Suppose that for a given optimisation problem (which might be multicriteria problem or a single-criteron problem), an additional objective function is introduced. How does the the set of solutions, i. e. the set of efficient points change when instead of the old problem the new multicriteria problem is considered? How does the set of properly efficient points, the set of weakly efficient points...

The continuing technological advances in different areas represent a challenge for researchers in computer science and in particular in the area of algorithms and theory. The gap between processing speed and data volume increases constantly, even though the performance of computers and their central processing units increases at a fast rate. This is because the data that surrounds us multiplies...

In this paper, we expose a new meta-heuristic based on the artificial behavior of bee colony (ABC) for solving the blocking permutation flow shop scheduling problem (BFSP) subject to regular objectives. On three main steps, the proposed algorithm iteratively solved the BFSP under three fixed measure (makespan, total flowtime, or total tardiness criterion). Discrete job permutations and operator...

0

Multi-objective problems are often solved by modifying them into equivalent single objective problems using pre-defined weights or utility functions. Then, a multi-objective problem is solved similar to a single objective problem returning a single solution. These methods can be problematic because assigning appropriate numerical values (i.e., weights) to an objective function can be challengin...

In this paper, we address the problem of routing school buses in a rural area. We approach this problem with a node routing model with multiple objectives that arise from conflicting viewpoints. From the point of view of cost, it is desirable to minimize the number of buses used to transport students from their homes to school and back. And from the point of view of service, it is desirable to ...

This article presents an efficient parametric optimization method for the biobjective optimal routing problem. The core process is a bounded greedy singleobjective shortest path approximation algorithm. This method avoids the computationally intensive dominance check with labeling methods and overcomes the deficiency with existing parametric methods that can only find extreme nondominated paths...

This research presents an application of the relatively new approach of ant colony optimization (ACO) to address a production-sequencing problem when two objectives are present Ð simulating the arti®cial intelligence agents of virtual ants to obtain desirable solutions to a manufacturing logistics problem. The two objectives are minimization of setups and optimization of stability of material u...

The 0-1 knapsack problem is a well-known combinatorial optimisation problem. Approximation algorithms have been designed for solving it and they return provably good solutions within polynomial time. On the other hand, genetic algorithms are well suited for solving the knapsack problem and they find reasonably good solutions quickly. A naturally arising question is whether genetic algorithms ar...