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1 – 10 of 58L.K. Tartibu, B. Sun and M.A.E. Kaunda
This paper aims to illustrate the use of the augmented epsilon-constraint method implemented in general algebraic modelling system (GAMS), aimed at optimizing the geometry of a…
Abstract
Purpose
This paper aims to illustrate the use of the augmented epsilon-constraint method implemented in general algebraic modelling system (GAMS), aimed at optimizing the geometry of a thermoacoustic regenerator. Thermoacoustic heat engines provide a practical solution to the problem of heat management where heat can be pumped or spot cooling can be produced. However, the most inhibiting characteristic of thermoacoustic cooling is their current lack of efficiencies.
Design/methodology/approach
Lexicographic optimization is presented as an alternative optimization technique to the common used weighting methods. This approach establishes a hierarchical order among all the optimization objectives instead of giving them a specific (and most of the time, arbitrary) weight.
Findings
A practical example is given, in a hypothetical scenario, showing how the proposed optimization technique may help thermoacoustic regenerator designers to identify Pareto optimal solutions when dealing with geometric parameters. This study highlights the fact that the geometrical parameters are interdependent, which support the use of a multi-objective approach for optimization in thermoacoustic.
Originality/value
The research output from this paper can be a valuable resource to support designers in building efficient thermoacoustic device. The research illustrates the use of a lexicographic optimization to provide more meaningful results describing the geometry of thermoacoustic regenerator. It applies the epsilon-constraint method (AUGMENCON) to solve a five-criteria mixed integer non-linear problem implemented in GAMS (GAM software).
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Shaghayegh Abolmakarem, Farshid Abdi, Kaveh Khalili-Damghani and Hosein Didehkhani
This paper aims to propose an improved version of portfolio optimization model through the prediction of the future behavior of stock returns using a combined wavelet-based long…
Abstract
Purpose
This paper aims to propose an improved version of portfolio optimization model through the prediction of the future behavior of stock returns using a combined wavelet-based long short-term memory (LSTM).
Design/methodology/approach
First, data are gathered and divided into two parts, namely, “past data” and “real data.” In the second stage, the wavelet transform is proposed to decompose the stock closing price time series into a set of coefficients. The derived coefficients are taken as an input to the LSTM model to predict the stock closing price time series and the “future data” is created. In the third stage, the mean-variance portfolio optimization problem (MVPOP) has iteratively been run using the “past,” “future” and “real” data sets. The epsilon-constraint method is adapted to generate the Pareto front for all three runes of MVPOP.
Findings
The real daily stock closing price time series of six stocks from the FTSE 100 between January 1, 2000, and December 30, 2020, is used to check the applicability and efficacy of the proposed approach. The comparisons of “future,” “past” and “real” Pareto fronts showed that the “future” Pareto front is closer to the “real” Pareto front. This demonstrates the efficacy and applicability of proposed approach.
Originality/value
Most of the classic Markowitz-based portfolio optimization models used past information to estimate the associated parameters of the stocks. This study revealed that the prediction of the future behavior of stock returns using a combined wavelet-based LSTM improved the performance of the portfolio.
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Ali Heidari, Din Mohammad Imani and Mohammad Khalilzadeh
This paper aims to study the hub transportation system in supply chain networks which would contribute to reducing costs and environmental pollution, as well as to economic…
Abstract
Purpose
This paper aims to study the hub transportation system in supply chain networks which would contribute to reducing costs and environmental pollution, as well as to economic development and social responsibility. As not all customers tend to buy green products, several customer groups should be considered in terms of need type.
Design/methodology/approach
In this paper, a multi-objective hub location problem is developed for designing a sustainable supply chain network based on customer segmentation. It deals with the aspects of economic (cost reduction), environment (minimizing greenhouse gas emissions by the transport sector) and social responsibility (creating employment and community development). The epsilon-constraint method and augmented epsilon-constraint (AEC) method are used to solve the small-sized instances of this multi-objective problem. Due to the non-deterministic polynomial-time hardness of this problem, two non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective grey wolf optimizer (MOGWO) metaheuristic algorithms are also applied to tackle the large-sized instances of this problem.
Findings
As expected, the AEC method is able to provide better Pareto solutions according to the goals of the decision-makers. The Taguchi method was used for setting the parameters of the two metaheuristic algorithms. Considering the meaningful difference, the MOGWO algorithm outperforms the NSGA-II algorithm according to the rate of achievement to two objectives simultaneously and the spread of non-dominance solutions indexes. Regarding the other indexes, there was no meaningful difference between the performance of the two algorithms.
Practical implications
The model of this research provides a comprehensive solution for supply chain companies that want to achieve a rational balance between the three aspects of sustainability.
Originality/value
The importance of considering customer diversity on the one hand and saving on hub transportation costs, on the other hand, triggered us to propose a hub location model for designing a sustainable supply chain network based on customer segmentation.
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Hiwa Esmaeilzadeh, Alireza Rashidi Komijan, Hamed Kazemipoor, Mohammad Fallah and Reza Tavakkoli-Moghaddam
The proposed model aims to consider the flying hours as a criterion to initiate maintenance operation. Based on this condition, aircraft must be checked before flying hours…
Abstract
Purpose
The proposed model aims to consider the flying hours as a criterion to initiate maintenance operation. Based on this condition, aircraft must be checked before flying hours threshold is met. After receiving maintenance service, the model ignores previous flying hours and the aircraft can keep on flying until the threshold value is reached again. Moreover, the model considers aircraft age and efficiency to assign them to flights.
Design/methodology/approach
The aircraft maintenance routing problem (AMRP), as one of the most important problems in the aviation industry, determines the optimal route for each aircraft along with meeting maintenance requirements. This paper presents a bi-objective mixed-integer programming model for AMRP in which several criteria such as aircraft efficiency and ferrying flights are considered.
Findings
As the solution approaches, epsilon-constraint method and a non-dominated sorting genetic algorithm (NSGA-II), including a new initializing algorithm, are used. To verify the efficiency of NSGA-II, 31 test problems in different scales are solved using NSGA-II and GAMS. The results show that the optimality gap in NSGA-II is less than 0.06%. Finally, the model was solved based on real data of American Eagle Airlines extracted from Kaggle datasets.
Originality/value
The authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.
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Iman Rastgar, Javad Rezaeian, Iraj Mahdavi and Parviz Fattahi
The purpose of this study is to propose a new mathematical model that integrates strategic decision-making with tactical-operational decision-making in order to optimize…
Abstract
Purpose
The purpose of this study is to propose a new mathematical model that integrates strategic decision-making with tactical-operational decision-making in order to optimize production and scheduling decisions.
Design/methodology/approach
This study presents a multi-objective optimization framework to make production planning, scheduling and maintenance decisions. An epsilon-constraint method is used to solve small instances of the model, while new hybrid optimization algorithms, including multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm, multi-objective harmony search and improved multi-objective harmony search (IMOHS) are developed to address the high complexity of large-scale problems.
Findings
The computational results demonstrate that the metaheuristic algorithms are effective in obtaining economic solutions within a reasonable computational time. In particular, the results show that the IMOHS algorithm is able to provide optimal Pareto solutions for the proposed model compared to the other three algorithms.
Originality/value
This study presents a new mathematical model that simultaneously determines green production planning and scheduling decisions by minimizing the sum of the total cost, makespan, lateness and energy consumption criteria. Integrating production and scheduling of a shop floor is critical for achieving optimal operational performance in production planning. To the best of the authors' knowledge, the integration of production planning and maintenance has not been adequately addressed.
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This study aims to evaluate the performance of the most popular multi-objective programming scalarization methods in the literature for the aircraft sequencing and scheduling…
Abstract
Purpose
This study aims to evaluate the performance of the most popular multi-objective programming scalarization methods in the literature for the aircraft sequencing and scheduling problem (ASSP). These methods are the weighted sum method, weighted goal programming, the ε-constraint method, the elastic constraint method, weighted Tchebycheff and augmented weighted Tchebycheff.
Design/methodology/approach
First, the ASSP for a single runway case was modeled using mixed-integer programming considering the safety and operational constraints and the objectives of the minimization of total delay and total flight time for a sample airport. The objectives were then combined by using the multi-objective programming scalarization methods and various expected times of arrival–departure samples were run for the mathematical models. Finally, the methods were evaluated in terms of the number of nondominated solutions, superior nondominated solution and the average solution time using the Measurement of Alternatives and Ranking according to Compromise Solution method, which is a popular multi-criteria decision-making method.
Findings
Augmented Weighted Tchebycheff was found to be the most effective approach to ASSP in terms of the evaluation criteria followed by Weighted Tchebycheff and then weighted sum method.
Practical implications
The methodology presented in this study could provide more efficient air traffic management in terminal maneuvering areas when multiple objectives need to be optimized.
Originality/value
Although there are studies including the comparison of several scalarization methods for other problems, the comparison of the methods for ASSP has not yet been handled in the literature. As there are several stakeholders in the air traffic system, ASSP includes several objectives, and as a result, this problem can benefit from analyses using this comparison.
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Souhil Mouassa and Tarek Bouktir
In the vast majority of published papers, the optimal reactive power dispatch (ORPD) problem is dealt as a single-objective optimization; however, optimization with a single…
Abstract
Purpose
In the vast majority of published papers, the optimal reactive power dispatch (ORPD) problem is dealt as a single-objective optimization; however, optimization with a single objective is insufficient to achieve better operation performance of power systems. Multi-objective ORPD (MOORPD) aims to minimize simultaneously either the active power losses and voltage stability index, or the active power losses and the voltage deviation. The purpose of this paper is to propose multi-objective ant lion optimization (MOALO) algorithm to solve multi-objective ORPD problem considering large-scale power system in an effort to achieve a good performance with stable and secure operation of electric power systems.
Design/methodology/approach
A MOALO algorithm is presented and applied to solve the MOORPD problem. Fuzzy set theory was implemented to identify the best compromise solution from the set of the non-dominated solutions. A comparison with enhanced version of multi-objective particle swarm optimization (MOEPSO) algorithm and original (MOPSO) algorithm confirms the solutions. An in-depth analysis on the findings was conducted and the feasibility of solutions were fully verified and discussed.
Findings
Three test systems – the IEEE 30-bus, IEEE 57-bus and large-scale IEEE 300-bus – were used to examine the efficiency of the proposed algorithm. The findings obtained amply confirmed the superiority of the proposed approach over the multi-objective enhanced PSO and basic version of MOPSO. In addition to that, the algorithm is benefitted from good distributions of the non-dominated solutions and also guarantees the feasibility of solutions.
Originality/value
The proposed algorithm is applied to solve three versions of ORPD problem, active power losses, voltage deviation and voltage stability index, considering large -scale power system IEEE 300 bus.
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Mohammad Khalilzadeh, Rose Balafshan and Ashkan Hafezalkotob
The purpose of this study is to provide a comprehensive framework for analyzing risk factors in oil and gas projects.
Abstract
Purpose
The purpose of this study is to provide a comprehensive framework for analyzing risk factors in oil and gas projects.
Design/methodology/approach
This paper consists of several sections. In the first section, 19 common potential risks in the projects of Pars Oil and Gas Company were finalized in six groups using the Lawshe validation method. These factors were identified through previous literature review and interviews with experts. Then, using the “best-worst multi-criteria decision-making” method, the study measured the weights associated with the performance evaluation indicators of each risk. Consequently, failure mode and effects analysis (FMEA) and the grey relational analysis (GRA)-VIKOR mixed method were used to rank and determine the critical risks. Finally, to assign response strategies to each critical risk, a zero-one multi-objective mathematical programming model was proposed and developed Epsilon-constraint method was used to solve it.
Findings
Given the typical constraints of projects which are time, cost and quality, of the projects that companies are often faced with, this study presents the identified risks of oil and gas projects to the managers of the oil and gas company in accordance with the priority given in the present research and the response to each risk is also suggested to be used by managers based on their organizational circumstances.
Originality/value
This study aims at qualitative management of cost risks of oil and gas projects (case study of Pars Oil and Gas Company) by combining FMEA, best worst and GRA-VIKOR methods under fuzzy environment and Epsilon constraints. According to studies carried out in previous studies, the simultaneous management of quantitative and qualitative cost of risk of oil and gas projects in Iran has not been carried out and the combination of these methods has also been innovated.
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Nurcan Deniz and Feristah Ozcelik
Although disassembly balancing lines has been studied for over two decades, there is a gap in the robotic disassembly. Moreover, combination of problem with heterogeneous employee…
Abstract
Purpose
Although disassembly balancing lines has been studied for over two decades, there is a gap in the robotic disassembly. Moreover, combination of problem with heterogeneous employee assignment is also lacking. The hazard related with the tasks performed on disassembly lines on workers can be reduced by the use of robots or collaborative robots (cobots) instead of workers. This situation causes an increase in costs. The purpose of the study is to propose a novel version of the problem and to solve this bi-objective (minimizing cost and minimizing hazard simultaneously) problem.
Design/methodology/approach
The epsilon constraint method was used to solve the bi-objective model. Entropy-based Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Preference Ranking Organization methods for Enrichment Evaluation (PROMETHEE) methods were used to support the decision-maker. In addition, a new criterion called automation rate was proposed. The effects of factors were investigated with full factor experiment design.
Findings
The effects of all factors were found statistically significant on the solution time. The combined effect of the number of tasks and number of workers was also found to be statistically significant.
Originality/value
In this study, for the first time in the literature, a disassembly line balancing and employee assignment model was proposed in the presence of heterogeneous workers, robots and cobots to simultaneously minimize the hazard to the worker and cost.
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Mohammad Khalilzadeh, Arya Karami and Alborz Hajikhani
This study aims to deal with supplier selection problem. The supplier selection problem has significantly become attractive to researchers and practitioners in recent years. Many…
Abstract
Purpose
This study aims to deal with supplier selection problem. The supplier selection problem has significantly become attractive to researchers and practitioners in recent years. Many real-world supply chain problems are assumed as multiple objectives combinatorial optimization problems.
Design/methodology/approach
In this paper, the authors propose a multi-objective model with fuzzy parameters to select suppliers and allocate orders considering multiple periods, multiple resources, multiple products and two-echelon supply chain. The objective functions consist of total purchase costs, transportation, order and on-time delivery, coverage and the weights of suppliers. Distance-based partial and general coverage of suppliers makes the number of orders of products more realistic. In this model, the weights of suppliers are determined by fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, as a multi-criteria decision analysis method, in the objective function. Also, the authors consider the parameters related to delays as triangular fuzzy numbers.
Findings
A small-sized numerical example is provided to clearly show the proposed model. The exact epsilon constraint method is used to solve this given multi-objective combinatorial optimization problem. Subsequently, the sensitivity analysis is conducted to testify the proposed model. The obtained results demonstrate the validity of the proposed multiple objectives mixed integer mathematical programming model and the efficiency of the solution approach.
Originality/value
In real-life situations, supplier selection parameters are uncertain and incomplete. Hence, the fuzzy set theory is used to tackle uncertainty. In this paper, a multi-objective supplier selection problem is formulated taking into consideration the coverage of suppliers and suppliers’ weights. Integrating coverage of suppliers to select and allocate the order to them can be mentioned as the main contribution of this study. The proposed model considers the delay from suppliers as fuzzy parameters.
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