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Seyed Taghi Akhavan Niaki

Sharif University of Technology, Industrial Engineering, Faculty Member

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Seyed Taghi Akhavan Niaki is Professor of Industrial
Engineering at Sharif University of Technology.
His research interests are in the areas of Quality
Engineering, Simulation Modeling and Analysis, Applied
Statistics, and Operations Research. Before joining
Sharif University of Technology, he worked as a systems engineer
and quality control manager for Iranian Electric Meters Company. He
received his Bachelor of Science in Industrial Engineering from Sharif
University of Technology in 1979, his Master’s and his Ph.D. degrees
both in Industrial Engineering from West Virginia University in 1989
and 1992, respectively. He is the Co-Editor-In-Chief of Scientia
Iranica, the Editor of Scinetia Iranica Transactions E, the Acting Editor
of Scientifi c and Research Journal of Sharif, a board member to several
international journals, and a member of ①ϖμ.
Supervisors: Prof. Wafik Iskander

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Papers by Seyed Taghi Akhavan Niaki

An optimal integrated lot sizing policy of inventory in a bi-objective multi-level supply chain with stochastic constraints and imperfect products

Journal of Industrial and Production Engineering, Oct 30, 2017

Abstract This paper provides a developed mathematical model to derive the optimal integrated lot ... more Abstract This paper provides a developed mathematical model to derive the optimal integrated lot sizing in a multi-level supply chain with imperfect quality products. The developed mathematical model has a bi-objective function, with conflicting goals, minimizing the chain inventory costs and maximizing the chain total profit aided to find optimum policy for integrated lot sizing. We further actualize the problem by assuming multiple stochastic constraints. The mathematical formulation of the problem is stochastic, nonlinear, and large. In this regard, the interior point algorithm that is developed as more effective algorithm with less iteration is used for solving the recent convex nonlinear model. Numerical example shows that the developed interior point method is an efficient method with excellent performance in terms of the quality of the solution. Also, Sensitivity analysis shows that the developed interior point method is an efficient method with excellent performance in terms of the quality of the solution.

Bi-objective optimisation of the joint replenishment problem in a two-echelon supply chain

International Journal of Services and Operations Management, 2021

A closed-loop supply chain considering carbon reduction, quality improvement effort, and return policy under two remanufacturing scenarios

Journal of Cleaner Production, Sep 1, 2019

A closed-loop supply chain considering carbon reduction, quality improvement effort, and return p... more

Blood plasma supply chain planning to respond COVID-19 pandemic: a case study

Environment, Development and Sustainability, Dec 8, 2022

The COVID-19 pandemic causes a severe threat to human lives worldwide. Convalescent plasma as sup... more The COVID-19 pandemic causes a severe threat to human lives worldwide. Convalescent plasma as supportive care for COVID-19 is critical in reducing the death rate and staying in hospitals. Designing an efficient supply chain network capable of managing convalescent plasma in this situation seems necessary. Although many researchers investigated supply chains of blood products, no research was conducted on the planning of convalescent plasma in the supply chain framework with specific features of COVID-19. This gap is covered in the current work by simultaneous regular and convalescent plasma flow in a supply chain network. Besides, due to the growing importance of environmental problems, the resulting carbon emission from transportation activities is viewed to provide a green network. In other words, this study aims to plan the integrated green supply chain network of regular and convalescent plasma in the pandemic outbreak of COVID-19 for the first time. The presented mixed-integer multi-objective optimization model determines optimal network decisions while minimizing the total cost and total carbon emission. The Epsilon constraint method is used to handle the considered objectives. The model is applied to a real case study from the capital of Iran. Sensitivity analyses are carried out, and managerial insights are drawn. Based on the obtained results, product demand impacts the objective functions significantly. Moreover, the systems' total carbon emission is highly dependent on the flow of regular plasma. The results also reveal that changing transportation emission unit causes significant variation in the total emission while the total cost remains fixed.

Designing a closed-loop blood supply chain network considering transportation flow and quality aspects

Sustainable operations and computers, 2021

Risk-adjusted frailty-based CUSUM control chart for phase I monitoring of patients’ lifetime

Journal of Statistical Computation and Simulation, Sep 7, 2020

Monitoring the mortality associated with a surgical procedure leads to the proper decision making... more Monitoring the mortality associated with a surgical procedure leads to the proper decision making in a healthcare system. However, the surgical outcomes depend not only on the risk factors of each patient but also on other categorical influential covariates which cannot be easily measured. Ignoring the unmeasured covariates leads to the poor performance of monitoring procedures. To deal with this significant issue, a general Phase-I risk-adjusted cumulative sum control chart is proposed using a combination of accelerated failure time and frailty models to monitor surgical outcomes. Extensive simulation studies are conducted which reveal that the proposed frailty-based CUSUM chart outperforms the simple CUSUM chart. Moreover, the proposed approach performs better than the existing dummy-based CUSUM chart in terms of detection power. Subsequently, the results of a real cardiac surgery dataset indicate that the inclusion of frailty variables in the risk-adjustment model can effectively model the heterogeneity of the surgical data.

An Agri-Fresh Food Supply Chain Network Design with Routing Optimization: A Case Study of ETKA Company

Advances in Mathematical Finance and Applications, 2022

The Supply Chain Network Design (SCND) with perishability is an active research topic. The Agri-f... more The Supply Chain Network Design (SCND) with perishability is an active research topic. The Agri-fresh Food Supply Chain (AFSC) is a relevant topic to SCND and this study aims to model a new AFSC for a real-world case study. Regarding the traditional AFSC, the geographically dispersed small farmers transport their product individually to market for selling. This leads to a higher transportation cost, which is the major cause of farmers' low profitability. This paper formulates a traditional product movement model to represent the existing AFSC. The concept of sharing economic approach is employed by the aggregate and collaborative transportation of products to minimize transportation inefficiency. This paper proposes an aggregate product movement with the vehicle routing model to redesign an AFSC for a case study in Iran based on the data of ETKA Company-the largest domestic agri-fresh food supply chain. A four-echelon, multi-period, Mixed Integer Non-Linear Programming (MINLP) approach for the proposed location-inventory-routing model is formulated for perishable products via considering the clustering of farmers to minimize the total distribution cost. According to the comparison analysis, the location-inventoryrouting model is effective and leads to a reduction of 33% in total distribution cost compared to the present supply chain.

A Scenario-Based Nonlinear Programming Model for a Two-Level Inventory Control Problem: A Case in Dairy Product Industry

Iranian journal of operations research, Jun 10, 2018

Here, a novel scenario-based two-level inventory control model with a limited budget is formulate... more Here, a novel scenario-based two-level inventory control model with a limited budget is formulated. The demand during the selling period is considered to follow a uniform probability distribution. It is assumed that there will be some customers who are willing to wait for their demands to be satisfied; thus, a service level is considered for these customers. The aim is to find the optimal order quantities of the products and the required raw materials at the beginning of the selling period such that the relevant expected total profit obtained during the period is maximized. A penalty function along with a barrier method is proposed to solve the developed nonlinear stochastic programming problem. The problem is solved under different scenarios including good, fair, and low demands. Finally, a case study in a dairy manufacturing company is provided to illustrate the application of the proposed methodology in real-world inventory control systems.

Bi-objective optimization of the joint replenishment problem in a two-echelon supply chain

International Journal of Services and Operations Management, 2020

Statistical Process Control for Correlated Data: A Case Study

Presenting a stochastic multi choice goal programming model for reducing wastages and shortages of blood products at hospitals

Journal of Industrial and Systems Engineering, Mar 22, 2017

One of the most fundamental components of healthcare systems is the blood supply chain management... more One of the most fundamental components of healthcare systems is the blood supply chain management. This chain has two significant components including collecting donor' bloods and supplying blood's products. The main purpose of this paper is to concentrate on supplying blood products and present a novel constrained biobjective mathematical model for a two-echelon blood supply chain network (BSCN). The aims of the proposed model are: 1) minimize products' waste and shortage costs in hospitals and 2) to minimize the maximum unsatisfied demand of different products among hospital demands supplied by blood transfusion centers. Some techniques are used to linearize the model's nonlinear terms and decrease presented model's complexity. A multi-choice goal programming (MCGP) technique is used to convert bi-objective model into a single objective one. The model is solved by mathematical software under 3 different scenarios and 18 time periods in realworld BSCN. Computational results showed that hospitals are tended in accepting products' holding and waste costs to satisfy demands of patients who are highly need for receiving blood products.

Resource Constrained Project Scheduling with Material Ordering: Two Hybridized Meta-Heuristic Approaches (TECHNICAL NOTE)

International Journal of Engineering, Sep 18, 2014

Optimal strategies for price, warranty length, and production rate of a new product with learning production cost

Scientia Iranica, Dec 1, 2013

This study investigates optimal strategies for price, warranty length, and production rate of a n... more This study investigates optimal strategies for price, warranty length, and production rate of a new product in which both static markets for non-durable and dynamic markets for durable products are involved. The mathematical model incorporates both the demand and the cost functions including production, warranty length, and inventory costs. Using the maximum principle approach, the optimal strategies and interactions among price, warranty length, and production rate in both markets are analyzed using some propositions. The analysis shows that to maximize pro ts in all cases, the price, the warranty length, and the production rate all must go up simultaneously, or one of them must increase and the other two must decrease concurrently.

Robust optimization of the design of monopropellant propulsion control systems using an advanced teaching-learning-based optimization method

Engineering Applications of Artificial Intelligence

An advanced teaching-learning-based algorithm to solve unconstrained optimization problems

Intelligent Systems with Applications

Unrelated parallel machine scheduling with machine processing cost

International Journal of Industrial Engineering Computations

In practical scheduling problems, some factors such as depreciation cost, green costs like the am... more In practical scheduling problems, some factors such as depreciation cost, green costs like the amount of energy consumption or carbon emission, other resources consumption, raw material cost, etc., are not explicitly related to the machine processing times. Most of these factors can be generally considered as machine costs. Considering the machine cost as another objective alongside the other classical time-driven decision objectives can be an attractive work in scheduling problems. However, this subject has not been discussed thoroughly in the literature for the case the machines have fixed processing costs. This paper investigates a general unrelated parallel machine scheduling problem with the machine processing cost. In this problem, it is assumed that processing a job on a machine incurs a particular cost in addition to processing time. The considered objectives are the makespan and the total cost, which are minimized simultaneously to obtain Pareto optimal solutions. The effic...

An Optimization Model for Cloud-based Supply Chain Network Design: Case Study in Banking Industry

International Journal of Communication Networks and Distributed Systems

An efficient solution method for an agri-fresh food supply chain: hybridization of Lagrangian relaxation and genetic algorithm

Environmental Science and Pollution Research, 2021

In the traditional agri-fresh food supply chain (AFSC), geographically dispersed small farmers tr... more In the traditional agri-fresh food supply chain (AFSC), geographically dispersed small farmers transport their products individually to the market for sale. This leads to a higher transportation cost, which is the primary cause of farmers’ low profitability. This paper formulates a traditional product movement problem in AFSC. First, the aggregate product movement model is combined with the vehicle routing model to redesign an existing AFSC (the ETKA Company; the most extensive domestic agri-fresh food supply chain in Iran) based on the available data. For the four-echelon, multi-period supply chain under investigation, a mixed integer linear programming (MILP) model is developed for the location-inventory-routing problem of perishable products via considering the clustering of farmers to minimize the total distribution cost. Considering the complexity of the problem, an efficient and effective “matheuristic” is introduced based on hybridizing the Lagrangian relaxation and genetic algorithm (GA). The solution obtained by the proposed “matheuristic” algorithm is robust and efficient in comparison with an exact solver, GA, and the Lagrangian relaxation approach individually. The comparison analysis reveals that the location-inventory-routing model is efficient, leading to a reduction in total distribution cost by 33% compared to the existing supply chain. Finally, the findings encourage further development and application of the proposed “matheuristic” to solve other complicated location-inventory-routing problems heuristically.

A bi-objective model for scheduling of multiple projects under multi-skilled workforce for distributed load energy usage

Operational Research, 2021

Satisfying the clients' uncompromising priorities is a challenge for decision makers of organ... more Satisfying the clients' uncompromising priorities is a challenge for decision makers of organizations that face multiple projects. This paper considers an organization with a multi-skilled workforce working on several predetermined projects under time-of-use energy tariffs where distributed load energy usage is the primary concern of energy suppliers. This paper also considers different time-of-use energy tariffs, which are among the most common strategies to reach a more balanced energy utilization. The problem is stated as a bi-objective mixed-integer programming model containing two conflicting objectives; to minimize the total cost of the multi-skilled workforce and obtain a sustainable schedule with minimum deviation from the projects' priorities. The problem is formulated mathematically, and the GAMS solver is applied for justifying the conflict between the objectives and validating the proposed formulation. In order to tackle real-life instances of the problem, intelligent algorithms based on cuckoo search, particle swarm, and genetic algorithms are developed. In the proposed algorithms, the application of a well-designed encoding and decoding structure efficiently ensures the generated solutions' feasibility. The Taguchi method is used for calibrating the parameters of the proposed meta-heuristics. Performance of the solving methods is evaluated based on some experiments, where ELECTRE method is utilized as a decision-making technique to prioritize the developed algorithms. To this aim, some well-known multi-objective measures are applied for comparative analysis of the results, where the supremacy of FSCS in terms of all metrics is declared.

Open-shop production scheduling with reverse flows

Computers & Industrial Engineering, 2021

An optimal integrated lot sizing policy of inventory in a bi-objective multi-level supply chain with stochastic constraints and imperfect products

Journal of Industrial and Production Engineering, Oct 30, 2017

Abstract This paper provides a developed mathematical model to derive the optimal integrated lot ... more Abstract This paper provides a developed mathematical model to derive the optimal integrated lot sizing in a multi-level supply chain with imperfect quality products. The developed mathematical model has a bi-objective function, with conflicting goals, minimizing the chain inventory costs and maximizing the chain total profit aided to find optimum policy for integrated lot sizing. We further actualize the problem by assuming multiple stochastic constraints. The mathematical formulation of the problem is stochastic, nonlinear, and large. In this regard, the interior point algorithm that is developed as more effective algorithm with less iteration is used for solving the recent convex nonlinear model. Numerical example shows that the developed interior point method is an efficient method with excellent performance in terms of the quality of the solution. Also, Sensitivity analysis shows that the developed interior point method is an efficient method with excellent performance in terms of the quality of the solution.

Bi-objective optimisation of the joint replenishment problem in a two-echelon supply chain

International Journal of Services and Operations Management, 2021

A closed-loop supply chain considering carbon reduction, quality improvement effort, and return policy under two remanufacturing scenarios

Journal of Cleaner Production, Sep 1, 2019

A closed-loop supply chain considering carbon reduction, quality improvement effort, and return p... more

Blood plasma supply chain planning to respond COVID-19 pandemic: a case study

Environment, Development and Sustainability, Dec 8, 2022

The COVID-19 pandemic causes a severe threat to human lives worldwide. Convalescent plasma as sup... more The COVID-19 pandemic causes a severe threat to human lives worldwide. Convalescent plasma as supportive care for COVID-19 is critical in reducing the death rate and staying in hospitals. Designing an efficient supply chain network capable of managing convalescent plasma in this situation seems necessary. Although many researchers investigated supply chains of blood products, no research was conducted on the planning of convalescent plasma in the supply chain framework with specific features of COVID-19. This gap is covered in the current work by simultaneous regular and convalescent plasma flow in a supply chain network. Besides, due to the growing importance of environmental problems, the resulting carbon emission from transportation activities is viewed to provide a green network. In other words, this study aims to plan the integrated green supply chain network of regular and convalescent plasma in the pandemic outbreak of COVID-19 for the first time. The presented mixed-integer multi-objective optimization model determines optimal network decisions while minimizing the total cost and total carbon emission. The Epsilon constraint method is used to handle the considered objectives. The model is applied to a real case study from the capital of Iran. Sensitivity analyses are carried out, and managerial insights are drawn. Based on the obtained results, product demand impacts the objective functions significantly. Moreover, the systems' total carbon emission is highly dependent on the flow of regular plasma. The results also reveal that changing transportation emission unit causes significant variation in the total emission while the total cost remains fixed.

Designing a closed-loop blood supply chain network considering transportation flow and quality aspects

Sustainable operations and computers, 2021

Risk-adjusted frailty-based CUSUM control chart for phase I monitoring of patients’ lifetime

Journal of Statistical Computation and Simulation, Sep 7, 2020

Monitoring the mortality associated with a surgical procedure leads to the proper decision making... more Monitoring the mortality associated with a surgical procedure leads to the proper decision making in a healthcare system. However, the surgical outcomes depend not only on the risk factors of each patient but also on other categorical influential covariates which cannot be easily measured. Ignoring the unmeasured covariates leads to the poor performance of monitoring procedures. To deal with this significant issue, a general Phase-I risk-adjusted cumulative sum control chart is proposed using a combination of accelerated failure time and frailty models to monitor surgical outcomes. Extensive simulation studies are conducted which reveal that the proposed frailty-based CUSUM chart outperforms the simple CUSUM chart. Moreover, the proposed approach performs better than the existing dummy-based CUSUM chart in terms of detection power. Subsequently, the results of a real cardiac surgery dataset indicate that the inclusion of frailty variables in the risk-adjustment model can effectively model the heterogeneity of the surgical data.

An Agri-Fresh Food Supply Chain Network Design with Routing Optimization: A Case Study of ETKA Company

Advances in Mathematical Finance and Applications, 2022

The Supply Chain Network Design (SCND) with perishability is an active research topic. The Agri-f... more The Supply Chain Network Design (SCND) with perishability is an active research topic. The Agri-fresh Food Supply Chain (AFSC) is a relevant topic to SCND and this study aims to model a new AFSC for a real-world case study. Regarding the traditional AFSC, the geographically dispersed small farmers transport their product individually to market for selling. This leads to a higher transportation cost, which is the major cause of farmers' low profitability. This paper formulates a traditional product movement model to represent the existing AFSC. The concept of sharing economic approach is employed by the aggregate and collaborative transportation of products to minimize transportation inefficiency. This paper proposes an aggregate product movement with the vehicle routing model to redesign an AFSC for a case study in Iran based on the data of ETKA Company-the largest domestic agri-fresh food supply chain. A four-echelon, multi-period, Mixed Integer Non-Linear Programming (MINLP) approach for the proposed location-inventory-routing model is formulated for perishable products via considering the clustering of farmers to minimize the total distribution cost. According to the comparison analysis, the location-inventoryrouting model is effective and leads to a reduction of 33% in total distribution cost compared to the present supply chain.

A Scenario-Based Nonlinear Programming Model for a Two-Level Inventory Control Problem: A Case in Dairy Product Industry

Iranian journal of operations research, Jun 10, 2018

Here, a novel scenario-based two-level inventory control model with a limited budget is formulate... more Here, a novel scenario-based two-level inventory control model with a limited budget is formulated. The demand during the selling period is considered to follow a uniform probability distribution. It is assumed that there will be some customers who are willing to wait for their demands to be satisfied; thus, a service level is considered for these customers. The aim is to find the optimal order quantities of the products and the required raw materials at the beginning of the selling period such that the relevant expected total profit obtained during the period is maximized. A penalty function along with a barrier method is proposed to solve the developed nonlinear stochastic programming problem. The problem is solved under different scenarios including good, fair, and low demands. Finally, a case study in a dairy manufacturing company is provided to illustrate the application of the proposed methodology in real-world inventory control systems.

Bi-objective optimization of the joint replenishment problem in a two-echelon supply chain

International Journal of Services and Operations Management, 2020

Statistical Process Control for Correlated Data: A Case Study

Presenting a stochastic multi choice goal programming model for reducing wastages and shortages of blood products at hospitals

Journal of Industrial and Systems Engineering, Mar 22, 2017

One of the most fundamental components of healthcare systems is the blood supply chain management... more One of the most fundamental components of healthcare systems is the blood supply chain management. This chain has two significant components including collecting donor' bloods and supplying blood's products. The main purpose of this paper is to concentrate on supplying blood products and present a novel constrained biobjective mathematical model for a two-echelon blood supply chain network (BSCN). The aims of the proposed model are: 1) minimize products' waste and shortage costs in hospitals and 2) to minimize the maximum unsatisfied demand of different products among hospital demands supplied by blood transfusion centers. Some techniques are used to linearize the model's nonlinear terms and decrease presented model's complexity. A multi-choice goal programming (MCGP) technique is used to convert bi-objective model into a single objective one. The model is solved by mathematical software under 3 different scenarios and 18 time periods in realworld BSCN. Computational results showed that hospitals are tended in accepting products' holding and waste costs to satisfy demands of patients who are highly need for receiving blood products.

Resource Constrained Project Scheduling with Material Ordering: Two Hybridized Meta-Heuristic Approaches (TECHNICAL NOTE)

International Journal of Engineering, Sep 18, 2014

Optimal strategies for price, warranty length, and production rate of a new product with learning production cost

Scientia Iranica, Dec 1, 2013

This study investigates optimal strategies for price, warranty length, and production rate of a n... more This study investigates optimal strategies for price, warranty length, and production rate of a new product in which both static markets for non-durable and dynamic markets for durable products are involved. The mathematical model incorporates both the demand and the cost functions including production, warranty length, and inventory costs. Using the maximum principle approach, the optimal strategies and interactions among price, warranty length, and production rate in both markets are analyzed using some propositions. The analysis shows that to maximize pro ts in all cases, the price, the warranty length, and the production rate all must go up simultaneously, or one of them must increase and the other two must decrease concurrently.

Robust optimization of the design of monopropellant propulsion control systems using an advanced teaching-learning-based optimization method

Engineering Applications of Artificial Intelligence

An advanced teaching-learning-based algorithm to solve unconstrained optimization problems

Intelligent Systems with Applications

Unrelated parallel machine scheduling with machine processing cost

International Journal of Industrial Engineering Computations

In practical scheduling problems, some factors such as depreciation cost, green costs like the am... more In practical scheduling problems, some factors such as depreciation cost, green costs like the amount of energy consumption or carbon emission, other resources consumption, raw material cost, etc., are not explicitly related to the machine processing times. Most of these factors can be generally considered as machine costs. Considering the machine cost as another objective alongside the other classical time-driven decision objectives can be an attractive work in scheduling problems. However, this subject has not been discussed thoroughly in the literature for the case the machines have fixed processing costs. This paper investigates a general unrelated parallel machine scheduling problem with the machine processing cost. In this problem, it is assumed that processing a job on a machine incurs a particular cost in addition to processing time. The considered objectives are the makespan and the total cost, which are minimized simultaneously to obtain Pareto optimal solutions. The effic...

An Optimization Model for Cloud-based Supply Chain Network Design: Case Study in Banking Industry

International Journal of Communication Networks and Distributed Systems

An efficient solution method for an agri-fresh food supply chain: hybridization of Lagrangian relaxation and genetic algorithm

Environmental Science and Pollution Research, 2021

In the traditional agri-fresh food supply chain (AFSC), geographically dispersed small farmers tr... more In the traditional agri-fresh food supply chain (AFSC), geographically dispersed small farmers transport their products individually to the market for sale. This leads to a higher transportation cost, which is the primary cause of farmers’ low profitability. This paper formulates a traditional product movement problem in AFSC. First, the aggregate product movement model is combined with the vehicle routing model to redesign an existing AFSC (the ETKA Company; the most extensive domestic agri-fresh food supply chain in Iran) based on the available data. For the four-echelon, multi-period supply chain under investigation, a mixed integer linear programming (MILP) model is developed for the location-inventory-routing problem of perishable products via considering the clustering of farmers to minimize the total distribution cost. Considering the complexity of the problem, an efficient and effective “matheuristic” is introduced based on hybridizing the Lagrangian relaxation and genetic algorithm (GA). The solution obtained by the proposed “matheuristic” algorithm is robust and efficient in comparison with an exact solver, GA, and the Lagrangian relaxation approach individually. The comparison analysis reveals that the location-inventory-routing model is efficient, leading to a reduction in total distribution cost by 33% compared to the existing supply chain. Finally, the findings encourage further development and application of the proposed “matheuristic” to solve other complicated location-inventory-routing problems heuristically.

A bi-objective model for scheduling of multiple projects under multi-skilled workforce for distributed load energy usage

Operational Research, 2021

Satisfying the clients' uncompromising priorities is a challenge for decision makers of organ... more Satisfying the clients' uncompromising priorities is a challenge for decision makers of organizations that face multiple projects. This paper considers an organization with a multi-skilled workforce working on several predetermined projects under time-of-use energy tariffs where distributed load energy usage is the primary concern of energy suppliers. This paper also considers different time-of-use energy tariffs, which are among the most common strategies to reach a more balanced energy utilization. The problem is stated as a bi-objective mixed-integer programming model containing two conflicting objectives; to minimize the total cost of the multi-skilled workforce and obtain a sustainable schedule with minimum deviation from the projects' priorities. The problem is formulated mathematically, and the GAMS solver is applied for justifying the conflict between the objectives and validating the proposed formulation. In order to tackle real-life instances of the problem, intelligent algorithms based on cuckoo search, particle swarm, and genetic algorithms are developed. In the proposed algorithms, the application of a well-designed encoding and decoding structure efficiently ensures the generated solutions' feasibility. The Taguchi method is used for calibrating the parameters of the proposed meta-heuristics. Performance of the solving methods is evaluated based on some experiments, where ELECTRE method is utilized as a decision-making technique to prioritize the developed algorithms. To this aim, some well-known multi-objective measures are applied for comparative analysis of the results, where the supremacy of FSCS in terms of all metrics is declared.

Open-shop production scheduling with reverse flows

Computers & Industrial Engineering, 2021