Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
25
4
2018
08
01
Bi-objective scheduling for the re-entrant hybrid flow shop with learning effect and setup times
2233
2253
EN
S.M.
Mousavi
Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
mousavi.sme@gmail.com
I.
Mahdavi
Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
J.
Rezaeian
Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
M.
Zandieh
Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, G. C., Tehran, Iran
10.24200/sci.2017.4451
The production scheduling problem in hybrid flow shops is a complex combinatorial optimization problem observed in many real-world applications. The standard hybrid flow shop problem involves often unrealistic assumptions. In order to address the realistic assumptions, four additional traits were added to the proposed problem. These include re-entrant line, setup times, position-dependent learning effects, and the consideration of maximum completion time together with total tardiness as objective function. Since the proposed problem is non-deterministic polynomial-time (NP)-hard, a meta-heuristic algorithm is proposed as the solution procedure. The solution procedure is categorized as an a priori approach. To show the efficiency and effectiveness of the proposed algorithm, computational experiments were done on various test problems. Computational results show that the proposed algorithm can obtain an effective and appropriate solution quality for our investigated problem
Re-entrant hybrid flow shop,Setup times,learning effect,Multi-objective problems,A priori approach
https://scientiairanica.sharif.edu/article_4451.html
https://scientiairanica.sharif.edu/article_4451_7c9099f9365f4664fc19f2f45c35b232.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
25
4
2018
08
01
Pricing and advertising decisions in a dominant-retailer supply chain: A multi-follower bi-level programming approach
2254
2266
EN
Maryam
Mokhlesian
Faculty of Industrial & Systems Engineering, Tarbiat Modares University, Tehran, Iran
Seyed Hessameddin
Zegordi
Faculty of Industrial & Systems Engineering, Tarbiat Modares University, Tehran, Iran
10.24200/sci.2017.4535
Pricing and advertising is one of the most important decisions in each supply chain especially in the competitive environment. In the previous studies, this is as a centralized decision. However, if each channel member makes its decision independently, the utility of all members is optimized. In such decentralized situations, the channel members may have different market power that they influence on the other members’ decisions. These issues can modeled through leader-follower Stackelberg game or bi-level programming. This study investigates coordination of pricing and cooperative advertising in a two-stage supply chain consisting of one dominant-retailer and multiple competitive manufactures which produce several perishable and substitutable products. This paper aims to determine pricing and cooperative advertising decisions expenditure as well as the amount of manufacturers’ production or retailer’s purchase such that utility of all members is met. Hence, the problem is modeled as a multi-follower bi-level programming problem. Since it is proved that the model is NP-hard, the proposed model is solved through simulated annealing. A numerical example is used to show the impact of demand’s variations on the members’ decisions.
Bi-level Programming,Pricing,Dominant-retailer supply chain,Substitutable and perishable products,cooperative advertising,Simulated annealing
https://scientiairanica.sharif.edu/article_4535.html
https://scientiairanica.sharif.edu/article_4535_722999d93fa822a922e650b230049e01.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
25
4
1999
11
30
Optimization of multi-response problems with continuous functional responses by considering dispersion effects
2267
2281
EN
Mohammad Hasan
Bakhtiarifar
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
Mahdi
Bashiri
0000-0002-5448-1773
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
bashiri@shahed.ac.ir
Amirhossein
Amiri
0000-0002-2385-8910
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
amirhossein.amiri@gmail.com
10.24200/sci.2017.4458
In some processes, quality of a product should be characterized by functional relationships between response variables and a signal factor. Hence the traditional methods cannot be used to find the optimum solution. In this paper, we propose a method which considers two different dispersion effects, i.e. in domain and between replicates variations in the functional responses. Besides, we propose an integral based measure to find the deviation from t
Multiple Responses Optimization,Functional Responses,Design of Experiments,Polynomial integral
https://scientiairanica.sharif.edu/article_4458.html
https://scientiairanica.sharif.edu/article_4458_bd1e2d61c4628ccde0e584f4013394c6.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
25
4
2018
08
01
Optimizing the number of outbound doors in the crossdock based on a new queuing system with the assumption of beta arrival time
2282
2296
EN
A.
Motaghedi-Larijani
Amirkabir University of Technology, Tehran, Iran
M.
Aminnayeri
Amirkabir University of Technology, Tehran, Iran
wxyjzzje@scientiaunknown.non
10.24200/sci.2017.4452
Crossdocking is one of the supply chain strategies that can reduce transportation and inventory costs. Many studies are conducted the problem of crossdocking by considering various characteristics of crossdocks. In this paper, a queuing model is proposed in order to optimize the number of outbound doors based on minimizing the total costs including the costs of adding a new outbound door and the expected waiting time of customers. The total number of trucks arriving for service is constant. Trucks arrive to outbound doors of the crossdock within a specified time window. Arrival times of trucks follow a beta distribution and customers to be served based on first in first out policy (FIFO). Since, the total number of customers as well as the time of arrivals are finite, the steady state distribution for the long run of the system is inapplicable. Instead, based on conditional joint probabilities, order statistics along with the Bayes theorem we calculate the total expected waiting time.
crossdock,queuing system,non-stationary,order statistics,conditional probability
https://scientiairanica.sharif.edu/article_4452.html
https://scientiairanica.sharif.edu/article_4452_f7ad89eac5cacd13265d42b94eceded0.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
25
4
2018
08
01
Robust-fuzzy model for supplier selection under uncertainty: An application to the automobile industry
2297
2311
EN
Masood
Rabieh
Department of Industrial Management, Shahid Beheshti University, Tehran, Iran
Mohammad
Modarres
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
modarres@sharif.edu
Adel
Azar
Department of Industrial Management, Tarbiat Modares University, Tehran, Iran
azara@modares.ac.ir
10.24200/sci.2017.4456
This paper proposes an innovative robust-fuzzy method for multi-objective, multi-period supplier selection problem under multiple uncertainties. This approach integrates robust optimization and fuzzy programming. Uncertain parameters are modeled as random variables that take value within a symmetrical interval. However, due to the complexity or ambiguity of some real world problems and specially the nature of some of the available input data, the length of interval is also highly uncertain. This ambiguity motivated us to present a new approach, which can be applicable to multiple uncertainties conditions. Thus, in our approach the half-length of these intervals is also represented by fuzzy membership function. We develop a model and a solution approach to select suppliers by considering risk. The proposed method is applied to a real case of supplier selection in automobile industry under uncertainty and ambiguity conditions. To verify the proposed model, we evaluated the results by simulation technique and compared values of objective function under different scenarios.
Supplier selection,uncertainty,robust optimization,Fuzzy programming,Robust-fuzzy model,Auto industry
https://scientiairanica.sharif.edu/article_4456.html
https://scientiairanica.sharif.edu/article_4456_8f3960b62e2faa205c7efcbffe8e6419.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
25
4
2018
08
01
A cluster-based emergency vehicle routing problem in disaster with reliability
2312
2330
EN
Zahra
Gharib
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Ali
Bozorgi-Amiri
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Reza
Tavakkoli-Moghaddam
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
tavakoli@gmail.com
Esmaeil
Najafi
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
10.24200/sci.2017.4450
In the event of natural disasters, relief distribution is the most challenging problem of emergency transportation. What is important in response to disaster is victims’ relief in disaster areas with the quick distribution of vital commodity. In this regard, damage to infrastructure (e.g., roads) can make trouble in designing a distribution network. So, this paper considers a problem using a three-stage approach. In the first stage, pre-processing of model inputs is done through an artificial neural fuzzy inference system (ANFIS) followed by investigating the safest route for each cluster using of decision-making techniques and graph theory. In the second stage, a heterogeneous multi-depots multi-mode vehicle routing problem is formulated for minimizing the transportation time and maximize the reliability. Finally, since the routing problem is NP-hard, two multi-objective meta-heuristic algorithms, namely non-dominated sorting genetic algorithm (NSGA-II) and multi-objective firefly algorithm (MOFA), are proposed to obtain the optimal solution and compared their performance through a set of randomly generated test problems. The results show that for this routing problem, the MOFF gives better solutions in comparison to NSGA-II and performs well in terms of accuracy and solution time.
disaster,Relief distribution,Vehicle routing problem,Clustering,Reliability,multi-objective meta-heuristics
https://scientiairanica.sharif.edu/article_4450.html
https://scientiairanica.sharif.edu/article_4450_caeea5190fbcd2e9d58fbdcb33557f73.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
25
4
2018
08
01
Multi-machine economic production quantity for items with scrapped and rework with shortages and allocation decisions
2331
2346
EN
Amir Hossein
Nobil
0000-0003-4769-4460
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
amirhossein.nobil@yahoo.com
Amir Hosein
Afshar Sedigh
Department of Information Science, University of Otago, Dunedin, New Zealand
Leopoldo Eduardo
Cárdenas-Barrón
School of Engineering and Sciences
Tecnológico de Monterrey
Ave. E. Garza Sada 2501 Sur, C.P. 64849, Monterrey, Nuevo León, México
10.24200/sci.2017.4453
This study considers a multi-product multi-machine economic production quantity inventory problem in an imperfect production system that produces two types of defective items: items that require rework and scrapped items. The shortage is allowed and fully backordered. The scrapped items are disposed with a disposal cost and the rework is done at the end of the normal production period. Moreover, a potential set of available machines for utilization is considered such that each has a specific production rate per item. Each machine has its own utilization cost, setup time and production rate per item. The considered constraints are initial capital to utilize machines and production floor space. The proposed inventory model is a mixed integer non-linear programing mathematical model. The problem is solved using a bi-level approach, first, the set of machines to be utilized and the production allocation of items on each machine are obtained thru a genetic algorithm. Then, using the convexity attribute of the second level problem the optimum cycle length per machine is determined. The proposed hybrid genetic algorithm outperformed conventional genetic algorithm and a GAMS solver, considering solution quality and solving time. Finally, a sensitivity analysis is also given.
EPQ,defective item,MINLP,Shortage,Hybrid algorithm
https://scientiairanica.sharif.edu/article_4453.html
https://scientiairanica.sharif.edu/article_4453_45f82911c5ba1e39afe0a362b254d314.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
25
4
2018
08
01
Inventory of complementary products with stock-dependent demand under vendor-managed inventory with consignment policy
2347
2360
EN
M.
Hemmati
Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez avenue, 1591634311, Tehran, Iran
m_hemmati@aut.ac.ir
S.M.T.
Fatemi Ghomi
0000-0003-4363-994X
Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez avenue, 1591634311, Tehran, Iran
fatemi@aut.ac.ir
Mohsen S.
Sajadieh
Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez avenue, 1591634311, Tehran, Iran
10.24200/sci.2017.4457
This paper proposes an integrated two-stage model, which consists of one vendor and one buyer for two complementary products. The vendor produces two types of products and delivers them to the buyer in distinct batches. Buyer stocks items in the warehouse and on the shelf. The demand for each product is sensitive to stock levels of both products. A vendor managed inventory with consignment stock policy is considered. The number of shipments and replenishment lot sizes are jointly determined as decision variables in such a way that total profit is maximized. The numerical study shows that as complementary rate increases, the quantity of transfers and demand of both products increase. Hence, ignoring the complementation between products leads to some customers lost.
supply chain coordination,Inventory control,Complementary products,Stock dependent demand,Consignment,vendor managed inventory
https://scientiairanica.sharif.edu/article_4457.html
https://scientiairanica.sharif.edu/article_4457_042b43b030527711b132eaa5edbcf032.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
25
4
2018
08
01
Efficient ratio-type estimators of finite population mean based on correlation coefficient
2361
2372
EN
Muhammad
Irfan
Department of Mathematics, Institute of Statistics, Zhejiang University, Hangzhou 310027, China
mirfan@zju.edu.cn
Maria
Javed
Department of Mathematics, Institute of Statistics, Zhejiang University, Hangzhou 310027 China
Zhengyan
Lin
Department of Mathematics, Institute of Statistics, Zhejiang University, Hangzhou 310027 China.
zlin@zju.edu.cn
10.24200/sci.2017.4455
We proposed efficient families of ratio-type estimators to estimate finite population mean using known correlation coefficient between study variable and auxiliary variable by adopting Singh and Tailor [Singh, H. P., and Tailor, R. “Use of known correlation coefficient in estimating the finite population means”, Statistics in Transition, 6(4), pp. 555-560 (2003)] estimator and Kadilar and Cingi [Kadilar, C., and Cingi, H. “An improvement in estimating the population mean by using the correlation coefficient”, Hacettepe Journal of Mathematics and Statistics, 35(1) pp. 103-109. (2006a)] class of estimators in simple random sampling without replacement. The newly proposed estimators behave efficiently as compared to the common unbiased estimator, traditional ratio estimator and the other competing estimators. Bias, mean squared error and minimum mean squared error of the proposed ratio-type estimators are derived. Moreover, theoretically findings are proved with cooperation of two real data sets.<br /> <strong> </strong>
Auxiliary variable,Bias,Correlation Coefficient,Efficiency,Mean squared error,Ratio-type estimators
https://scientiairanica.sharif.edu/article_4455.html
https://scientiairanica.sharif.edu/article_4455_e8c488486d6fffdaf17aa071e1ebec7f.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
25
4
2018
08
01
A novel correlation coefficient of intuitionistic fuzzysets based on the connection number of set pair analysis and its application
2373
2388
EN
Harish
Garg
0000-0001-9099-8422
School of Mathematics, Thapar University Patiala 147004, Punjab, India
harishg58iitr@gmail.com
Kamal
Kumar
School of Mathematics, Thapar University Patiala 147004, Punjab, India
10.24200/sci.2017.4454
Set pair analysis (SPA) is an updated theory for dealing with the uncertainty, which overlaps the other<br />theories of uncertainty such as probability, vague, fuzzy and intuitionistic fuzzy set (IFS). Considering the<br />fact that the correlation coecient plays an important role during the decision-making process, in this paper,<br />after pointing out the weakness of the existing correlation coecients between the IFSs, we propose a novel<br />correlation coecient and weighted correlation coecients formulation to measure the relative strength of the<br />dierent IFSs. For it, rstly corresponding to each intuitionistic fuzzy number, the connection number of the<br />SPA theory has been formulated in the form of the degree of identity, discrepancy and contrary and then<br />based on its, a novel correlation coecient measures have been dened. Pairs of identity, discrepancy and<br />contrary of the connection number have been taken as a vector representation during the formulation. Lastly,<br />a decision-making approach based on the proposed measures has been presented which has been illustrated<br />by two numerical examples in pattern recognition and medical diagnosis.
Set pair analysis,Connection number,Intuitionistic fuzzy set,Pattern recognition,Medical diagnosis,decision making
https://scientiairanica.sharif.edu/article_4454.html
https://scientiairanica.sharif.edu/article_4454_c5b19f1ec5c2826fda26dbc02bab5382.pdf