Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
22
3
2015
06
01
Service Centers Location Problem Considering Service Diversity within Queuing Framework
1103
1116
EN
Fezzeh
Partovi
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
fezi_parto@yahoo.com
Mehdi
Seifbarghy
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
m.seifbarghy@qiau.ac.ir
In this paper, a new model is developed considering diversity of service in service centers location problem. It is assumed that different services can be provided at each service center. The model has three objective functions including: minimizing the sum of customers’ travel time and waiting time in service centers, balancing service loads among the given centers, and minimizing the total establishment costs of service centers and assignment costs of servers. Different number of servers can be assigned to each service center. Regarding the allocation of customers to the service centers, each customer patronizes with respect to the distance to the center, the attractiveness of each service center’s site for the customer and the number of located servers at the service center. Since the proposed model is of nonlinear integer programming type and is of high complexity in solving, two meta-heuristic based heuristics using particle swarm optimization (PSO) and variable neighborhood search (VNS) are proposed inorder to solve the problem. Different sizes of numerical examples are designed and solved in order to compare the efficiency of the heuristics.
location,Queuing systems,Service diversity,particle swarm optimization,Variable neighborhood search
http://scientiairanica.sharif.edu/article_3704.html
http://scientiairanica.sharif.edu/article_3704_2814d53e65bae49427c161d14e289536.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
22
3
2015
06
01
A Novel Approach in Multi Response Optimization for Correlated Categorical Data
1117
1129
EN
Reza
Kamranrad
Department of Industrial Engineering, Shahed University, Tehran, Iran
Mahdi
Bashiri
Department of Industrial Engineering, Shahed University, Tehran, Iran
bashiri.m@gmail.com
The main purpose of this paper is the optimization of multiple categorical correlated responses. So, a heuristic approach and log-linear model has been used to simultaneous estimation of responses surface parameters. Parameters estimation has been performed with the aim of maximizing the number of concordance. The concordance means that the joint probability for the occurrence of dependent responses in each treatment is more than the otherprobabilities inthe same treatment. The second step of this research is the optimization of multi correlated responses for categorical data using some practical Meta heuristic algorithms such as Simulated Annealing, Tabu Search and Genetic Algorithm. Using each Meta heuristic algorithm, best controllable factors are selectedto maximizing the joint probability of success. Three simulated numerical examples with different sizes have been used to describe the proposed algorithms. Results show the superiority of the joint success probability values in the Tabu Search algorithm comparing to the other approaches.
Multi response optimization,Categorical data,Correlated responses,parameter estimation,concordance,Meta heuristic algorithm
http://scientiairanica.sharif.edu/article_3705.html
http://scientiairanica.sharif.edu/article_3705_f206254696456e04dfde5173ce873944.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
22
3
2015
06
01
An Evolutionary Algorithm for Supplier Order Allocation with Fuzzy Parameters Considering Linear and Volume Discount
1130
1141
EN
Mahsa
Soufi Neyestani
IE Department, University of Tafresh, Tafresh, Iran
Fariborz
Jolai
IE Department, College of engineering, University of Tehran, Tehran, Iran
fjolai@ut.ac.ir
Hamid Reza
Golmakani
IE Department, University of Tafresh, Tafresh, Iran
golmakni@mie.utoronto.ca
In this research, supplier order allocation problem is investigated. The problem is that one buyer wants to allocate required products to pre-selected suppliers. Allocation is considered under some constraints such as capacity, delivery rate, linear discount and volume discount. Objectives of the model are maximizing the total value of purchase, minimizing the total cost of purchase and minimizing the total number of defective products purchased. We propose a Multi-Objective Mixed Integer Non-Linear (MOMINL) model, for multi-period suppliers order allocation, in situation where suppliers offer discount. In practice, some information such as buyer demand and suppliers delivery rate is uncertain, so fuzzy sets are applied for handling uncertainty. Since PSO and GA are one of the most effective methods to find a good solution to a difficult Multi-Objective Problem (MOP), a multi-objective optimization algorithm based on PSO and GA (MOPSOGA) is developed to solve the model and give a set of Pareto optimal solutions. The efficiency of the Pareto Archive obtained from the algorithm is evaluated based on spacing and diversity metrics.
multi-period multi-product supplier order allocation,linear discount,volume discount,Jimenez method,PSO,GA
http://scientiairanica.sharif.edu/article_3706.html
http://scientiairanica.sharif.edu/article_3706_51c26c6afd25fd85eaf3cd55cd6ee13e.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
22
3
2015
06
01
A Revision on Area Ranking and Deviation Degree Methods of Ranking Fuzzy Numbers
1142
1154
EN
Reza
Ghasemi
Department of Industrial Engineering, K.N Toosi University of Technology, Tehran, Iran
rghasemi@mail.kntu.ac.ir
Mohsen
Nikfar
Department of Industrial Engineering, K.N Toosi University of Technology, Tehran, Iran
mohsen.nikfar@gmail.com
Emad
Roghanian
Department of Industrial Engineering, K.N Toosi University of Technology, Tehran, Iran
e_roghanian@kntu.ac.ir
Recently two important methods ([1],[2]) [Wang. Zh.X, Liu. Y.J, and Feng. B, “Ranking L–R fuzzy number based on deviation degree”. information science(2009). pp 2070-2077.],and [Wang.Y.M, and Luo. Y, “Area ranking of fuzzy numbers based on positive and negative ideal points.’’ Computers and Mathematics with Applications(2009). pp 1769-1779.] proposed for ranking fuzzy numbers. But we found that they both have a same basic disadvantage. In this paper after a short review on different proposed fuzzy number ranking methods, we explain the drawback on deviation degree and the area ranking methods and provide an improvement method to overcome this shortage. Our approach is based on the maximization set and minimization set methods concepts. The results show the superiority of the proposed method in comparison with other ranking methods, especially when the ranking of the inverse and the symmetry of the fuzzy numbers is of interest.
Fuzzy number,Deviation degree,Area rankingRisk attitude,Maximization set,Minimization set
http://scientiairanica.sharif.edu/article_3707.html
http://scientiairanica.sharif.edu/article_3707_73aea3ce9022b2b6ac8dbc2aec3173e8.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
22
3
2015
06
01
An ε-constraint multi-objective optimization model for web-based convergent product networks using the Steiner tree
1155
1170
EN
Reza
Hassanzadeh
Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
rhz_1974@ustmb.ac.ir
Iraj
Mahdavi
Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
irajarash@rediffmail.com
Nezam
Mahdavi-Amiri
Faculty of Mathematical Sciences, Sharif University of Technology, Tehran, Iran
nezamm@sharif.edu
Convergent product is an assembly shape concept integrating functions and sub-functions to form a final product. To conceptualize the convergent product problem, a web-based network is considered in which a collection of base functions and sub-functions configure the nodes and each arc in the network is considered to be a link between two nodes. The aim is to find an optimal tree of functionalities in the network adding value to the product in the web environment. First, an algorithm is proposed to assign the links among bases and sub-functions. Then, numerical values as benefits and costs are determined for arcs and nodes, respectively, using a mathematical approach. Also, a customer’s value corresponding to the benefits is considered. Finally, the Steiner tree methodology is adapted to a multi-objective model optimized by an augmented ε-constraint method.Anexample is worked out to illustrate the proposed approach.
Convergent product,web-based (digital) network,multi-objective programming,Steiner tree,ε-constraint
http://scientiairanica.sharif.edu/article_3708.html
http://scientiairanica.sharif.edu/article_3708_449413b9e20318f5df8c73e176809625.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
22
3
2015
06
01
Fractional order grey relational analysis and its application
1171
1178
EN
Lifeng
Wu
College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Sifeng
Liu
College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Ligen
Yao
School of Economics and Management, Hebei University of Engineering, Handan 056038, China
Liang
Yu
College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
The main idea behind this study is introduce fractional order grey relational degree to analyze the relationship between sci-tech input and economic growth of China. Based on fractional order dierence operator, fractional order grey relational analysis (FGRA) is dened. The eect of dierent orders on grey relational analysis is discussed. Two examples show the process and eciency of its application.
Grey Relational Analysis,fractional order,R&D,GDP,high technology output value
http://scientiairanica.sharif.edu/article_3709.html
http://scientiairanica.sharif.edu/article_3709_62344970983251bd254951c87b5dac6e.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
22
3
2015
06
01
Integrated Life Cycle Assessment- Activity Based Life Cycle Costing approach for an automotive product
1179
1188
EN
M.S.
SHAMA
SCMS School of Engineering and Technology, Ernakulam, Kerala, India
mail2shamams@gmail.com
S.
VINODH
Department of Production Engineering, National Institute of Technology, Tiruchirappalli – 620 015, India
vinodh_sekar82@yahoo.com
K.
JAYAKRISHNA
Department of Production Engineering, National Institute of Technology, Tiruchirappalli-620 015, India
mail2jaikrish@gmail.com
The manufacturing organizations are adopting the environmentally friendlier practices to sustain in the competitive business environment. Automotive industries are adopting the environmental management standards to comply with government norms. Life Cycle Assessment (LCA) enables the evaluation of environmental impacts associated with the processes. Life Cycle Costing (LCC) enables the attainment of economic aspect of sustainability. This article presents an integrated approach of LCA- Activity Based LCC to minimize the environmental impact across the life cycle as well as to identify the costs associated with life cycle activities. Different scenarios are being analyzed from the sustainability view point and critical activities are also being identified so as to improve sustainability.
Sustainability,Life Cycle Assessment,Life Cycle Costing,Activity Based Life Cycle Costing,Environmental Impact,Resource consumption
http://scientiairanica.sharif.edu/article_3710.html
http://scientiairanica.sharif.edu/article_3710_37f9674e8287e830957de54e4fcee898.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
22
3
2015
06
01
Robust economic-statistical design of multivariate exponentially weighted moving average control chart under uncertainty with intervaldata
1189
1202
EN
Amirhossein
Amiri
0000-0002-2385-8910
Industrial Engineering Department, Faculty of Engineering, Shahed University, Tehran, P.O. Box 18151-159, Iran
amirhossein.amiri@gmail.com
Anahita
Sherbaf Moghaddam
Industrial Engineering Department, Faculty of Engineering, Shahed University, Tehran, P.O. Box 18151-159, Iran
a.sherbaf@shahed.ac.ir
Zahra
Aghababaee
Industrial Engineering Department, Sharif University of Technology, Tehran, P.O. Box 11155-9414, Iran
z_aghababaei@ie.sharif.edu
The cost parameters in economic-statistical models of control charts are usually assumed to be deterministic in the literature. Considering uncertainty in the cost parameters of control charts is very common in application. So, several researchers used scenario-based approach for robust economic-statistical design of control charts. In this paper, we specifically concentrate on the multivariate exponentially weighted moving average (MEWMA) control chart and consider interval uncertainty in the cost parameters of the MEWMA control chart and develop a robust economic-statistical design of the MEWMA control chart by using interval robust optimization technique. Meanwhile, the Lorenzen and Vance cost function is used and to calculate the average run length criterion, the Markov chain approach is applied. Then, genetic algorithm for obtaining optimal solution of the proposed robust model is used and effectiveness ofthis model is illustratedthrough a numerical example. Also, a comparison with certain situation of the cost parameters is performed. Finally, a sensitivity analysis is done to investigate the effect of changing the intervals of cost parameters of the Lorenzen and Vance model on the optimal solutions. Furthermore, a sensitivity analysis on the other certain cost parameters of the Lorenzen and Vance model is done.
Statistical process control,MEWMA control chart,Robust economic-statistical design,Interval robust optimization,Genetic Algorithm,Markov chain
http://scientiairanica.sharif.edu/article_3711.html
http://scientiairanica.sharif.edu/article_3711_fe93cd5407f7ec16dbd72d88a0c6eaa7.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
22
3
2015
06
01
An empirical comparison of simulated annealing and iterated local search for the hierarchical single allocation hub median location problem
1203
1217
EN
Mohammad Hossein
Fazel Zarandi
Department of Industrial Engineering, AmirKabir University of Technology, 424 Hafez Avenue, Tehran, Iran
zarandi@aut.ac.ir
Soheil
Davari
Department of Manufacturing Systems and Industrial Engineering, Sabanci University, Orhanli-Tuzla, 34956 Istanbul, Turkey
Ali
Haddad Sisakht
Department of Industrial and Manufacturing Systems Engineering, Iowa State University, 50011 Ames, USA
Hub location problem (HLP) has been an attractive area of research for more than four decades. A recently proposed problem in the area of hub location is the hierarchical single-allocation hub median problem (SA-H-MP) which is associated with finding the location of a number of hubs and central hubs, so that the total routing cost is minimized. Owing to the problem’s complexity and intractability, this paper puts forward two metaheuristics, simulated annealing (SA) and iterated local search (ILS), and compares their performances. Results show that while both algorithms are able to reach optimal solutions on the standard CAB dataset, their runtimes are negligible and considerably lower compared to the runtimes of exact methods.
Location,Simulated annealing,Iterated local search,Heuristics,Metaheuristics
http://scientiairanica.sharif.edu/article_3712.html
http://scientiairanica.sharif.edu/article_3712_315de3effa11e762de565bac7eb5a1de.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
22
3
2015
06
01
Robust M-estimation of Multivariate FIGARCH Models for Handling Volatility Transmission: A Case study of Iran, United Arab Emirates and the Oil Global Price Index
1218
1226
EN
Seyed Babak
Ebrahimi
Department of industrial engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
b_ebrahimi@iust.ac.ir
Seyed Mohammad
Seyedhosseini
Department of industrial engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
seyedhosseini@iust.ac.ir
The stochastic nature of price volatility, as an important issue in stock markets, significantly affects decision makers’ decisions. In this paper, a new multivariate fractionally integratedgeneralized autoregressive conditional heteroscedasticity (MVFIGARCH) model is proposed. Being more comprehensive in comparison with the models in the literature, the proposed model considers long term parameter which is estimated simultaneously with other parameters. One of the well-known methods of MVFIGARCH estimation is the Gaussian quasi-maximum likelihood method. The Gaussian quasi-maximum likelihood estimator of MVFIGARCH model is known to be sensitive to data outliers. To correct the vulnerability of this method to outliers in data, robust M-estimators are introduced for MVFIGARCH models. Volatility models with bounded innovation propagation property are introduced to increase the robustness of the estimations. The applicability of the proposed model is justified by the volatility transmission among Tehran stock index, Dubai stock index and oil global price index using MVFIGARCH model within the time span from December 5, 2006 to January 30, 2012 is investigated. The result of estimation in different models generally shows volatility transmission from oil global market to Tehran and Dubai markets. Volatility transmission from Dubai market to Tehran was meaningfully observed as well. However, the effect of transmission was not observed in reverse direction.
GARCH Models,MVFIGARCH model,Volatility,time series analysis,M-estimation
http://scientiairanica.sharif.edu/article_3713.html
http://scientiairanica.sharif.edu/article_3713_97d1579b64d248621bde568a09544392.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
22
3
2015
06
01
A Hybrid Cultural-Harmony Algorithm for Multi-Objective Supply Chain Coordination
1227
1241
EN
Saeed
Alaei
Department of Industrial Engineering, KNT University of Technology, Tehran, Iran
alaei.saeed@gmail.com
Farid
Khoshalhan
Department of Industrial Engineering, KNT University of Technology, Tehran, Iran
khoshalhan@kntu.ac.ir
We investigate a one-buyer-multi-vendor co-ordination model with vendor selection problemin a centralized supply chain. In the proposed model, the buyer selects one or more vendorsand orders an appropriate quantity. The quantity discount mechanism is used by all vendors with the aim of coordinating the supply chain. We formulate the problem as a multi objective mixed integer nonlinear mathematical model. Using the Global Criterion method, the proposed model is transformed into a single objective optimization problem. Since, the problem is NP-hard, we propose four meta-heuristic algorithms: Particle Swarm Optimization (PSO), Scatter Search (SS), Population based Harmony Search (HS-pop) and Harmony Search based Cultural Algorithm (HS-CA). The Taguchi’s robust tuning method is applied in order to estimate the optimum values of parameters. Then, the solution quality and computational time of algorithms are compared.
supply chain coordination,Meta-heuristics,Taguchi method,Supplier selection,Cultural algorithm,Harmony search
http://scientiairanica.sharif.edu/article_3714.html
http://scientiairanica.sharif.edu/article_3714_d463507f6995fbbeaa4bd42efcd09640.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
22
3
2015
06
01
Two meta-heuristic algorithms for the dual-resource constrained flexible job-shop scheduling problem
1242
1257
EN
M.
Yazdani
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
mehdi_yazdani2007@yahoo.com
M.
Zandieh
Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, G.C., Tehran, Iran
R.
Tavakkoli-Moghaddam
0000-0002-6757-926X
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
tavakoli@ut.ac.ir
F.
Jolai
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
gmzvcalo@scientiaunknown.non
Systems where both machines and workers are treated as constraints are termed dual- resource constrained (DRC) systems. In the last few decades, DRC scheduling has attracted much attention from researchers. This paper addresses the dual-resource constrained flexible job-shop scheduling problem (DRCFJSP) to minimize makespan. This problem is NP-hard and mainly includes three sub-problems: (1) assigning each operation to a machine out of a set of compatible machines, (2) determining a worker among a set of skilled workers for operating each operation on the selected machine, and (3) sequencing the operations on the machines considering workers in order to optimize the performance measure. This paper presents two meta-heuristic algorithms, namely simulated annealing (SA) and vibration damping optimization (VDO), to solve the DRCFJSP. The proposed algorithms make use of various neighborhood structures to search in the solution space. The Taguchi experimental design method as an optimization technique is employed to tune different parameters and operators of the presented algorithms. Numerical experiments with randomly generated test problems are used to evaluate performance of the developed algorithms. A lower bound is used to obtain the minimum value of makespan for the test problems. The computational study confirms the proper quality of results of the proposed algorithms.
Flexible job-shop scheduling,Dual-resource constrained,Simulated annealing,Vibration damping optimization,Taguchi experimental design
http://scientiairanica.sharif.edu/article_3715.html
http://scientiairanica.sharif.edu/article_3715_ceaed57d2c49f19ac28c448626182280.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
22
3
2015
06
01
MergedAutomobile Maintenance Part Delivery Problem Using an Improved Artificial Bee Colony Algorithm
1258
1270
EN
Baozhen
YAO
School of Automotive Engineering, Dalian University of Technology, Dalian, 116024, China
yaobaozhen@hotmail.com
Ping
HU
School of Automotive Engineering, Dalian University of Technology, Dalian, 116024, China
Lan
YU
School of International Trade and Commerce, Yanching Institute of Technology, 065201, Beijing, China
Mingheng
ZHANG
School of Automotive Engineering, Dalian University of Technology, Dalian, 116024, China
Junjie
GAO
School of Automotive Engineering, Dalian University of Technology, Dalian, 116024, China
The merged automobile maintenance part delivery problem will attract interests from the merged company due to the reduced delivery cost by collaborative delivery among several automobile part depots. Since the delivery problem is a very complex problem, Voronoi diagram is adopted to simplify this delivery problem by splitting customers into several sets. Then, this paper attempts to solve this delivery problem by using of artificial bee colony algorithm. To improve the performance of the artificial bee colony algorithm, an adaptive strategy is used to control the proportion of scouts and leaders. At last, the computational results for 23 benchmark problems indicate that the proposed algorithm is an effective method to solve the multi-depot vehicle routing problem. Furthermore, the results of a merged automobile maintenance part delivery problem also indicate that the improved artificial bee colony algorithm with Voronoi diagram is feasible for solving this kind of delivery problem.
Automobile maintenance part delivery problem,Multi-depot vehicle routing problem,Voronoi diagram,Adaptive strategy,artificial bee colony algorithm,Merged
http://scientiairanica.sharif.edu/article_3716.html
http://scientiairanica.sharif.edu/article_3716_157ff9b34cb9056df573918dc1f15433.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
22
3
2015
06
01
Improved estimation of finite population median under two-phase sampling when using two auxiliary variables
1271
1277
EN
J.
Shabbir
Department of Statistics, Quaid-i-Azam University, Islamabad 45320, Pakistan
S.
Gupta
Department of Mathematics and Statistics, University of North Carolina at Greensboro, Greensboro, NC 27412, USA
Z.
Hussain
Department of Mathematics and Statistics, University of North Carolina at Greensboro, Greensboro, NC 27412, USA
zhlangh@yahoo.com
We propose an efficient estimator for population median under two-phase sampling when using two auxiliary variables on the lines of Diana [Diana, G. “A class of estimators of the population mean in stratified random sampling”, Statistica, 1, pp. 59-66 (1993)] and Jhajj and Walia [Jhajj, H. S. and Walia, G. S. “A generalized difference-cum-ratio type estimator for the population variance in double sampling”, Communications in Statistics-Simulations and Computation, 41, 58-64. (2012)]. The expressions for mean squared errors are presented, correct to first order of approximation. Both theoretical and numerical comparisons reveal that the proposed estimator performs better than the unbiased sample median estimator, ratio estimator, and estimators by Srivastava et al. [Srivastava, S. K., Rani, S., Khare, B. B., and Srivastava, S. R. “A generalized chain ratio estimator for mean of finite population”, Journal of the Indian Society of Agricultural Statistics, 42(1), pp. 108-117 (1990)] and Gupta et al. [Gupta, S., Shabbir, J., Ahmad, S. “Estimation of median in two phase sampling using two auxiliary variables”, Communications in Statistics-Theory and Methods, 37(11), pp. 1815−1822 (2008)].
Auxiliary variables,Mean squared error (MSE),Median,Efficiency
http://scientiairanica.sharif.edu/article_3717.html
http://scientiairanica.sharif.edu/article_3717_5dbe79001ebb8e2ac18a4e3ac88150ee.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
22
3
2015
06
01
Interrelating physical and financial flows in a bi-objective closed-loop supply chain network problem with uncertainty
1278
1293
EN
Majid
Ramezani
Faculty of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
ramezani.m@aut.ac.ir
Ali Mohammad
Kimiagari
Faculty of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
kimiagar@aut.ac.ir
Behrooz
Karimi
Faculty of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
b.karim@aut.ac.ir
This paper presents a bi-objective logistic design problem integrating the financial and physical flows of a closed-loop supply chain in which the uncertainty of demand and the return rate described by a finite set of possible scenarios. The main idea of this paper consists of the joint integration of enterprise finance with the company operations model, where financial aspects are explicitly considered as exogenous variables. The model addressesthe company operationsdecisions as well as the finance decisions. Moreover, the change in equity is considered as objective function along with the profit to evaluate the business aspects.Since the logistic network design is a strategic problem and the change of configuration is not easy in the future,a bi-objective robust optimization with the max-min versionis extended to cope with the uncertainty of parameters. In addition, to obtain solutions with a better time, the scenario relaxation algorithm is adapted for the proposed approach. The numerical examples are presented to show the applicability of the model along with a sensitivity analysis on financial parameters. The obtained results illustrate the importance of such modelling systems leading to more overall earnings and expressingfurther insights on the interactions between operations and finances.
Closed-loop supply chain,Finances,uncertainty,Multi-objective robust optimization,Scenario relaxation algorithm
http://scientiairanica.sharif.edu/article_3718.html
http://scientiairanica.sharif.edu/article_3718_3e7f2749a4e7b6f20715d1c7b5a848c6.pdf
Sharif University of Technology
Scientia Iranica
1026-3098
2345-3605
22
3
2015
06
01
Comparing four ordering policies in a lost sales inventory model with Poisson demand and zero ordering cost
1294
1298
EN
Rasoul
Haji
Department of Industrial Engineering, Sharif University of Technology, Tehran, Zip 14588-89694, Iran
haji@sharif.edu
Hamed
Tayebi
Department of Industrial Engineering, Sharif University of Technology, Tehran, Zip 14588-89694, Iran
tayebi@mehr.sharif.edu
In this paper, we compare four ordering policies in a lost sales inventory model with zero ordering cost, constant lead time, and Poisson demand process. These ordering policies are 1) base stock policy, 2) full delay policy, 3) simple delay policy and 4) a recently developed ordering policy called (1, T) or one for one period policy. Our work can be considered as an expansion of a previous research which compared the first three policies. We show that, for any fixed value of the ratio of unit lost sales cost over unit holding cost, there is a specific value of lead time demand beyond which the cost of (1, T) policy is lower than the costs of other three policies. Furthermore, the superiority of (1, T) policy is more significant for low values of the above ratio and becomes more pronounced as the lead time demand increases.
Lost sales,Base Stock policy,one-for-one-period policy,Poisson demand
http://scientiairanica.sharif.edu/article_3719.html
http://scientiairanica.sharif.edu/article_3719_ae3991d597d2ca4ec518cd655db66ff3.pdf