فهرست مطالب ali salmasnia

One of the important problems in managing construction projects is selecting the best alternative for activities' execution to minimize the project's total cost and time. However, uncertain factors often have negative effects on activity duration and cost. Therefore, it is crucial to develop robust approaches for construction project scheduling to minimize sensitivity to disruptive noise factors. Additionally, existing methods in the literature rarely focus on environmentally conscious construction management. Achieving these goals requires incorporating the project scheduling problem with multiple objectives. This study proposes a robust optimization approach to determine the optimal construction operations in a project scheduling problem, considering time, cost, and environmental impacts (TCE) as objectives. An analytical algorithm based on Benders decomposition is suggested to address the robust problem, taking into account the inherent uncertainty in activity time and cost. To evaluate the performance of the proposed solution approach, a computational study is conducted using real construction project data. The case study is based on the wall of the east coast of Amirabad port in Iran. The results obtained using the suggested solution approach are compared to those of the CPLEX solver, demonstrating the appropriate performance of the proposed approach in optimizing the time, cost, and environment trade-off problem.

Cloud manufacturing is a service-oriented production model that centralizes production resources to respond to specific customer needs in different locations. One of the main issues in cloud manufacturing systems is the proper allocation of sub-tasks to enterprises and their optimal scheduling. Most studies assume that all tasks have only one type of structure, although it is possible to have tasks with different structures in one order set. Furthermore, existing scheduling models in the cloud manufacturing literature tend to assume that all tasks are available at time zero and that the logistics time/cost among enterprises is negligible. Therefore, in this study, an optimization model with three objective functions of task completion time, the cost imposed on the cloud manufacturing system, and the quality of the selected services are developed to get closer to the real world; three features are included in it: 1) the possibility of tasks with two structures, series and parallel, 2) different arrival times of tasks in the cloud manufacturing system, and 3) time/cost of logistics between different enterprises.

First, six examples with different numbers of tasks and subtasks are designed with both sequential and parallel structures. To solve the proposed model accurately and achieve the global optimum, the CPLEX solver is used in the GAMS software.

To verify the importance of the characteristics of the developed model, two comparative studies are carried out. In the first comparative study, the presented model is compared with a similar model in which it is assumed that all tasks are available at time zero. The second comparative study examines the effect of considering logistics time/costs between enterprises when allocating subtasks to services. The results of the comparative studies show the misleading level of the cloud manufacturing manager in the face of the reduced models.

Originality/Value:

 This research aims to present a model for the simultaneous scheduling of tasks with sequential and parallel structures, considering the different task arrival times and logistics in the cloud manufacturing system.

This article develops an integrated model of transmitting strategies and operational activities to enhance the efficiency of supply chain management. As the second objective, this paper aims to improve supply chain performance management (SCPM) by employing proper decision-making approaches. The proposed model optimizes the performance indicator based on SCOR metrics. A process-based method is utilized for high-level decisions, while a mathematical programming method is proposed for low-level decisions. The suggested operational model takes some major supply chain properties such as multiple suppliers, multiple plants, multiple materials, and multiple produced items over several time periods into account. To solve the operational multi-objective optimization model, a goal programming approach is applied. The computational results are explained in terms of a numerical example, and a sensitivity analysis is performed to investigate how the performance of the supply chain is influenced by strategic scenario planning.

In some applications, the number of quality characteristics is larger than the number of observations within subgroups. Common multivariate control charts to monitor the variability of such high-dimensional processes are unsuitable because the sample covariance matrix is not positive semi-definite and invertible. Moreover, the impact of gauge imprecision on detection capability of multivariate control charts under high-dimensional setting has been clearly neglected in the literature. To overcome these shortcomings, this paper develops a ridge penalized likelihood ratio chart for Phase II monitoring of high-dimensional process in the presence of measurement system errors. The developed control chart departures from the assumption of sparse variability shifts in which the assignable cause can only affects a few elements of the covariance matrix. Then, to compensate for the adverse impact of gauge impression, the developed chart is extended by employing multiple measurements on each sampled item. Simulation studies are carried out to study the impact of imprecise measurements on detectability of the developed monitoring scheme under different shift patterns. The results show that the gauge inability negatively affects the run-length distribution of the developed control chart. It is also found that the extended chart under multiple measurements strategy can effectively reduce the error impact.

In this study, a hybrid method based on Decision Making Trial and Evaluation Laboratory (DEMATEL) technique, analytical network process (ANP), and factor analysis is developed to prioritize economic, social, and environmental indicators of sustainable development in free. For this purpose, first, the indicators are classified into two internal and external ones and their latent factors are identified and named using the factor analysis method. Then, the DEMATEL technique is employed for identifying the internal relationship among the indicators as well as determining the intensity of their influential indicators and influence. Moreover, the causal diagrams, the internal relationships of internal and external indicators, and the cause or effect of the indicators are explained by the DEMATEL technique. Finally, the relative importance of the indicators is obtained based on the ANP method, and by calculating the weighted and limit super matrixes, the ranking of sustainable development indicators in both internal and external parts is obtained. The results obtained from 44 internal and 24 external indicators show that "welfare and services" and "culture making" factors are the most important internal and external ones, respectively, while "providing conditions for investment and support for investment" and "prevention of marine pollution by ships" indicators are the most important internal and external ones in the sustainable development of free zones.

Aim and introduction. Today’s business leaders are seeking to achieve sustainable development in order to succeed in the competitive market. Based on the theoretical framework, research background and experts' opinions, it can be concluded that despite the increase in the number of free zones in Iran, they have mainly focused on importing different products to the country. In other words, there is not a balanced growth in these areas in terms of sustainable development indicators, including economic, environmental and social indicators. In addition, the performance of the free zones has not been satisfactory from attracting foreign investment and employment viewpoints. Therefore, considering the high potential of free trade-industrial zones in economic growth of the country, it is necessary to take the all dimensions of sustainable development into account. Methodology.The main purpose of this study is to identify and analyze the indicators of sustainable development in  free zones of Iran. To accomplish that, considering three main areas including commercial, industrial and tourism, the sustainable development indicators of free zones under both internal and external dimensions from three aspects of environmental, economic and social are studied using fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) technique. This technique is a structural method that is able to represent the causal relationships among factors which uses a straightforward diagram and some matrices. This technique contains eight steps of 1) defining appropriate scales, 2) establishing the direct-influence matrix, 3) normalizing the direct-influence matrix, 4) constructing the total-influence matrix, 5) deriving the influential relation map (IRM), 6) designing a scatter diagrams, 7) setting the threshold values, 8) building cause and effect relationship diagrams.In this regard, a DEMATEL questionnaire based on the 5-choice verbal values ​​scale consisting of 44 internal and 24 external indicators is designed. Then, the results received from eight experts are analyzed in MATLAB and Excel software. The obtained results show that the variability of internal indicators is more than the that of external indicators which implies that the internal indicators have a wider range of relationships than the external indicators. Moreover, the dispersion of internal effect indicators is more than that of internal cause ones while there is a slight difference between the dispersions of external cause and effect indicators. Besides, the existence of complex relations among both internal and external indicators is confirmed. The obtained results from internal indicators show that three indicators: 1) separation, collection, transportation, recycling and disposal of industrial waste, 2) reducing the use of hazardous industrial waste, and 3) Satisfaction of supply chain stakeholders, respectively, have the most relationship with other internal indicators. In other words, focusing on the mentioned indicators can make a significant improvement in other internal indicators. Furthermore, three internal indicators of 1) providing interest and services for stakeholders, 2) proper strategy to increase market share, and 3) encouragement of employees to participate in sustainable development, respectively, have the least relations with other internal indicators. Consequently, concentrating on these indicators leads to little changes in other internal indicators. The results obtained from external indicators confirm that three indicators of 1) culture of compliance with safety and environmental standards by companies, 2) prevention of marine pollution by ships, and 3) raising the awareness of tourists and customers regarding traffic issues, respectively, are the most related indicators to other external ones. As a result, changes in these indicators can significantly affect the other external indicators.Discussion and Conclusion.In addition, three indicators of: 1) encouraging tourists and customers to buy eco-friendly products, 2) cooperating with charities and organizations to support the local community, and 3) informing tourists and customers to respect the customs, traditions and comfort of the local community, respectively have the least interaction with the other external ones. Hence, paying attention to these three indicators results in a little changes in other external indicators. As another important finding, it can be referred to the influential internal and external indicators. In this regard, the indicators: 1) providing sustainable development policies, 2) air quality monitoring and reduction of greenhouse gas emissions, and 3) establishment of industries are the most effective internal indicators, respectively, while 1) educating local community regarding sustainable development, 2) reducing energy consumption by companies, and 3) cooperation with international companies are the most influential foreign indicators, respectively.Finding. Although, this study determines the complex causal structure and identifies influential and influenced internal and external indicators, it does not calculate the relative importance of the indicators. Besides, despite the internal relation among internal and external indicators of sustainable development, the proposed method ignores the multiple relations among them. Therefore, future researchers can be directed to use of multi-criteria decision-making techniques to quantify the relative importance of sustainable development criteria of free trade-industrial zones.

This paper designs a Scenario analysis approach to determine the joint production policy for two products under possible substitution. The Scenario analysis is designed to improve decision making by considering possible outcomes and their implications. The traditional multi-products production models assume that there is no possible substitution between products. However, in real-world cases, there are many substitutable products where substitution may occur in the event of a product stock-out. The proposed model optimizes production quantities for two products under substitution with the aim of minimizing the total cost of inventory system, including setup and holding costs, subject to a resource constraint. To analyze the problem, four special Scenarios are derived and discussed in detail. Furthermore, the total cost functions are derived for each Scenario separately, and then a solution procedure is suggested based on the Scenarios developed. The numerical examples are implemented, and the results are discussed in detail.

In recent years, it has been proven that integrating statistical process control, maintenance policy, and production can bring more benefits for the entire production systems. In the literature of triple-concept integrated models, it has generally been assumed that the observations are independent. However, the existence of correlated structures in some practical applications put the traditional control charts in trouble. The mixed EWMA-CUSUM (MEC) control chart and the ARMA control chart are effective tools to monitor the mean of autocorrelated processes. This paper proposes an integrated model subject to some constraints for determining the decision variables of triple concepts in the presence of autocorrelated data. Three types of autocorrelated processes are investigated to study their effects on the results. Moreover, the results of the MEC and ARMA charts are compared. Due to the complexity of the model, a particle swarm optimization (PSO) algorithm is applied to select optimal decision variables. An industrial example and extensive comparisons are provided

Recently, design of preventive maintenance (PM) policies during the warranty period has attracted the attention of researchers. The methods mainly design warranty servicing strategies in a way that reduce the cost imposed on the manufacturer without considering the impact of customer dissatisfaction. While dissatisfaction with a product is an important issue which may result in the loss of potential buyers and switching existing buyers to competitors. Therefore, this study develops a bi-objective model which simultaneously minimizes the manufacturer and the buyer cost under a Non-homogeneous Poisson Process framework. Also, a non-periodic preventive maintenance strategy is presented in which PM actions are performed at discrete time instants in a way that the expected number of failures remains a constant value over all PM intervals. Furthermore, it is a known fact that the value of money reduces over time due to different reasons and has a significant impact on long-term contracts. Since PMs and repairs are conducted at different times, the time value of money is considered to estimate the cost more accurately. A comparative study is conducted to support this claim that the presented non-periodic reliability-based PM policy has a better performance in comparison with a periodic PM policy.

On one hand the presence of Iran as an observer in the WTO, and on the other hand the development of private banks, encounter the Iranian banks with a much more competitive environment. Survive in such situations requires its own mechanism, tools and strategies. Among these tools, we can mention to a marketing plan to achieve a sustainable ompetitive advantage. In this research, three-ramification model including structural, environmental, and behavioral aspects is used for investigating the pathology of marketing program of Export Development Bank of Iran. In this regard, a questionnaire based on three-ramification model is provided that its validity and stability are evaluated using experts and Cronbach's alpha, respectively. The results show that none of the structural, environmental, and behavioral aspects are not in a suitable situation.

The production-inventory models are traditionally adopted for manufacturing systems. A relatively new area of production-inventory planning is related to livestock growing process. This research aims to propose a class of production-inventory model for new items titled growing items. In such a case, a rancher orders a quantity of newborn livestock like chicks or lambs, grow them to an appropriate weight during the growing period, slaughter them and then sells them to the meat market. The goal is to calculate the economic order quantity of the growing items at the start of a growing cycle, the optimal length of the growing cycle and the optimal total profit. We, in this research, extend the previous work to the case of joint growing and deteriorating items, where livestock grows at growing period, and, additionally, the slaughtered livestock may be deteriorated during the sales period. Moreover, since some amount of slaughtered livestock is waste and should be disposed, a weight reduction factor is considered when livestock is slaughtered. The inventory models are constructed for this case, a heuristic solution algorithm is presented, a numerical example is discussed, and finally, sensitivity analysis is carried out to investigate the applicability of the problem.

Statistical process monitoring, maintenance policy, and production cycle length usually have been investigated separately while they are three dependent aspects in the industrial systems. Moreover, most of the papers that integrated simultaneously these aspects, suffer from three major drawbacks as follows: (1) Optimizing the production cost without considering the time value of money to simplify the model; (2) Considering the fixed shift size while it is a random variable in the real condition; (3) Economic design of control charts ignoring the statistical properties that lead to reduce the control chart power, extremely. To eliminate these weaknesses, this paper presents an integrated model of production cycle length, maintenance policy, and economic-statistical design considering the time value of money and the stochastic shift size. Furthermore, to maintain the reliability of the system at an acceptable level, the presented model uses non-uniform sampling. Finally, three comparative studies on the main contributions are presented to illustrate the advantages of the model and a sensitivity analysis is implemented on the several parameters to extend insights into the matter.

Control charts are powerful tools to monitor quality characteristics of services or production processes. However, in some processes, the performance of process or product cannot be controlled by monitoring a characteristic; instead, they require to be controlled by a function that usually refers as a profile. This study suggests employing exponentially weighted moving average (EWMA) and range (R) control charts for profile monitoring, simultaneously. For this purpose, the parameters of these control charts should be determined in a way that the expected total cost is minimized. In order to evaluate the statistical performance of the proposed model, the in-control and out-of-control average run lengths are applied. Moreover, the existence of uncertain parameters in many processes is a barrier to attain the best design of control charts in practice. In this paper, the economic-statistical design of control charts for linear profile monitoring under uncertain conditions is investigated. A genetic algorithm is used for solving the proposed robust model, and the Taguchi experimental design is employed for tuning its parameters. Furthermore, the effectiveness of the developed model is illustrated through a numerical example.

Most of the inventory control models assume that quality defect never happens, which means production process is perfect. However, in real manufacturing processes, the production process starts its operation in the in-control state; but after a period of time, shifts to the out-of-control state because of occurrence of some disturbances. In this paper, in order to approach the model to real manufacturing conditions, a process is considered in which quality defect and machine deterioration may occur. Since the adaptive control charts detect the occurrence of assignable cause quicker than the traditional control charts, an adaptive non-central chi-square control chart is designed, which monitors the process mean and variance, simultaneously. In addition, to reduce the failure rate of the machine, two types of maintenance policies consisting of reactive and preventive are planned. Then, the particle swarm optimization algorithm is employed to minimize the overall cost per cycle involving inventory cost, quality loss cost, inspection cost and maintenance cost subject to statistical quality constraints. Finally, to demonstrate the effectiveness of the suggested approach, two comparative studies are presented. The first one confirms that integration of production planning, maintenance policy and statistical process monitoring leads to a significant increase in the cost savings. The second one indicates superiority of the developed adaptive control chart in comparison with the control chart with the fixed parameters.

To attain a setting of decision variables that minimize the total cost of supply chain is the one of important problems in the literature review of the supply chain. Recently, several methods have suggested approaches addressing these problem but most of them assume that the parameters such as demands, supply and deliveries to be deterministic and ignore potential correlation among the supply chain objectives. In this study, a desirability function-based optimization approach is proposed that not only considers uncertainty in parameters and sets simultaneously all objectives in a minimum desirability level but also takes into account potential correlation between objectives and minimizes the effect of uncontrollable variables or noise factors.

Headways (i.e. the time period between the departure times of two consecutive transportation vehicles) is an important issue for urban railway companies. In this problem we are facing with two conflicting objectives, average passenger travel time and rate of carriage fullness. Until now different multi-response optimization procedure for solving this problem is studied but these approaches do not consider responses variance and covariance between them in optimization process. Therefore this research presents a modeling and solution approach based on discrete-event simulation and response surface methodology that not only puts average of response in satisfactory region but also try to minimize the variance of responses relative to noise variable and also consider covariance between them. In order to evaluate the performance of the proposed approach, Tehran metro line 4 has been assessed. The results show that the proposed approach is superior to existing techniques.

This paper addresses a situation in which a firm is willing to locate several new multi-server facilities in a geographical area to provide a service to his customers within the M/M/m/K queue system. As a new assumption, it is also considered that there is already operating competitors in such system. This paper is going to find the location of facilities in a way that the market share of entering firm is maximized. For this purpose, simultaneous minimization of total cost and maximum idle time in each facility is considered as two objective functions in the model. The total cost consists of two parts: (1) the fixed cost for opening a new facility, and (2) the operational costs regarding to the customers, which depends on travel time to the facility and the waiting time at the facility. In addition, in order to make the problem more adapted to real-world situations, two new constraints on budget and number of the servers in each facility are added to the model. Eventually, to tackle the suggested problem, a non-dominated sorting genetic algorithm (NSGA-II) and a non-dominated ranked genetic algorithm (NRGA) are utilized. Finally, the performance of algorithms are investigated via analyzing a set of test problems.

In this study, a multi-state degraded system is studied, where status of system is degrading over time continuously. As time progresses, system may either deteriorate gradually and go to lower performance state or it may fail suddenly. If the system fails, some repairs are carried out to restore the system to the previous state. When the inspections revealed that the system has reached its last acceptable state, a PM is carried out to restore the system to the higher performance states. The goal is to find the optimal PM level so that the mean availability of the system is maximized and the total cost of the system is minimized. In this regard, Markov process is employed to represent different states of system. An integrated optimization approach is also suggested based on the desirability function statistical approach. The suggested aggregation method is robust to the potential dependency between the total cost and the mean availability. It also ensures that both objective functions fall in decision maker’s acceptable region. In order to show the efficiency of the proposed approach, a numerical example is presented and analyzed.

This study aims to select optimal maintenance strategy for components of an electric motor of the National Iranian Oil Refining and Distribution Company. In this regard, a method based on revised multi choice goal programming and analytic hierarchy process (AHP) is presented. Since improving the equipment reliability is an important issue, reliability centered maintenance (RCM) strategies are introduced in this paper. Furthermore, on one hand, we know that maintenance cost consists of a considerable percentage of production cost; on the other hand, the risk of equipment failure is a main factor on personnel’s safety. Consequently, the cost and risk factors are selected as important criteria of maintenance strategies.

Achieving optimal production cycle time for improving manufacturing processes is one of the common problems in production planning. During recent years, different approaches have been developed for solving this problem, but most of them assume that mean quality characteristic is constant over production run length and sets it on customer’s target value. However, the process mean may drift from an in-control to an out-of-control at a random point in time. This study aims to select the production cycle time and the initial setting of mean quality characteristic, so that the expected total cost, consisting of quality loss and maintenance costs as well as ordering and holding costs, already considered in the classic models is minimized. To investigate the effect of mean process setting, a computational analysis on a real world example is performed. Results show the superiority of the proposed approach compared to the classical economic production quantity model.

On one hand the presence of Iran as an observer in the WTO, and on the other hand the development of private banks, encounter the Iranian banks with a much more competitive environment. Survive in such situations requires its own mechanism, tools and strategies. Among these tools, we can mention to a marketing plan to achieve a sustainable ompetitive advantage. In this research, three-ramification model including structural, environmental, and behavioral aspects is used for investigating the pathology of marketing program of Export Development Bank of Iran. In this regard, a questionnaire based on three-ramification model is provided that its validity and stability are evaluated using experts and Cronbach's alpha, respectively. The results show that none of the structural, environmental, and behavioral aspects are not in a suitable situation.