Layered Fixed Point Logic

We present a logic for the specification of static analysis problems that goes beyond the logics traditionally used. Its most prominent feature is the direct support for both inductive computations of behaviors as well as co-inductive specifications of properties. Two main theoretical contributions are a Moore Family result and a parametrized worst case time complexity result. We show that the logic and the associated solver can be used for rapid prototyping and illustrate a wide variety of applications within Static Analysis, Constraint Satisfaction Problems and Model Checking. In all cases the complexity result specializes to the worst case time complexity of the classical methods

An Efficient Consistency Algorithm for the Temporal Constraint Satisfaction Problem

Dechter et al. [5] proposed solving the Temporal Constraint Satisfaction Problem (TCSP) by modeling it as a meta-CSP, which is a finite CSP with a unique global constraint. The size of this global constraint is exponential in the number of time points in the original TCSP, and generalized-arc consistency is equivalent to finding the minimal network of the TCSP, which is NP-hard. We introduce _AC, an efficient consistency algorithm for filtering the meta-CSP. This algorithm significantly reduces the domains of the variables of the meta-CSP without guaranteeing arc-consistency. We use _AC as a preprocessing step to solving the meta-CSP. We show experimentally that it dramatically reduces the size of a meta-CSP and significantly enhances the performance of search for finding the minimal network of the corresponding TCS

An Efficient Consistency Algorithm for the Temporal Constraint Satisfaction Problem

Dechter et al. [5] proposed solving the Temporal Constraint Satisfaction Problem (TCSP) by modeling it as a meta-CSP, which is a finite CSP with a unique global constraint. The size of this global constraint is exponential in the number of time points in the original TCSP, and generalized-arc consistency is equivalent to finding the minimal network of the TCSP, which is NP-hard. We introduce _AC, an efficient consistency algorithm for filtering the meta-CSP. This algorithm significantly reduces the domains of the variables of the meta-CSP without guaranteeing arc-consistency. We use _AC as a preprocessing step to solving the meta-CSP. We show experimentally that it dramatically reduces the size of a meta-CSP and significantly enhances the performance of search for finding the minimal network of the corresponding TCS

Planning of Optimal Fuel Supply of International Transport Activity

The selection of the petrol station for refuel and the amount of refilled fuel are not determined centrally at most transport companies, but depend on the individual decision of the driver, so that the total cost of the burned fuel is not minimal. The goal of this study is to elaborate a precise and reliable mathematical model and method for the determination of the optimal refuel points and the amount of loaded fuel to fulfil specific transport tasks. The model is a mixed valued non-linear programming model, which can be handled by optimization procedures. Based on the elaborated model and method, a decision-supporting software was developed, which provides the required information that is necessary for the economical fulfilment of transport trips

Planning of Optimal Fuel Supply of International Transport Activity

The selection of the petrol station for refuel and the amount of refilled fuel are not determined centrally at most transport companies, but depend on the individual decision of the driver, so that the total cost of the burned fuel is not minimal. The goal of this study is to elaborate a precise and reliable mathematical model and method for the determination of the optimal refuel points and the amount of loaded fuel to fulfil specific transport tasks. The model is a mixed valued non-linear programming model, which can be handled by optimization procedures. Based on the elaborated model and method, a decision-supporting software was developed, which provides the required information that is necessary for the economical fulfilment of transport trips

Comparative study of pheromone control heuristics in ACO algorithms for solving RCPSP problems

Constraint Satisfaction Problems (CSP) belong to a kind of traditional NP-hard problems with a high impact on both research and industrial domains. The goal of these problems is to find a feasible assignment for a group of variables where a set of imposed restrictions is satisfied. This family of NP-hard problems demands a huge amount of computational resources even for their simplest cases. For this reason, different heuristic methods have been studied so far in order to discover feasible solutions at an affordable complexity level. This paper elaborates on the application of Ant Colony Optimization (ACO) algorithms with a novel CSP-graph based model to solve Resource-Constrained Project Scheduling Problems (RCPSP). The main drawback of this ACO-based model is related to the high number of pheromones created in the system. To overcome this issue we propose two adaptive Oblivion Rate heuristics to control the number of pheromones: the first one, called Dynamic Oblivion Rate, takes into account the overall number of pheromones produced in the system, whereas the second one inspires from the recently contributed Coral Reef Optimization (CRO) solver. A thorough experimental analysis has been carried out using the public PSPLIB library, and the obtained results have been compared to those of the most relevant contributions from the related literature. The performed experiments reveal that the Oblivion Rate heuristic removes at least 79% of the pheromones in the system, whereas the ACO algorithm renders statistically better results than other algorithmic counterparts from the literature