scholarly journals Genetic Algorithm for Solving Multi-Objective Optimization in Examination Timetabling Problem

2021 ◽  
Vol 16 (11) ◽  
pp. 4
Author(s):  
Son Tung Ngo ◽  
Jafreezal B Jaafar ◽  
Izzatdin Abdul Aziz ◽  
Giang Hoang Nguyen ◽  
Anh Ngoc Bui
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Hard Problem ◽  
Multi Objective Optimization ◽  
Time Requirement ◽  
Examination Timetabling ◽  
Education Management ◽  
Timetabling Problem ◽  
Multi Objective ◽  
Np Hard Problem

Examination timetabling is one of 3 critical timetabling jobs besides enrollment timetabling and teaching assignment. After a semester, scheduling examinations is not always an easy job in education management, especially for many data. The timetabling problem is an optimization and Np-hard problem. In this study, we build a multi-objective optimizer to create exam schedules for more than 2500 students. Our model aims to optimize the material costs while ensuring the dignity of the exam and students' convenience while considering the rooms' design, the time requirement of each exam, which involves rules and policy constraints. We propose a programmatic compromise to approach the maximum tar-get optimization model and solve it using the Genetic Algorithm. The results show the effectiveness of the introduced algorithm.

scholarly journals Multi-objective Optimization Model of Multi-modal Transport Based on Regional Sustainability Indicators

2020 ◽  
Vol 325 ◽  
pp. 03001
Author(s):  
Shengjiao Yang ◽  
Zuoling Song
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Social Indicators ◽  
Free Trade Area ◽  
Third Party ◽  
Local Optimum ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Freight Forwarder ◽  
Population Genetic Algorithm

With the development of “One Belt, One Road” initiative and free trade area, the volume of cross-border international logistics involving multiple modes of transport has surged. Meanwhile, the proportion of using integrated transportation system in domestic trunk transport has increased. Multi-modal transport (MMT) based on green transport can realize intensive utilization of transport capacity resources, and implement sustainable transport management with three bottom lines of economic, environmental and social aspects. In this paper, the carbon emission index and regional transportation infrastructure utilization index are introduced to construct a multi-objective optimization model with sustainable goals of environmental protection, cost saving and social contribution. The poly-population genetic algorithm (PPGA) is used to overcome the limitation of the traditional genetic algorithm running to the local optimum. The model proposed by this paper quantifies environmental and social indicators, balances comprehensive performance of environment, economy and society, and provides quantitative decision making support for carriers, international freight forwarder or third party logistics to carry out green MMT.

Research on the Multi-Objective Optimization Model of System-Level BIT Testability Index Determination

2011 ◽  
Vol 121-126 ◽  
pp. 2223-2227 ◽  
Author(s):  
Chun Sheng Zhu ◽  
Qi Zhang ◽  
Fan Tun Su ◽  
Hong Liang Ran
Keyword(s):  
Genetic Algorithm ◽  
Life Cycle ◽  
Mathematical Models ◽  
Optimization Model ◽  
System Reliability ◽  
Life Cycle Cost ◽  
System Level ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Life Circle

By weighing reliability, maintainability, availability and life-cycle cost of equipment which are influenced by testability，the testability indexes of system level BIT are determined on the basis of maximum system reliability & maintainability and minimum the life-circle cost. The influence mathematical models of system reliability, maintainability, availability and life-circle cost are established. According to these mathematical models, the multi-objective optimization model of system-level BIT testability indexes is established. The multi-objective optimization model is solved using Non-dominated Sorting Genetic Algorithm II, and the validity of the multi-objective optimization model is proved through an example.

scholarly journals Perbandingan Fluid Genetic Algorithm dan Genetic Algorithm untuk Penjadwalan Perkuliahan

2020 ◽  
Vol 9 (3) ◽  
pp. 350
Author(s):  
Muhammad Ezar Al Rivan ◽  
Bhagaskara Bhagaskara
Keyword(s):  
Genetic Algorithm ◽  
Learning Rate ◽  
Hard Problem ◽  
Np Hard ◽  
Global Learning ◽  
Mutation Process ◽  
Hard Constraint ◽  
Multi Objective ◽  
Several Variables ◽  
Np Hard Problem

The lecture schedule is a problem that belongs to the NP-Hard problem and multi-objective problem because it has several variables that affect the preparation of the schedule and has limitations that must be met. One solution that has been found is using a Genetic Algorithm (GA). GA has been proven to be able to provide a schedule that can meet limitations in scheduling. Besides, it also found a new concept of thought from GA, namely the Fluid Genetic Algorithm (FGA). The most visible difference between FGA and GA is that there is no mutation process in each iteration. FGA has a new stage, namely individual born and new constants, namely global learning rate, individual learning rate, and diversity rate. This concept of thinking was tested in previous studies and found that FGA is superior to GA for the problem of finding the optimum value of a predetermined function, but this function is not included in the multi-objective problem. In this study, the testing and comparison of FGA and GA were conducted for the problem of scheduling lectures at STMIK XYZ. Based on the results obtained, FGA can produce a schedule without any hard constraint violations. FGA can be used to solve multi-objective problems. FGA has a smaller number of generations than GA. However, overall GA is superior in producing schedules without any problems.

Adaptive multi-objective traffic signal control using NLRMNSGA-II algorithm

2018 ◽  
Vol 45 (11) ◽  
pp. 973-985
Author(s):  
Yuan-Yang Zou ◽  
Xue-Guo Xu ◽  
Gui-Hua Lin
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Efficient Algorithm ◽  
Numerical Experiments ◽  
Adaptive System ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Multi Objective Optimization ◽  
New Model ◽  
Multi Objective

In this paper, we consider an adaptive system for controlling green times at junction. For this adaptive system, we present a multi-objective optimization model, which is much easier to solve than some existing models. Furthermore, to solve the new model, we suggest an algorithm, called NLRMNSGA-II, which is based on the nonlinear least regression and a modified non-dominated sorting genetic algorithm. Our numerical experiments indicate that the NLRMNSGA-II is an efficient algorithm for the considered adaptive system.

A partner selection model for collaborative product innovation from the viewpoint of knowledge collaboration

Kybernetes ◽  
2019 ◽  
Vol 49 (6) ◽  
pp. 1623-1644 ◽  
Author(s):  
Jie Jian ◽  
Milin Wang ◽  
Lvcheng Li ◽  
Jiafu Su ◽  
Tianxiang Huang
Keyword(s):  
Optimization Model ◽  
Product Innovation ◽  
Partner Selection ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Proposed Model ◽  
Np Hard Problem ◽  
Selection For ◽  
Knowledge Collaboration

Purpose Selecting suitable and competent partners is an important prerequisite to improve the performance of collaborative product innovation (CPI). The purpose of this paper is to propose an integrated multi-criteria approach and a decision optimization model of partner selection for CPI from the perspective of knowledge collaboration. Design/methodology/approach First, the criteria for partner selection are presented, considering comprehensively the knowledge matching degree of the candidates, the knowledge collaborative performance among the candidates, and the overall expected revenue of the CPI alliance. Then, a quantitative method based on the vector space model and the synergetic matrix method is proposed to obtain a comprehensive performance of candidates. Furthermore, a multi-objective optimization model is developed to select desirable partners. Considering the model is a NP-hard problem, a non-dominated sorting genetic algorithm II is developed to solve the multi-objective optimization model of partner selection. Findings A real case is analyzed to verify the feasibility and validity of the proposed model. The findings show that the proposed model can efficiently select excellent partners with the desired comprehensive attributes for the formation of a CPI alliance. Originality/value Theoretically, a novel method and approach to partner selection for CPI alliances from a knowledge collaboration perspective is proposed in this study. In practice, this paper also provides companies with a decision support and reference for partner selection in CPI alliances establishment.

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