This paper investigates scheduling heuristics to seek the minimum of a positively weighted convex sum of makespan and the number of tardy jobs in a hybrid flow shop environment where at least one production stage is made up of unrelated parallel machines. Sequence-and machine-dependent setup times are considered. The problem is a combinatorial optimization problem which is difficult to be solve...

In this paper, we have conducted a literature review on the recent developments and publications involving the vehicle routing problem and its variants, namely vehicle routing problem with time windows (VRPTW) and the capacitated vehicle routing problem (CVRP) and also their variants. The VRP is classified as an NP-hard problem. Hence, the use of exact optimization methods may be difficult to s...

The genetic algorithm (GA) paradigm has attracted considerable attention as a promising heuristic approach for solving optimization problems. Much of the development has related to problems of optimizing functions of continuous variables, but recently there have been several applications to problems of a combinatorial nature. What is often found is that GAs have fairly poor performance for comb...

Knapsack problems are important NP-Complete combinatorial optimization problems. Although nearly all the classical instances can be solved in pseudo-polynomial time nowadays, yet there are a variety of test problems which are hard to solve for the existing algorithms. In this paper we propose a new approach based upon binary particle swarm optimization algorithm (BPSO) to find solutions of thes...

This paper presents algorithms, heuristics and lower bounds addressing optimization issues in many-to-many multicasting. Two main problems are addressed: (1) a precise combinatorial comparison of optimal multicast trees with optimal multicast rings, (2) an optimized sharing of network resources (i.e., nodes and links) among multiple multicast groups that coexist. The former is central to the ch...

We present a new approach to automatic test pattern generation for very large scale integrated sequential circuit testing. This approach is more eecient than past test generation methods, since it exploits knowledge of potential circuit defects. Our method motivates a new combinatorial optimization problem, the Tour Covering Problem. We develop heuristics to solve this optimization problem, the...

Most problems of combinatorial optimization like routing, task allocation, or scheduling belong to the class of NP-complete problems and can be solved efficiently only by heuristics. Both, genetic algorithms and evolutionary strategies have a number of drawbacks that reduce their applicability to that kind of problems. In order to overcome some of these problems, this paper looks upon the stand...

Numerous design challenges in system synthesis can be formulated as combinatorial optimization problems. Unfortunately, most of these problems are NP-complete or NP-hard. As fabrication technology improves and transistors become smaller and more numerous, we will continue to rely on design automation techniques to create and use future computing systems. Therefore, there is a substantial need f...

Travelling salesman problem (TSP) is a combinatorial optimization problem. It is NP hard problem and TSP is the most intensively studied problem in the area of optimization. There are a number of approximation algorithms and heuristics proposed in the literature which can yield to good solutions. But with the increase in the number of cities, the complexity of the problem goes on increasing. Th...