Real Case Study In Iran Research Articles

Optimal pricing strategy in the closed-loop supply chain using game theory under government subsidy scenario: A case study

Automotive battery production always receives government supports which directly affect the recycling of used products collected from secondary markets. Accordingly, an optimal product pricing strategy and efficient policy suggestions must be provided for this industry. This study used Stackelberg models to analyze three categories of pricing strategies related to the recycling process of car batteries when government subsidy is paid. These models are applied to make optimal decisions in two centralized and decentralized modes to investigate the links between and influences of different parameters of the problem on one another. Finally, a numerical example based on a real case study in Iran is applied to the models. The results indicate that making centralized decisions leads to a higher level of profitability than decentralized decision-making in the under-study industry. Accordingly, maintaining coordination and integration among the supply chain components in the decision-making process increases the profitability of the entire network.

A hybrid ANN-MILP model for agile recovery production planning for PPE products under sharp demands

Today, supply chains (SCs) have been struggling with a new type of disruption known as outbreaks such as epidemics or pandemics. This type of disruption has features such as long-term and uncertain lifespan and leads to severe fluctuations in product demand. This paper elaborates on a hybrid approach based on artificial neural networks (ANN) and mathematical programming techniques to efficiently deal with this type of disruption in the production planning of SCs. In the first phase of the hybrid approach, a multi-layer perceptron ANN model with an optimised structure is developed to efficiently predict the demand and its peak points. In the second phase, the predicted demands are considered as input in a new multiobjective agile recovery production planning model. The proposed model minimises total costs and delivery times and maximises responsiveness. A real case study in Iran is conducted to verify and validate the proposed hybrid approach. The prediction error of the ANN method is about 1 percent. According to the predicted demand, optimal decisions are determined by the proposed model. The impact of under-estimation and over-estimation of demand is evaluated in terms of total costs, delivery time, responsiveness and shortage costs in the SCs.

Optimizing the dam site selection problem considering sustainability indicators and uncertainty: An integrated decision-making approach

Changes of climate, population growth, extended droughts, water scarcity, and sustainability considerations are amongst the concerns that stress more than before the significance of water resources management (WRM). One of the approaches that may assist decision-makers (DMs) in handling water resources is the proper site selection for dam construction. The dam site selection decision is a huge long-term investment and due to multiple decision-making factors, stakeholders or DMs, non-deterministic information in the decision-making process, and ripple effect on many other elements of the economy, meticulous analysis is needed. In this study, dam site selection problem (DSSP) is modeled in the form of binary programming under certainty, uncertainty, and hybrid circumstances. Hence, nadir compromise programming (NCP) model is executed in order to tackle the problem with crisp data. Further, a novel model based on NCP and stochastic programming is suggested to treat the uncertainty. The integration of the proposed models is able to address the problem with crisp and random data. The evaluation criteria are categorized based on sustainability (social, economic, and environmental) and technical indicators for dam site selection. To obtain the criteria weight, an interval group decision-making trial and evaluation laboratory (IGDEMATEL) approach is also applied where experts are able to state their opinions on linguistic terms and interval numbers. A real case study in Iran is then investigated in order to appraise the applicability of the developed methodology. A simulation method is further proposed to assess the obtained results and validate the suggested model's performance. Finally, the IGDEMATEL results are employed to survey the relationships between cause-and-effect criteria. The results obtained from the proposed and simulation models are similar in 83% of the cases, and the remaining difference of 17% is because of the uncertainty governing the problem, as well as the lack of sufficient data for precise decision-making.

Toward circular economy for pomegranate fruit supply chain under dynamic uncertainty: A case study

Food security is a fundamental prerequisite of human survival. This study focuses on providing a novel bi-objective model for the design of a green closed-loop pomegranate supply chain based on the value chain. To maximize the benefits of circular economy, we propose a thermochemical conversion process and develop a novel hybrid risk-neutral and risk-averse multi-stage stochastic programming (RNRAMSSP) approach to cope with supply uncertainty. We employ the expected value of perfect information (EVPI) and value of stochastic solution (VSS) to validate the proposed approach. For the VSS metric, we define a novel measure to compare the worst-case cost of the stochastic and deterministic model solutions. To demonstrate the applicability of the proposed model, we provide a real case study in Iran. Based on the VSS metric, the risk-neutral and risk-averse stochastic model solutions save up to 1% and 1.76% in the worst-case cost, respectively, compared to the deterministic model.

Robust sustainable canola oil-based biodiesel supply chain network design under supply and demand uncertainty.

The excessive consumption of fossil fuels has sparked debates and caused environmental damage, leading the global community to search for a suitable alternative. To achieve sustainable development goals and prevent harmful climate scenarios, the world needs to increase its use of renewable energy. Biodiesel, a clean and eco-friendly fuel with a high flash point and more lubrication than petroleum-based fuels, and without the emission of harmful environmental gases, has emerged as one of the fossil fuel alternatives. To promote the mass-level production of biodiesel, a sustainable supply chain (SC) that does not depend on laboratory production is necessary. For this purpose, this research proposes a multi-objective mixed-integer non-linear mathematical programming (MINLP) model to design a sustainable canola oil-based biodiesel supply chain network (CO-BSCND) under supply and demand uncertainty. This mathematical model aims to minimize the total cost (TC) and total carbon emission while maximizing the total number of job opportunities simultaneously. A scenario-based robust optimization (SBRO) approach is applied to deal with uncertainty. The proposed model is implemented in a real case study in Iran, and numerical experiments and sensitivity analysis are conducted to demonstrate its applicability. The results of this research demonstrate that designing a sustainable supply chain network for the production and distribution of biodiesel fuel is achievable. Moreover, this mathematical modeling makes mass-scale production of biodiesel fuel a possibility. In addition, the SBRO method adopted in this research enables managers and researchers to explore the design conditions of the supply chain network by controlling the uncertainties that affect it. This approach allows the chain's performance to be as close as possible to the actual conditions. As a result, the SBRO method enhances the efficiency of the supply chain network and boosts productivity toward achieving desired goals.

Thermal power plants pollution assessment based on deep neural networks, remote sensing, and GIS: A real case study in Iran

To investigate the impact of the Bandar Abbas thermal power plant on the waters of the Persian Gulf coast, a combination of satellite images and ground data was utilized to determine the Sea Surface Temperature (SST) as a thermal index, Total Organic Carbon (TOC) and Chemical Oxygen Demand (COD) as biological indices. Additionally, measurements of SO2, O3, NO2, CO2, CO, and CH4 values in the atmosphere were taken to determine the plant's impact on air pollution. Temperature values of the water for different months were predicted using Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and Cascade neural networks. The results indicate that the waters near thermal power plants exhibit the highest temperatures in July and September, with temperatures reaching approximately 50 °C. Furthermore, the SST values were found to be strongly correlated with ecological indices. The Multiple Linear Regression (MLR) analysis revealed a strong correlation between the temperature and TOC, COD, and O2 in water (RTOC2=0.98), RO22=−0.89, RCOD2=0.87 and O3, NO3, CO2, and CO in the air (RO32=0.99,RNO32=0.97,RCO22=0.95,RCO2=0.96). Finally, the results demonstrate that the LSTM method exhibited high accuracy in predicting the water temperature (R2 = 0.98).

Designing a portfolio-based closed-loop supply chain network for dairy products with a financial approach: Accelerated Benders decomposition algorithm

Product portfolio design is one of the important and effective factors in the financial and physical flows of various supply chains, especially dairy products. Accordingly, the financial and physical flows of the portfolio should be taken into consideration during the supply chain design. In this study, a closed-loop supply chain (CLSC) network is designed for dairy products aiming at maximizing the net cash flow from assets and maximizing the amounts paid to shareholders simultaneously. To find the optimal policy, an accelerated Benders decomposition (ABD) algorithm is implemented to tackle the complexity of the model. Moreover, three multi-objective optimization solution approaches of the weighted sum method (WSM), augmented ε-constraint (AEC), and fuzzy multi-objective programming (FMOP) are implemented to tackle the bi-objectiveness of the model. Next, a real case study in Iran is investigated to reveal the applicability of the developed methodology. Numerical results reveal that the ABD method reduces CPU time by about 10.8%. Moreover, the results of the case study demonstrate that by integrating financial and physical flows, an improvement of 4.8478% in the net cash flow from assets and a 2.3% improvement in the amounts paid to shareholders compared to the current situation.

Investigating social risks of construction projects in historic tourism sites in urban districts of developing countries: social network analysis approach

PurposeSocial risk management has recently come to the fore as a significant feature of project management. This prominence is particularly evident in urban construction projects that take place in cultural heritage and tourism historic sites. Accordingly, this study aims to adopt social network analysis (SNA) to investigate social risks in construction projects occurring in urban districts rife with historically and culturally significant tourism sites.Design/methodology/approachThe present study analyzed a real case study in Iran as an emergent economy and a developing country. Primarily, the study reviewed previous literature on social risks and relevant stakeholders. Next, the judgments of experts through the content validity ratio analysis confirmed 12 social risks and 9 key stakeholders. Finally, SNA is used to determine the relations between the social risks and stakeholders as well as the significance of each risk.FindingsThe investigation demonstrated that the most important social risks in the construction projects of the case study are “Psychological disorders”, “Environmental pollution” and “Cultural conflicts”.Practical implicationsThe findings could help policymakers, urban planners and project managers in developing countries with a rich cultural heritage to reduce social risks and improve the efficiency of their projects.Originality/valueTo the best of the authors’ knowledge, the present study is one of the first instances to investigate construction projects implemented in densely populated urban areas hosting cultural heritage and historic tourism sites.

Viable healthcare supply chain network design for a pandemic.

The recent COVID-19 pandemic revealed that healthcare networks must have a flexible and effective structure. In this study, we develop a viable healthcare network design for a pandemic using a multi-stage stochastic approach. We propose a multi-level network that includes health centers, computed tomography scan centers, hospitals, and clinics. Patients have conditions to returning to normal life or quarantining at home. Three objectives are defined: maximizing the probability of patient recovery, minimizing the costs of all centers in the network, and minimizing the Coronavirus death rate. We investigate a real case study in Iran to demonstrate the model’s applicability. Finally, we compare the healthcare supply chain network design in a pandemic with a normal situation to advise how the network can continue to remain viable.

A comparison between multivariate adaptive regression splines and regressive convolution neural network with support vector regression for pipe burst rate prediction on limited dataset

ABSTRACT By accurate predicting of pipe bursts, it is possible to schedule pipe maintenance, rehabilitation and improve level of services in Water Distribution Networks (WDNs). In recent years, artificial intelligence techniques have been used vastly in pipe burst prediction. These techniques need big input dataset for deep learning but big dataset might not be available in old WDNs. This paper aimed to compare the performance of multivariate adaptive regression splines (MARS) and regressive convolution neural network with least square support vector regression (RCNN-LSSVR) for predicting of Pipe Burst Rate (PBR) with limited dataset. Pipes age, diameter, depth of installation, length, average and maximum hydraulic pressure are considered as effective parameters of PBR. Data collected from real-case study in Iran include 158 cases for polyethylene and 124 cases for asbestos cement pipes during 2012–2019. The results indicate that RCNN-LSSVR model has a great performance of PBR prediction on limited dataset.

A novel mathematical optimization model for a preemptive multi-priority M/M/C queueing system of emergency department’s patients, a real case study in Iran

The Covid-19 pandemic crisis has caused many difficulties worldwide. One of the most critical problems is that the emergency departments (EDs) have become overcrowded. Because this problem can increase patients’ queue length and waiting times in EDs, this paper provides a mixed-integer non-linear mathematical model considering a preemptive M/M/C queueing system to solve the problem and optimize a benefit function concerning the number of servers and treatment rate. In this model, different patient priorities, which are modified according to Covid-19 patients, are considered. This model is then solved using an exact approach and a meta-heuristic algorithm, the grasshopper optimization algorithm, for two shifts of the ED of a hospital in order to consider non stationery arrival rate in Varamin, Iran. The results of both algorithms confirmed the effectiveness of the proposed model. Moreover, to justify using the preemptive model, a comparison between the preemptive and non-preemptive models is conducted. An extensive sensitivity analysis is presented, and finally, a list of managerial insights is provided for managers to improve their service system further.

Green Design of Regional Wheat Supply Chains under Uncertainty

This study presents a fuzzy mathematical programming model to optimize regional wheat hub center in Iran with the aim of achieving sustainable self-sufficiency, swap and export of wheat to neighboring countries. The proposed model is developed for a 10-year planning horizon with real-world assumptions under uncertainty. By the proposed model, the optimal decisions are made on the amount of wheat cultivation areas in different provinces, capacity of silos, amount of import, swap and export of wheat, transportation mode and storage amount of wheat in different periods. Two objective functions including economic and environmental goals are optimized by the proposed model. The proposed model is examined under uncertainty conditions and the possibilistic programming approach is used to deal with the uncertainty of parameters. Finally, the presented model is validated through investigating a real case study in Iran. The results show the efficiency of the model for making optimal strategic and tactical decisions in wheat supply chains.

Investigating a Dual-Channel Network in a Sustainable Closed-Loop Supply Chain Considering Energy Sources and Consumption Tax.

This paper proposes a dual-channel network of a sustainable Closed-Loop Supply Chain (CLSC) for rice considering energy sources and consumption tax. A Mixed Integer Linear Programming (MILP) model is formulated for optimizing the total cost, the amount of pollutants, and the number of job opportunities created in the proposed supply chain network under the uncertainty of cost, supply, and demand. In addition, to deal with uncertainty, fuzzy logic is used. Moreover, four multi-objective metaheuristic algorithms are employed to solve the model, which include a novel multi-objective version of the recently proposed metaheuristic algorithm known as Multi-Objective Reptile Search Optimizer (MORSO), Multi-Objective Simulated Annealing (MOSA), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Grey Wolf (MOGWO). All the algorithms are evaluated using LP-metric in small sizes and their results and performance are compared based on criteria such as Max Spread (MS), Spread of Non-Dominance Solution (SNS), the number of Pareto solutions (NPS), Mean Ideal Distance (MID), and CPU time. In addition, to achieve better results, the parameters of all algorithms are tuned by the Taguchi method. The programmed model is implemented using a real case study in Iran to confirm its accuracy and efficiency. To further evaluate the current model, some key parameters are subject to sensitivity analysis. Empirical results indicate that MORSO performed very well and by constructing solar panel sites and producing energy out of rice waste up to 19% of electricity can be saved.

Designing an integrated responsive-green-cold vaccine supply chain network using Internet-of-Things: artificial intelligence-based solutions.

In this paper, a new responsive-green-cold vaccine supply chain network during the COVID-19 pandemic is developed for the first time. According to the proposed network, a new multi-objective, multi-period, multi-echelon mathematical model for the distribution-allocation-location problem is designed. Another important novelty in this paper is that it considers an Internet-of-Things application in the COVID-19 condition in the suggested model to enhance the accuracy, speed, and justice of vaccine injection with existing priorities. Waste management, environmental effects, coverage demand, and delivery time of COVID-19 vaccine simultaneously are therefore considered for the first time. The LP-metric method and meta-heuristic algorithms called Gray Wolf Optimization (GWO), and Variable Neighborhood Search (VNS) algorithms are then used to solve the developed model. The other significant contribution, based on two presented meta-heuristic algorithms, is a new heuristic method called modified GWO (MGWO), and is developed for the first time to solve the model. Therefore, a set of test problems in different sizes is provided. Hence, to evaluate the proposed algorithms, assessment metrics including (1) percentage of domination, (2) the number of Pareto solutions, (3) data envelopment analysis, and (4) diversification metrics and the performance of the convergence are considered. Moreover, the Taguchi method is used to tune the algorithm’s parameters. Accordingly, to illustrate the efficiency of the model developed, a real case study in Iran is suggested. Finally, the results of this research show MGO offers higher quality and better performance than other proposed algorithms based on assessment metrics, computational time, and convergence.

Disaster relief supply chain design for personal protection equipment during the COVID-19 pandemic

The global epidemic caused by novel coronavirus continues to be a crisis in the world and a matter of concern. The way the epidemic has wreaked havoc on the international level has become difficult for the healthcare systems to supply adequately personal protection equipment for medical personnel all over the globe. In this paper, considering the COVID-19 outbreak, a multi-objective, multi-product, and multi-period model for the personal protection equipment demands satisfaction aiming to optimize total cost and shortage, simultaneously, is developed. The model is embedded with instances and validated by both modern and classic multi-objective metaheuristic algorithms. Moreover, the Taguchi method is exploited to set the metaheuristic into their best performances by finding their parameters’ optimum level. Furthermore, fifteen test examples are designed to prove the established PPE supply chain model and tuned algorithms’ applicability. Among the test examples, one is related to a real case study in Iran. Finally, metaheuristics are evaluated by a series of related metrics through different statistical analyses. It can be concluded from the obtained results that solution methods are practical and valuable to achieve the efficient shortage level and cost.

Designing and planning the animal waste-to-energy supply chains: A case study

• Biogas of animal waste could be used in distributed electricity generation. • Developing an MILP model for planning animal waste-to-energy supply chain. • Considering multi-period conditions for multiple suppliers. • Optimizing the capacity levels of collection and production centers. • Applying the proposed model in a real case study in Iran. The need for clean energy and fossil fuel depletion are the reasons behind the increasing tendency to utilize renewable energy resources. Biogas as the final product of anaerobic fermentation of some sources including animal waste, could be used in distributed electricity generation. This paper presents a mixed linear programming model for designing and planning of electricity production supply chain from animal manure. The proposed model determines the optimum locations for establishing facilities, optimum capacity level, and material flow throughout the supply chain. Multiple suppliers, collection centers and production centers, and multi-period conditions are considered in the proposed model. The proposed model is applied in a real case of Iran including all its 30 provinces for 10 years planning horizon. The achieved results confirm the capability of the proposed model in efficient design of electricity production chain network.

A multi-objective robust possibilistic programming approach to coordinating procurement operations in the disaster supply chain using a multi-attribute reverse auction mechanism

This paper presents a multi-attribute reverse auction framework to facilitate the purchase operations between suppliers and a relief organization. The proposed auction framework consists of announcement, construction, and evaluation phases. In the announcement phase, the relief organization provides suppliers with information about demand and required relief items. In the construction phase, a fuzzy bi-objective mathematical model is developed from a supplier’s perspective; by solving the model, the suppliers offer their bids, each of which stipulating price, quantity, and delivery time for each item. The evaluation phase is formulated from the relief organization’s perspective; suppliers are first ranked based on their proposed bids and other qualitative criteria by the fuzzy PROMETHEE method. Then, a fuzzy tri-objective mathematical model is solved to determine winner(s) and allocate orders. For the first time, this paper develops a multi-objective robust possibilistic programming (MORPP) approach to cope with the uncertainty of parameters in the relief chain. Furthermore, multi-objective models are solved by an augmented ∊-constraint method. Finally, applicability and validity of the proposed auctioning framework are tested through a real case study in Iran and the managerial insights are provided.

Bi-objective robust optimization for reliability-oriented power network planning by considering distributed generation effects: A case study in Iran

In the present study, a new mixed-integer linear programming is presented for a reliability-oriented power network. The reliability approach is conducted using the Average Energy Not Supplied (AENS) index and System Average Interruption Duration Index (SAIDI) based on the feeder’s failure rate. In this formulation, the constraints include standard ranges for electric power and current magnitude, acceptable voltage drop, power law, and power balance considering loss power. Moreover, the relation between feeder failure rate and electric power level and subsequently SAIDI for outage duration control is added in constraints to improve network reliability. The model is studied on a real case study in Iran in two scenarios with or without considering DG. The obtained results indicate that optimization for DG location and its capacity in the power system decrease AENS cost. Furthermore, DG incorporation in the grid reduces load congestion in substations and system’ total cost. Augmented Epsilon Constraint (AEC) method is utilized to solve two-objective mixed-integer linear programming. To overcome the uncertain environment, the robust optimization is applied, and the effect of Γ parameters is investigated on Pareto sets and system’s total costs. As confirmed by numerical results, by increasing demand constraint conservative level, the objective function gets away from the nominal value, requiring that Decision-Makers create an appropriate balance between conservative levels and system costs. The obtained results reveal the efficiency and effectiveness of the proposed mathematical model in the real world. • New reliability-oriented multi stage power network planning. • Determination the feeders failure rates via power storage and AENS cost. • Considering uncertainty in demand forecasting and facility installation costs. • DG incorporation as a facility to costs reduction and reliability reinforcement.

Utilizing IoT to design a relief supply chain network for the SARS-COV-2 pandemic

The current universally challenging SARS-COV-2 pandemic has transcended all the social, logical, economic, and mortal boundaries regarding global operations. Although myriad global societies tried to address this issue, most of the employed efforts seem superficial and failed to deal with the problem, especially in the healthcare sector. On the other hand, the Internet of Things (IoT) has enabled healthcare system for both better understanding of the patient’s condition and appropriate monitoring in a remote fashion. However, there has always been a gap for utilizing this approach on the healthcare system especially in agitated condition of the pandemics. Therefore, in this study, we develop two innovative approaches to design a relief supply chain network is by using IoT to address multiple suspected cases during a pandemic like the SARS-COV-2 outbreak. The first approach (prioritizing approach) minimizes the maximum ambulances response time, while the second approach (allocating approach) minimizes the total critical response time. Each approach is validated and investigated utilizing several test problems and a real case in Iran as well. A set of efficient meta-heuristics and hybrid ones is developed to optimize the proposed models. The proposed approaches have shown their versatility in various harsh SARS-COV-2 pandemic situations being dealt with by managers. Finally, we compare the two proposed approaches in terms of response time and route optimization using a real case study in Iran. Implementing the proposed IoT-based methodology in three consecutive weeks, the results showed 35.54% decrease in the number of confirmed cases.

An integrated gas-oil and bio-diesel supply network model with strategic and tactical applications considering the environmental aspects

In recent years, research has shown that biomass can be a source of energy to replace fossil fuels and effectively reduce current environmental crises. Researchers have studied how biofuels are supplied through the oil supply chain to achieve tangible results. This paper presents an optimization model for the gas-oil and biodiesel supply chains suggesting the optimization of both supply chains simultaneously for the first time. The proposed model deals with the connection points of two supply chains and determines the two chains’ connection points by considering two economic and environmental objective functions. The model can be used to make decisions on issues such as location, allocation, production planning, inventory management, capacity expansion, and so forth. The proposed programming model’s performance has been studied through a real case study in Iran and the sensitivity analyses have been performed. The ε-constraint method was used to solve the multi-objective model. The proposed model is expected to be effective in the future management of countries’ fuel sources, particularly to be used as an alternative to fossil fuels. Also, this research can provide a basis for more extensive research on fuel supply chain integration.