Scheduling problem for repetitive construction projects involves three conflicting objectives. These objectives are project duration, project total cost, and project total interruption time. This paper presents a multi-objective fuzzy linear programming model (FLP) for resolving this problem. Literature concerned with scheduling problems for repetitive construction projects was reviewed. Multi-...

We study a generic minimization problem with separable non-convex piecewise linear costs, showing that the linear programming (LP) relaxation of three textbook mixed-integer programming formulations each approximates the cost function by its lower convex envelope. We also show a relationship between this result and classical Lagrangian duality theory.

Linear programming is described as a tool for selecting breeding stock in a production unit facing constraints of resources, marketing, or preference. The predicted performance of an animal for major input and output traits is incorporated into the objective function reflecting, for example, farm profits, and into a matrix of coefficients specifying the constraints. An example demonstrates the ...

This paper presents a novel solution method for joint energy and Spinning Reserve (SR) dispatch problem. In systems in which the Lost Opportunity Cost (LOC) should be paid to generators, if the LOC is not considered in the dispatch problem, the results may differ from the truly optimum solution. Since the LOC is a non-differentiable function, including it in the formulation makes the problem so...

This paper presents a new method for regression model prediction in an uncertain environment. In practical engineering problems, in order to develop regression or ANN model for making predictions, the average of set of repeated observed values are introduced to the model as an input variable. Therefore, the estimated response of the process is also the average of a set of output values where th...

Linear programming (LP) is an important tool for many inter-disciplinary optimization problems. The Simplex method is the most widely used algorithm to solve LP problems and has immense impact on several developments in various fields. With development of public domain and commercial software solvers, it has been automated and made available for use. A serious bottleneck in implementation of Si...

we present a new model and a new approach for solving fuzzylinear programming (flp) problems with various utilities for the satisfactionof the fuzzy constraints. the model, constructed as a multi-objective linearprogramming problem, provides flexibility for the decision maker (dm), andallows for the assignment of distinct weights to the constraints and the objectivefunction. the desired solutio...

Lifted inference approaches have rendered large, previously intractable probabilistic inference problems quickly solvable by handling whole sets of indistinguishable objects together. Triggered by this success, we show that another important AI technique is liftable, too, namely linear programming. Intuitively, given a linear program (LP), we employ a lifted variant of Gaussian belief propagati...

Approximate linear programming (ALP) is an efficient approach to solving large factored Markov decision processes (MDPs). The main idea of the method is to approximate the optimal value function by a set of basis functions and optimize their weights by linear programming (LP). This paper proposes a new ALP approximation. Comparing to the standard ALP formulation, we decompose the constraint spa...

Recent combinatorial algorithms for linear programming can also be applied to certain non-linear problems. We call these Generalized Linear Programming, or GLP, problems. We connect this class to a collection of results from combinatorial geometry called Helly-type theorems. We show that there is a Helly-type theorem about the constraint set of every GLP problem. Given a family H of sets with a...