Y.C. Zanjani; Z. Rafie Majd; A. Mirzazadeh
Volume 2, Issue 1 , June 2015, , Pages 1-15
Abstract
Fuzzy reliability is often used in analyzing the reliability in the large industrial systems. In this paper, a relatively new method is presented to analyze Neishabour (also called Nishapur, a city in Iran) train disaster. In this regards, by using the certain and uncertain propositions, unreliability ...
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Fuzzy reliability is often used in analyzing the reliability in the large industrial systems. In this paper, a relatively new method is presented to analyze Neishabour (also called Nishapur, a city in Iran) train disaster. In this regards, by using the certain and uncertain propositions, unreliability circuit of the system is depicted .Due to the inability to provide exact values for the unreliability of each subsystem, regarding the opinion of experts, fuzzy logic is applied and triangular and Gaussian membership functions are attributed depending to the type of each subsystem and the fuzzy unreliability value of the system is calculated. Finally, by defuzzification and comparing the obtained value with the classification table of linguistic variables, unreliability of the system is identified.
M. Hajian Heidary; A. Aghaie
Volume 2, Issue 2 , December 2015, , Pages 1-12
Abstract
Nowadays, logistics and supply chain management (SCM) is critical to compete in the current turbulent markets. In addition, in the global context, there are many uncertainties which affect on the market. One of the most important risks is supplier disruption. The first step to cope with these uncertainties ...
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Nowadays, logistics and supply chain management (SCM) is critical to compete in the current turbulent markets. In addition, in the global context, there are many uncertainties which affect on the market. One of the most important risks is supplier disruption. The first step to cope with these uncertainties is quantifying them. In this regard many researches have focused on the problem but measurement of the risk in the global SCM is yet a challenge. In the uncertain conditions, simulation is a good tool to study the system. This paper aims to study a global supply chain with related risks and measurement of the risks using simulation. Global aspects considered in the paper are: 1- currency exchange rate, 2- extended leadtime for abroad supplies, 3- regional and local uncertainties. In this regard, two popular risk measurement approaches (VaR and CVaR) are used in the simulation of uncertainties in the global supply chain. Results showed that adopting risk averse behavior to cope with the uncertainties leads to the lower stockouts and also higher costs.
M. Zandieh; M.M. Asgari Tehrani
Volume 1, Issue 1 , November 2014, , Pages 1-19
Abstract
Make-to-order is a production strategy in which manufacturing starts only after a customer's order is received; in other words, it is a pull-type supply chain operation since manufacturing is carried out as soon as the demand is confirmed. This paper studies the order acceptance problem with weighted ...
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Make-to-order is a production strategy in which manufacturing starts only after a customer's order is received; in other words, it is a pull-type supply chain operation since manufacturing is carried out as soon as the demand is confirmed. This paper studies the order acceptance problem with weighted tardiness penalties in permutation flow shop scheduling with MTO production strategy, the objective function of which is to maximize the total net profit of the accepted orders. The problem is formulated as an integer-programming (IP) model, and a cloud-based simulated annealing (CSA) algorithm is developed to solve the problem. Based on the number of candidate orders the firm receives, fifteen problems are generated. Each problem is regarded as an experiment, which is conducted five times to compare the efficiency of the proposed CSA algorithm to the one of simulated annealing (SA) algorithm previously suggested for the problem. The experimental results testify to the improvement in objective function values yielded by CSA algorithm in comparison with the ones produced by the formerly proposed SA algorithm.
A. M. Kimiagari; F. Lotfian Delouyi; M. Shabani
Volume 3, Issue 1 , June 2016, , Pages 1-14
Abstract
In the recent years, renewable energy sources are an important component of world energy consumption. GDP is one of the main measures of a country’s economic activity. Most of the studies examine the impact of renewable energy consumption on GDP with single equation model and the others use dynamic ...
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In the recent years, renewable energy sources are an important component of world energy consumption. GDP is one of the main measures of a country’s economic activity. Most of the studies examine the impact of renewable energy consumption on GDP with single equation model and the others use dynamic panel data. Since the Granger causality analysis’s findings of this paper establish bidirectional causality between GDP and renewable energy consumption, the purpose of this study is to develop a simultaneous-equations model to explore the interaction between GDP and renewable energy consumption in a dynamic panel data. This model uses GDP and renewable energy consumption as endogenous variables and seven factors as exogenous variables. By using a dynamic panel data of 34 OECD countries from 1990 to 2012, the model is estimated by using the two-stage least-squares method. The results confirm the important influence of renewables and non-renewables as well as capital and labor force on GDP in OECD countries. Based on the results, both GDP and real oil price play an important role in renewable energy consumption. Our findings suggest that energy planners and policy makers need to increase renewable energy investment to ensure sustainable economic development in future.
M.S. Fallahnezhad; V. Golbafian
Volume 3, Issue 2 , December 2016, , Pages 1-16
Abstract
CCC-r chart extended approach of CCC charts, is a technique applied when nonconforming items are rarely observed. However, it is usually assumed that the inspection process is perfect in the implementation control charts imperfect inspections may have a significant impact on the performance of the control ...
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CCC-r chart extended approach of CCC charts, is a technique applied when nonconforming items are rarely observed. However, it is usually assumed that the inspection process is perfect in the implementation control charts imperfect inspections may have a significant impact on the performance of the control chart and setting the control limits. This paper first investigates the effect of inspection errors on the formulation of CCC-r chart, then an economic model is presented in the presence of inspection errors to design control chart so that the average cost per item minimized. The r parameter in the chart is optimized with respect to the economic objective function, Modified Consumer Risk, and Modified Producer Risk.
Fahimeh Tanhaie; Nasim Nahavandi
Abstract
The theory of constraints is an approach to production planning and control that emphasizes on the constraints to increase throughput by effectively managing constraint resources. One application in theory of constraints is product mix decision. Product mix influences the performance measures in multi-product ...
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The theory of constraints is an approach to production planning and control that emphasizes on the constraints to increase throughput by effectively managing constraint resources. One application in theory of constraints is product mix decision. Product mix influences the performance measures in multi-product manufacturing system. This paper presents an alternative approach by using of goal programming to determine the product mix of the manufacturing system. The objective of paper is to provide a methodology in order to make product mix decision. Key point of the proposed methodology is considering decision maker idea to determine the weights of objective functions that are throughput and bottleneck exploitation. Therefore the weights of the objective functions are determined by the information get from decision maker. Through an example, inefficiency of theory of constraints in multiple bottleneck problems has been showed. Comparison of theory of constraints, linear programming and other methods to product mix problem has also discussed to show the advantages of the proposed method.
Masoud Rabbani; Leyla Aliabadi; Razieh Heidari; Hamed Farrokhi-Asl
Abstract
This study considers a reliable location – inventory problem for a supply chain system comprising one supplier, multiple distribution centers (DCs), and multiple retailers in which we determine DCs location, inventory replenishment decisions and assignment retailers to DCs, simultaneously. Each ...
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This study considers a reliable location – inventory problem for a supply chain system comprising one supplier, multiple distribution centers (DCs), and multiple retailers in which we determine DCs location, inventory replenishment decisions and assignment retailers to DCs, simultaneously. Each DC is managed through a continuous review (S, Q) inventory policy. For tackling real world conditions, we consider the risk of probabilistic distribution center disruptions, and also uncertain demand and lead times, which follow Poisson and Exponential distributions, respectively. A new mathematical formulation is proposed and we model the proposed problem in two steps, in the first step, a queuing system is applied to calculate mean inventory and mean reorder rate of steady-state condition for each DC. Next, regarding the results obtained from the first step, we formulate a mixed integer nonlinear programming model which minimizes the total expected cost of inventory, location and transportation and can be solved efficiently by means of LINGO software. Finally, several test problems and sensitivity analysis of key parameters are conducted in order to illustrate the effectiveness of the proposed model.
Sorour Farokhi; Emad Roghanian; Yaser Samimi
Abstract
One of the main challenges of strategic management is implementing the strategies. Designing the strategy map in Balanced Scorecard framework to determine the causality between strategic objectives is one of the most important issues in implementing the strategies. In designing the strategy map with ...
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One of the main challenges of strategic management is implementing the strategies. Designing the strategy map in Balanced Scorecard framework to determine the causality between strategic objectives is one of the most important issues in implementing the strategies. In designing the strategy map with intuition and judgment, the link between strategic objectives is not clear and it is not obvious which strategic objectives are related and influenced each other. Hence, it is essential to offer a quantitative and accurate method to design the strategy map and clarify these relationships. In this paper, after reviewing the methods for determining the causal relationships among BSC perspectives in the literature, a framework on the basis of historical data analysis and multi-response surface regression analysis is offered to determine causal relationships among strategic objectives with respect to data of key performance measures of past years in order to obtain the coefficients and equations that can be used in the prediction of the responses. Using statistically significant models, the correlations between the factors and several responses were acquired. The presented quantitative approach is useful for determining the causal relationships resulting in an accurate strategy map and is a supporting approach for improving decision makers’ opinions and enabling them to reach a more accurate picture of the relationships. This research also presents a case study to demonstrate the applicability of the proposed approach. The application and implication of the proposed method in a real case show that the contributions of the research are not only theoretical but practical as well. The strategy map constructed in this study can also serve as a reference point for similar businesses.
Seyed Mahdi Sadatrasoul; Zeynab Hajimohammadi
Abstract
Credit risk management is a process in which banks estimate probability of default (PD) for each loan applicant. Data sets of previous loan applicants are built by gathering their data, and these internal data sets are usually completed using external credit bureau’s data and finally used for estimating ...
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Credit risk management is a process in which banks estimate probability of default (PD) for each loan applicant. Data sets of previous loan applicants are built by gathering their data, and these internal data sets are usually completed using external credit bureau’s data and finally used for estimating PD in banks. There is also a continuous interest for bank to use rule based classifiers to build their default prediction models. However, in practice the data records are usually incomplete and have some missing values and this make problems for banks, especially in credit risk portfolios which are low default and makes model rule based building complex. Several strategies could be used in order to handle the missing data issue. This paper used five missing value handling strategies including; ignoring, replacing with random, mean, C&R tree induced values and elimination strategies in a real credit scoring dataset. Experimental results show that ignoring strategy consistently outperforms other methods on test data set, and suggest that the CHAID is a useful classifier for handling low default portfolios with missing value.
Taha-Hossein Hejazi; Pardis Roozkhosh
Abstract
The nature of input materials is changed as long as the product reaches the consumer in many types of manufacturing processes. In designing and improving multi-stage systems, the study of the steps separately may not lead to the greatest possible improvement in the whole system, therefore the study of ...
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The nature of input materials is changed as long as the product reaches the consumer in many types of manufacturing processes. In designing and improving multi-stage systems, the study of the steps separately may not lead to the greatest possible improvement in the whole system, therefore the study of inputs and outputs of each stage can be effective in improving the output quality characteristics. In this study, the double sampling method is applied for inspection where decision variables are the sample size per sampling time and the maximum amount of defective items in the first and second samples in each stage. Furthermore, uncertainty in parameters such as production, inspection, and replacement costs are included in the objective function and handled by a Monte-Carlo based optimization method. In order to show the efficacies of the proposed method, a numerical example has been designed, and further analyses on solutions have been conducted.
Malek Hassanpour
Abstract
Iranian electronic products supplier industries are developing day by day and modern techniques and facilities are assigning as well as many promotions about green products supply chain as input materials introduced into the generation cycle of industries. Current cluster study of Iranian Electronic ...
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Iranian electronic products supplier industries are developing day by day and modern techniques and facilities are assigning as well as many promotions about green products supply chain as input materials introduced into the generation cycle of industries. Current cluster study of Iranian Electronic Products Manufacturing Industries (IEPMI) comprised a technical and hierarchical evaluation carried out as the objective of current research. It was used SPSS and Excel Software to classify and analysis about 33 IEPMI via an unsupervised model, Additive Ratio Assessment (ARAS), Simple Additive Weighting (SAW) and Data Envelopment Analysis (DEA) models. Finally, a hierarchical cluster classification has developed for the 33 industries pertaining to 5 main criteria as well as the total inventory of input, output materials and facilities employed. It was found that the ranking systems based on ARAS and SAW presented the same results for IEPMI. DEA model was also classified IEPMI in terms of efficiency score.
Morteza Shafiee; Sara Emadi
Abstract
Today, outsourcing is recognized as one of the most effective strategies in the business world. In this regard, outsourcing of business processes is considered to be one of the most common forms of outsourcing. The purpose of this study is to provide In-depth and quantitative analysis of the benefits ...
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Today, outsourcing is recognized as one of the most effective strategies in the business world. In this regard, outsourcing of business processes is considered to be one of the most common forms of outsourcing. The purpose of this study is to provide In-depth and quantitative analysis of the benefits of BPO in a dairy plant in Iran and how these benefits affect the willingness of senior plant managers to increase the levels of outsourcing of business processes. Therefore, Structural Equation Modeling (SEM) based on BPO Benefit Analysis is used. The population of the study consisted of 50 managers who all answered a questionnaire containing 20 questions. Responses were analyzed using the Partial Least Squares (PLS) method. The research method is a quantitative experimental one. The findings of this study show that cost planning has a higher value than real cost savings and this is one of the benefits of BPO.
Javad Behnamian
Abstract
This research extends a two-phase algorithm for parallel job scheduling problem by considering earliness and tardiness as multi-objective functions. Here, it is also assumed that the jobs may use more than one machine at the same time, which is known as parallel job scheduling. In the first phase, jobs ...
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This research extends a two-phase algorithm for parallel job scheduling problem by considering earliness and tardiness as multi-objective functions. Here, it is also assumed that the jobs may use more than one machine at the same time, which is known as parallel job scheduling. In the first phase, jobs are grouped into job sets according to their machine requirements. For this, here, a heuristic algorithm is proposed for coloring the associated graph. In the second phase, job sets will be sequenced as a single machine scheduling problem. In this stage, for sequencing the job sets which are obtained from the first phase, a discrete algorithm is proposed, which comprises two well-known metaheuristics. In the proposed hybrid algorithm, the genetic algorithm operators are used to discretize the particle swarm optimization algorithm. An extensive numerical study shows that the algorithm is very efficient for the instances which have different structures so that the proposed algorithm could balance exploration and exploitation and improve the quality of the solutions, especially for large-sized test problems.
Reza Pakdel Mehrabani; Abbas Seifi
Abstract
This paper investigates the Stackelberg equilibrium for pricing and ordering decisions in a multi-channel supply chain. We study a situation where a manufacturer is going to open a direct online channel in addition to n existing traditional retail channels. It is assumed that the manufacturer is the ...
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This paper investigates the Stackelberg equilibrium for pricing and ordering decisions in a multi-channel supply chain. We study a situation where a manufacturer is going to open a direct online channel in addition to n existing traditional retail channels. It is assumed that the manufacturer is the leader and the retailers are the followers. The situation has a hierarchical nature and is formulated as a bi-level programming problem. The upper level problem is a mathematical model dealing with decisions of the manufacturer, while the lower level is a Nash equilibrium model determining the retail prices and order quantities by formulating the competition between the physical retailers. We consider a price-sensitive linear demand model with an additive uncertain part and analyze the optimal decisions for each sales channel. To enable supply chain coordination, we propose a particular revenue-sharing contract. This contract enables the retailers to set pricing and ordering policies that are equivalent to those in an integrated supply chain. Finally, we examine the impact of the model parameters on the equilibrium with a comprehensive numerical study.
N. Mahmoodi Darani; A. Dolatnejad; M. Yousefikhoshbakht
Volume 2, Issue 2 , December 2015, , Pages 13-25
Abstract
The traveling salesman problem (TSP) is a well-known optimization problem in graph theory, as well as in operations research that has nowadays received much attention because of its practical applications in industrial and service problems. In this problem, a salesman starts to move from an arbitrary ...
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The traveling salesman problem (TSP) is a well-known optimization problem in graph theory, as well as in operations research that has nowadays received much attention because of its practical applications in industrial and service problems. In this problem, a salesman starts to move from an arbitrary place called depot and after visits all of the nodes, finally comes back to the depot. The objective is to minimize the total distance traveled by the salesman. Because this problem is a non-deterministic polynomial (NP-hard) problem in nature, it requires a non-polynomial time complexity at runtime to produce a solution. Therefore, a reactive bone route algorithm called RBRA is used for solving the TSP in which several local search algorithms as an improved procedure are applied. This process avoids the premature convergence and makes better solutions. Computational results on several standard instances of TSP show the efficiency of the proposed algorithm compared to other meta-heuristic algorithms.
Hamed Soleimani; Tahere Fattahi Ferdos
Abstract
Today, HSE (health, safety, and environment) systems play a vital role in green and sustainable aspects of the companies. However, performance evaluation of HSE systems is a crucial issue in industry and academia. This paper tries to identify and prioritize the effective factors in HSE performance in ...
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Today, HSE (health, safety, and environment) systems play a vital role in green and sustainable aspects of the companies. However, performance evaluation of HSE systems is a crucial issue in industry and academia. This paper tries to identify and prioritize the effective factors in HSE performance in Iran Khodro (the largest automotive company in Iran) and Tabriz Petrochemical (one of the biggest Iranian petrochemical company). The factors are achieved through the literature and recent publications and then they are customized by the expert's opinions. Finally, a hybrid Fuzzy DEMATEL ANP approach is developed for prioritization of the factors. Indeed, Fuzzy DEMATEL is used in order to determine the relations among factors and sub factors and to help in providing ANP super matrix. Afterward, the Fuzzy ANP is proposed to find the final weights of the factors and sub factors. The weights are used in order to prioritize the factors for two selected companies.
Hoda Moradi; Mozhde Rabbani; Hamid Babaei Meybodi; Mohammad Taghi Honari
Abstract
This paper presents a common set of weights (CSWs) method for multi-stage or network structured decision-making units (DMUs). The decision-making approaches proposed here consist of three stages. In the first step, a hybrid dynamic network data envelopment analysis (DNDEA) model is used to determine ...
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This paper presents a common set of weights (CSWs) method for multi-stage or network structured decision-making units (DMUs). The decision-making approaches proposed here consist of three stages. In the first step, a hybrid dynamic network data envelopment analysis (DNDEA) model is used to determine the efficiency values of the supply chain. Next, a CSW model is developed using the range-adjusted measure (RAM). In the third step, the extracted CSWs are used to compute a separate weight for each component of each DMU. the extracted CSWs are then used in the third step to calculate DMUs weights separately for each component. Then the overall efficiency is obtained by weighted averaging of the efficiency of individual components. Thus, this model evaluates the overall efficiency of a network process as well as the contribution of individual network components. The results of this study demonstrate the model’s capability to evaluate the efficiency of dynamic network structures with very high discriminatory power. In an implementation of the model in a case study, only one supplier (KARAN) earned the maximum efficiency value, and the efficiency scores of other suppliers were in the range of 0.6409-0.9983. After applying the CSWs, KARAN remained the most efficient supplier, and the efficiency scores of other suppliers moved to the range of 0.5002-0.9349. The range shifted to 0.4823-0.9921 after applying the stages weights. This weighting method should be considered an integral part of such modeling procedures, Given the enhancement observed in the results of CSW after incorporating the component weights.
M. Sayyah; H. Larki; M. Yousefikhoshbakht
Volume 3, Issue 1 , June 2016, , Pages 15-38
Abstract
One of the most important extensions of the capacitated vehicle routing problem (CVRP) is the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) where customers require simultaneous delivery and pick-up service. In this paper, we propose an effective ant colony optimization (EACO) ...
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One of the most important extensions of the capacitated vehicle routing problem (CVRP) is the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) where customers require simultaneous delivery and pick-up service. In this paper, we propose an effective ant colony optimization (EACO) which includes insert, swap and 2-Opt moves for solving VRPSPD that is different with common ant colony optimization (ACO). ACO is a meta-heuristic algorithm inspired by the foraging behavior of real ants. Artificial ants are used to build a solution for the problem by using the pheromone information from previously generated solutions. An extensive numerical experiment is performed on 68 benchmark problem instances involving up to 200 customers available in the literature. The computational result shows that EACO not only presented a very satisfying scalability, but also was competitive with other meta-heuristic algorithms such as tabu search, large neighborhood search, particle swarm optimization and genetic algorithm for solving VRPSPD problems.
F. Tavan; S.M. Sajadi
Volume 2, Issue 1 , June 2015, , Pages 16-26
Abstract
This paper, considers Network Failure Manufacturing System (NFPMS) and production control policy of unreliable multi-machines, multi-products with perishable items. The production control policy is based on the Hedging Point Policy (HPP). The important point in the simulation of this system ...
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This paper, considers Network Failure Manufacturing System (NFPMS) and production control policy of unreliable multi-machines, multi-products with perishable items. The production control policy is based on the Hedging Point Policy (HPP). The important point in the simulation of this system is assumed that the customers who receive perishable item are placed in priority queue of the customers who are faced with shortage. The main goal of this paper is determining of optimal production rates that minimizes average total cost consist of shortage, production, holding and perishable costs. Because of uncertainly and complexity of this system, simulation optimization of this system using ARENA software has been done. A numerical example will show the efficiency of the proposed approach.
Z. Hajirahimi; M. Khashei
Volume 3, Issue 2 , December 2016, , Pages 17-32
Abstract
Despite several individual forecasting models that have been proposed in the literature, accurate forecasting is yet one of the major challenging problems facing decision makers in various fields, especially financial markets. This is the main reason that numerous researchers have been devoted to develop ...
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Despite several individual forecasting models that have been proposed in the literature, accurate forecasting is yet one of the major challenging problems facing decision makers in various fields, especially financial markets. This is the main reason that numerous researchers have been devoted to develop strategies to improve forecasting accuracy. One of the most well established and widely used solutions is hybrid methodologies that combine linear statistical and nonlinear intelligent models. The main idea of these methods is based on this fact that real time series often contain complex patterns. So single models are inadequate to model and process all kinds of existing relationships in the data, comprehensively. In this paper, the auto regressive integrated moving average (ARIMA) and artificial neural networks (ANNs), which respectively are the most important linear statistical and nonlinear intelligent models, are selected to construct a set of hybrid models. In this way, three combination architectures of the ARIMA and ANN models are presented in order to lift their limitations and improve forecasting accuracy in financial markets. Empirical results of forecasting the benchmark data sets including the opening of the Dow Jones Industrial Average Index (DJIAI), closing of the Shenzhen Integrated Index (SZII) and closing of standard and poor’s (S&P 500) indicates that hybrid models can generate superior results in comparison with both ARIMA and ANN models in forecasting stock prices.
Kamran Karimi Movahed; Ali Ghodratnama
Abstract
In this paper, we consider a newsvendor who is going to invest on dedicated or flexible capacity, our goal is to find the optimal investment policy to maximize total profit while the newsvendor faces uncertainty in lead time and demand simultaneously. As highlighted in literature, demand is stochastic, ...
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In this paper, we consider a newsvendor who is going to invest on dedicated or flexible capacity, our goal is to find the optimal investment policy to maximize total profit while the newsvendor faces uncertainty in lead time and demand simultaneously. As highlighted in literature, demand is stochastic, while lead time is constant. However, in reality lead time uncertainty decreases newsvendor's performance and increases purchasing cost. Analytical results suggest an approach for decision makers to decide which situation is optimal to invest in flexible capacity. Furthermore, we derive a closed-form solution for optimal production and capacity under dedicated and flexible policy when demand and lead time follow uniform and normal distribution. An approximation method introduced in this paper to find the optimal production quantity and investment policy results show that this approximation is useful when the coefficient of variation is low under uniform distribution, and it is useful when the coefficient of variation is high under normal distribution. Finally, we show a threshold, considering the fact that it is optimal for the newsvendor to invest on flexible capacity when flexible capacity cost is less than the threshold. To sum up, we measure the effect of lead time variability on optimal solution.
Mohammad Saber Fallah nezhad; Yousof Shamstabar; Mohammad Mahdi Vali Siar
Abstract
The CCC-r chart is developed based on cumulative count of a conforming (CCC) control chart that considers the cumulative number of items inspected until observing r nonconforming ones. Typically, the samples obtained from the process are analyzed through 100% inspection to exploit the CCC-r chart. However, ...
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The CCC-r chart is developed based on cumulative count of a conforming (CCC) control chart that considers the cumulative number of items inspected until observing r nonconforming ones. Typically, the samples obtained from the process are analyzed through 100% inspection to exploit the CCC-r chart. However, considering the inspection cost and time would limit its implementation. In this paper, we investigate the performance of CCC-r chart with variable sampling interval (CCC-rVSI chart). The efficiency of CCC-rVSI chart is compared with fixed sampling interval (FSI) scheme of CCC-r chart (CCC-rFSI chart) and CCCVSI chart. The comparison results show that CCC-rVSI chart is more efficient than the CCCVSI chart in reducing the average time to signal (ATS) and also CCC-rVSI chart performs better than CCC-rFSI chart. In addition, some sensitivity analyses are performed to illustrate the effect of the input parameters on the performance of CCC-rVSI chart.
Behzad Maleki Vishkaei; Iraj Mahdavi; Nezam Mahdavi Amiri; Esmaile Khorram
Abstract
Public Bicycle Sharing System (PBSS) is used as a way to reduce traffic and pollution in cities. Its performance is related to availability of bicycles for picking up and free docks to return them. Existence of different demand types leads to the emergence of imbalanced stations. Here, we try to balance ...
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Public Bicycle Sharing System (PBSS) is used as a way to reduce traffic and pollution in cities. Its performance is related to availability of bicycles for picking up and free docks to return them. Existence of different demand types leads to the emergence of imbalanced stations. Here, we try to balance inventory of stations via defining maximal response rates for each type of rental request. If the maximal response rate for a destination is lower than 100 percent, a part of the proposed destination requests is rejected in the hope of balancing the inventory. The goal is to minimize the mean extra inventory and the mean rejected requests by providing proper amounts of the maximal response rates. An approximation method named as Mean Value Analysis (MVA) is used to develop a genetic algorithm for solving the problem. Different examples are worked through to examine the applicability of the proposed method. The results show that the proposed policy leads to a significant improvement and reduces the users’ dissatisfaction.
T.H. Hejazi; I. Soleimanmeigouni
Volume 1, Issue 1 , November 2014, , Pages 20-30
Abstract
Long-term planning is a challenging process for dealing with problems in big industries. Quick and flexible process of responding to the existing variable requirements are considered in such problems. Some of important strategic decisions which should be made in this field are, namely the way that manufacturing ...
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Long-term planning is a challenging process for dealing with problems in big industries. Quick and flexible process of responding to the existing variable requirements are considered in such problems. Some of important strategic decisions which should be made in this field are, namely the way that manufacturing facilities should be applied as well as assignment and design the system of delivery of orders. On the other hand, by using the small core and big network viewpoint in planning, such decisions should be made in a concentrated way. In this paper, a robust multi criteria group decision making model based on TOPSIS method is proposed, which evaluates the requirements of a real case study. In this regard, firstly important criteria in such environments would be determined. Secondly, using expert’s opinions and statistical analysis methods the group multi criteria decision making model would be constructed.
Pooyan Hajy Alikhani; Mohamad Reza Sadeghi Moghadam; Seyed Mostafa Razavi; Ali Mohaghar
Abstract
Retailers commonly offer discounts to encourage consumers to purchase more products thereby increasing retailers’ revenues. This article focuses on modeling the seller pricing decisions by using agent-based approach when the price, as a tool of revenue management, decreases. Considering the seller ...
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Retailers commonly offer discounts to encourage consumers to purchase more products thereby increasing retailers’ revenues. This article focuses on modeling the seller pricing decisions by using agent-based approach when the price, as a tool of revenue management, decreases. Considering the seller as an agent who uses price changes to maximize its total revenues, the objective of this research is to find the proper seller’s decision about the rate of discount on products in 3 different scenarios. In the first scenario, all products’ price elasticity of demand are the same and the products have relatively elastic demand. In the second scenario, all goods have the same price elasticity of demand and have relatively inelastic demands. The third scenario presents a combination of the first and the second scenarios in which the price elasticity of demand of products are different and goods with elastic and inelastic demand are placed next to each other. Also, all goods in each scenario are substitutes. In the first scenario, reducing the price causes the downward trend in rate of profit even though the discount could increase the revenue. In the second scenario, the agent behaves differently which offering the discount does not increase the revenue. In the third scenario, the products’ discount increases the revenue with a slope less than the first scenario. Also, the discount for all products doesn’t cause income growth. Therefore, some goods without any discount remain in shelf. Consequently, the proposed model in this research shows the proper rate of discount on each product in different product layouts.