Functional logic languages combine lazy (demand-driven) evaluation strategies from functional programming with non-deterministic computations from logic programming. The lazy evaluation of non-deterministic subexpressions results in a demand-driven exploration of the search space: if the value of some subexpression is not required, the complete search space connected to it is not explored. On t...

The advent of multicore processors has made concurrent programming languages mandatory. However, most concurrent programming models come with two major pitfalls: non-determinism and deadlocks. By determinism, we mean the output behavior of the program is independent of the scheduling choices (e.g., the operating system) and depends only on the input behavior. A few concurrent programming models...

Functional logic languages extend the setting of functional programming by non-deterministic choices, free variables and narrowing. Most existing approaches to simulate logic features in functional languages do not preserve laziness, i.e., they can only model strict logic programming like in Prolog. Lazy functional logic programming however, has interesting properties supporting a more declarat...

The mine production planning defines a sequence of block extraction to obtain the highest NPV under a number of constraints. Mathematical programming has become a widespread approach to optimize production planning, for open pit mines since the 1960s. However, the previous and existing models are found to be limited in their ability to explicitly incorporate the ore grade uncertainty into the p...

Non-determinism is of great importance in functional logic programming. It provides expressiveness and efficiency to functional logic computations. In this paper we describe an implementation of the multiparadigm functional logic language Curry. The evaluation strategy employed by the implementation is based on definitional trees and needed narrowing for deterministic operations, while non-dete...

In this paper, a comprehensive mathematical model for designing an electric power supply chain network via considering preventive maintenance under risk of network failures is proposed. The risk of capacity disruption of the distribution network is handled via using a two-stage stochastic programming as a framework for modeling the optimization problem. An applied method of planning for the net...

Deterministic parsing techniques are typically used in favor of general parsing algorithms for efficiency reasons. However, general algorithms such as Earley’s method are more powerful and also easier for developers to use, because no seemingly arbitrary restrictions are placed on the grammar. We describe how to narrow the performance gap between general and deterministic parsers, constructing ...

The models described in this paper for treating congestion in DEA are extended by according them chance constrained programming formulations. The usual route used in chance constrained programming is followed here by replacing these stochastic models with their ‘‘deterministic equivalents.’’ This leads to a class of non-linear problems. However, it is shown to be possible to avoid some of the n...

We study a cement transportation problem with asset-management (equipment selection) for strategic planning scenarios involving multiple forms of transportation and network expansion. A deterministic mixed integer programming approach is difficult due to the underlying capacitated multi-commodity network flow model and the need to consider a time-space network to ensure operational feasibility....

The rollout algorithm is a suboptimal control method for deterministic and stochastic problems that can be solved by dynamic programming. In this short note, we derive an extension of the rollout algorithm that applies to constrained deterministic dynamic programming problems, and relies on a suboptimal policy, called base heuristic. Under suitable assumptions, we show that if the base heuristi...