A flow shop scheduling problem involves scheduling jobs on multiple machines in series in order to optimize a given criterion. The flow time of a job is the amount of time the job spent before its completion and the stretch of the job is the ratio of its flow time to its processing time. In this paper, a hybrid genetic algorithm (HGA) approach is proposed for minimizing the total stretch in flo...

In this paper, the problem of lot sizing, scheduling and delivery of several items in a two-stage supply chain over a finite planning horizon is studied. Single supplier via a flexible flow line production system (FFL), produces several items and delivers them directly to an assembly facility. Based on basic period (BP) strategy, a new mixed zero-one nonlinear programming model has been develop...

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 pap...

Minimum vertex cover problem (MVCP) is an NP-hard problem and it has numerous real life applications. This paper presents hybrid genetic algorithm (HGA) to solve MVCP efficiently. In this paper we have demonstrated that when local optimization technique is added to genetic algorithm to form HGA, it gives near to optimal solution speedy. We have developed new heuristic vertex crossover operator ...

A hybrid genetic algorithm (HGA) combines a genetic algorithm (GA) with an individual learning procedure. One such learning procedure is a local search technique (LS) used by the GA for refining global solutions. A HGA is also called a memetic algorithm (MA), one of the most successful and popular heuristic search methods. An important challenge of MAs is the trade-off between global and local ...

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. A mixed integer programming model is developed for the given problem that belongs to NP-hard class. In this case, traditional approaches cannot reach to an optimal solution in a reasonable...

the main design objective of axial compressor is the efficiency increasing, pressure ratio and weight loss. these three parameters are used as an objective function and they are computed according to adopted design procedures. in this paper, a design method is presented that named quasi-three-dimensional. physical flow conditions are applied to design by some constraints to avoid irrational res...

In this thesis, a BP neural network based GA (Genetic Algorithm) is proposed to take advantage of their complementary ability of local and global search for optimum solutions. To show the effectiveness of this novel HGA (Hybrid GA), We have respectively developed two application-oriented algorithm for the design of simulation experiments that are widely used in a variety of data processing prob...

This study considers the production environment of the re-entrant flow-shop (RFS). In a RFS, all jobs have the same routing over the machines of the shop and the same sequence is traversed several times to complete the jobs. The aim of this study is to minimize makespan by using the genetic algorithm (GA) to move from local optimal solution to near optimal solution for RFS scheduling problems. ...

The ability to track the optimum of dynamic environments is important in many practical applications. In this paper, the capability of a hybrid genetic algorithm (HGA) to track the optimum in some dynamic environments is investigated for different functional dimensions, update frequencies, and displacement strengths in different types of dynamic environments. Experimental results are reported b...