Multi objective quadratic fractional programming (MOQFP) problem involves optimization of several objective functions in the form of a ratio of numerator and denominator functions which involve both contains linear and quadratic forms with the assumption that the set of feasible solutions is a convex polyhedral with a nite number of extreme points and the denominator part of each of the objecti...

With increasing competition in the business world and the emergence and development of new technologies, many companies have turned to integrated production and distribution for timely production and delivery at the lowest cost of production and distribution and with the least delay in delivery. By increasing human population and the increase in greenhouse gas emissions and industrial waste, in...

In this research, the multi-objective project management decision problem with fuzzy goals and fuzzy constraints are considered. We constitute α-cut approach and two various fuzzy goal programming solution methods for solving the Multi-Objective Project Management (MOPM) decision problem under fuzzy environments. The Interactive fuzzy multi-objective linear programming (i-FMOLP) and Weighted Ad...

The quadratic knapsack problem (QKP) is the problem of maximizing a quadratic objective function subject to a single knapsack constraint. It is a well-known NP-hard combinatorial problem. In this paper, we examine the use of Alienor method for solving QKP. Alienor method is a new global optimization algorithm based on a simple idea consisting in approximating an n variables function by a single...

in this paper, a stochastic multi-depot multi-objective vehicle routing problem (vrp) is studied. based on observations in real-world problems, we develop an aspect of the problem, such as stochastic availability of routes. to solve the problem in this situation, a two-objective mathematical model is developed, in which the related objective functions are: (1) minimizing transportation costs an...

We analyze combinatorial optimization problems with ordinal, i.e., non-additive, objective functions that assign categories (like good, medium and bad) rather than cost coefficients to the elements of feasible solutions. review different optimality concepts for ordinal discuss their similarities differences. then focus on two prevalent are shown be equivalent. Our main lies investigation a bije...