Perishability of blood products as well as uncertainty in demand amounts complicate the management of blood supply for blood centers. This paper addresses a mixed-integer linear programming model for blood platelets production planning while integrating the processes of blood collection as well as production/testing, inventory control and distribution. Whole blood-derived production methods for...

This paper studies binary linear programming problems in the presence of uncertainties that may cause solution values to change during implementation. type uncertainty, termed implementation is modeled explicitly affecting decision variables rather than model parameters. The nature invalidates use existing models for this uncertainty. robust solutions obtained are optimal a worst-case min-max o...

Solving games played on timed automata is a well-known problem and has led to tools and industrial case studies. In these games, the first player (Controller) chooses delays and actions and the second player (Perturbator) resolves the non-determinism of actions. However, the model of timed automata suffers from mathematical idealizations such as infinite precision of clocks and instantaneous sy...

Probabilistic logic programming under the distribution semantics has been very useful in machine learning. However, inference is expensive so machine learning algorithms may turn out to be slow. In this paper we consider a restriction of the language called hierarchical PLP in which clauses and predicates are hierarchically organized. In this case the language becomes truth-functional and infer...

Anglican is a probabilistic programming system designed to interoperate with Clojure and other JVM languages. We describe the implementation of Anglican and illustrate how its design facilitates both explorative and industrial use of probabilistic programming.

We present ProbLog2, the state of the art implementation of the probabilistic programming language ProbLog. The ProbLog language allows the user to intuitively build programs that do not only encode complex interactions between a large sets of heterogenous components but also the inherent uncertainties that are present in real-life situations. The system provides efficient algorithms for queryi...

This paper addresses two central problems for probabilistic processing models: parameter estimation from incomplete data and efficient retrieval of most probable analyses. These questions have been answered satisfactorily only for probabilistic regular and context-free models. We address these problems for a more expressive probabilistic constraint logic programming model. We present a log-line...