scholarly journals Determining decoupling points in a supply chain networks using NSGA II algorithm

2017 ◽  
Vol 10 (2) ◽  
pp. 352 ◽  
Author(s):  
Mina Ebrahimiarjestan ◽  
Guoxin Wang
Keyword(s):  
Heuristic Method ◽  
Production Costs ◽  
Pareto Optimal ◽  
Delivery Time ◽  
Nsga Ii ◽  
Supply Chain Networks ◽  
Proposed Model ◽  
Product Delivery ◽  
Objective Model ◽  
Decision Making Model

Purpose: In the model, we used the concepts of Lee and Amaral (2002) and Tang and Zhou (2009) and offer a multi-criteria decision-making model that identify the decoupling points to aim to minimize production costs, minimize the product delivery time to customer and maximize their satisfaction.Design/methodology/approach: We encounter with a triple-objective model that meta-heuristic method (NSGA II) is used to solve the model and to identify the Pareto optimal points. The max (min) method was used.Findings: Our results of using NSGA II to find Pareto optimal solutions demonstrate good performance of NSGA II to extract Pareto solutions in proposed model that considers determining of decoupling point in a supply network.Originality/value: So far, several approaches to model the future have been proposed, of course, each of them modeled a part of this concept. This concept has been considered more general in the model that defined in follow.  In this model, we face with a multi-criteria decision problem that includes minimization of the production costs and product delivery time to customers as well as customer consistency maximization.

A bi-objective model for vessel emergency maintenance under a condition-based maintenance strategy

SIMULATION ◽  
2017 ◽  
Vol 94 (7) ◽  
pp. 609-624 ◽  
Author(s):  
Jinlou Zhao ◽  
Liqian Yang
Keyword(s):  
Complex Problem ◽  
Spare Parts ◽  
Condition Based Maintenance ◽  
Maintenance Strategy ◽  
Nsga Ii ◽  
Maintenance Costs ◽  
Inventory Costs ◽  
Penalty Costs ◽  
Proposed Model ◽  
Objective Model

When sailing on the open seas, far from onshore dockyards, if a crucial part of the ship’s machinery fails, the ship will experience a costly event that carries a high risk of seriously affecting ship operations. If the ship receives warning of an impending defect, then it can try to sail to a dockyard and simultaneously order the spare parts needed to fix the problem. In this paper, we define this type of maintenance situation as ‘vessel emergency maintenance’. It is a complex problem, due to uncertainties with both the machinery condition development and spare parts delivery. To solve this problem, our paper proposes a bi-objective model under a condition-based maintenance strategy, with the aim of simultaneously minimizing maintenance costs and maximizing ship reliability. Maintenance costs include four things: (1) fuel consumption costs; (2) renting extra vessels; (3) shipping delay penalty costs; and (4) spare parts inventory costs. Ship reliability is represented by the reliability of the ship’s main engine, and can be described through a stochastic process. To solve this bi-objective model, we employ a non-dominated sorting genetic algorithm II (NSGA-II) to generate the Pareto optimal front of the two objectives. A numerical experiment is presented to demonstrate the applicability of the proposed model. The results indicate that the proposed model can provide emergency maintenance decision support for ship operators while they are sailing at sea.

scholarly journals Using NSGAII to Solve Bi-objective Bed Allocation Problems

2015 ◽  
Vol 1 (3) ◽  
pp. 397
Author(s):  
Jalal A. Sultan ◽  
Ban A. Mitras ◽  
Raghad M. Jasim
Keyword(s):  
Queuing Theory ◽  
Optimization Problems ◽  
Admission Rate ◽  
Nsga Ii ◽  
Multi Objective ◽  
Decision Aiding ◽  
Proposed Model ◽  
Objective Model ◽  
Bed Allocation ◽  
Suitable Framework

The Bed Allocation Problem (BAP) is NP-complete and always high dimensional. In this paper, a bi-objective decision aiding model based on queuing theory is introduced for allocation of beds in a hospital. The problem is modeled as an M/PH/n queue. The objectives include maximizing the patient admission rate human resources, in particular, maximization of the nursing work hours. The proposed model is solved by using Non-dominated Sorting Genetic Algorithm-II (NSGA-II), which is a very effective algorithm for solving multi-objective optimization problems and finding optimal Pareto front. The paper describes an application of the model, dealing with a public hospital in Iraq. The results related that multi-objective model was presented suitable framework for bed allocation and optimum use.

A fuzzy based multiobjective optimization of multi echelon supply chain network

2020 ◽  
Vol 39 (3) ◽  
pp. 3057-3066
Author(s):  
Prashant K. Jamwal ◽  
Shahid Hussain
Keyword(s):  
Supply Chain ◽  
Simultaneous Optimization ◽  
Pareto Optimal ◽  
Supply Chain Network ◽  
Final Solution ◽  
Nsga Ii ◽  
Supply Chain Networks ◽  
Pareto Optimal Set ◽  
Customer Service Level ◽  
Optimal Set

Globalization of business around the world has turned individual firms into groups of collaborating business units whereby companies do not operate in isolation but function as integral part of big supply chain networks (SCN). Organization of SCN is quite complex as they operate with uncertainty in demands and operations. However, supply chain networks are required to be optimized in order to reduce the overall supply chain cost and increase service levels. Since these objectives are normally conflicting and incommensurable, instead of a singular solution, it is preferred to obtain a set of equitable solutions which is commonly referred to as set of Pareto optimal solutions. Subsequently, a suitable solution can be chosen by the user from the set of equitable solutions. In the present research, a multi-echelon SCN problem is formulated and two important objectives are identified. It is desired to minimize the total cost of supply chain network and at the same time maximize customer service level in terms of supply to demand ratio. Simultaneous optimization of these objectives has been carried out using an evolutionary algorithm (EA) called NSGA-II, which works with population of SCN solutions and is more likely to provide set of globally optimized solutions. However, at the conclusion of optimization, user needs to select a final solution from the Pareto optimal set of solutions after careful analysis. Existing approaches to carry out such analysis are complex and time consuming. We propose a novel method involving fuzzy logic in this research by which fuzzy indices corresponding to each of the solutions in the Pareto Front (PF) are obtained. Fuzzy indices of all the Pareto optimal SCN solutions are later compared to reach to a final solution from the Pareto optimal set.

scholarly journals MULTI-OBJECTIVE GREEN MIXED VEHICLE ROUTING PROBLEM UNDER ROUGH ENVIRONMENT

Transport ◽  
2021 ◽  
Vol 0 (0) ◽  
pp. 1-13
Author(s):  
Joydeep Dutta ◽  
Partha Sarathi Barma ◽  
Anupam Mukherjee ◽  
Samarjit Kar ◽  
Tanmay De ◽  
...  
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Point Of View ◽  
Third Party ◽  
Third Party Logistics ◽  
Nsga Ii ◽  
Routing Problem ◽  
Multi Objective ◽  
Proposed Model ◽  
Objective Model

This paper proposes a multi-objective Green Vehicle Routing Problem (G-VRP) considering two types of vehicles likely company-owned vehicle and third-party logistics in the imprecise environment. Focusing only on one objective, especially the distance in the VRP is not always right in the sustainability point of view. Here we present a bi-objective model for the G-VRP that can address the issue of the emission of GreenHouse Gases (GHGs). We also consider the demand as a rough variable. This paper uses the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to solve the proposed model. Finally, it uses Multicriteria Optimization and Compromise Solution (abbreviation in Serbian – VIKOR) method to determine the best alternative from the Pareto front.

scholarly journals An Evolutionary Algorithm for Multiobjective Fuzzy Portfolio Selection Models with Transaction Cost and Liquidity

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Wei Yue ◽  
Yuping Wang ◽  
Cai Dai
Keyword(s):  
Evolutionary Algorithm ◽  
Transaction Cost ◽  
Portfolio Selection ◽  
Stock Exchange ◽  
Optimal Investment ◽  
Pareto Optimal ◽  
Nsga Ii ◽  
Fuzzy Portfolio Selection ◽  
Proposed Model ◽  
Mean Variance

The major issues for mean-variance-skewness models are the errors in estimations that cause corner solutions and low diversity in the portfolio. In this paper, a multiobjective fuzzy portfolio selection model with transaction cost and liquidity is proposed to maintain the diversity of portfolio. In addition, we have designed a multiobjective evolutionary algorithm based on decomposition of the objective space to maintain the diversity of obtained solutions. The algorithm is used to obtain a set of Pareto-optimal portfolios with good diversity and convergence. To demonstrate the effectiveness of the proposed model and algorithm, the performance of the proposed algorithm is compared with the classic MOEA/D and NSGA-II through some numerical examples based on the data of the Shanghai Stock Exchange Market. Simulation results show that our proposed algorithm is able to obtain better diversity and more evenly distributed Pareto front than the other two algorithms and the proposed model can maintain quite well the diversity of portfolio. The purpose of this paper is to deal with portfolio problems in the weighted possibilistic mean-variance-skewness (MVS) and possibilistic mean-variance-skewness-entropy (MVS-E) frameworks with transaction cost and liquidity and to provide different Pareto-optimal investment strategies as diversified as possible for investors at a time, rather than one strategy for investors at a time.

AN UNBIASED BI-OBJECTIVE OPTIMIZATION MODEL AND ALGORITHM FOR CONSTRAINED OPTIMIZATION

2014 ◽  
Vol 28 (08) ◽  
pp. 1459008 ◽  
Author(s):  
NING DONG ◽  
YUPING WANG
Keyword(s):  
Constrained Optimization ◽  
Optimization Model ◽  
Optimization Problem ◽  
Selection Criterion ◽  
Optimal Solution ◽  
Pareto Optimal ◽  
Optimal Vector ◽  
Pareto Ranking ◽  
Proposed Model ◽  
Objective Model

Transforming a constrained optimization problem (COP) into a bi-objective optimization problem (BOP) is an efficient way to solve the COP. However, how to obtain a good balance between the objective function and the constraint violation function is not easy in BOP. To handle this issue, a novel unbiased bi-objective optimization model is proposed, in which both objective functions are equally treated. Furthermore, the novel model is shown to have the unique Pareto optimal vector under proper condition, and the Pareto optimal vector is exactly corresponding to the optimal solution of COP. Moreover, the relationship between the existing biased bi-objective model and the proposed unbiased one is analyzed in detail. For the unbiased model, a generic multi-objective optimization evolutionary algorithm, i.e. a differential evolution (DE), can be used to solve it, and Pareto ranking is employed as the unique selection criterion. The experiments are conducted on 24 well-known benchmark test instances and the results illustrate that the proposed model is not only effective but also efficient.

scholarly journals Economical Speed for Optimizing the Travel Time and Energy Consumption in Train Scheduling using a Fuzzy Multi-Objective Model

2021 ◽  
Author(s):  
Ahmad Reza Jafarian-Moghaddam
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Pollutant Emissions ◽  
Scheduling Problems ◽  
Train Scheduling ◽  
Multi Objective ◽  
Consumption Ratio ◽  
Proposed Model ◽  
Objective Model ◽  
Time And Energy

AbstractSpeed is one of the most influential variables in both energy consumption and train scheduling problems. Increasing speed guarantees punctuality, thereby improving railroad capacity and railway stakeholders’ satisfaction and revenues. However, a rise in speed leads to more energy consumption, costs, and thus, more pollutant emissions. Therefore, determining an economic speed, which requires a trade-off between the user’s expectations and the capabilities of the railway system in providing tractive forces to overcome the running resistance due to rail route and moving conditions, is a critical challenge in railway studies. This paper proposes a new fuzzy multi-objective model, which, by integrating micro and macro levels and determining the economical speed for trains in block sections, can optimize train travel time and energy consumption. Implementing the proposed model in a real case with different scenarios for train scheduling reveals that this model can enhance the total travel time by 19% without changing the energy consumption ratio. The proposed model has little need for input from experts’ opinions to determine the rates and parameters.

A multi-objective supplier selection model for green supply chain network under uncertainty

2018 ◽  
Vol 13 (3) ◽  
pp. 605-625 ◽  
Author(s):  
Mohammad Khalilzadeh ◽  
Hadis Derikvand
Keyword(s):  
Supply Chain ◽  
Supplier Selection ◽  
Objective Function Value ◽  
Selection Problem ◽  
Green Supply Chain ◽  
Content Type ◽  
Multi Objective ◽  
Proposed Model ◽  
Objective Model ◽  
Supplier Selection Problem

Purpose Globalization of markets and pace of technological change have caused the growing importance of paying attention to supplier selection problem. Therefore, this study aims to choose the best suppliers by providing a mathematical model for the supplier selection problem considering the green factors and stochastic parameters. This paper aims to propose a multi-objective model to identify optimal suppliers for a green supply chain network under uncertainty. Design/methodology/approach The objective of this model is to select suppliers considering total cost, total quality parts and total greenhouse gas emissions. Also, uncertainty is tackled by stochastic programming, and the multi-objective model is solved as a single-objective model by the LP-metric method. Findings Twelve numerical examples are provided, and a sensitivity analysis is conducted to demonstrate the effectiveness of the developed mathematical model. Results indicate that with increasing market numbers and final product numbers, the total objective function value and run time increase. In case that decision-makers are willing to deal with uncertainty with higher reliability, they should consider whole environmental conditions as input parameters. Therefore, when the number of scenarios increases, the total objective function value increases. Besides, the trade-off between cost function and other objective functions is studied. Also, the benefit of the stochastic programming approach is proved. To show the applicability of the proposed model, different modes are defined and compared with the proposed model, and the results demonstrate that the increasing use of recyclable parts and application of the recycling strategy yield more economic savings and less costs. Originality/value This paper aims to present a more comprehensive model based on real-world conditions for the supplier selection problem in green supply chain under uncertainty. In addition to economic issue, environmental issue is considered from different aspects such as selecting the environment-friendly suppliers, purchasing from them and taking the probability of defective finished products and goods from suppliers into account.

scholarly journals STUDY AND APPLY ACO ALGORITHM IN TIME-COST OPTIMIZATION OF CONSTRUCTION PROJECT

2010 ◽  
Vol 13 (1) ◽  
pp. 17-30
Author(s):  
Luan Hong Pham ◽  
Nhan Thanh Duong
Keyword(s):  
Cost Optimization ◽  
Construction Project ◽  
Heuristic Method ◽  
Ant Colony ◽  
Ant Colony System ◽  
Time Cost ◽  
Project Duration ◽  
Proposed Model ◽  
Feasible Solutions ◽  
Computational Resources

Time-cost optimization problem is one of the most important aspects of construction project management. In order to maximize the return, construction planners would strive to optimize the project duration and cost concurrently. Over the years, many researches have been conducted to model the time-cost relationships; the modeling techniques range from the heuristic method and mathematical approach to genetic algorithm. In this paper, an evolutionary-based optimization algorithm known as ant colony optimization (ACO) is applied to solve the multi-objective time-cost problem. By incorporating with the modified adaptive weight approach (MAWA), the proposed model will find out the most feasible solutions. The concept of the ACO-TCO model is developed by a computer program in the Visual Basic platforms. An example was analyzed to illustrate the capabilities of the proposed model and to compare against GA-based TCO model. The results indicate that ant colony system approach is able to generate better solutions without making the most of computational resources which can provide a useful means to support construction planners and managers in efficiently making better time-cost decisions.

scholarly journals Analisis Perbandingan Aggregat Of Function (AOF) dengan Non-Dominated Sorting Genetic Algorithm (NSGA-II) dalam Menentukan Optimasi Multi-Objective pada Penjadwalan Mesin Produksi Flow Shop

2018 ◽  
Vol 17 (2) ◽  
pp. 158
Author(s):  
Fifin Sonata ◽  
Dede Prabowo Wiguna
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Total Tardiness ◽  
Pareto Optimal ◽  
Nsga Ii ◽  
Trade Off ◽  
Multi Objective

Penjadwalan mesin produksi dalam dunia industri memiliki peranan penting sebagai bentuk pengambilan keputusan. Salah satu jenis sistem penjadwalan mesin produksi adalah sistem penjadwalan mesin produksi tipe flow shop. Dalam penjadwalan flow shop, terdapat sejumlah pekerjaan (job) yang tiap-tiap job memiliki urutan pekerjaan mesin yang sama. Optimasi penjadwalan mesin produksi flow shop berkaitan dengan penyusunan penjadwalan mesin yang mempertimbangkan 2 objek yaitu makespan dan total tardiness. Optimasi kedua permasalahan tersebut merupakan optimasi yang bertolak belakang sehingga diperlukan model yang mengintegrasikan permasalahan tersebut dengan optimasi multi-objective A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimazitaion : NSGA-II. Dalam penelitian ini akan dibandingkan 2 buah metode yaitu Aggregat Of Function (AOF) dengan NSGA-II agar dapat terlihat nilai solusinya. Penyelesaian penjadwalan mesin produksi flow shop dengan algoritma NSGA-II untuk membangun jadwal dengan meminimalkan makespan dan total tardiness.Tujuan yang ingin dicapai adalah mengetahui bahwa model yang dikembangkan akan memberikan solusi penjadwalan mesin produksi flow shop yang efisien berupa solusi pareto optimal yang dapat memberikan sekumpulan solusi alternatif bagi pengambil keputusan dalam membuat penjadwalan mesin produksi yang diharapkan. Solusi pareto optimal yang dihasilkan merupakan solusi optimasi multi-objective yang optimal dengan trade-off terhadap seluruh objek, sehingga seluruh solusi pareto optimal sama baiknya.

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