فهرست مطالب abdul sattar safaei

Online group buying emerged as one of the most successful online business models. Online group buying refers to the online buying community's purchase of products and services significantly reduced from the regular retail price. According to previous studies, many factors can affect purchase intention on such a platform. This study developed a model that explains the factors influencing purchase intention in an online group buying website, it also proposed a model to study online group buying sustainability from the customer perspective. It considers the impact of sustainability dimensions, customer satisfaction, and website quality on customers' intention to buy. This study examines three dimensions of social, economic, and environmental factors on customers' intention to buy from online group buying websites for the first time. This study also addressed the related relationship between the sustainability dimensions in such a platform. The results show that each social, economic, and environmental dimension positively affects customer satisfaction. Moreover, the sustainability dimensions positively influence the purchase intention, while the environmental dimension has less impact, and the studied online group buying customers pay less attention to it, also, the satisfaction and quality of the website affect the purchase intention.

Self-compacting concrete is an innovation in the field of concrete that has resolved one of the main problems in the construction of concrete structures, namely the problem of concrete compaction. This type of concrete has caused a uniform composition in concrete with a new composition and the invention of superplasticizer. In the present study, the effect of introducing the additives of zeolite and micro silica and their replacement with a part of cement has been evaluated by testing the mechanical properties including compressive, tensile, flexural and ultrasonic waves tests on the control specimen of self-compacting concrete and expansive self-compacting concrete. Also, the short-term deformation due to shrinkage of self-compacting concrete caused by drying for a period of 210 days after the fabrication of specimens has been investigated. Experimental results have shown that micro silica and zeolite pozzolans improve the microstructure of the evaluated concrete, which improves the mechanical properties. In fact, the replacement of cement with pozzolans has made the parts of the transition zone of the specimens better and condensed. Shrinkage test results also show that zeolite and micro silica pozzolans lead to a significant reduction in drying shrinkage. Drying shrinkage of the concrete containing 15% zeolite and 7.5% micro silica decreased by 32% and 15% compared to the control concrete containing expansive materials at the age of 210 days, respectively.

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.

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.

A profile is a relationship between a response variable and one or more independent variables being controlled during the time. Process Capability Indices (PCI) are measured to evaluate the performance of processes in producing conforming products. Despite frequent applications of profile and a variety of available methods to monitor its different types, little researches have been carried out on determining capability indices of profile process. PCIs such as  and in profile state, are used to evaluate process capability in producing conforming profiles. This paper presents a functional approach for nonlinear profiles which usually expressed as nonlinear regression. Thus, functions such as pertaining technical specification limits, mean and natural tolerance limits are determined as nonlinear profiles and also functional limit is applied to determine Functional Capability Indices (FCI)  and  of functional nonlinear profile. Easy calculation and the ability to calculate FC in each period and at each point are from the advantages of this method over the other methods.

Logistics planning plays an important role in providing services to the disaster -stricken areas. In this study, a scenario-based multi-objective model is presented to locate the distribution and evacuation centers and distribute the relief commodities with an appropriate allocation. It aims to serve the earthquake-stricken areas that are classified according to their construction qualities. The objective functions of cost, responsiveness and demand coverage are considered in the proposed optimization model. Moreover, due to the uncertain nature of a disaster and uncertainty in some model parameters, a robust optimization approach is utilized. A revised multi-choice goal programming method is applied to solve the multi-objective model. The proposed model is validated through a case study conducted in the city of Amol. The computational results show the efficiency of the proposed model in a real–world disaster situation.

This study aims to discuss the solution methodology for a closed-loop supply chain (CLSC) network that includes the collection of used products as well as distribution of the new products. This supply chain is presented on behalf of the problems that can be solved by the proposed meta-heuristic algorithms. A mathematical model is designed for a CLSC that involves three objective functions of maximizing the profit, minimizing the total risk and shortages of products. Since three objective functions are considered, a multi-objective solution methodology can be advantageous. Therefore, several approaches have been studied and an NSGA-II algorithm is first utilized, and then the results are validated using an MOSA and MOPSO algorithms. Priority-based encoding, which is used in all the algorithms, is the core of the solution computations. To compare the performance of the meta-heuristics, random numerical instances are evaluated by four criteria involving mean ideal distance, spread of non-dominance solution, the number of Pareto solutions, and CPU time. In order to enhance the performance of the algorithms, Taguchi method is used for parameter tuning. Finally, sensitivity analyses are performed and the computational results are presented based on the sensitivity analyses in parameter tuning.

The common consideration on economic model is that there is knowledge about the risk of occurrence of an assignable cause and the various cost parameters that does not always adequately describe what happens in practice. Hence, there is a need for more realistic assumptions to be incorporated. In order to reduce cost penalties for not knowing the true values of some parameters, this paper aims to develop a bi-objective model of the economic-statistical design of the S control chart to minimize the mean hourly loss cost while minimizing out-of-control average run length and maintaining reasonable in-control average run length considering Taguchi loss function. The purpose of Taguchi loss function is to reflect the economic loss associated with variation in, and deviations from, the process target or the target value of a product characteristic. In contrast to the existing modeling approaches, the proposed model and given Pareto-optimal solution sets enables the chart designer to obtain solutions that is effective even for control chart design problems in uncertain environments. A comparison study with a traditional economic design model reveals that the proposed chart presents a better approach for quality system costs and the power of control chart in detecting the assignable cause.

Supply chain management is a set of guidelines for integration of chains, which aims to reduce costs and increase system services to customers. One of the most important issues for supply chain stockholders is procuring and purchasing requested items in a timely manner. Thus, a variety of models have been proposed as purchasing strategy in supply chains. Group purchasing is a system which leads to decline expenses, such as ordering, administrative, transportation and purchasing costs. This research suggests a modeling approach that develops a purchasing group organization for a set of pharmacies in the field of healthcare. The proposed model determines a cooperation strategy based on various factors such as purchasing cost, pharmacies distance and demand difference within a given group. To illustrate the application of the proposed model, designing a purchasing group organization for Chalus city pharmacies is investigated.