فهرست مطالب mohammadmahdi paydar

In the two last decades, the analysis of the agriculture supply chain (ASC) has been paid attention by academics and practitioners. However, the issue of coordinating ASC is not considered so much where weak collaborations make lower profits and efficiencies. In this research, the distribution of profits and coordination of a three-level ASC including a gardener, a major buyer, and a retailer are investigated. The problem has been developed for both centralized and decentralized models. The optimal strategies of ASC are obtained for both models. In this study, the wholesale price contractual mechanism is investigated where the buyer’s and retailers’ wholesale price and the farm size are decision variables to find win-win situations under coordination. The proposed models were solved and sufficient propositions were developed. The numerical study is illustrated. The results show that with increasing farm area, the optimal harvest amount per unit area decreases for the centralized model. Furthermore, with increasing farm size, the gardener harvest amount per unit area and the supply amount increase where the retail selling price is almost constant for both cases. At the same level, with increasing farm size, the supply chain profit increases. In decentralized analysis with a wholesale price contract, the gardener's harvest amount per unit area and the supply amount increase by increasing the gardener's wholesale price. Moreover, by increasing the gardener’s wholesale price, its profit increases, and the profit of the buyer and the retailer decreases. For future studies, analyzing and comparing other coordinating contracts, such as revenue sharing and traditional ASCs contracts is proposed.

Having a good stock portfolio, which is one of the most important factors in making money in the stock market, requires the correct choice of criteria. This issue for new stock traders in the Tehran Stock Exchange (TSE) who do more than 50 percent of daily transactions in this market, due to their lack of sufficient experience, seems thoroughly essential. As a result, newcomers who were trading in the Tehran Stock Exchange in 2020 have been invited to participate in this study. After identifying the most influential variables in portfolio selection via the Delphi method, these factors have been ranked based on Shannon’s Entropy Approach. The results show that Familiarity, Net Profit Ratio, and Stock Price are respectively the main priorities of new entrants in choosing the stock portfolio. Besides, risk-related variables have generally the least importance in stock portfolio selection from the perspective of new entrants.

This study proposes a multi-stage supply chain model with direct and reverse flows of goods to assess the effects of risk on the profit of a supply chain network and the realization of demand. The studied network aims to maximize profit, minimize unmet demand, reduce delivery time, alleviate disruption risks in facilities and transportation, and decrease supply chain visibility. We created a system for quantifying the disruption risk ratings of supply chain components. To help the company better understand its suppliers, address essential network components, and prioritize risk management initiatives, the evaluation may be useful. For our supply chain optimization models, we rely on the predicted disruption risk ratings as a basis. Goal programming is used to solve the multi-criteria model. The resiliency of the supply chain network is shown numerically. In order to build the model, the designer had to make strategic judgments. Risk mitigation methods such as extra inventory and backup suppliers are adopted to increase the supply chain network’s resiliency. Short-term disruptions may be mitigated by stockpiling additional raw materials to avoid component shortages. A cost-benefit analysis shows that every risk reduction strategy is worthwhile.

Supply chain management is one of the requirements for achieving economic growth in any supply chain. If managers' decisions are optimally allocated, it will be possible for companies and industries with a competitive and profitable advantage to grow and develop. The main desire of any company for survival is to minimize costs and maximize profitability. Due to the increasing complexity and dynamics of the situation, decision-making in this area requires more advanced analytical methods. Accordingly, the Real options theory has emerged, which introduces a new way of thinking about investing, especially in conditions of uncertainty. In this paper, a multi-period model is considered that examines the demand uncertainty in each period and also uses the Real options theory to seek the optimal strategy for investors in conditions of uncertainty and the effect of investors’ discretion on it. Using a decision tree to estimate the probable demand in each period and using Monte Carlo simulations to identify the lowest cost scenario in each period, the model has been solved in this research. In the case of the uncertainty parameter, sensitivity analysis is performed, and under different values ​​of this parameter, the obtained result is evaluated and validated. And the extension of outsourcing will increase the company’s profitability and meet higher demand and lower costs.

The design and establishment of a logistics network is a strategic decision that lasts several years to work and the parameters of customer demand and return may be changed during this time. Therefore, an efficient logistics network should be designed in a way that can respond to uncertainties. The applications of such a network can be found in different industries like the battery industry. This study aims to determine the number of products sent among the centers at each time so that the total cost of reverse logistics and delay time is minimized. To address the uncertainty in the reverse logistics network (RLN), a fuzzy programming method is utilized. To tackle the complexity of the problem, the cuckoo optimization algorithm (COA) and genetic algorithm (GA) were developed. To compare these two optimization algorithms and find the superiority of them, a series of problem instances were generated. The obtained results demonstrated a satisfactory efficacy for both meta-heuristic algorithms. It was also revealed that the sum of values sent to the main manufacturer is equal to the values obtained from the exact solution method.

Companies and firms, nowadays, due to mounting competition and product diversity, seek to apply virtual cellular manufacturing systems to reduce production costs and improve quality of the products. In addition, as a result of rapid advancement of technology and the reduction of product life cycle, production systems have turned towards dynamic production environments. Dynamic cellular manufacturing environments examine multi-period planning horizon, with changing demands for the periods. A dynamic virtual cellular manufacturing system is a new production approach to help manufacturers for decision making. Here, due to variability of demand rates in different periods, which turns to flow variability, a mathematical model is presented for dynamic production planning. In this model, we consider virtual cell production conditions and worker flexibility, so that a proper relationship between capital and production parameters (part-machine-worker) is determined by the minimum lost sales of products to customers, a minimal inventory cost, along with a minimal material handling cost. The problems based on the proposed model are solved using LINGO, as well as an epsilon constraint algorithm.

Nowadays, the main attention and tendencies of tourists are towards ecotourism and its related activities. Ecotourism centers are an active business in the ecotourism industry that provides necessary services for tourists. Today, the number of ecotourism centers in some areas is increasing and it caused an intensively competitive environment between these centers to attract customers. In other words, the big concern of the manager and owners of these centers is knowing the variables which affect the decisions of tourists to choose an ecotourism center. From what we know, no research has been done on this subject. Therefore, these matters encouraged us to do an essential investigation. In this paper, firstly, the factors affecting a tourist's decision to choose an ecotourism center are identified. Three types of customers (tourists) are considered for ecotourism centers and the weight of each of the considered factors is calculated using the fuzzy DEMATEL method based on the opinion of each customer group. Then, the factors are ranked using the TOPSIS method. The outcomes manifest that the personnel of the centers and the brand of each center have the most and the least impact on the selection process by the tourist, respectively.

New concepts of -feasibility and -efficiency of solutions for fuzzy mathematical programming problems are used, where  is a vector of distinct satisfaction degrees. Recently, a special kind of fuzzy mathematical programming entitled Fuzzy Flexible Linear programming (FFLP) is attracted many interests. Using the mentioned concepts, we propose a two-phase approach to solve FFLP. In the first phase, the original FFLP problem converts it to a Multi-Parametric Linear Programing (MPLP) problem, and then in phase II using the convenient optimal solution with the higher feasibility degree is concluded. Using this concept, we have solved the problem of the animal diet. In the process of milk production, the highest cost relates to animal feed. Based on reports provided by the experts, around seventy percent of dairy livestock costs included feed costs. In order to minimize the total price of livestock feed, according to the limits of feed sources in each region or season, and also the transportation and maintenance costs and ultimately milk price reduction, optimization of the livestock nutrition program is an essential issue. Because of the uncertainty and lack of precision in the optimal food ration done with existing methods based on linear programming, there is a need to use appropriate methods to meet this purpose. Therefore, in this study formulation of completely mixed nutrient diets of dairy cows is done by using a fuzzy linear programming in early lactation. Application of fuzzy optimization method and floating price make it possible to formulate and change the completely mixed diets with adequate safety margins. Therefore, applications of fuzzy methods in feed rations of dairy cattle are recommended to optimize the diets. Obviously, it would be useful to design suitable software, which provides the possibility of using floating prices to set feed rations by the use of fuzzy optimization method.

This study considers outsourcing decisions in a flexible flow shop scheduling problem, in which each job can be processed by either an in-house production line or outsourced. The selected objective function aims to minimize the weighted sum of tardiness costs, in-house production costs, and outsourcing costs with respect to the jobs due date. The purpose of the problem is to select the jobs that must be processed in-house, schedule processing of the jobs in-house, and finally select and assign other jobs to the subcontractors. We develop a mixed-integer linear programming (MILP) model for the research problem. Regarding the complexity of the research problem, the MILP model cannot be used for large-scale problems. Therefore, four metaheuristic algorithms, including SA, GA, PSO, hybrid PSO-SA, are proposed to solve the problem. Furthermore, some random test problems with different sizes are generated to evaluate the effectiveness of the proposed MILP model and solution approaches. The obtained results demonstrate that the GA can obtain better solutions in comparison to the other algorithms.

Natural crises Threaten Human life and property every year. So planning for disaster Preparation is essential. Flood Threatens Thousands of People around the World. Flood damage is different in different areas. Therefore, identifying and classifying floodplains in each region is one of the measures that can be taken to manage and reduce flood damage. By identifying and prioritizing flood vulnerable areas can Reduce flood damage. In this study pre-identified flood-vulnerable areas in Amol city are prioritized according to criteria such as population density, distressed areas, distance from rivers and access to cities and roads. Using analytic hierarchy process (AHP) five flood-vulnerable areas are prioritized. Then, a bi-objective mathematical model is provided to determine the best locations to set up relief sites and the amount of relief goods and machines required as preparation for quick disaster response. Finally, solutions are provided to increase the allocation to areas with higher accountability priorities and lower costs.

The purpose of this study is to provide a model for evaluating the flexibility between different components of the supply chain. Supply chain flexibility is presented and its significance is examined. This is trailed by a survey of the flexibility literature. Next, devices for assessing flexibility are talking about: Decision analysis, decision trees, and real options. This paper analyzes how firms got to be get arranged against unsettling impact in their supply chain and centers on how they can utilize real options to respect twofold sourcing procedures. As a presentation, it characterizes a common structure to assist chiefs with considering around store orchestrate interruptions and select adjusted systems. In the first place it sets up a general survey of the conceivable answers for overseeing chance interruptions. It centers on the specific approach of twofold sourcing and outlines that the real options thought may be a adjusted contraption to overview such a technique and builds up an instructive appear to look at and respect the focuses of intrigued of depending on twofold sourcing.

Here, a new mathematical model for cellular manufacturing systems considering three important features of part priority, levels of machine’s technology, and the operator’s skill is developed. Simultaneous consideration of these features provides a more realistic analysis of the problems in cellular manufacturing systems. A model with multiple design features including cell formation, human resources flexibility with different skills, machines flexibility, operational sequence, processing time, and the capacity of machine and manpower is proposed in this article. Ourfocus is on the design of cells to implementtwo dissimilar goals. The first goal is the reduction of inter-cellular movements of parts and workers. The second goal is the creation of efficient cellsby making cell’s quality level identical for produced products so that the production of all the different parts have good quality. Two approaches of augmented ε-constraint and non-dominated sorting genetic algorithm II (NSGA-II) are used to solve this model. By comparison of these two approaches, we realizethat the multi-objective evolutionary optimization algorithm creates a Pareto-optimal front in a reasonable amount of time forlarge-scale problems

This paper presents a structure that empower designing supervisory groups to survey the estimation of real options in projects of enormous scale, incompletely standardized frameworks actualized a couple of times over the medium term. Specific options writing is done using a methodology of planning the design and making prior decisions regarding the arrangements of specific options, with a recreation-based value measure designed to be near-current construction rehearsals and to resolve financial problems in particular cases. To study the case and demonstrate the actual application of this method, drug chain modeling at the tactical level was investigated. The physical and financial flow and their disturbance are simultaneously modulated. In order to complete the financial flow, financial ratios are also entered into the model. Problem uncertainty has been modeled using one of the most recent robust optimization approaches called Robust Possibilistic Programming (RPP) in combination with real options theory. The model was applied to a case study and its results were analyzed and validated by GAMS software. The results show that without violating the limitations of the problem, it shows appropriate decisions to deal with the problem.

Nowadays, factories should be coordinated with changes in the dynamic environment due to the intense competition in the businesses. Different strategies and systems are existing to help factories in a dynamic situation. In this article, a new multi-objective mathematical model is presented by the implementation of dynamic virtual cellular manufacturing and also considering new product development which enables factories to be successful in their business. This paper contains three objectives including maximizing the total profits of the factory in all the periods, the grouping efficacy and also the number of the new product. After linearization of the proposed model, multi-choice goal programming with utility function is used to solve the model. In addition, a case study has been conducted in the real world to show the effectiveness of the proposed model and finally, the results show that the integration of virtual cellular manufacturing with new product development can be helpful for managers and companies and leads to more efficiency.

Supplier selection, order allocation and production planning are important and challenging decisions in supply chain management. There are many studies on mentioned topics separately. In this paper, a multi-objective mathematical model proposed to optimize a sustainable supplier selection problem with order allocation and production planning simultaneously. This study considers a multi-supplier, multi-product, multi-item and multi-period supply chain. The designed mathematical model seeks to maximize total profit and minimize unsatisfied demand and total risk along with enforcing sustainability criteria in selecting suppliers. Supplier selection is a virtual process in every manufacturing company. On the other hand, this research considers all the important aspects of this problem. Therefore, the proposed framework can be implemented in many different companies like electronic, food, chemical industry. The proposed model is solved utilizing two metaheuristic algorithms including NSGA II and MOPSO. Moreover, algorithms are tuned utilizing Taguchi analysis. Furthermore, ten sample problems are generated and results are compared to identify the best algorithm for the proposed model.

Cell formation problem (CFP) is one of the main problems in cellular manufacturing systems. Minimizing exceptional elements and voids is one of the common objectives in the CFP. The purpose of the present study is to propose a new model for cellular manufacturing systems to group parts and machines in dedicated cells using a part-machine incidence matrix to minimize the voids. After identifying the exceptional elements, the machines required for processing the remained operations of corresponding parts which are not processed in the dedicated cells are specified. This results in a new matrix called part family-machine. Then, by clustering the part family-machine incidence matrix, the part families that should be assigned to a specific cell to achieve the highest similarity can be determined. The similarity can be translated to sharing machines required for completing the processes and form new cells called shared cells to minimize the number of exceptional elements and voids. Unlike previous models in which the similarity is considered only in the dedicated cells, in the proposed model, the similarity would be monitored and observed in the entire production process. Due to the complexity of our model, two meta-heuristic algorithms including artificial immune system (AIS) and simulated annealing (SA) are proposed. The efficiency of the algorithms is compared to that of exact solutions. Also, the algorithms are compared regarding the quality of solutions. Finally, according to grouping efficacy measure, SA algorithm has a superior performance in comparison with AIS by spending less CPU time.

In this study, tactical decisions considering the material and financial flows in a supply chain have been made. To achieve these aims and some effective solutions, a multi-objective mathematical model proposed for an integrated supply chain master planning problem. The multi-product, multi-period and capacitated supply chain network has three objective functions. Two first objective functions are maximizing the net present value of manufacturing centers and suppliers’ cash flow, and the third one minimizes the market price of the final product. Besides we considered the market price as a key variable in the model and investigate its effects. Then, improved multi-choice goal programming is used to transform the multi-objective model to its single-objective one. To find out the appropriateness of the proposed model, the results of an industrial example are illustrated, and sensitivity analyses to evaluate the results are provided to obtain better insight and access to different aspects of the problem.

New product development is an essential requirement in any company and every company needs to develop its technology and products to survive in a competitive market. Most organizations nowadays have more than ever realized that relying on traditional competitive levers such as quality enhancement, cost reduction and differentiation is not enough to provide products and services, but it is important to pay attention to new product entry as well as early product exit. In this research we examine supply chain network design when a new product is added to the product line. Also, the important topic of customer relationship management, which is one of the factors driving the increase in product sales as a result of profitability for the organization, has been designed as a mathematical model. Finally, an improved version of the benders decomposition algorithm is presented as the accelerating benders for the proposed problem. The computational results show the superior performance of the solution method.

Regarding population growth and prompt development in developing countries, municipal solid waste management is always a great challenge for governments. Waste to energy conversion is an efficient approach with respect to overcoming not only the challenge of municipal solid waste management but also environmental challenges related to energy consumption like global warming and fossil fuel depletion. One of the substantial problems throughout the implementation of waste to energy approach is process selection. The selected process should be technically feasible and should have a high level of compliance with environmental standards. Owing to an inevitable significance of process selection, this paper focuses on defining the best process by relying on multi-criteria decision-making tools and network analytic process. Considering the effective parameters such as cost, efficiency in material diversity, productivity rate, energy consumption, pollutant emissions, toxic substances, and process time, the result indicates that the physico-chemical process is superior process for pretreatment of material.

To enhance agility and quick responding to customers' demand, manufacturing processes are rearrenged according to different systems. The efficient execution of a manufacturing system depends on various factors. Among them, cell design and human issue are the pivotal ones. The proposed model designs cellular manufacturing systems using three objective functions from three different perspectives, to reflect a more realistic picture of the cell formation problem. This paper presents a model with the goals of maximizing the total value of grouping efficacy and minimizing the total costs and total non-interest workers in cells in a dynamic environment for several consecutive periods.  The main idea of the proposed model is enhancing cell efficiency through an assigning the group of workers who have a mutual interest in working with each other. For solving the current model, the revised multi-choice goal programming method has been employed. Finally, computational results and sensitivity analysis are discussed.

In the recent decades, tourism industry has considered as the most important industry and has grown dramatically therefore, the share of international tourism in the global economic activities are continuously increasing. Today, the tourism industry is too important in the economic and social development of a country thus economists call it invisible exports. Ecotourism is a type of tourism in which tourists travel to visit and enjoy the world's undisturbed natural areas and watch plants, birds, fish, and other animals.The objectives of ecotourism are educating and making the ecotourists ready to protect and respect ecosystems and natural resources and to become familiar with diverse cultures, beliefs and human rights. Also ecotourism plays a major role in the economic and political development of local communities. According to the importance of ecotourism, this study presents a mathematical model for ecotourism supply chain. The proposed model is single-objective one which maximizing the chain’s profit. Due to the demand uncertainty, the fuzzy method is used to deal with uncertainty. An applicable numerical example was also presented and the mathematical model was solved by Lingo software and the results were examined.

The present study aimed to design a bi-objective bi-level mathematical model for multi-dimensional cellular manufacturing system. Minimizing the total number of voids and balancing the assigned workloads to cells are regarded as two objectives of the upper level of the model. However, the lower level attempts to maximize the workers' interest to work together in a special cell. To this aim, two nested bi-level metaheuristics including particle swarm optimization (NBL-PSO) and a population-based simulated annealing algorithm (NBL-PBSA) were implemented to solve the model. In addition, the goal programming approach was utilized in the upper level procedure of these algorithms. Further, nine numerical examples were applied to verify the suggested framework and the TOPSIS method was used to find the better algorithm. Furthermore, the best weights for upper level objectives were tuned by using a weight sensitivity analysis. Based on computational results, all three objectives were different from their ideal goals when decisions about inter and intra-cell layouts, and cell formation to balance the assigned workloads by considering voids and workers' interest were simultaneously madeby considering a wide assumption-made problem closer to the real world. Finally, NBL-PBSA could perform better than NBL-PSO, which confirmed the efficiency of the proposed framework.

In many real scheduling situations, it is necessary to deal with the worker assignment and job scheduling together. However, in traditional scheduling problems, only the machine is assumed to be a constraint and there isn’t any constraint about workers. This assumption could be due to the lower cost of workers compared to machines or the complexity of workers' assignment problems. This research proposes a flexible flow shop scheduling problem with two simultaneous issues: finding the best worker assignment, and solving the corresponding scheduling problem. We present a mathematical model that extends flexible flow shop scheduling problem to admit the worker assignment. Due to the NP-hardness of the research problem, two approximation approaches based on particle swarm optimization, named PSO and SPSO, are applied to minimize the makespan. The experimental results show that the proposed algorithms can efficiently minimize the makespan but the SPSO generates better solutions especially for large-size problems.

The Department of Medical Services has a long-standing goal to improve the quality and operation of clinical management by efficient use of materials. The present undertaking structures and aspects of the continuing critical evaluation of current support operations in the Medical service department (MSD) -constraint semi-governmental emergency centers, including the re-appropriation and eventual management of temporary workers and services. The key test is to see how maintenance activities can be managed even more easily and conducted more cleverly. In this paper, a mechanism was developed to answer these questions: only with correcting the processes and at the lowest cost possible, how can we improve the maintenance operations?

For this purpose, current procedures and circumstances in 2 general hospitals in Mazandaran province in Iran are studied and data were gathered via questionnaire, interviews, and observation from 66 people including managers, supervisors, and employees. 56 questions were posed about 5 factors affecting maintenance management and their reliability was confirmed through the Cronbach's alpha method. To analyze these questions, a wide range of statistical techniques such as Spearman's rank correlation coefficient, Chi-squared test, Cramer’s V and Factor Analysis were used by SPSS software. Then for a more accurate survey, tools like Critical Success Factors, Key Performance Indicator, the theory of constraints & the Current Reality Tree are utilized.

Results have shown that effective factors in the improvement of maintenance management can be divided into 4 important principles called continuous learning, clarity in policies and procedures, encouraging innovation and balancing the number of maintenance employees with the work volume.

These four principles can form a useful framework with the lowest cost to improve the current maintenance management procedure in the hospitals which are under review in this paper’s case study.

Transporting the injured people from the affected areas to the hospitals and medical centers and distributing the relief commodities are the most important activities during a disaster’s response phase. An efficient relief logistics planning can reduce fatalities in a disaster situation. In this study, a multi-objective, multi-commodity model is presented for locating the temporary medical centers in appropriate locations and allocating the affected areas to the hospitals and the temporary medical centers. Moreover, the relief commodities and injured people logistics are considered as well. The proposed model attempts to minimize the total cost and the weighted sum of unserved injured people. The total cost consists of the procurement cost of relief commodity, transportation cost, set up and shortage cost. The Lp-Metric method is applied to solve the multi-objective model. Finally, the proposed model is analyzed through a case study on an earthquake scenario in the Amol city of Mazandaran province.