performance deep‐sea container terminal is given. This shows that the function‐programming system of SERVICES is generic, adequate and effective and that it favours distributed control.

Discusses the design of large‐scale distribution networks which entails taking decisions on a large number of issues that are all closely interrelated, making it difficult to develop a competitive distribution strategy. Many support systems for distribution planning have been developed, but, they do not facilitate a coherent approach to the design process. Presents a logistic planning system that provides dedicated support for all issues involved in distribution planning. The foundation of which is an integrated planning support framework. Proposes that small models dedicated to only part of the total design process provide the best support for logistic planning and that such models encapsulated in a framework lead to optimal system design. Suggests that when the support system is applied to strategic/tactical distribution planning, the design process requires less time and less expert capacity, while resulting in a more competitive logistic supply chain.

A general concave ∞-horizon optimization model is analyzed with the help of a special convexity concept, which combines both the usual convexity and the dynamic structure. The axiomatic setup leads to a perfect symmetry between the primal and dual problems. After introducing a particular dynamic feasibility hypothesis, the following results are presented: (i) boundedness of trajectories as a necessary condition for optimally, (ii) the existence of primal and dual optimal trajectories, (iii) approximation by finite horizon models, and (iv) necessary and sufficient conditions for optimality.