amir-mohammad golmohammadi

This study proposes a multi-objective location-routing problem considering the capacity of vehicles to decline the system's costs. The model considers probabilistic times of traveling, service, and waiting by vehicles while guaranteeing the least probability which the cumulative values of these parameters are less than a pre-determined value when minimization of this value is considered an objective function.  To cope with uncertainty, fuzzy numbers for important parameters of customer demand, vehicle capacity, variable and fixed transportation costs, and depot opening costs are used. Moreover, the nonlinear constraints are linearized to reduce computational time. We also use a fuzzy ranking method to transform the presented model into an equivalent auxiliary crisp model. As the model is NP-hard, we introduce a novel Multi-Objective Imperialist Competitive Algorithm (MOICA) to address the issue. The efficacy of the presented MOICA is evaluated by comparing its performance against two well-established multi-objective metaheuristics, Pareto Archived Evolution Strategy (PAES), and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). Leveraging Response Surface Methodology (RSM), the mutation and crossover operators employed by each algorithm were meticulously tuned. Subsequently, the performance of all three algorithms was examined using four benchmark comparison metrics across a range of established benchmark examples. The results demonstrably substantiate the superiority of the proposed MOICA in achieving optimal solutions.

One of the main aspects of the production industry is the optimization of acceptance sampling plans. Sampling plan performance depends on many uncertain factors that are difficult to model, especially in a multi-stage process. Therefore, it requires an innovative procedure to optimize it. This study presents an innovative double sampling plan for a multi-stage process based on discrete event simulation (DES) toward proposing an applicable plan to the inspection of the product whose accepting probability follows the hypergeometric distribution for a finite lot without replacement. This paper focuses on the five economic parameters of a double sampling plan that are determined by minimizing the average sample number (ASN) with the help of DES results and optimization methods. Several experiments based on DES were tested to determine the regression function of the ASN simulation study was carried out using Enterprise Dynamic software (ED). Our economic statistical lot acceptance sampling plan based on minimizing the average sample number has been developed to determine the acceptance parameters including the first acceptance number, first rejection, number, first sample size, second sample size, and second acceptance number in a multi-stage process. According to all runs of the simulation model, we concluded at a 95% level of confidence that ASN ranges from 530.16 to 554.93, which is given in detail in the paper.

Today, the progress of industries depends on quality optimization, and improving production processes through setting controllable parameters of the process can greatly help this. The present research shows the practical experience gained in the development of the resistance welding quality of a pressure vessel, in which the well-known Taguchi method is used in the design of experiments to minimize the effect of uncontrollable factors on the target value. In this research, the pressure between the electrodes, the size and shape of the electrodes and the working surface (smoothness and cleanliness) were considered as the most important controlled variables. By conducting the designed test in 5 repetitions, the quality of the resistance welding improved significantly. It was observed that among the parameters affecting the quality of spot welding, the welding time has the greatest effect on the strength of the weld. The investigation of the current intensity showed that the increase of this factor as well as the welding time decreases the hardness of the welding center.

Enhancing the efficacy and productivity of transportation system has been on the most common issues in recent decades, noteworthy to the industrial managers and expert so that the products are delivered to the clients at right time and the least costs. Therefore, there are two important issues; one is to create hub as the as intermediaries for streaming from multiple origins to multiple destinations and also responding to the tours of every hub at the proper time. The other is a route where the vehicles should pay at time window of each destination node. On the other hand, these problems may cause cost differences between hub and interruption of their balance. Accordingly, this paper presents a model dealing with cost balancing among the vehicles as well as reducing the total cost of the system. Given the multi-objective and NP-Hard nature of the issue, a multi-objective imperialist competitive algorithm (MOICA) is suggested to provide Pareto solutions. The provided solutions are at small, average and large scales are compared with the solutions provided by Non-Dominated Sorting Genetic Algorithm (NSGA-II) algorithm. Then, its performance is determined using the index for evaluating the algorithm performance efficacy to solve the problem at large dimensions.</span></span></span></span></span></span>

The circular economy is one of the most important issues in the optimal use of resources all around the world. The combination of circular economy and supply chain creates a new concept called circular supply chain, which seeks to increase the efficiency of the supply chain by making the best use of resources. In this research, the main purpose is to apply a hybrid Multi-Criteria Decision-Making (MCDM) method to evaluate the effective factors in implementing the circular supply chain. First, the effective factors in the field of the circular supply chain are identified, and in the next step, the weight of the factors is obtained by implementing the Analytic Hierarchy Process (AHP) method. Next, the intensity of the effect of each factor is calculated. Moreover, the correlation between the factors affecting the circular supply chain and the effectiveness of the factors is analyzed using the Decision Making Trial and Evaluation Laboratory (DEMATEL) method. Finally, using the Simple Additive Weighting (SAW) method, the most important factors in the implementation of the circular supply chain are identified. The core results of this research show that the quality of final products is the most important factor in implementing a circular supply chain. Moreover, applying the circular economy approach leads to the zero-waste goal, which can increase the efficiency of supply chains.

In the industrial world today, manufacturing units are trying to locate your requirements and the depot vehicle routing in order to transport the goods for reduce your cost. Needless to mention that the location of the warehouse is effective for vehicle routing. Therefore, in this paper, a mathematical programming model to optimize the storage location and vehicle routing are presented. The objective function of the model is minimizing the total cost associated with the transportation and storage of rental fee. Limitations of the model include vehicle capacity, the maximum distance traveled by vehicles and etc. In addition, labor costs, such as salaries, rent, warehouses, rental vehicles and etc. Approach to model the real world has been considered.Also, since each location and routing issues alone are a NP-hard problem, then location routing problem can be combined problem and It requires the use of meta- heuristic algorithms to solve.

Development of supply chains is one of the practical concepts in the field of production and sales in competitive conditions. Accordingly, it is necessary to properly study the competitive conditions in which supply chain networks can be designed. In this regard, the present research contributes to the field by incorporating the market share and customer satisfaction to the competitive conditions of supply chains. For this purpose, a nonlinear mathematical model is presented in order to find locations and perform distributions in a closed-loop supply chain under competitive conditions. This model has two objectives including profit maximization and market share maximization. To solve the model, LP-metric and goal programming are implemented, and then the results of these two methods are discussed. Comparisons are also made in terms of the value of the objective functions as well as the solution time. Finally, the simple weighted sum method is used to select the superior method. The results show that the LP-metric method is worth performing to solve the mathematical model of the research.

Today the competitive position of organizations is defined according to the capabilities, skills, and knowledge embedded in their organizational processes. The business processes are one of the key assets of organizations. However, the lack of available resources and simultaneously the differences between processes from different aspects, indicate the need for prioritization of improvement projects in organizations. These differences are expressed in various aspects such as output value, level of impact in the organization, complexity, and the time and capital required for improvement projects. To analyze the processes from these aspects, it is often necessary to obtain the experts’ judgments. For this purpose, the present study employs the fuzzy hierarchical process based on a combination of hesitant fuzzy sets and type-2 fuzzy sets. This approach has been applied in the Markazi electricity power Distribution Company. The proposed approach provides a flexible structure to obtain the experts’ opinions and the uncertainty and ambiguity embedded in these opinions. Meanwhile, it provides an organizational group decision-making process without simplifying and reducing the complexity of verbal judgments. The results indicate the high priority of these business processes to implement the improvement projects: the operation and maintenance of the power distribution network, the construction, and development of the distribution network, and the financial management process.

Supply chain field has always been an aspiration for competitiveness in manufacturing organizations. Any organization’s conditions can be judged based on several criteria, such as robustness, rapid reconfiguration, lead time compression, etc. These criteria effectively determine the kind of supply chain strategy; it should be noted that this strategy varies in different markets and industries. Therefore, considering an appropriate strategy for the supply chain is an essential issue for most managers. Hence, this study, using a fuzzy expert system, shows how to select the best supply chain strategy. Three popular strategies, i.e., lean, agile, and leagile, are the main elements in this research. In addition, five applicable criteria are applied for selecting a supply chain strategy. A fuzzy expert system based on if-then rules was designed to connect these criteria to three strategies and select an appropriate supply chain strategy. Hence, criteria and supply chain strategies are taken to be input and output of this expert system, respectively, which lead to faster decision-making in the selection of supply chain strategy.

The fundamental function of a cellular manufacturing system (CMS) is based on the definition and recognition of the type of similarity among the parts that should be produced in a planning period. Cell formation (CF) and cell/machine layouts are two major steps in implementing the CMS design. This paper aims to propose a new mathematical nonlinear programming model for cell formation that employs the rectilinear distance concept to determine layout in a continuous space. In the proposed model, the benefits of cellular layout consideration are used, and the objective function computes the cost of cell reconfiguration and the costs of intra-cell and inter-cell material handling movements. Due to its problem complexity, a genetic algorithm (GA) and a particle swarm optimization (PSO) algorithm are proposed to solve the problem. To address the efficiency of the linearized model and solution methods, the production information of a real case study is used and 30 test problems in different dimensions are presented.

 Design/methodology/approach:

 In this paper, a mathematical programming model of cell formation and cell layout has been proposed in a continuous space, using the concept of rectilinear distance. In the proposed model, production information similar to the production flow between machines, alternative process routing, cells capacity, and the inter-cell and intra-cell transportation costs has been considered. Due to the nature and complexity of the proposed model, two metaheuristic algorithms, i.e., GA and PSO have been also developed for larger problems. 

In this paper a real case study in the BATA company was studied and the result of configuration was illustrated. By computing the efficiency of the linearized model and solution methods, the production information of a real case study was used and 30 test problems in different dimensions were presented. Findings highlighted the high efficiency of the genetic algorithm in solving large-scale problems. 

Research limitations/implications:

 Issues such as assuming similar dimensions for machines or their constant availability are considered as the limitations of this study. For future research, the following subjects can be attractive and the present study can provide the necessary background for researchers who seek to work on such subjects:Considering unequal dimensions for machines; in the proposed model, machines were considered as squares of equal area with unit dimension. To obtain a more appropriate schema from the space of a job shop, machines’ dimensions can be assumed as the input parameters.Developing probabilistic models and fuzzy model factors (e.g., available machines, operation time, costs, transportation time, and demand for each part) can be considered fuzzy or probabilistic.To make the model more realistic, production data such as setup times and holding inventory between periods can be incorporated. Also, the proposed model can be integrated with the scheduling problem. 

Practical implications:

 CMS, which is the most important application of GT, overcomes the inefficiency of traditional approaches by reducing transportation time and distance. A flow shop layout has high efficiency in a mass production system, while a job shop is a very flexible system for producing various parts. Each of these systems does not have any other benefits. The CMS is an approach between the two manufacturing systems and aims to improve flexibility and efficiency to produce manufacturing groups in different sizes. In a CMS, machines and parts assignment to cells must impose a minimum cost on the system. Due to the practical nature of the proposed mathematical model, a real case was studied in which, the production information of the BATA company was used. 

Originality/value: 

In this study, a new non-linear mixed-integer programming model was proposed which considered the simultaneous cell formation and intra-cell and inter-cell layouts in a continuous space. The model aimed to determine the cell formation and the intra and inter-cell layout concurrently in a way that the total transportation cost of parts and the reconfiguration cost of cells were minimized. The proposed model attempted at calculating the material handling costs realistically. The material handling cost was computed based on the actual location of machines and cells on the shop floor regarding the dimensions of equal-sized machines. Handling both intra/intercellular materials using batch sizes for transferring parts was taken into account in calculating the transportation cost. The transportation cost was calculated based on the rectilinear travel distance, according to the center-to-center interval among machines.

The study aims to present a mathematical model for the reduction of the system costs through the proper location of the required warehouses and the routing of vehicles that carry the products from the warehouses within a time window.

With regard to the specific nature of a location-routing problem, the consumption of fuel and the depreciation of vehicles are directly affected by the distance covered. The model proposed in this research seeks to minimize the undue length of the distance that vehicles have to travel. Moreover, for the approximation of the model to real-world conditions as much as possible, the concept of ‘time window’ is employed to determine the maximum allowable time for the distribution of goods.

Three metaheuristic algorithms including NSGA-II, PAES and MOICA are used to solve the proposed model. To evaluate the efficiency of the solutions, several problems of different sizes are introduced and solved, and then the results are compared in terms of the SM, MID and QM criteria. The comparative results suggest the superiority of the MOICA algorithm for big-size problems.

Setting a time window for the reduction of the distance traveled by the vehicles gets the model close to real-world conditions. It also makes it possible to estimate the costs more accurately.

The main objective of designing the supply chain is to increase profitability. For this reason, a state-of-the-art model of a three-echelon closed-loop supply chain is proposed that consists of the manufacturer, retailer, and collection centers. For the first time, a new separate and autonomous channel is considered in this model for the sale of Reman products aiming at increasing the manufacturer's profitability. In this model, location, inventory, and pricing of the product are also taken into consideration. Lingo software is utilized to solve nonlinear objective functions at a small scale, and metaheuristic genetic algorithms and particle swarm optimization were utilized to solve at a large scale. The research results depict that the state-of-the-art model design of the closed-loop supply chain network is credible and the optimal location of supply chain components, optimal response of product flow, and product price are determined in a proper manner. As a result, the profitability of the whole closed-loop supply chain network increased. Sensitivity analysis of mathematical model depicts that this model shows higher sensitivity to retailer replacement factor, price and purchasing power of collection centers. Also, the genetic algorithm shows better performance on a large scale in terms of response quality. On the other hand, the time required by particle swarm optimization to reach a response is far better than the genetic algorithm. Ultimately, practical suggestions for the managers are presented considering the state-of-the-art model design of a closed-loop supply chain network.

The subjects of reliability acceptance sampling plans and failure-censored life tests have usually been investigated from the viewpoint of statistical properties; indeed, few researchers have shed light on the economic aspects of these issues. In this research, a constrained mathematical model is developed to optimally design a reliability sampling plan under failure censoring life testing. Minimizing the expected total cost (ETC) involved in the sampling and life testing is considered as the objective function of the model. Ensuring the producer’s and the consumer’s risks is taken into consideration as the constraint of the model. To minimize the ETC, the model optimally determines three decision variables including the total number of the items put to the life test, the number of the failed items to terminate the test, and a criterion to make decisions about the acceptance or rejection of the lot. Examples are provided and analyses are conducted to gain some insight regarding the model performance.

The facility layout design and Cell Formation (CF) problems are the important sectors in designing a cellular manufacturing system (CMS). These problems are interrelated and simultaneous consideration of them is essential for a successful design of CMS. In this paper, a new non-linear mixed integer programming model is presented to solve the integrated cell formation and inter/intra cell layouts in continuous space. The proposed approach incorporated machine reliability with a stochastic time between failures. Some important factors such as stochastic process time, part demand, cell size, variable process routing, and both inter-cell and intra-cell layout are considered in proposed model. The objective is to minimize the total inter/intra cell transportation cost and total breakdown cost. The proposed model is then linearized to reduce computation time and an exact solver by using GAMS is proposed to tackle the computational complexity of the developed model. Results indicate the efficiency and the application of proposed model in the area of CMS conceptually

There is still a great deal of attention in cellular manufacturing systems and proposing capable metaheuristics to better solve these complicated optimization models. In this study, machines are considered unreliable that life span of them follows a Weibull distribution. The intra and inter-cell movements for both parts and machines are determined using batch sizes for transferring parts are related to the distance traveled through a rectilinear distance. The objectives minimize the total cost of parts relocations and maximize the processing routes reliability due to alternative process routing. To solve the proposed problem, Genetic Algorithm (GA) and two recent nature-inspired algorithms including Keshtel Algorithm (KA) and Red Deer Algorithm (RDA) are employed. In addition, the main innovation of this paper is to propose a novel hybrid metaheuristic algorithm based on the benefits of aforementioned algorithms. Some numerical instances are defined and solved by the proposed algorithms and also validated by the outputs of exact solver. A real case study is also utilized to validate the proposed solution and modeling algorithms. The results indicate that the proposed hybrid algorithm is more appropriate than the exact solver and outperforms the performance of individual ones.

Joint economic lot sizing (JELS) addresses integrated inventory models in a supply chain. Most of the studies in this field either do not consider the role of the transportation cost in their analysis or consider transportation cost as a fixed part of the ordering costs. In this article, a model is developed to analyze an incremental quantity discount in transportation cost. Appropriate equations are derived to compute the costs related to the inventory systems in the buyer and vendor sites. Then, a procedure including five steps is proposed to optimize the model and determine the values of the decision variables. To analyze the performance of the incremental discount, the JELS problem is studied in two other states of transportation costs.  These states include fixed transportation cost and all-unit quantity discount.  Moreover, some numerical analyses are carried out to show the impact of transportation costs and inventory-related parameters on the system performance. According to the results of the sensitivity analyses, it is observed that all-unit quantity discount leads to a better performance of the system in comparison with the incremental quantity discount.

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

The fundamental function of a cellular manufacturing system (CMS) is based on definition and recognition of a type of similarity among parts that should be produced in a planning period. Cell formation (CF) and cell layout design are two important steps in implementation of the CMS. This paper represents a new nonlinear mathematical programming model for dynamic cell formation that employs the rectilinear distance notion to determine the layout in the continuous space. In the proposed model, machines are considered unreliable with a stochastic time between failures. The objective function calculates the costs of inter and intra-cell movements of parts and the cost due to the existence of exceptional elements (EEs), cell reconfigurations and machine breakdowns. Due to the problem complexity, the presented mathematical model is categorized in NP-hardness; thus, a genetic algorithm (GA) is used for solving this problem. Several crossover and mutation strategies are adjusted for GA and parameters are calibrated based on Taguchi experimental design method. The great efficiency of the proposed GA is then demonstrated via comparing with particle swarm optimization (PSO) and the optimum solution via GAMS considering several small/medium and large-sized problems.

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.