Integer linear fractional programming problem with multiple objective MOILFP is an important field of research and has not received as much attention as did multiple objective linear fractional programming. In this work, we develop a branch and cut algorithm based on continuous fractional optimization, for generating the whole integer efficient solutions of the MOILFP problem. The basic idea of...

We propose an Integer Linear Programming (ILP) approach for solving integer programming problems with bilinear objectives and linear constraints. Our approach is based on nding upper and lower bounds for the optimal bilinear objective function, and using the upper bound to produce a tight binary decomposition of an ensemble in the bilinear objective function. This allows us to transform the ori...

This note comments on a paper published by Wagner and Davis ( Decision Sciences (2001), 32(4), 557–573 ) . These authors present an integer-programming model for the single-item discrete sequential search problem with group activities. Based on their experiments, they conjecture that the problem can be solved as a linear program. In this note, we provide a counterexample for which the optimal v...

Linear bilevel programming is a decision making problem with a two-level decentralized organization. The leader is in the upper level and the follower, in the lower level. This study addresses linear bilevel multi-objective multi-follower programming (LB-MOMFP) problem, a special case of linear bilevel programming problems with one leader and multiple followers where each decision maker has sev...

We propose a mixed integer programming formulation for the single machine scheduling problem with release times and the objective of minimizing the weighted sum of the start times The basic formulation involves start time and sequence determining variables and lower bounds on the start times Its linear programming relaxation solves problems in which all release times are equal For the general p...

We introduce a new Integer Linear Programming (ILP) approach for solving Integer Programming (IP) problems with bilinear objectives and linear constraints. The approach relies on a series of ILP approximations of the bilinear IP. We compare this approach with standard linearization techniques on random instances and a set of real-world product bundling problems.

Increasing economy’s resistance against the menace of sanctions, various risks, shocks, and internal and external threats are one of the main national policies which can be implemented through bank investments. Investment project selection is a complex and multi-criteria decision-making process that is influenced by multiple and often some conflicting objectives. This paper studies portfolio inve...