eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2018-08-01
25
4
2233
2253
10.24200/sci.2017.4451
4451
Bi-objective scheduling for the re-entrant hybrid flow shop with learning effect and setup times
S.M. Mousavi
mousavi.sme@gmail.com
1
I. Mahdavi
2
J. Rezaeian
3
M. Zandieh
4
Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, G. C., Tehran, Iran
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
http://scientiairanica.sharif.edu/article_4451_7c9099f9365f4664fc19f2f45c35b232.pdf
Re-entrant hybrid flow shop
Setup times
learning effect
Multi-objective problems
A priori approach
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2018-08-01
25
4
2254
2266
10.24200/sci.2017.4535
4535
Pricing and advertising decisions in a dominant-retailer supply chain: A multi-follower bi-level programming approach
Maryam Mokhlesian
1
Seyed Hessameddin Zegordi
2
Faculty of Industrial & Systems Engineering, Tarbiat Modares University, Tehran, Iran
Faculty of Industrial & Systems Engineering, Tarbiat Modares University, Tehran, Iran
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.
http://scientiairanica.sharif.edu/article_4535_722999d93fa822a922e650b230049e01.pdf
Bi-level Programming
Pricing
Dominant-retailer supply chain
Substitutable and perishable products
cooperative advertising
Simulated annealing
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2018-08-01
25
4
2267
2281
10.24200/sci.2017.4458
4458
Optimization of multi-response problems with continuous functional responses by considering dispersion effects
Mohammad Hasan Bakhtiarifar
1
Mahdi Bashiri
bashiri@shahed.ac.ir
2
Amirhossein Amiri
amirhossein.amiri@gmail.com
3
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
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 target function. A probabilistic method is applied to consider the correlation structure of functional responses. Three numerical examples and a real case from literature are studied to show the efficiency of the proposed method
http://scientiairanica.sharif.edu/article_4458_bd1e2d61c4628ccde0e584f4013394c6.pdf
Multiple Responses Optimization
Functional Responses
Design of Experiments
Polynomial integral
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2018-08-01
25
4
2282
2296
10.24200/sci.2017.4452
4452
Optimizing the number of outbound doors in the crossdock based on a new queuing system with the assumption of beta arrival time
A. Motaghedi-Larijani
1
M. Aminnayeri
wxyjzzje@scientiaunknown.non
2
Amirkabir University of Technology, Tehran, Iran
Amirkabir University of Technology, Tehran, Iran
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.
http://scientiairanica.sharif.edu/article_4452_f7ad89eac5cacd13265d42b94eceded0.pdf
crossdock
queuing system
non-stationary
order statistics
conditional probability
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2018-08-01
25
4
2297
2311
10.24200/sci.2017.4456
4456
Robust-fuzzy model for supplier selection under uncertainty: An application to the automobile industry
Masood Rabieh
1
Mohammad Modarres
modarres@sharif.edu
2
Adel Azar
azara@modares.ac.ir
3
Department of Industrial Management, Shahid Beheshti University, Tehran, Iran
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
Department of Industrial Management, Tarbiat Modares University, Tehran, Iran
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.
http://scientiairanica.sharif.edu/article_4456_8f3960b62e2faa205c7efcbffe8e6419.pdf
Supplier selection
uncertainty
robust optimization
Fuzzy programming
Robust-fuzzy model
Auto industry
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2018-08-01
25
4
2312
2330
10.24200/sci.2017.4450
4450
A cluster-based emergency vehicle routing problem in disaster with reliability
Zahra Gharib
1
Ali Bozorgi-Amiri
2
Reza Tavakkoli-Moghaddam
tavakoli@gmail.com
3
Esmaeil Najafi
4
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
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.
http://scientiairanica.sharif.edu/article_4450_caeea5190fbcd2e9d58fbdcb33557f73.pdf
disaster
Relief distribution
Vehicle routing problem
Clustering
Reliability
multi-objective meta-heuristics
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2018-08-01
25
4
2331
2346
10.24200/sci.2017.4453
4453
Multi-machine economic production quantity for items with scrapped and rework with shortages and allocation decisions
Amir Hossein Nobil
amirhossein.nobil@yahoo.com
1
Amir Hosein Afshar Sedigh
2
Leopoldo Eduardo Cárdenas-Barrón
3
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Department of Information Science, University of Otago, Dunedin, New Zealand
School of Engineering and Sciences Tecnológico de Monterrey Ave. E. Garza Sada 2501 Sur, C.P. 64849, Monterrey, Nuevo León, México
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.
http://scientiairanica.sharif.edu/article_4453_45f82911c5ba1e39afe0a362b254d314.pdf
EPQ
defective item
MINLP
Shortage
Hybrid algorithm
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2018-08-01
25
4
2347
2360
10.24200/sci.2017.4457
4457
Inventory of complementary products with stock-dependent demand under vendor-managed inventory with consignment policy
M. Hemmati
m_hemmati@aut.ac.ir
1
S.M.T. Fatemi Ghomi
fatemi@aut.ac.ir
2
Mohsen S. Sajadieh
3
Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez avenue, 1591634311, Tehran, Iran
Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez avenue, 1591634311, Tehran, Iran
Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez avenue, 1591634311, Tehran, Iran
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.
http://scientiairanica.sharif.edu/article_4457_042b43b030527711b132eaa5edbcf032.pdf
supply chain coordination
Inventory control
Complementary products
Stock dependent demand
Consignment
vendor managed inventory
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2018-08-01
25
4
2361
2372
10.24200/sci.2017.4455
4455
Efficient ratio-type estimators of finite population mean based on correlation coefficient
Muhammad Irfan
mirfan@zju.edu.cn
1
Maria Javed
2
Zhengyan Lin
zlin@zju.edu.cn
3
Department of Mathematics, Institute of Statistics, Zhejiang University, Hangzhou 310027, China
Department of Mathematics, Institute of Statistics, Zhejiang University, Hangzhou 310027 China
Department of Mathematics, Institute of Statistics, Zhejiang University, Hangzhou 310027 China.
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.
http://scientiairanica.sharif.edu/article_4455_e8c488486d6fffdaf17aa071e1ebec7f.pdf
Auxiliary variable
Bias
Correlation Coefficient
Efficiency
Mean squared error
Ratio-type estimators
eng
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
2018-08-01
25
4
2373
2388
10.24200/sci.2017.4454
4454
A novel correlation coefficient of intuitionistic fuzzysets based on the connection number of set pair analysis and its application
Harish Garg
harishg58iitr@gmail.com
1
Kamal Kumar
2
School of Mathematics, Thapar University Patiala 147004, Punjab, India
School of Mathematics, Thapar University Patiala 147004, Punjab, India
Set pair analysis (SPA) is an updated theory for dealing with the uncertainty, which overlaps the othertheories of uncertainty such as probability, vague, fuzzy and intuitionistic fuzzy set (IFS). Considering thefact that the correlation coecient plays an important role during the decision-making process, in this paper,after pointing out the weakness of the existing correlation coecients between the IFSs, we propose a novelcorrelation coecient and weighted correlation coecients formulation to measure the relative strength of thedierent IFSs. For it, rstly corresponding to each intuitionistic fuzzy number, the connection number of theSPA theory has been formulated in the form of the degree of identity, discrepancy and contrary and thenbased on its, a novel correlation coecient measures have been dened. Pairs of identity, discrepancy andcontrary of the connection number have been taken as a vector representation during the formulation. Lastly,a decision-making approach based on the proposed measures has been presented which has been illustratedby two numerical examples in pattern recognition and medical diagnosis.
http://scientiairanica.sharif.edu/article_4454_c5b19f1ec5c2826fda26dbc02bab5382.pdf
Set pair analysis
Connection number
Intuitionistic fuzzy set
Pattern recognition
Medical diagnosis
decision making