A Genetic Algorithm Approach for Multi-Product Multi-Machine CONWIP Production System

2011 ◽  
Vol 110-116 ◽  
pp. 3624-3630
Author(s):  
Saeede Ajorlou ◽  
Issac Shams ◽  
Mirbahador G. Aryanezhad
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Programming ◽  
Programming Model ◽  
Search Algorithms ◽  
Mixed Integer ◽  
Simplified Model ◽  
Mathematical Programming Model ◽  
Production Lines ◽  
Work In Process ◽  
Genetic Algorithm Approach

In this paper, a new mathematical programming model is developed to address common issues relating to single-stage CONstant-Work-In-Process based production lines. A Ge-netic Algorithm (GA) approach is then proposed to directly solve the model in order to simultaneously determines the optimal job sequence and WIP level. Unlike many existing approaches, which are based on deterministic search algorithms such as nonlinear programming and mixed integer programming, our proposed method does not rely on a linearized or simplified model of the system. results from a comprehensive numerical example indicate computational efficiency and validation of our method.

scholarly journals Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1452
Author(s):  
Cristian Mateo Castiblanco-Pérez ◽  
David Esteban Toro-Rodríguez ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Power Balance ◽  
Continuous Version ◽  
Discrete Part

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

scholarly journals Modeling the Scheduling Problem in Cellular Manufacturing Systems Using Genetic Algorithm as an Efficient Meta-Heuristic Approach

2021 ◽  
Author(s):  
Amin Rezaeipanah ◽  
Musa Mojarad
Keyword(s):  
Genetic Algorithm ◽  
Manufacturing Systems ◽  
Cellular Manufacturing ◽  
Programming Model ◽  
Heuristic Method ◽  
Cell Formation ◽  
Mixed Integer ◽  
Test Problems ◽  
Cellular Manufacturing Systems ◽  
Machine Grouping

This paper presents a new, bi-criteria mixed-integer programming model for scheduling cells and pieces within each cell in a manufacturing cellular system. The objective of this model is to minimize the makespan and inter-cell movements simultaneously, while considering sequence-dependent cell setup times. In the CMS design and planning, three main steps must be considered, namely cell formation (i.e., piece families and machine grouping), inter and intra-cell layouts, and scheduling issue. Due to the fact that the Cellular Manufacturing Systems (CMS) problem is NP-Hard, a Genetic Algorithm (GA) as an efficient meta-heuristic method is proposed to solve such a hard problem. Finally, a number of test problems are solved to show the efficiency of the proposed GA and the related computational results are compared with the results obtained by the use of an optimization tool.

GA-CDFM Based Hybrid Optimization Method for Steelmaking Scheduling and Caster Operation

2012 ◽  
Vol 424-425 ◽  
pp. 994-998 ◽  
Author(s):  
Xiao Chuan Luo ◽  
Chong Zheng Na
Keyword(s):  
Genetic Algorithm ◽  
Integer Programming ◽  
Mixed Integer Programming ◽  
Programming Model ◽  
Optimization Method ◽  
Optimal Performance ◽  
Hybrid Optimization ◽  
Mixed Integer ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization

In steelmaking plant, the process times of machines change frequently and randomly for the reason of metallurgical principle. When those change happen, the plant scheduling and caster operation must respond to keep the optimal performance profile of plant. Therefore, the integration of plant scheduling and caster operation is a crucial task. This paper presents a mixed-integer programming model and a hybrid optimized algorithm for caster operation and plant scheduling, which combine the genetic algorithm optimization and CDFM process status verification. Data experiments illustrate the efficiency of our model and algorithm.

An Efficient Batch Scheduling Model for Hospital Sterilization Services Using Genetic Algorithm

2021 ◽  
pp. 928-946
Author(s):  
Shubin Xu ◽  
John Wang
Keyword(s):  
Genetic Algorithm ◽  
Medical Devices ◽  
Programming Model ◽  
Flow Time ◽  
Nonlinear Integer Programming ◽  
Batch Scheduling ◽  
Scheduling Problem ◽  
Work In Process ◽  
Completion Times ◽  
Sum Of Completion Times

A major challenge faced by hospitals is to provide efficient medical services. The problem studied in this article is motivated by the hospital sterilization services where the washing step generally constitutes a bottleneck in the sterilization services. Therefore, an efficient scheduling of the washing operations to reduce flow time and work-in-process inventories is of great concern to management. In the washing step, different sets of reusable medical devices may be washed together as long as the washer capacity is not exceeded. Thus, the washing step is modeled as a batch scheduling problem where washers have nonidentical capacities and reusable medical device sets have different sizes and different ready times. The objective is to minimize the sum of completion times for washing operations. The problem is first formulated as a nonlinear integer programming model. Given that this problem is NP-hard, a genetic algorithm is then proposed to heuristically solve the problem. Computational experiments show that the proposed algorithm is capable of consistently obtaining high-quality solutions in short computation times.

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