Health service management plays a crucial role in human life. Blood related operations are considered as one of the important components of the health services. This paper presents a bi-objective mixed integer linear programming model for dynamic location-allocation of blood facilities that integrates strategic and tactical decisions. Due to the epistemic uncertain nature of ...

stochastic approach to vehicle routing problem: development and theories abstract in this article, a chance constrained (ccp) formulation of the vehicle routing problem (vrp) is proposed. the reality is that once we convert some special form of probabilistic constraint into their equivalent deterministic form then a nonlinear constraint generates. knowing that reliable computer software for lar...

The term probabilistic constrained programming means the same as chance constrained programming, i.e., optimization of a function subject to certain conditions where at least one is formulated so that a condition, involving random variables, should hold with a prescribed probability. The probability is usually not prescribed exactly but a lower bound is given instead which is in practice near u...

We propose to represent a probability distribution as a program in a general-purpose programming language rather than a special language built from scratch. This approach makes it easier for the probabilistic-reasoning and programming-language communities to share their work. To demonstrate that this representation is simple and efficient, we implement inference by variable elimination and impo...

In recent years, declarative programming languages specialized for probabilistic modeling has emerged as distinct class of languages. These languages are predominantly written by researchers in the machine learning field and concentrate on generalized MCMC inference algorithm. Unfortunately, all these languages are too slow for practical adoption. In my talk, I will outline several places where...

Spreadsheet workbook contents are simple programs. Because of this, probabilistic programming techniques can be used to perform Bayesian inversion of spreadsheet computations. What is more, existing execution engines in spreadsheet applications such as Microsoft Excel can be made to do this using only built-in functionality. We demonstrate this by developing a native Excel implementation of bot...

This paper shows how probabilistic reasoning can be applied to the predicative style of programming.

Formal modelling languages such as process algebras are widespread and effective tools in computational modelling. However, handling data and uncertainty in a statistically meaningful way is an open problem in formal modelling, severely hampering the usefulness of these elegant tools in many real world applications. Here we introduce ProPPA, a process algebra which incorporates uncertainty in t...

Of all scientiic investigations into reasoning with uncertainty and chance, probability theory is perhaps the best understood paradigm. Nevertheless, all studies conducted thus far into the semantics of quantitative logic programming(cf.) have restricted themselves to non-probabilistic semantical characterizations. In this paper, we take a few steps towards rectifying this situation. We deene a...

In recent years sports analytics has gotten more and more popular. We propose a model for Rugby data in particular to model the 2014 Six Nations tournament. We propose a Bayesian hierarchical model to estimate the characteristics that bring a team to lose or win a game, and predict the score of