We investigate cumulative scheduling in uncertain environments, using constraint programming. We detail in this paper the dynamic sweep filtering algorithm of the FlexC global constraint.

In practice, due to the lack of information, imprecise variables which come from experts’ empirical data usually appear. In order to deal with these imprecise variables, uncertainty theory is proposed and has been proved to be an efficient method. This paper introduces uncertainty theory into travelling salesman problem (TSP), in which the link travel times are assumed to be uncertain variables...

The importance of social responsibility of corporate and business units is increasingly emphasized by researchers and practitioners in recent years. Since supply chains play important roles in today’s business environment, the issue of social responsibility should be considered carefully when designing and planning of supply chains to move towards sustainability. This paper addresses the proble...

In this paper, we deal with the vehicle routing problem where vehicles have finite capacities and demands of customers are uncertain. We represent the uncertain demands by triangular fuzzy numbers and interpret them as possibility distributions. According to the same consideration as the stochastic programming with recourse, we treat the influence of the fuzziness of customers’ demands as recou...

Logic programming, object-oriented programming and soft computing have provided advantageous methodologies and techniques for computer-based problem solving. This paper proposes a framework that combines these three disciplines to exploit their own advantages in dealing with real world problems. The framework is a logic-based one in which class and object properties are represented by clauses. ...

The real life decisions are usually made in the state of uncertainty. This fact providesa motivation to study the behavior of uncertain phenomena. In order to deal withsubjective uncertainty, Liu (2007) founded an uncertainty theory that is a branch ofmathematics based on normality, monotonicity, self-duality, countable subadditivity,and product measure axioms. After that, u...

Preferences have a multi-faceted relationship with several knowledge representation domains and they have been understood in different ways in the Artificial Intelligence literature. In this thesis, we study and clarify the role that preferences can play in the setting of Answer Set Programming. Keywords— preferences, nonmonotonic reasoning, uncertain nonmonotonic reasoning, decision under unce...

Support logic programming and its practical implementation (Fril) integrates probabilistic and fuzzy uncertainty into logic programming using mass assignments. This paper presents a snapshot of current research, aimed at combining the best aspects of inductive logic programming with the uncertainty representation of Fril to create a sophisticated and novel approach to knowledge discovery. An ex...

This paper presents a new method for regression model prediction in an uncertain environment. In practical engineering problems, in order to develop regression or ANN model for making predictions, the average of set of repeated observed values are introduced to the model as an input variable. Therefore, the estimated response of the process is also the average of a set of output values where th...

With globalization and rapid technological-economic development accelerating the market dynamics, consumers' demand is becoming more volatile diverse. In this situation, capacity adjustment as an operational strategic decision plays a major role to ensure supply chain responsiveness while maintaining costs at reasonable norm. This study contributes literature by developing computationally effic...