ali mostafaeipour

In this paper, the problem of product pricing in a sustainable supply chain consisting of several production centers, several distribution centers, and customers in two direct and indirect channels is modeled. This model aims to achieve more profit in a sustainable supply chain by complying with environmental and social considerations. In order to solve the model, a hybrid of genetic algorithm (GA) and grey wolf optimizer (GWO) called genetic grey wolf optimizer (GGWO) was developed, with the ability to search the problem-solving space based on the simultaneous use of the operators of the two algorithms. Analysis of the numerical example with GGWO and its comparison with other solution methods indicate that pricing in direct and indirect channels is strongly related to the price elasticity coefficient of the substitute product and the price elasticity of demand so that the market boom and bust of the depends on the proper determination of these model parameters. It is also observed that in the conditions if market boom, demand increased in both direct and indirect channels and that product prices decreased in both channels. Therefore, the marginal profit increased due to the sale of more products. The GGWO algorithm has faster convergence compared to GA and GGWO, while the solution time in this method is higher than other solution methods. Analyzing different numerical examples suggest that the efficiency of the used algorithms, in particular GGWO, is higher than exact methods.

Hydropower boasts the capability to consistently generate electricity throughout the year, offering the lowest operating costs and the longest lifespan among renewable energy technologies. Given the aforementioned considerations and the absence of prior investigations into Iran's hydropower potential, this study employs HOMER software to explore the feasibility of supplying electricity to a village comprising 10 households near Koohrang Tunnel in Chaharmahal and Bakhtiari Province, leveraging solar, wind, and hydro turbine renewable energies. Three distinct scenarios, centered around hydro turbine utilization, were examined. These scenarios aimed to provide electricity in off-grid (1st scenario) and grid-connected modes (2nd scenario), as well as to generate electricity and hydrogen in a grid-connected mode (3rd scenario). In the first scenario, the most economically viable design yielded a cost of $0.187 per kWh of generated electricity, with 99% of the electricity sourced from the hydro turbine and the remaining 1% from a diesel generator. This scenario resulted in a CO2 emission of 23.2 kg/y. In the second scenario, the most cost-effective option supplied 94% of the electricity from the hydro turbine and the remaining portion from the main grid, at a cost of $0.033 per kWh. Notably, surplus electricity sold to the main grid facilitated an annual reduction of 1297 kg of CO2 emissions. The third scenario, which combined the hydro turbine with the main grid, presented the most financially viable option. Here, the costs of per kWh of generated electricity and per kg of produced hydrogen were $2.012 and $0.49, respectively.

Compared to coal and other fossil fuels, renewable energy (RE) sources emit significantly less carbon dioxide (CO2). In this sense, switching to such sources brings many positive effects to the environment through mitigating climate change, so the terms green energy and clean energy, have been derived from these constructive environmental impacts. Given the utmost importance of RE development, the primary objective of this study was to identify and prioritize the effective RE development strategies in Mazandaran Province, Iran, using different methods, including the Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, along with other decision-making techniques. Recruiting a team of 11 industrial and academic experts, the strategies to implement in this region were developed in line with the RE development plans. For this purpose, the Multi-Criteria Decision-Making (MCDM) methodologies were utilized within the gray fuzzy environment to manage the existing uncertainties. The Gray-Additive Ratio Assessment System (Gray ARAS) was further applied to rank the main factors at each level. According to the SWOT analysis and the Stepwise Weight Assessment Ratio Analysis (SWARA) outcomes, among the major factors shaping RE development in Mazandaran Province, Iran, the economic criterion, with the final weight of 0.24, was ranked first; and then the geographical and environmental criteria, having the final weights of 0.23 and 0.19, were put in the second and third places, respectively. In this regard, appropriate location, with the final weight of 0.226, was ranked first; and subsequently pollution reduction and energy production costs, receiving the final weights of 0.103 and 0.094, were the second and third sub-criteria, respectively. As a final point, the validation results based on the Gray-Weighted Aggregated Sum Product Assessment (Gray-WASPAS) and ranking obtained through the Gray-ARAS were confirmed.

A multi-level sustainable supply chain is related to a system that includes all activities necessary to transfer and supply materials and services from the producer to the consumer. In this system, the focus is on providing materials and services based on a number of objectives, such as reducing costs, increasing quality, and preserving the environment. Due to the increase of uncertainty in the supply chain, organizations need to use resources for the prediction of internal uncertainties, needs, and supply, thereby minimizing vulnerability and elevating the tolerance of their supply. Understanding the uncer-tainties and the parameters causing factors causes the problem of risk management to be raised in some cases. Therefore, main contribution of current study is multi-objective planning for a sustainable, multi-level, multi-period model, consid-ering the determined conditions and boom as uncertainty scenarios, has been specifically considered. The most important goal of the research is to determine the best units of each level (suppliers, factories, ...) of chain networks according to the points and criteria determined in the model and network, design and determine the best communication routes (network) between the selected units Each level is optimal with other levels as well as determining the volume of transported goods in these routes. For this purpose, a mathematical model has been developed, which is solved through the limited epsilon method and NSGA-II meta-heuristic algorithm. Data comparing the mathematical model and NSGA-II meta-heuristic algorithm show the calculated errors of 0.022, which considering that it is less than 0.1, the calculation error is acceptable and can be compared to the results of the error methods. The sensitivity analysis on the probability of the boom scenario showed the value of the objective function can change between 7398.51 and 3245.73. Finally, the sensitivity analysis of the probability of recession scenario showed the value of the objective function can change between 3291.64 and 9364.35. The findings of this research show that using the multi-objective planning model in the sustainable supply chain, taking into account the boom and bust of the market, can create significant improvements in the performance and profitability of the supply chain.

The vehicle routing problem has attracted much attention in the recent decade. Considering the real-world constraints, many extensions have been developed. This paper develops a new model for the green vehicle routing problem with simultaneous pickup and delivery under demand uncertainty. Due to the problem's complexity, the standard solvers are only able to solve small-scale instances. To solve the large-scale problems, a two-stage algorithm based on the modified AVNS is proposed. Extensive computational experiments are conducted using modified versions of Solomon’s benchmark instances to show the performance of the algorithm. The results affirm that the two-stage algorithm is capable of generating optimal solutions for small-size instances and the planned routes generated for large-size instances were significantly more robust against the increase of uncertainty parameters.

The competitive environment in the global market makes most countries look for better ways to solve problems in order to earn more money. One of the strategies proposed as a competitive one is to use a stable closed loop to improve performance. The present study, which has not reported any research in this field, proposes a multi-level sustainable chain-loop supply chain (SCLSC) network for pomegranate fruit. The mathematical model has been designed with the aim of offering the lowest price, the amount of response received and the reduction of costs. Our study distinguishes itself from other studies by considering the costs of using artificial intelligence in the production chain and in the reverse logistics sector, converting pomegranate waste into recycled products including ethanol for car fuel and organic fertilizer production. In order to examine the research gap and approach real-world applications, an applied example in Iran has been studied. Also, NSGA-II and MOPSO algorithms are used to solve the model, and in the new solution method, the HSA&TS multi-objective hybrid algorithm is proposed. In addition, in the comparison of algorithms, indicators in the one-way variance analysis table, the best time is . Therefore, the practical result show that the combined development algorithm of HSA&TS is a suitable technique and it is superior to other selected methods, it is also recommended, usable and implementable for the development of the logistics network.

The health insurance system can play an effective role to control health expenditures. The purpose of this study is to provide a model for estimating the physician visit tariffs. To achieve this goal, a hybrid model was used. fuzzy logic is the most appropriate tool for controlling systems and deriving rules for the relationship between inputs and outputs. So, the output of the data mining techniques enter the fuzzy logic as an input variable. The data were collected from the Health Insurance Organization of Iran in two sections including the physicians' costs and physicians' deductions. Owing to the techniques used in this model, NN had the least error, as compared to other data mining techniques (0.0034 and 0.0013, respectively). After defining the variables, membership functions and fuzzy logic rules, the accuracy of the whole control model was confirmed by random data. This research has dealt with the domains of health insurance , their connections and defining effective variables better and more extensively than the other studies in the field.

The majority of sustainability assessments of the bio based industries are primarily focused on the environmental and economic aspects, while social impacts are rarely considered. While overlooking social dimension can have a serious harmful impact across supply chains. To address this issue, this study proposes a modified systemic approach for a social sustainability impact assessment of the technology treatment for converting municipal solid waste to bioenergy based on a review on the common methodologies for assessing social impacts. To show the applicability and efficiency of the proposed framework, a sample of 8 experts were used to evaluate and prioritize social sustainability criteria, using a multi-criteria decision-making method called the ‘best worst method’ (BWM). The criteria are ranked according to their average weight obtained through BWM. The results of this study help bio industry managers, decision-makers and practitioners decide where to focus their attention during the implementation stage, to increase social sustainability in their bioenergy supply chains derived waste and move towards sustainable development.

The majority of sustainability assessments of the bio based industries are primarily focused on the environmental and economic aspects, while social impacts are rarely considered. While overlooking social dimension can have a serious harmful impact across supply chains. To address this issue, this study proposes a modified systemic approach for a social sustainability impact assessment of the technology treatment for converting municipal solid waste to bioenergy based on a review on the common methodologies for assessing social impacts. To show the applicability and efficiency of the proposed framework, a sample of 8 experts were used to evaluate and prioritize social sustainability criteria, using a multi-criteria decision-making method called the ‘best worst method’ (BWM). The criteria are ranked according to their average weight obtained through BWM. The results of this study help bio industry managers, decision-makers and practitioners decide where to focus their attention during the implementation stage, to increase social sustainability in their bioenergy supply chains derived waste and move towards sustainable development.

In hot and dry regions, air conditioning is used for many different applications like residential, industry, and agriculture and dairy products. This research studies the applicability of wind and solar energies for cooling fruit storage warehouse in the hot and dry region of Yazd in Iran. The studied case is a fruit warehouse with an area of 4240 m2 resulting in a storage capacity of about 1000 tons. For this purpose, the heat gain of the warehouse is determined, and the obtained cooling load is then used to examine the solar and wind energy to power a conventional warehouse system. Different scenarios are examined for this research such as solar air conditioner, solar absorption chiller, wind catcher, and a combination of solar air conditioners and solar absorption chiller for cooling the fruit warehouse. Comparison and economic evaluation of different scenarios show that the solar air conditioning ranks first for this purpose. Results are then validated using value engineering methodology. Solar air conditioning with the highest net present value (NPV) of 4,865,040,418 Rials and the best internal rate of return (IRR) value of 182.98 % was determined to be the best approach among the studied methods. The results of this research can be applied to other regions with similar climatic conditions too.

Fuel cells are potential candidates for storing energy in many applications; however, their implementation is limited due to poor efficiency and high initial and operating costs. The purpose of this research is to find the most influential fuel cell parameters by applying the adaptive neuro-fuzzy inference system (ANFIS). The ANFIS method is implemented to select highly influential parameters for proton exchange membrane (PEM) element of fuel cells. Seven effective input parameters are considered including four parameters of semi-empirical coefficients, parametric coefficient, equivalent contact resistance, and adjustable parameter. Parameters with higher influence are then identified. An optimal combination of the influential parameters is presented and discussed. The ANFIS models used for predicting the most influential parameters in the performance of fuel cells were performed by the well-known statistical indicators of the root-mean-squared error (RMSE) and coefficient of determination (R2). Conventional error statistical indicators, RMSE, r, and R2, were calculated. Values of R2 were calculated as of 1.000, 0.9769, and 0.9652 for three different scenarios, respectively. R2 values showed that the ANFIS could be properly used for yield prediction in this study

This paper proposes a mathematical model for ride-sharing vehicles with a common destination. A number of cars should assign to individuals by a company to pick up other participants in their way to the common destination. Traveling time as an important parameter is considered an uncertain parameter to enhance the applicability of the model which is formulated using fuzzy programming and necessity concept. Moreover, to have a better solution with better productivity, maximizing the earliest departure time of the individuals is considered beside of minimizing total traveling time. This helps to make justice among individuals for departure time. Goal programming is employed to work with objective functions and solve the model. Furthermore, a numerical example is implemented on the model to evaluate the applicability of the model which indicates the efficiency of employing fuzzy programming and considering both of the objective functions using goal programming. Results of the numerical example indicate the importance of considering both of the objective functions together in which ignoring each of them leads to inefficient solutions.

In this research, the factors affectinguniversity employees’ motivation and productivity are identified and classified in seven groups; the impact of each motivation factor on the productivity is presented by ANP fuzzy model.Eight universities in Iran were analyzed in this research work. The aim of this study is to explore the productivity of employees. This paper attempts to give new insights intodesigning the portfolio factors, motivating employees for productivity improvement by implementing BLP and ANP fuzzy models.The research results show that there is a positive and significant relationship among reward system, motivation factors, and human resources productivity. In addition, among the options of reward system, the factors of internal (inherent) reward, non-financial external reward, and financial external reward had the highestimpact on increasing motivation and productivity factors. At the next stage, a BLP model is designed according to the importance and impact of each reward system option on motivation and productivity factors and organization limitations, including budget, facilities, and conditions to design portfolio factors motivating employees with the aim of improving productivity. The research results show that actualizing performance evaluation, receiving the feedback from the results of doing tasks by different ways, providing an opportunity for all employees to progress, coordination between job specifications and employees’ abilities, and a manager competency are very critical for improving the organization productivity.

Competitive location deals with the problem of locating facilities to provide the service to the customers where other competing facilities offering the same services. Many competitive location models are presented in the literature. However, the literature on competitive location considering reverse logistic in closed-loop supply chain is rather scarce. Also, there are two main approaches in the related literature; increasing profit, and increasing market share of the service centers. Most of the studies in the literature consider one of these issues as the objective function. But, in this study it is shown that these objectives may have conflict with each other. Therefore, addressing these issues simultaneously is important for a successful design of supply chain. This study addresses a novel bi-objective competitive facility location problem in a closed-loop supply chain in a manner that increasing profit and market share are considered simultaneously. On the other hand, in the real world, the customer may choose facilities that are not necessarily close to them, because of the greater attractiveness of other facilities. Hence, in this study, a new relationship is introduced to determine the attractiveness of each potential center for customers based on the distance and quality of service centers. To solve the proposed model and tackle the computational complexity of the proposed model, two approaches are developed: LP-metric and NSGAII. Furthermore, multiple numerical instances are defined and solved with exact approach of LP-metric through GAMS and the results are evaluated in order to validate the accuracy of the proposed model. The best value of “P” in LP-metric approach is also obtained via analyzing the results. Furthermore, the performance of the NSGAII is analyzed by comparing with exact solution of LP-metric through GAMS. The results indicate that the proposed NSGAII is more appropriate than LP-metric and consequently solving the large size problems through GAMS in a logical time is impossible.

The present study aimed at ranking and selecting the superior geothermal project for hydrogen production in 14 provinces of Iran using a multi-objective optimization fuzzy hybrid approach through analyzing the ratio (fuzzy Moora) and expanded entropy weighting method. In this research, the extended entropy weighing method and the Fuzzy-Moora approach were utilized to weigh the criteria and project the ranking, respectively. In this research, 13 criteria for ranking the geothermal projects in Iran have been selected for hydrogen production. At first, the technical-economic feasibility of the projects was carried out in Homer software, and then the ranking process was performed with the proposed hybrid approach. The results showed that among 14 studied provinces using geothermal energy, the provinces of Bushehr, Hormozgan, Isfahan, Mazandaran, East Azarbaijan, Fars, Qazvin, Zanjan, Ardebil, Khorasan Razavi, Kerman, Sistan and Baluchestan, South Khorasan and West Azarbaijan were ranked in that order in terms of hydrogen production.

The solutions used to solve bi-level congestion pricing problems are usually based on heuristic network optimization methods which may not be able to find the best solution for these type of problems. The application of meta-heuristic methods can be seen as viable alternative solutions but so far, it has not received enough attention by researchers in this field. Therefore, the objective of this research was to compare the performance of two meta-heuristic algorithms namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), with each other and also with a conventional heuristic method in terms of degree of optimization, computation time and the level of imposed tolls. Hence, a bi-level congestion pricing problem formulation, for simultaneous optimization of toll locations and toll levels on a road network, using these two meta-heuristic methods, was developed. In the upper level of this bi-level problem, the objective was to maximize the variation in the Net Social Surplus (NSS) and in the lower level, the Frank-Wolfe user equilibrium method was used to assign traffic flow to the road network. PSO and GA techniques were used separately to determine the optimal toll locations and levels for a Sioux Falls network. The numerical results obtained for this network showed that GA and PSO demonstrated an almost similar performance in terms of variation in the NSS. However, the PSO technique showed 45% shorter run time and 24% lower mean toll level and consequently, a better overall performance than GA technique. Nevertheless, the number and location of toll links determined by these two methods were identical. Both algorithms also demonstrated a much better overall performance in comparison with a conventional heuristic algorithm. The results indicates the capability and superiority of these methods as viable solutions for application in congestion pricing problems.

The energy insecurity, environmental pollution, climate change and even reduced rainfall in some countries are prime examples of consequences of the world’s excessive reliance on fossil fuels. This study suggests that in some southern islands and coastal areas of Iran, two such problems, namely the growing shortage of potable water and air pollution can be addressed by building a wind-powered seawater desalination plant at the locations. To evaluate such project, first the sites that may provide the highest efficiency need to be identified. In this study, 10 ports and 5 islands in southern Iran, which suffer from water shortage but have access to seawater, are identified as preliminary candidate sites for such project. The criteria influencing the suitability of a location are considered to be wind power density, economic feasibility, topographic condition, frequency of natural disasters, population, and the wind farm’s distance from desalination facility. After analyzing and weighting the criteria, the locations are ranked using the ELECTRE III method, and the results are validated using the PROMETHEE method. In conclusion, the results of ranking techniques show that Qeshm Island is the best location for construction of a wind-powered seawater desalination plant.

Supply chain management (SCM) addresses the management of materials and information across the entire chain from suppliers to producers, distributors, retailers, and customer. The theory of supply chain management suggests that lead time reduction is a pioneer to the use of market mediation to reduce transaction uncertainty in the chain, which can be conceptualized as the primary goal of supply chain management. In the past few decades, scholars gave ample attention about the impact of inventory on SCM. This paper relates to the development of a lot sizing model for a single component multiple delivery system with variable demand and lead time of a multinational transformer company. Two models and the modification were developed on the basis of the following assumptions. For first model distribution of demand is normal, distribution of procurement lead time is exponential and the quantity is coming in a single lot. For second model distribution of demand is normal distribution of ‘procurement’ and ‘administrative delay’ lead time is exponential and the quantity is coming in a single lot.
Modification of the first model has been done by taking the effect of multiple deliveries in the models and correcting the Re-order point as obtained from the previous models. The results were observed by the second model and analysis has been done for different parametric conditions. The effect of multiple deliveries is also taken into account. The optimum re-order point and economic ordering quantity with various different inputs have been discussed.

Effective preventive healthcare services have a significant role in reducing fatality and medical expenses in all human societies and the level of accessibility of customers to these services can be considered as a measure of their efficiency and effectiveness. The main purpose of this paper is to develop a service network design model of preventive healthcare facilities with the principal objective of maximizing participation in the offered services. While considering utility constraints and incorporating demand elasticity of customers due to travel distance and congestion delays, optimal number, locations and capacities of facilities as well as customer assignment o facilities are determined. First, the primary nonlinear integer program is transformed, then the linearized model is solved by developing an exact algorithm. Computational results show that large-sized instances can be solved in a reasonable amount of time. An illustrative case study of network of hospitals in Shiraz, Iran, is used to demonstrate the model and the managerial insights are discussed.

Manufacturers around the globe are competing for the identification of innovative value propositions to survive in the competitive and complex market. This paper is intended to investigate implementation of Value Engineering (VE) technique into the product design concept for necessary changes in design of the humidifier system in order to lower unnecessary costs and to increase quality of the product. Humidifiers are used for ventilation and cooling the air at most houses in cities located in hot and dry areas. Value Engineering as a systematic attempt is used to increase efficiency of the product, and optimize the life cycle cost. This leads to a shift from traditional design towards new efficient designs. For this study, an 8 stage job plan is used for VE job plan. In this article, different components of humidifier were analyzed thoroughly and then numerous suggestions were made at the brainstorming sessions. Function Analysis System Technique (FAST) was also utilized at the first stage. Main findings lead to a conclusion that there were more suggestions at the brain storming session. Main findings lead to a conclusion that best suggestion for improved design of the humidifier is change the material of fan cover from galvanized iron to hard plastic or fiber plastic.

Aquaculture is among the oldest occupations of human being. Over the past quarter of century, the aquaculture industry has grown rapidly. The effect of water containing sodium sulfate on long term compressive strength of concrete of fishing ponds and channels is investigated in this paper. Aim of this paper was to analyze the strength of concrete channels and of aquaculture which are in direct contact with dissolved sodium sulfate. This is an ongoing laboratory investigation which consisted of 480 standard casting concrete cube mix designs and subjecting them to different curing condition environments. Analyzing laboratory results, it was found that for short period of time, the effect was negligible, but for longer periods up to seven months, EC (electrical conductivity) of water had a low negative effect on compressive strength of concrete, while specimens were placed in waters with different ECs. On the other hand, average compressive strength of concrete was almost 25 kg/cm2 lower than estimated. However, loading the sample concretes up to failure resulted in strength loss of up to 10%. To solve this problem, designed compressive strength must be considered 10% higher than actual in order to have an acceptable concrete strength for water channels and ponds which are in direct contact with sodium sulfate ions in the water.

In order to perform Preventive Maintenance (PM), two approaches have evolved in the literature. The traditional approach is based on the use of statistical and reliability analysis of equipment failure. Under statistical-reliability (S-R)-based PM, the objective of achieving the minimum total cost is pursued by establishing fixed PM intervals, which are statistically optimal, at which to replace or overhaul equipments or components. The second approach involves the use of sensor-based monitoring of equipment condition in order to predict occurrence of machine failure. Under condition-based (C-B) PM, intervals between PM works are no longer fixed, but are performed only “when needed”. It is obvious that Condition Based Maintenance (CBM) needs an on-line inspection and monitoring system that causes CBM to be expensive. Whenever this cost is infeasible, we can develop other methods to improve the performance of traditional (S-R)-based PM method. In this research, the concept of Bayesian inference was used. The time between machine failures was observed, and with combining Bayesian Inference with (S-R)-based PM, it is tried to determine the optimal checkpoints. Therefore, this approach will be effective when it is combined with traditional (S-R)-based PM, even if large number of data is gathered.