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 develope...

In this paper, a new method is presented for TAM/wrapper co-optimization based on hybrid genetic algorithm and twodimensional packing problem. In this method, core test is represented by rectangles, and a hybrid genetic algorithm that provides highly optimal solution for two-dimensional packing problem is introduced for TAM allocation and test scheduling. During the scheduling, the TAM width as...

This paper proposes a Hybrid Genetic Algorithm (HGA) for the Resource Constrained Project Scheduling Problem (RCPSP). Resources are renewable and there is a unique way to perform the activities. This work employs Genetics Algorithms (GA) and Hybrid Genetic Algorithms to schedule project activities to minimize makespan subject to precedence constraints and resources availability. The algorithms ...

A hybrid genetic algorithm (HGA) approach is proposed for a lot-streaming flow shop scheduling problem, in which a job (lot) is split into a number of smaller sublots so that successive operations can be overlapped. The objective is the minimization of the mean weighted absolute deviation of job completion t imes from due dates.

In this paper, we propose a new optimization technique, the hierarchical multi-space competitive distributed genetic algorithm (HmcDGA), which is effective for the hierarchical optimization problem. It is an extension of the multi-space competitive distributed genetic algorithm (mcDGA), which was proposed by the authors. The mcDGA efficiently finds an optimal solution with a low computational c...

The internet of medical things (IoMT) empowers patients to get adaptable, and virtualized gear over the internet. Task scheduling is most fundamental problem in IoMT-cloud since cloud execution commonly relies on it. Thus, a proposition being made for distinct technique suitably meet these solicitations. To manage issue, an artificial intelligence (AI) method known as hybrid genetic algorithm (...

We study a multiple crossdocks problem with supplier and customer time windows, where any violation of time windows will incur a penalty cost and the flows through the crossdock are constrained by fixed transportation schedules and crossdock capacities. We prove this problem to be NP-hard in the strong sense and therefore focus on developing efficient heuristics. Based on the problem structure,...

new blind estimation of MIMO channels using HGA was proposed for the frequency selective channel. By exploiting the output statistics, an objective function that coming from blind estimation of MIMO channels based on the subspace could be obtained. Then the fitness function based on the objective function is proposed for optimization by a hybrid genetic algorithm (HGA). We have proved this meth...

Conventional Multi-User Detection (MUD) algorithms for the Multi-Input, Multi-Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system fail to consider the detection performance and algorithm complexity simultaneously. To address this problem, a new Joint Intelligent MUD (JI-MUD) algorithm that aims to improve the MUD performance of the MIMO-OFDM system and to reduce the complexity ...

In this work, multicriteria decision making objective for flow shop scheduling with sequence dependent set up time and due dates have been developed. Multicriteria decision making objective includes total tardiness, total earliness and makespan simultaneously which is very effective decision making for scheduling jobs in modern manufacturing environment. As problem of flow shop scheduling is NP...