Adaptive Weight Approach Research Articles

Process Planning and Scheduling in Distributed Manufacturing System Using Multiobjective Genetic Algorithm

AbstractThis paper proposed a multiobjective genetic algorithm approach for solving process planning and scheduling (PPS) problems in a distributed manufacturing system (DMS) environment. In this environment, factories processing various machines and tools at different geographical locations are often combined to produce various parts with different resource constraints. This paper proposed a fast multiobjective genetic algorithm with archive (fmoGA‐A) mechanism to deal with PPS problem with considering the minimization of the maximum total processing time and minimization of the maximum variation of workload of machine in DMS. The proposed algorithm has been compared with other approaches to testify and benchmark the optimization reliability on several PPS problems. These comparisons demonstrate the importance of PPS in DMS and indicate fmoGA‐A is better than adaptive weight approach genetic algorithm (awaGA) did on efficacy and efficient; the efficacy is not less than non‐dominated sorting genetic algorithm II (NSGA‐II) and strength Pareto evolutionary algorithm 2 (SPEA2) and the efficient is better than NSGA‐II and SPEA2. Copyright © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Real-time task scheduling by multiobjective genetic algorithm

Real-time tasks are characterized by computational activities with timing constraints and classified into two categories: a hard real-time task and a soft real-time task. In hard real-time tasks, tardiness can be catastrophic. The goal of hard real-time tasks scheduling algorithms is to meet all tasks’ deadlines, in other words, to keep the feasibility of scheduling through admission control. However, in the case of soft real-time tasks, slight violation of deadlines is not so critical. In this paper, we propose a new scheduling algorithm for soft real-time tasks using multiobjective genetic algorithm (moGA) on multiprocessors system. It is assumed that tasks have precedence relations among them and are executed on homogeneous multiprocessor environment. The objective of the proposed scheduling algorithm is to minimize the total tardiness and total number of processors used. For these objectives, this paper combines adaptive weight approach (AWA) that utilizes some useful information from the current population to readjust weights for obtaining a search pressure toward a positive ideal point. The effectiveness of the proposed algorithm is shown through simulation studies.

Applying an Ant Colony Optimization Algorithm-Based Multiobjective Approach for Time–Cost Trade-Off

The time–cost trade-off is one of the most crucial aspects of construction project planning, which in fact is a combinatorial optimization problem. This technical note employed an evolutionary algorithm—ant colony optimization (ACO) algorithm to deal with the time–cost trade-off problems. Combining with the modified adaptive weight approach, the ACO algorithm can find out the optimal solutions, and define the Pareto front as well. The development of the ACO-based multiobjective approach in this technical note provides an attractive alternative to solving construction time–cost optimization.

Scheduling algorithm for real-time tasks using multiobjective hybrid genetic algorithm in heterogeneous multiprocessors system

The scheduling problem for real-time tasks on multiprocessor is one of the NP-hard problems. This paper proposes a new scheduling algorithm for real-time tasks using multiobjective hybrid genetic algorithm (mohGA) on heterogeneous multiprocessor environment. In solution algorithms, the genetic algorithm (GA) and the simulated annealing (SA) are cooperatively used. In this method, the convergence of GA is improved by introducing the probability of SA as the criterion for acceptance of new trial solution. The proposed algorithm has a multiobjective to minimize the total tardiness and completion time simultaneously. For these conflicting objectives, this paper combines adaptive weight approach (AWA) that utilizes some useful information from the current population to readjust weights for obtaining a search pressure toward a positive ideal point. The effectiveness of the proposed algorithm is shown through simulation studies. In simulation studies, the results of the proposed algorithm are better than that of other algorithms.

Bicriteria Network Optimization Problem using Priority-based Genetic Algorithm

Network optimization is being an increasingly important and fundamental issue in the fields such as engineering, computer science, operations research, transportation, telecommunication, decision support systems, manufacturing, and airline scheduling. In many applications, however, there are several criteria associated with traversing each edge of a network. For example, cost and flow measures are both important in the networks. As a result, there has been recent interest in solving Bicriteria Network Optimization Problem. The Bicriteria Network Optimization Problem is known a NP-hard. The efficient set of paths may be very large, possibly exponential in size. Thus the computational effort required to solve it can increase exponentially with the problem size in the worst case. In this paper, we propose a genetic algorithm (GA) approach used a priority-based chromosome for solving the bicriteria network optimization problem including maximum flow (MXF) model and minimum cost flow (MCF) model. The objective is to find the set of Pareto optimal solutions that give possible maximum flow with minimum cost. This paper also combines Adaptive Weight Approach (AWA) that utilizes some useful information from the current population to readjust weights for obtaining a search pressure toward a positive ideal point. Computer simulations show the several numerical experiments by using some difficult-to-solve network design problems, and show the effectiveness of the proposed method.