Sharif University of TechnologyScientia Iranica1026-309817120100601An Analytic Variable Limit np Control Chart3317ENM.AminnayeriDepartment of Industrial Engineering,Amirkabir University of TechnologyM.H.AbooieDepartment of Industrial Engineering,Amirkabir University of TechnologyJournal Article20100721The Shewhart np control chart is often used to monitor the quantity of nonconforming, but
it is slow in detecting small deviations. This paper proposes an efficient approach to monitor the quantity
of nonconforming. The novelty of the paper is utilization of an initial belief to construct an analytic
variable limit np control chart. The proposed method uses all gathered data, sequentially. This approach
is signicantly faster than some existent eective approaches in detecting small deviations. These charts
are mainly used for evaluation of the initial setup in the process. The simulated results for the average run
length profiles demonstrate the superiority of the new approach against the standard np chart, binomial
CUSUM, binomial EWMA and moving average approach.http://scientiairanica.sharif.edu/article_3317_ce56854bbb87ed70167f42e392938943.pdfSharif University of TechnologyScientia Iranica1026-309817120100601Hybrid Electromagnetism-Like Algorithm for Supplier Selection in Make-to-Order Planning3318ENF.JolaiDepartment of Industrial Engineering,University of TehranS. M. T.Fatemi GhomiDepartment of Industrial Engineering,Amirkabir University of Technology0000-0003-4363-994XM.MirabiDepartment of Industrial Engineering,University of YazdJournal Article20100721An electromagnetism algorithm is a meta-heuristic proposed to derive approximate solutions
for computationally hard problems. In the literature, several successful applications have been
reported for graph-based optimization problems, such as scheduling problems. This paper presents an
application of the electromagnetism algorithm to supplier selection in a production planning process
where there are multiple products and multiple customers and also capacity constraints. We consider
a situation where the demand quantity of multiple discrete products is known over a planning horizon.
The required raw material for each of these products can be purchased from a set of approved suppliers.
Also, a demand-dependent delivery time (due date) and maximum delivery time (deadline) apply for each
demand. Problems containing all these assumptions have not been addressed previously in the literature.
A decision needs to be made regarding what raw material to order and in what quantities, which suppliers
and, nally, at which periods. Numerical results indicate that the electromagnetism algorithm exhibits
impressive performances with small error ratios. The results support the success of the electromagnetism
algorithm application to the supplier selection problem of interest.http://scientiairanica.sharif.edu/article_3318_021cb5b19cb1f01a582179e8fc3de882.pdfSharif University of TechnologyScientia Iranica1026-309817120100601Phase II Monitoring of Autocorrelated Polynomial Proles in AR(1) Processes3319ENR.NoorossanaDepartment of Industrial Engineering,Iran University of Science and TechnologyR.B.KazemzadehDepartment of Industrial Engineering,Tarbiat Modares UniversityA.AmiriDepartment of Industrial Engineering,Tarbiat Modares UniversityJournal Article20100721In many practical situations, the quality of a process or product can be characterized by
a function or prole. Here, we consider a polynomial prole and investigate the eect of the violation
of a common independence assumption, implicitly considered in most control charting applications, on
the performance of the existing monitoring techniques. We specically consider a case when there is
autocorrelation between proles over time. An autoregressive model of order one is used to model the
autocorrelation structure between error terms in successive proles. In addition, two remedial methods,
based on time series approaches, are presented for monitoring autocorrelated polynomial proles in phase
II. Their performances are compared using a numerical simulation runs in terms of an Average Run
Length (ARL) criterion. The eects of assignable cause and autocorrelation coecient on the shape of
proles are also investigated.http://scientiairanica.sharif.edu/article_3319_500425c8655eeadb8af1fe47f7c78a65.pdfSharif University of TechnologyScientia Iranica1026-309817120100601Economic Production Quantity Model with Scrapped Items and Limited Production Capacity3320ENS.T.Akhavan NiakiDepartment of Industrial Engineering,Sharif University of Technology0000-0001-6281-055XA.TaleizadehDepartment of Industrial Engineering,Iran University of Science and TechnologyA.A.NajafiDepartment of Industrial Engineering,Qazvin Islamic Azad UniversityJournal Article20100721In this paper, an Economic Production Quantity (EPQ) model is studied, in which the
production defective-rate follows either a uniform or a normal probability distribution. Shortages are
allowed and take a backorder state, and the existence of only one machine causes a limited production
capacity for the common cycle length of all products. The aim of this research is to determine the
optimal production quantity of each product, such that the expected total cost including holding, shortage,
production, setup and defective items cost is minimized. The mathematical model of the problem is derived,
for which the objective function is proved to be convex. Then, a derivative approach is utilized to obtain
the optimal solution. At the end, two numerical examples are provided to illustrate the practical usage of
the proposed method.http://scientiairanica.sharif.edu/article_3320_b491a1be4fb86856a1b2d9205b725d32.pdfSharif University of TechnologyScientia Iranica1026-309817120100601Resource-Constrained Project Scheduling Problem with Flexible Work Profiles: A Genetic Algorithm Approach3321ENM.RanjbarDepartment of Industrial Engineering,Sharif University of TechnologyF.KianfarDepartment of Industrial Engineering,Sharif University of TechnologyJournal Article20100721This paper deals with the resource-constrained project scheduling problem with
exible
work proles. In this problem, a project contains activities interrelated by nish-start-type precedence
constraints with a time lag of zero. In many real-life projects, however, it often occurs that only one
renewable bottleneck resource is available and that activities do not have a xed prespecied duration and
associated resource requirement, but a total work content, which essentially indicates how much work has
to be performed on them. Based on this work content, all feasible work proles have to be specied for
the activities, each characterized by a xed duration and a resource requirement prole. The task of the
problem is to nd the optimum work prole and start time of each activity in order to minimize the project
makespan. We propose a procedure to nd all feasible work profiles of each activity and we use a genetic
algorithm with a new crossover operator to schedule the activities. Computational results on a randomly
generated problem set are presented.http://scientiairanica.sharif.edu/article_3321_4c7518d92dc20c600dbd55e66337d77f.pdfSharif University of TechnologyScientia Iranica1026-309817120100601A Corporate Supply Optimizer with Flow Network3322ENM.SepehriDepartment of Engineering,Sharif University of TechnologyF.GhasemzadehSchool of Business,Sharif University of TechnologyK.FayazbakhshDepartment of Computer Science and Engineering,Amirkabir University of TechnologyJournal Article20100721A holding or a multi-business corporate seeks to coordinate its supply for minimum overall
costs. A Corporate Supply Optimizer (CSO), as a central entity taking advantage of the notion of
ow
networks, gathers necessary operational information from members of the corporate supply chain. The
CSO then guides supply chain members on ordering decisions for a minimum overall cost for the entire
supply chain. Its computational engine models the entire supply chain with multiple members in four
stages to satisfy customer demand. The CSO seeks a solution with minimum total costs, unlike noncooperative
supply chains where individual members compete to optimize their local costs. The existing
literature stays with restrictive assumptions on the number of supply chain stages, disallowing a case of
multiple products. Simulation results indicate an approximately 26% reduction in total costs of the supply
chain utilizing the CSO.http://scientiairanica.sharif.edu/article_3322_354c23283151ec6d72d7662b46cec469.pdfSharif University of TechnologyScientia Iranica1026-309817120100601A Heuristic Algorithm and a Lower Bound for the Two-Echelon Location-Routing Problem with Soft Time Window Constraints3323ENS.H.ZegordiDepartment of Industrial Engineering,Tarbiat Modares UniversityE.NikbakhshDepartment of Industrial Engineering,Tarbiat Modares UniversityJournal Article20100721The location-routing problem is one of the most important location problems for designing
integrated logistics systems. In the last three decades, various types of objective function and constraints
have been considered for this problem. However, time window constraints have received little attention,
despite their numerous real-life applications. In this article, a new 4-index mathematical model, an
ecient and fast heuristic and a lower bound for the two-echelon location-routing problems with soft
time window constraints are presented. The proposed heuristic tries to solve the problem via creating
an initial solution, then improving it by searching on six neighborhoods of the solution, and using the
Or-opt heuristic. At the end, computational results show the eciency of the proposed heuristic, using
the proposed lower bound.http://scientiairanica.sharif.edu/article_3323_914c872ce83884ecb171cbce20a7717b.pdf