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Pervasive computing envisions seamless support for user tasks through cooperating devices that are present in an environment. Fluctuating availability of devices, induced by mobility and failures, requires mechanisms and algorithms that allow applications to adapt to their ever‐changing execution environments without user intervention. To ease the development of adaptive applications, Becker et al. (3) have proposed the peer‐based component system PCOM. This system provides fundamental mechanisms to support the automated composition of applications at runtime. In this article, we discuss the requirements on algorithms that enable automatic configuration of pervasive applications. Furthermore, we show how finding a configuration can be interpreted as Distributed Constraint Satisfaction Problem. Based on this, we present an algorithm that is capable of finding an application configuration in the presence of strictly limited resources. To show the feasibility of this algorithm, we present an evaluation based on simulations and real‐world measurements and we compare the results with a simple greedy approximation.

The purpose of this paper is the need to build a novel approach that would allow flexible modeling and solving of food supply chain management (FSCM) problems. The models developed would use the data (data-driven modeling) as early as possible at the modeling phase, which would lead to a better and more realistic representation of the problems being modeled.

An essential feature of the presented approach is its declarativeness. The use of a declarative approach that additionally includes constraint satisfaction problems and provides an opportunity of fast and easy modeling of constrains different in type and character. Implementation of the proposed approach was performed with the use of an original hybrid method in which constraint logic programming (CLP) and mathematical programming (MP) are integrated and transformation of a model is used as a presolving technique.

The proposed constraint-driven approach has proved to be extremely flexible and efficient. The findings obtained during part of experiments dedicated to efficiency were very interesting. The use of the constraint-driven approach has enabled finding a solution depending on the instance data up to 1,000 times faster than using the MP.

Due to the limited use of exact methods for NP-hard problems, the future study should be to integrate the CLP with environments other than the MP. It is also possible, e.g., with metaheuristics like genetic algorithms, ant colony optimization, etc.

There is a possibility of using the approach as a basis to build a decision support system for FSCM, simple integration with databases, enterprise resource planning systems, management information systems, etc.

The new constraint-driven approach to FSCM has been proposed. The proposed approach is an extension of the hybrid approach. Also, a new decision-making model of distribution and logistics for the food supply chain is built. A presolving technique for this model has been presented.

In the grid‐based simulation, the resource application needed is distributed in the grid environment as grid service, and time management is a key problem in the simulation system. Grid workflow provides convenience for grid user to management and executes grid services. But it emphasizes process and no time‐management, so a temporally constrained grid workflow model is pointed out based on grid flow with temporally constraint to schedule resources and manage time.

The temporally constrained grid workflow model is distributed model: the federate has local temporal constraints and interactive temporal constraints among federates. The problem to manage time is a temporally distributed constraint satisfaction problem given deadline time and duration time of grid services. Multi‐asynchronous weak‐commitment search (AWS) algorithm is an approach to resolve DCSP, so a practical example of a simulation project‐based grid system was presented to introduce application of Multi‐AWS algorithm.

The temporally constrained grid workflow is based temporal reasoning and grid workflow description about grid services.

The new problem about scheduling resources and managing time in the grid‐based simulation is pointed out; and the approach to resolve the problem is applied into a practical example.

Currently, the European air transport system is experiencing an annual growth of 7%. With an increasing number of flights, airports are reaching their capacity limits and are becoming a bottleneck in the system. Mantea is a European Commission funded project dealing with this issue. This paper focuses on planning decision support tools for airport traffic controllers.

The objective of our planning tools is to achieve a better use of the available airport infrastructure (taxiways and runways). To generate a safe plan, many rules must be taken into account that restrict the usage of airport tarmac: international regulations, airport operational procedures, aircraft performance, weather conditions and sometimes even controller “usual practices”. To generate a realistic plan, extensive monitoring of the traffic situation as well as suitable timing must be achieved. In the life cycle of a flight, 11 out of 15 possible causes of delay occur in an interval of 10-20 minutes, between aircraft start-up request and push-back. This means that precise planning before the end of this period is highly improbable. On the other hand, planning after this period implies the need for fast responses from the system.

In the Mantea project, an architecture is proposed in which a co-operative approach is taken towards planning aircraft movements at the airport. Controllers will be supported by planning tools that help assigning routes and departure times to controlled vehicles, in planning runway allocation (departure sequence) and occupancies, and in monitoring plan progress during flight phases. The planning horizon relates to medium term operations, i.e. 2-20 minutes ahead. The Mantea planning tools implement the following functions: runway departure planning, routing, and plan conformance monitoring. The tools will reduce the controller's workload, increase the level of safety for airport surface movements, and reduce the number of delays and operating costs for the airliners.

In this paper, we will focus on the constraint satisfaction programming techniques used in Mantea for (1) runway departure planning, (2) itinerary search and taxi planning functions. The airport tarmac and runway vicinity air routes have been modelled as a graph. Real time constraints have brought us to develop an algorithm linear in complexity for the itinerary search problem. Operational pressure has led us to develop fast search strategies for scheduling (i.e. use of heuristics, hill climbing…).

The purpose of this paper is to present a new method for solving parametric programming problems; a new scheme of constraints fuzzification. In the proposed approach, constraints are learned based on deductive learning.

Adaptive neural‐fuzzy inference system (ANFIS) is used for constraint learning by generating input and output membership functions and suitable fuzzy rules.

The experimental results show the ability of the proposed approach to model the set of constraints and solve parametric programming. Some notes in the proposed method are clustering of similar constraints, constraints generalization and converting crisp set of constraints to a trained system with fuzzy output. Finally, this idea for modeling of constraint in the support vector machine (SVM) classifier is used and shows that this approach can obtain a soft margin in the SVM.

Properties of the new scheme such as global view of constraints, constraints generalization, clustering of similar constraints, creation of real fuzzy constraints, study of constraint strength and increasing the degree of importance to constraints are different aspects of the proposed method.

Product configuration is considered as one of the most successful applications of knowledge‐based approaches in the past decade. Knowledge‐based configurations can be classified into three different approaches, namely, rule‐based, model‐based and case‐based approaches. Past research has mainly focused on the development of reasoning techniques for mapping requirements to configurations. Despite the success of certain conventional approaches, the acquisition of configuration knowledge is usually done manually. This paper aims to explore fundamental issues in product configuration system, and propose a novel approach based on data mining techniques to automatically discover configuration knowledge in constraint‐based configurations.

Given a set of product data comprising product requirements specification and configuration information, the paper adopted an association rule mining algorithm to discover useful patterns between requirement specification and product components, as well as the correlation among product components. A configuration was developed which takes XML‐based requirement specification as input and bases on a constraint knowledge base to produce product configuration as output consisting of a list of selected components and the structure and topology of the product. Three modules are developed, namely product data modelling, configuration knowledge generation and product configuration generation module. The proposed approach is implemented in the configuration knowledge generation module. The configuration generation module realizes a resolution of constraint satisfaction problem to generate the output configuration.

The significance and effectiveness of the proposed approach is demonstrated by its incorporation in our configuration system prototype. A case study was conducted and experimental results show that the approach is promising in finding constraints with given sufficient data.

Novel knowledge generation approach is proposed to assist constraint generation for Constraint‐based product configuration system.

The purpose of this paper is to present research in the area of the applications of knowledge‐based and constraint programming (CP)‐driven methodology in production planning and development of decision‐making software supporting scheduling of multi‐robot in a multi‐product job shop, taking into account imprecise (fuzzy) activity specification, and resource sharing by some industrial processes that simultaneously produce different products.

Applications of the knowledge‐based, logic‐algebraic and CP‐driven approach for multi‐robot task allocation problem and generating of fuzzy plan/schedule of production activities for a given period of time.

This paper illustrates the useful information that can be obtained from fuzzy and crispy‐like schedule describing production activities in a multi‐product job shop.

The use of knowledge‐based and CP‐driven methodology for production planning in a multi‐product job shop was a very effective method dedicated to solve typical decision problems in the area of project‐driven production flow management applied in make‐to‐order manufacturing.

The methodology discussed in the paper can be used to design fuzzy Gantt diagrams, which define admissible schedule of production orders for a given period of time.

The paper's contribution covers various issues of decision making while employing the knowledge‐ and CP‐based framework. The proposed approach provides the framework allowing one to take into account distinct (pointed), and imprecise (fuzzy) data, in a unified way and treat it in a unified form of a discrete, constraint satisfaction problem.

The Physical Internet (PI) application in a supply chain is explored by automakers to achieve a digital supply chain to challenge timely delivery while maintaining high customised production at the lowest operating cost.

A bi-objective mixed integer model is formulated, where production is performed in multistage manufacturing systems (MMS) and then delivered in a two-level distribution system. Next, a hybrid iterative method algorithm is developed to solve the practical-scale problem within an admissible time. Finally, PI's benefits on production and supply chain operation are discussed through extensive computational experiments in different supply chain configurations.

Three significant findings are obtained. First, PI can achieve a comparable or better service level, while the cost is always lower. Second, PI can improve the utilisation of production and transportation resources. Third, with a more complex supply chain and a higher production cost or truck fixed cost, PI's advantages over traditional supply chain become more vigorous, but the increase in orders will weaken it.

The auto enterprise should adopt a PI-enabled supply chain (PI-SC), especially with the increase of network complexity and specific cost factors.

Importance should be attached to the PI-SC to make customers better involved in the supply chain.

First, the application of PI in the existing plant is described. Second, MMS production with multi-mode transportation is jointly scheduled. Third, the decision support of the PI-SC is provided for auto enterprises.

This paper aims to present an approach to decision‐making in disaster response operations. The approach is based on ontology‐driven knowledge sharing and application of well‐developed tasks from the area of production network management, that in turn, enables using the existing problem‐solving methods and tools.

The approach applies the decision‐making tasks used in production network management to solving the above‐mentioned problem.

It is shown that there exist many common features and requirements for decision‐making in industrial environment and in disaster relief operations. They both require applying such technologies as ontology and context management, constraint satisfaction and profiling. Sample tasks used in the considered problem domains are presented.

The described research is a step forward in extension of integrating relatively well‐developed technologies implemented in production networks to the quite new areas of disaster relief and humanitarian logistics.

A logistics‐based project is described which addresses the need for better intermodal transport, whilst balancing economic and environmental gains through the use of Internet technologies. Pipeline intermodal system to support control, expedition and scheduling (PISCES) provides an integrating platform for using these technologies in processing and sharing commercially sensitive data within transport chains (i.e. road, rail and barge). The paper demonstrates how information from an Internet‐based system can be used to drive a scheduling tool to provide appropriate routes for the transport of goods, using a multimodal transport model.