Based on an extremely simple program notation more advanced program features can be developed in linear projective program syntax such as conditional statements, while loops, recursion, use of an evaluation stack, object classes, method calls etc. Taking care of a cumulative and bottom up introduction of such complex features while providing appropriate projections into the lower levels of lang...

this paper studies the hybrid flow shop scheduling where the optimization criterion is the minimization of total tardiness. first, the problem is formulated as a mixed integer linear programming model. then, to solve large problem sizes, an artificial immune algorithm hybridized with a simple local search in form of simulated annealing is proposed. two experiments are carried out to evaluate th...

In this lecture, we study the topic of Job Scheduling. The problem of job scheduling is that if we have n jobs and m machines and we have a processing time pij of each machine i for each job j, the task is to find a schedule to assign the jobs to the m machines to complete all the n jobs such that the completion time of all jobs will be minimized. If each job j has the same running time pj on e...

We present a novel linear program for the approximation of the dynamic programming cost-to-go function in high-dimensional stochastic control problems. LP approaches to approximate DP naturally restrict attention to approximations that are lower bounds to the optimal cost-to-go function. Our program – the ‘smoothed approximate linear program’ – relaxes this restriction in an appropriate fashion...

This paper describes an implementation ofthe one-phase primal-dual path-following algorithm for solving linear programming problems. The design is intended to be simple, portable and robust. These design goals are achieved without sacrificing stateofthe-art performance. We give a rather complete description of the algorithm and the implementation. Computational results obtained for the NET LIB ...

We present a novel linear program for the approximation of the dynamic programming costto-go function in high-dimensional stochastic control problems. LP approaches to approximate DP have typically relied on a natural ‘projection’ of a well studied linear program for exact dynamic programming. Such programs restrict attention to approximations that are lower bounds to the optimal cost-to-go fun...

Program equivalence in linear contexts, where programs are used or executed exactly once, is an important issue in programming languages. However, existing techniques like those based on bisimulations and logical relations only target at contextual equivalence in the usual (non-linear) functional languages, and fail in capturing non-trivial equivalent programs in linear contexts, particularly w...