scholarly journals A Compromise Programming for Multi-Objective Task Assignment Problem

Computers ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 15
Author(s):  
Son Tung Ngo ◽  
Jafreezal Jaafar ◽  
Izzatdin Abdul Aziz ◽  
Bui Ngoc Anh
Keyword(s):  
Web Application ◽  
Optimal Solution ◽  
Task Assignment ◽  
Compromise Programming ◽  
Programming Approach ◽  
Multi Objective ◽  
Faculty Expectations ◽  
Conflicting Objectives ◽  
University Lecturers ◽  
Task Assignment Problem

The problem of scheduling is an area that has attracted a lot of attention from researchers for many years. Its goal is to optimize resources in the system. The lecturer’s assigning task is an example of the timetabling problem, a class of scheduling. This study introduces a mathematical model to assign constrained tasks (the time and required skills) to university lecturers. Our model is capable of generating a calendar that maximizes faculty expectations. The formulated problem is in the form of a multi-objective problem that requires the trade-off between two or more conflicting objectives to indicate the optimal solution. We use the compromise programming approach to the multi-objective problem to solve this. We then proposed the new version of the Genetic Algorithm to solve the introduced model. Finally, we tested the model and algorithm with real scheduling data, including 139 sections of 17 subjects to 27 lecturers in 10 timeslots. Finally, a web application supports the decision-maker to visualize and manipulate the obtained results.

Concurrent Optimization of Product Module Selection and Assembly Line Configuration: A Multi-Objective Approach

2005 ◽  
Vol 127 (4) ◽  
pp. 875-884 ◽  
Author(s):  
Zhonghui Xu ◽  
Ming Liang
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Solution Procedure ◽  
Programming Approach ◽  
Reconfigurable Manufacturing ◽  
Multi Objective ◽  
Modular Product ◽  
Conflicting Objectives ◽  
Module Selection ◽  
Line Design

Both modular product design and reconfigurable manufacturing have a great potential to enhance responsiveness to market changes and to reduce production cost. However, the two issues have thus far mostly been investigated separately, thereby causing possible mismatch between the modular product structure and the manufacturing or assembly system. Therefore, the potential benefits of product modularity may not be materialized due to such mismatch. For this reason, this paper presents a concurrent approach to the product module selection and assembly line design problems to provide a set of harmonic solutions to the two problems and hence avoid the mismatch between design and manufacturing. The integrated nature of the problem leads to several noncommensurable and often conflicting objectives. The modified Chebyshev goal programming approach is applied to solve the multi-objective problem. A genetic algorithm is further developed to provide quick and near-optimum solutions. The proposed approach and the solution procedure have been applied to an ABS motor problem. The performance of the genetic algorithm has also been examined.

scholarly journals A Compromise Programming to Task Assignment Problem in Software Development Project

2021 ◽  
Vol 69 (3) ◽  
pp. 3429-3444
Author(s):  
Ngo Tung Son ◽  
Jafreezal Jaafar ◽  
Izzatdin Abdul Aziz ◽  
Bui Ngoc Anh ◽  
Hoang Duc Binh ◽  
...  
Keyword(s):  
Software Development ◽  
Assignment Problem ◽  
Task Assignment ◽  
Development Project ◽  
Compromise Programming ◽  
Software Development Project ◽  
Task Assignment Problem

A Tradeoff-Based Interactive Multi-Objective Optimization Method Driven by Evolutionary Algorithms

2017 ◽  
Vol 21 (2) ◽  
pp. 284-292 ◽  
Author(s):  
Lu Chen ◽  
◽  
Bin Xin ◽  
Jie Chen ◽  
◽  
...  
Keyword(s):  
Evolutionary Algorithms ◽  
Optimal Solution ◽  
Optimization Method ◽  
Normal Vector ◽  
Pareto Optimal ◽  
Management Problem ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Tangent Hyperplane ◽  
Conflicting Objectives

Multi-objective optimization problems involve two or more conflicting objectives, and they have a set of Pareto optimal solutions instead of a single optimal solution. In order to support the decision maker (DM) to find his/her most preferred solution, we propose an interactive multi-objective optimization method based on the DM’s preferences in the form of indifference tradeoffs. The method combines evolutionary algorithms with the gradient-based interactive step tradeoff (GRIST) method. An evolutionary algorithm is used to generate an approximate Pareto optimal solution at each iteration. The DM is asked to provide indifference tradeoffs whose projection onto the tangent hyperplane of the Pareto front provides a tradeoff direction. An approach for approximating the normal vector of the tangent hyperplane is proposed which is used to calculate the projection. A water quality management problem is used to demonstrate the interaction process of the interactive method. In addition, three benchmark problems are used to test the accuracy of the normal vector approximation approach and compare the proposed method with GRIST.

scholarly journals Hazardous Materials Transportation with Multiple Objectives: A Case Study in Taiwan

2016 ◽  
Author(s):  
Ta-Yin Hu ◽  
Ya-Han Chang
Keyword(s):  
Hazardous Materials ◽  
Multiple Objectives ◽  
Compromise Programming ◽  
Programming Approach ◽  
Risk Indicator ◽  
Compromise Solution ◽  
Hazardous Material ◽  
Conflicting Objectives ◽  
Hazmat Transportation ◽  
Kaohsiung City

Hazardous material (hazmat) transportation has been an important issue for handling hazardous materials, such as gases and chemical liquids. In the past, researchers have made great efforts to develop policies and route planning methods for hazmat transportation problems. In 2014, Kaohsiung City in Taiwan suffered a gas pipeline explosion at midnight; 32 people were killed, and hundreds of people were injured. After the incident, policies and routing strategies for hazardous materials (hazmat) transportation in Kaohsiung were initiated to avoid pipeline transportation. Although methodologies for hazmat transportation have been proposed and implemented to minimize potential risks, multiple objectives need to be considered in the process to facilitate hazmat transportation in Taiwan. In order to consider both government and operators’ aspects, a multi-objective formulation for the hazmat problem is proposed and a compromise programming method is applied to solve the problem with two objectives: travel cost and risk. The path risk is defined based on risk assessment indexes, such as road characteristics, population distribution, link length, hazardous material characteristics, and accident rates. An aggregate risk indicator is proposed for roadway segments. The compromise programming approach is developed from the concept of compromise decision and the main idea is to search the compromise solution closest to the ideal solution. The proposed method is applied to Kaohsiung City, Taiwan. The results show that two conflicting objectives keep making trade-offs between each other until they finally reach a compromise solution.

scholarly journals GOAL PROGRAMMING APPROACH TO CHANCE CONSTRAINED MULTI-OBJECTIVE LINEAR FRACTIONAL PROGRAMMING PROBLEM BASED ON TAYLOR’S SERIES APPROXIMATION

2012 ◽  
Vol 2 (2) ◽  
pp. 77-80
Author(s):  
Durga Banerjee ◽  
Surapati Pramanik
Keyword(s):  
Programming Problem ◽  
Goal Programming ◽  
Fractional Programming ◽  
Random Variables ◽  
Optimal Solution ◽  
Programming Approach ◽  
Objective Functions ◽  
Linear Fractional Programming ◽  
Multi Objective ◽  
Linear Fractional Programming Problem

This paper deals with goal programming approach to chance constrained multi-objective linear fractional programming problem based on Taylor’s series approximation. We consider the constraints with right hand parameters as the random variables of known mean and variance. The random variables are transformed into standard normal variables with zero mean and unit variance. We convert the chance constraints with known confidence level into equivalent deterministic constraints. The goals of linear fractional objective functions are determined by optimizing it subject to the equivalent deterministic system constraints. Then the fractional objective functions are transformed into equivalent linear functions at the optimal solution point by using first order Taylor polynomial series. In the solution process, we use three minsum goal programming models and identify the most compromise optimal solution by using Euclidean distance function.

Multiobjective Optimization in Water and Environmental Systems Management- MODE Approach

2016 ◽  
pp. 120-136
Author(s):  
Janga Reddy Manne
Keyword(s):  
Optimization Problems ◽  
Effective Means ◽  
Optimal Solution ◽  
Simultaneous Optimization ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Systems Management ◽  
Multi Objective ◽  
Environmental Systems ◽  
Conflicting Objectives

Many real world problems are characterized by multiple goals, often conflicting in nature and compete with one another. Multi-objective optimization problems (MOOPs) require the simultaneous optimization of several non-commensurable and conflicting objectives. In the past, several studies have used conventional approaches to solve the MOOPs by adopting weighted approach or constrained approach, which may face difficulties while generating Pareto optimal solutions, if optimal solution lies on non-convex or disconnected regions of the objective function space. An effective algorithm should have an ability to learn from earlier performance to direct proper selection of weights for further evolutions. To achieve these goals, multi-objective evolutionary algorithms (MOEAs) have become effective means in recent past, which can generate a population of solutions in each iteration and offer a set of alternatives in a single run. This chapter presents an effective MOEA, namely multi-objective differential evolution (MODE) for problems of solving water, environmental systems.

GOAL PROGRAMMING APPROACH TO CHANCE CONSTRAINED MULTI-OBJECTIVE LINEAR FRACTIONAL PROGRAMMING PROBLEM BASED ON TAYLOR’S SERIES APPROXIMATION

2012 ◽  
Vol 2 (2) ◽  
pp. 77-80 ◽  
Author(s):  
Durga Banerjee ◽  
Surapati Pramanik
Keyword(s):  
Programming Problem ◽  
Goal Programming ◽  
Fractional Programming ◽  
Random Variables ◽  
Optimal Solution ◽  
Programming Approach ◽  
Objective Functions ◽  
Linear Fractional Programming ◽  
Multi Objective ◽  
Linear Fractional Programming Problem

This paper deals with goal programming approach to chance constrained multi-objective linear fractional programming problem based on Taylor’s series approximation. We consider the constraints with right hand parameters as the random variables of known mean and variance. The random variables are transformed into standard normal variables with zero mean and unit variance. We convert the chance constraints with known confidence level into equivalent deterministic constraints. The goals of linear fractional objective functions are determined by optimizing it subject to the equivalent deterministic system constraints. Then the fractional objective functions are transformed into equivalent linear functions at the optimal solution point by using first order Taylor polynomial series. In the solution process, we use three minsum goal programming models and identify the most compromise optimal solution by using Euclidean distance function.

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