eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2017-06-01
24
3
1483
1492
10.24200/sci.2017.4129
4129
Vendor-managed inventory system with partial backordering for evaporating chemical raw material
A.A. Taleizadeh
1
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Consider a supply chain including a renery producing evaporating chemical product, an exporter, and one or some engine oil producers outside the exporter's country. The exporter uses vendor-managed inventory system implemented between renery and exporter to decrease his/her inventory cost. This paper develops two models with partial backordering for evaporating chemical product developed in a two-layer chain includingsingle renery and single exporter with one product before and after utilizing vendormanaged inventory policy. Demand and partial backordering rates are deterministic and constant. A numerical example is provided to illustrate the applicability of the proposed model and solution method.
http://scientiairanica.sharif.edu/article_4129_8b0ae9b4308b2991e6dcf996d8a4884e.pdf
Supply chain management
Inventory
Partial backordering
Evaporating product
Deterioration
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2017-06-01
24
3
1493
1504
10.24200/sci.2017.4130
4130
Sequencing of mixed models on U-shaped assembly lines by considering effective help policies in make-to-order environment
M. Rabbani
1
N. Manavizadeh
2
N. Shabanpour
3
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, P.O. Box 11155-4563, Iran
Department of Industrial Engineering, Khatam University, Tehran, Iran.
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, P.O. Box 11155-4563, Iran
Mixed-Model Assembly Line (MMAL) is a type of production line where a variety of products' models similar to products' characteristics are assembled in the same line. Many manufacturers tend to use mixed-model assembly line in their production lines, since this policy makes it possible to assemble various products in the Make-To-Order (MTO) environment. In this research, the sequence of U-type mixed-model assembly lineis achieved through considering downstream help and storage of kits as eective help policies for reducing total line stoppages and tardiness in delivery time of products to customers. Since this problem is NP-hard, hybrid GA-Beam search algorithm is developed to solve the problem. Numerical experiments are used to evaluate the performance and effectiveness of the proposed algorithm. To the best of our knowledge, this is the first study that considers getting help from downstream worker or using storehouse of kits, which has ready-toassemble parts in the conditions that workers cannot complete the remained task in the work horizon.
http://scientiairanica.sharif.edu/article_4130_15ad642dd9480e7631ebdeccacc68678.pdf
MMAL
MTO
Kits' storage
Downstream help
Hybrid GA-beam search
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2017-06-01
24
3
1505
1518
10.24200/sci.2017.4131
4131
A projection-based approach to intuitionistic fuzzy group decision making
Z. Yue
1
Y. Jia
2
College of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang, China
Library of Guangdong Ocean University, Zhanjiang, China
Group Decision Making (GDM) is usually used for solving complex decision problems, which is an important part of modern decision science. Weight of the Decision Maker (DM) plays an important role in the GDM process, and the projection-based approach is a comprehensive consideration between decision objects. It is a valuable work to determine the weights of DMs by a projection measurement. This paper investigates a GDMmethod based on projection measurement in an intuitionistic fuzzy environment. First, this article introduces an ideal decision among all individual decisions, and the weights of DMs are determined by using a projection measurement. Then, the individual decisions are aggregated into a collective decision. Finally, the preference order of alternatives is identied by using the score and accuracy function of the intuitionistic fuzzy numbers.In addition, a comparison with another GDM method is provided. Feasibility and practicability of the developed method are illustrated by an experimental analysis. The experimental result shows that the projection-based method is a high-resolution decision method.
http://scientiairanica.sharif.edu/article_4131_b13c47748cd8c9b24dd7f0c11129e446.pdf
group decision making
Intuitionistic fuzzy number
Weight of decision maker
Projection measurement
Aggregation
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2017-06-01
24
3
1519
1532
10.24200/sci.2017.4132
4132
Development of a Cournot-oligopoly model for competition of multi-product supply chains under government supervision
A. Hafezalkotob
1
S. Borhani
2
S. Zamani
3
College of Industrial Engineering, Islamic Azad University, South Tehran Branch, Entezari Alley, Oskoui Alley, Choobi Bridge, Tehran, P.O. Box 1151863411, Iran
College of Industrial Engineering, Islamic Azad University, South Tehran Branch, Entezari Alley, Oskoui Alley, Choobi Bridge, Tehran, P.O. Box 1151863411, Iran
College of Industrial Engineering, Islamic Azad University, South Tehran Branch, Entezari Alley, Oskoui Alley, Choobi Bridge, Tehran, P.O. Box 1151863411, Iran
Globalization, increased governmental regulations, and customer demands regarding environmental issues have led the organizations to review the measures necessary for the implementation of the Green Supply Chain Management (GSCM) to improve the environmental and economical performances. The paper proposes a Cournot-oligopoly model for green supply chain management. It provides a novel approach to construct amodel that maximizes government tari and prots of the suppliers and manufacturers in all the GSCs. The bi-level model is converted to a single-level model by replacing the second level with its Karush Kuhn Tucker (KKT) conditions and linearization techniques. Then, a Genetic Algorithm (GA) is utilized to solve the single-level model using MATLAB software. Afterwards, the obtained results are compared with optimal solutions acquired by Enumerative Method (EM) to evaluate the validity and feasibility of the proposed GA. The sensitivity analysis of this model indicates that the scal policy of the government greatly affects the reduction of environmental pollution costs caused by industrial activities such as automobile production in a competitive market. Therefore, the amount of non-green products' taxes is directly related to the decrease of environmental pollution.
http://scientiairanica.sharif.edu/article_4132_ebf263e545f6210e8576949c41abe4f8.pdf
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2017-06-01
24
3
1533
1546
10.24200/sci.2017.4133
4133
A robust optimization approach for an inventory problem with emergency ordering and product substitution in an uncertain environment: A case study in pharmaceutical industry
E. Mardan
1
M. Sadegh Amalnick
2
M. Rabbani
3
F. Jolai
gmzvcalo@scientiaunknown.non
4
Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
This paper presents a multi-product, multi-period inventory problem in an uncertain environment where the main suppliers are prone to yield uncertainty. In order to overcome the arisen uncertainties, two basic approaches of emergency ordering and product substitutability are taken into consideration. In the proposed emergency ordering scheme, two sets of suppliers, i.e. cheap unreliable and expensive reliable (emergency) suppliers, are considered and a tradeo between the cheap price of the main suppliers and reliabilityof emergency supplier is attained. In product substitution scheme, the demand of each product is fullled directly by the related product or other substitute products. A riskaverse decision maker is taken into consideration whose risk-averseness level is controlled by the portion of demand which should be denitely satised and not backordered or lost. A robust optimization approach with two variability measures is proposed to minimize the variability of the model. The results reveal the value of emergency ordering and productsubstitution. In addition, the results suggest which measure should be selected according to the decision maker's attitude toward the desired prot, variability, and service level.
http://scientiairanica.sharif.edu/article_4133_f41d626a0c12eda244631ea7827a2885.pdf
Substitutable products
Emergency ordering
Yield uncertainty
Inventory problem
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2017-06-01
24
3
1547
1560
10.24200/sci.2017.4134
4134
Solving the redundancy allocation problem of k-out-of-n with non-exponential repairable components using optimization via simulation approach
P. Azimi
p.azimi@yahoo.com
1
M. Hemmati
m_hemmati@aut.ac.ir
2
A. Chambari
3
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
In this article, a new model and a novel solving method are provided to address the non-exponential redundancy allocation problem in series-parallel k-out-of-n systems with repairable components based on Optimization Via Simulation (OVS) technique. Despite the previous studies, in this model, the failure and repair times of each component were considered to have non-negative exponential distributions. This assumption makes the model closer to the reality where the majority of used components have greater chance to face a breakdown in comparison to new ones. The main objective of this research is the optimization of Mean Time to the First Failure (MTTFF) of the system via allocating the best redundant components to each subsystem. Since this objective function of the problem could not be explicitly mentioned, the simulation technique was applied to model the problem, and dierent experimental designs were produced using DOE methods. To solve the problem, some meta-Heuristic Algorithms were integrated with the simulation method. Several experiments were carried out to test the proposed approach; as a result, the proposed approach is much more real than previous models, and the near optimum solutions are also promising.
http://scientiairanica.sharif.edu/article_4134_eab1f7aed4b74bb17b82ae2a57e7325f.pdf
Redundancy allocation problem
k-out-of-n systems
meta-heuristic algorithms
Simulation Methods
Enterprise Dynamic (ED) software
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2017-06-01
24
3
1561
1570
10.24200/sci.2017.4135
4135
Fuzzy multi-objective optimization of linear functions subject to max-arithmetic mean relational inequality constraints
F. Kouchakinejad
1
M. Mashinchi
fvjdicgp@scientiaunknown.non
2
E. Khorram
3
Department of Mathematics, Graduate University of Advanced Technology, End of Haft Bagh-e-Alavi Highway, Kerman, Iran
Department of Statistics, Faculty of Mathematics and Computer Science, Shahid Bahonar University of Kerman, Pajohesh Square, 22nd Bahman Blvd, Kerman, Iran
Faculty of Mathematics and Computer Sciences, Amirkabir University of Technology, 424 Hafez Ave, Tehran, Iran.
The main goal of this work is to nd a better solution to a kind of multiobjective optimization problem subject to a system of fuzzy relational inequalities with max-arithmetic mean composition. First, this problem is solved and, then, in the case that the decision maker is not satised with any of the solutions, by assigning linear membership functions to the inequalities in the constraints and objective functions and using Bellman-Zadeh decision, a new solution is found. This new solution does not belong to the feasible domain but is considered acceptable based on the decision maker's view. In order to found this solution easier, some simplication processes are given. Afterwards, an algorithm is presented to generate the new solution. Finally, an example is given to illustrate the steps of the algorithm.
http://scientiairanica.sharif.edu/article_4135_0a276849c3dae68b89c77b153ab921c4.pdf
Fuzzy inequality
Fuzzy relational inequalities
Fuzzy solution
Linear objective function
Max-arithmetic mean composition
multi-objective optimization
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2017-06-01
24
3
1571
1584
10.24200/sci.2017.4136
4136
A new proactive-reactive approach to hedge against uncertain processing times and unexpected machine failures in the two-machine flow shop scheduling problems
D. Rahmani
1
Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
In this paper, a proactive-reactive approach has been considered for achieving stable and robust schedules despite uncertain processing times and unexpected machine failures in a two-machine flow shop system. In the literature, Surrogate Measures (SMs) have been developed for achieving stable and robust solutions against the occurrence of stochastic disruptions. These measures proactively provide an approximation of the real conditions of the system in the event of a disruption. Because of the discrepancies of these measures with their real values, a dierent approach is developed in this paper in two-step structure. First, an initial robust schedule is produced and then, based on a multi-component measure, an appropriate reaction is adopted against unexpected machine failures. Computational results indicate that this method produces better solutions compared to the other two classical scheduling approaches considering their eectivenessand performance.
http://scientiairanica.sharif.edu/article_4136_458f9edb40148e46e3b1b1bf7d7b51f2.pdf
disruption
robustness
stability
Nervousness
Flow shop
Proactive-reactive approach
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2017-06-01
24
3
1585
1602
10.24200/sci.2017.4137
4137
A bi-objective model to optimize reliability and cost of k-out-of-n series-parallel systems with tri-state components
Pe. Pourkarim Guilani
1
A. Zaretalab
2
S.T. A. Niaki
3
Pa. Pourkarim Guilani
4
Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Ave., Tehran, Iran
Department of Industrial Engineering, Sharif University of Technology, Tehran, P.O. Box 11155-9414, Iran
Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
Redundancy Allocation Problem (RAP) is one way to increase system reliability. In most of the models developed so far for the RAP, system components are considered to have a binary state consisting of \working perfect" or \completely failed". However, to suit the real-world applications, this assumption has been relaxedin this paper, such that components can have three states. Moreover, a Bi-Objective RAP (BORAP) is modeled for a system with serial subsystems, in which non-repairable tri-state components of each subsystem are congured in parallel and the subsystem works under k-out-of-n policy. Furthermore, to enhance system reliability, technical and organizational activities that can aect failure rates of the components, and hence can improve the system performance are also taken into account. The aim is to nd the optimum number of redundant components in each subsystem, such that the system reliability is maximized while the cost is minimized within some real-world constraints. In order to solve the complicated NP-hard problem at hand, the multi-objective Strength Pareto EvolutionaryAlgorithm (SPEA-II) is employed. As there is no benchmark available, the Non-dominated Sorting Genetic Algorithm (NSGA-II) is used to validate the results obtained. Finally, theperformances of the algorithms are analyzed using 20 test problems.
http://scientiairanica.sharif.edu/article_4137_fe5f2d01eddabc7d801fe5e36f01148e.pdf
Reliability
Redundancy allocation problem
Tri-state components
bi-objective optimization
SPEA-II
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2017-06-01
24
3
1603
1614
10.24200/sci.2017.4138
4138
Monitoring of serially correlated processes using residual control charts
R. Osei-Aning
1
S.A. Abbasi
2
M. Riaz
3
Department of Mathematics and Statistics, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia
Department of Mathematics and Statistics, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia
Department of Mathematics and Statistics, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia
Control charts act as the most important tool for monitoring of process parameters. The assumption of independence that underpins the implementation of the charts is violated when process observations are correlated. The eect of this issue can lead to the malfunctioning of the usual control charts by causing a large number of false alarms or slowing the detection ability of the chart in unstable situations. In this paper, weinvestigated the performance of the Mixed EWMA-CUSUM and Mixed CUSUM-EWMA charts for the ecient monitoring of autocorrelated data. The charts are applied to the residuals obtained from tting an autoregressive (AR) model to the autocorrelated observations. The performance of these charts is compared with the performances of the residual Shewhart, EWMA, CUSUM, combined Shewhart-CUSUM, and combinedShewhart-EWMA charts. Performance criteria such as Average Run Length (ARL) and Extra Quadratic Loss (EQL) are used for the evaluation and comparison of the charts. Illustrative examples are presented to demonstrate the application of the charts to serially correlated observations
http://scientiairanica.sharif.edu/article_4138_561a0fe1bf01766e340e58342fcc4fdc.pdf
Autocorrelation
Average Run Length
CUSUM
EWMA
Extra Quadratic Loss
Residuals