Flow-shop scheduling problem (FSP) deals with the scheduling of a set of n jobs that visit a set of m machines in the same order. As the FSP is NP-hard, there is no efficient algorithm to reach the optimal solution of the problem. To minimize the holding, delay and setup costs of large permutation flow-shop scheduling problems with sequence-dependent setup times on each machine, this paper deve...

This paper presents hybrid genetic algorithms to optimize the structure of the main parts of hydroelectric sets based on the ®nite element method. Firstly, the optimal model of the main parts of hydroelectric sets is established including an objective function and some constraint conditions. Afterwards, the stochastic direction method (SDM) and a genetic algorithm (GA) method are applied to sol...

This paper presents a hybrid genetic algorithm to solve the uncapacitated location allocation problems as a combinatorial optimization problem. The proposed method incorporates a modified K-means algorithm that clusters the customers into groups based on the rectilinear distance, and then the initial population of solutions is calculated according to the derived centers of clusters. The hybrid ...

• HGA for online product allocation reduces give-away and achieves target throughput. outperforms current practice in terms of giveaway throughput adherence a case study. Incorporating prediction the fitness function improves performance. Poultry processing plants utilize batchers to produce products, aiming minimize operational costs while adhering imposed by their customers. This paper studie...

Author Affiliation: Department of Electrical and Electronic Engineering, Nigde University, Nigde, Turkey. Abstract: This letter outlines a hybrid genetic algorithm (HGA) for solving the economic dispatch problem. The algorithm incorporates the solution produced by an improved Hopfield neural network (NN) [1] as a part of its initial population. Elitism, arithmetic crossover, and mutation are us...

This paper presents a new multi-objective job shop scheduling with sequence-dependent setup times. The objectives are to minimize the makespan and sum of the earliness and tardiness of jobs in a time window. Scince a job shop scheduling problem has been proved to be NP-hard in a strong, traditional approaches cannot reach to an optimal solution in a reasonable time. Thus, we propose an efficien...

This study is an extension of the Joint Replenishment Problem (JRP) that takes into accounts warehouse-space restrictions. The focus of this study is to determine the lot size of each product under power-of-two policy to minimize the total cost per unit time and to generate a feasible replenishment schedule of multiple products without exceeding the available warehouse-space. In order to solve ...

Our paper focuses on the generation of optimal test sequences and test cases using Intelligent Agents for highly reliable systems. Test sequences support test case generation for these types of systems. Our system is modeled through UML state charts. Conventional test generation techniques do not worry about optimization and dynamic nature of such systems. In the case of highly reliable Softwar...

A no-wait job shop (NWJS) describes a situation where every job has its own processing sequence with the constraint that no waiting time is allowed between operations within any job. A NWJS problem with the objective of minimizing total completion time is a NP-hard problem and this paper proposes a hybrid genetic algorithm (HGA) to solve this complex problem. A genetic operation is defined by c...

Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems (FIS) are nowadays widely adopted as hybrid techniques in commercial and industrial environment. In this paper we present an interesting application of the fuzzy-GA paradigm to Smart Grids. The main aim consists in performing decision making for power flow management tasks in the proposed microgrid model equipped by ren...