2019-07-16T21:22:18Z
http://jqepo.shahed.ac.ir/?_action=export&rf=summon&issue=37
Journal of Quality Engineering and Production Optimization
2015
1
1
Flow shop Scheduling Problem with Maintenance Coordination: a New Approach
Mostafa
Khatami
Hessameddin
Zegordi
This study investigates the coordination of production scheduling and maintenance planning in theflow shop scheduling environment. The problem is considered in a bi-objective form, minimizing themakespan as the production scheduling criterion and minimizing the system unavailability as themaintenance planning criterion. The time interval between consecutive maintenance activities as well as thenumber of maintenance activities on each machine are assumed to be non-fixed. A mixed integerprogramming formulation of the problem is presented. A special case of the problem, named as single servermaintenance is also studied. Then, a bi-objective ant colony system algorithm is presented to solve theproblem in focus. To obtain the appropriate components of the proposed algorithm, two sets of experimentsare provided. Firstly, experiments are carried out to select the suitable heuristic method to build the heuristicinformation part of the algorithm between CDS and NEH. Secondly, experiments are reported to select thelocal search algorithm between iterated local search and adjacent pair-wise interchange. At last, experimentsare generated to evaluate the performance of the proposed algorithm, comparing it to the results of anexhaustive search algorithm.
Flow Shop Scheduling
Preventive maintenance
Coordination
Non-fixed time interval
Ant colony system
2015
02
26
1
11
http://jqepo.shahed.ac.ir/article_184_d19b88e9039fe0d19aa809128dfbb107.pdf
Journal of Quality Engineering and Production Optimization
2015
1
1
A New Optimization via Invasive Weeds Algorithm for Dynamic Facility Layout Problem
Farnaz
Barzinpour
Mohammad
Mohammadpour Omran
Seyed Farzad
Hoseini
Kaveh
Fahimi
Farshid
Samaei
The dynamic facility layout problem (DFLP) is the problem of finding positions of departments onthe plant floor for multiple periods (material flows between departments change during the planning horizon)such that departments do not overlap, and the sum of the material handling and rearrangement costs isminimized. In this paper a new optimization algorithm inspired from colonizing weeds, Invasive WeedsOptimization (IWO) is utilized to solve the well-known DFLP. IWO is a simple algorithm which uses basiccharacteristics of a colony of weeds such as proliferation, growth and competition.A set of reference numerical problems is taken in order to evaluate the efficiency of the algorithm comparedwith the Dynamic Programming method which had been applied to solve the addressed problem. In order toverify the efficiency of the proposed algorithm a wide range of experiments are carried out to comparethe proposed algorithm. Computational results have indicated that the DIWO algorithm is capable ofobtaining optimal solutions for small and medium-scaled problems very efficiently.
Discrete Invasive Weed Optimization
Dynamic Facility layout Problem
Dynamic Programming
2015
04
01
11
20
http://jqepo.shahed.ac.ir/article_185_c3454f1b1a19429fba382986bb1ae3be.pdf
Journal of Quality Engineering and Production Optimization
2015
1
1
Bi-objective Optimization for Just in Time Scheduling: Application to the Two-Stage Assembly Flow Shop Problem
Sahar
Tadayoni Rad
Saiedeh
Gholami
Rasoul
Shafaei
Hany
Seidgar
This paper considers a two-stage assembly flow shop problem (TAFSP) where m machines are in the first stage and an assembly machine is in the second stage. The objective is to minimize a weighted sum of earliness and tardiness time for n available jobs. JIT seeks to identify and eliminate waste components including over production, waiting time, transportation, inventory, movement and defective products.Two-stage assembly flow shop is a combinational production system in which different parts are manufactured on parallel machines independently. This system can be used as a method to produce a variety of products through assembling and combining different set of parts. We apply e-constraint method as an exact approach to validate the proposed model and to obtain fronts of the solutions in the solution spaceThe goal of the proposed problem is trade off between two objectives, minimization makespan and total weighted tardiness and earliness. To analyze effects of n and m factors on the efficiency and performance of the proposed algorithm, we calculate the complexity of sub problems based on factors n and m and the computational results demonstrate that the computational time increases with increasing in n and m, in other words, complexity of the problem increases.
Two-Stage Assembly flow shop problem
Just in time scheduling
-constraint method
2015
02
26
21
32
http://jqepo.shahed.ac.ir/article_186_84e9d0778cddba9ee10b2b155b4d1659.pdf
Journal of Quality Engineering and Production Optimization
2015
1
1
Solving Single Machine Sequencing to Minimize Maximum Lateness Problem Using Mixed Integer Programming
Amir Hossein
Parsamanesh
Rashed
Sahraeian
Despite existing various integer programming for sequencing problems, there is not enoughinformation about practical values of the models. This paper considers the problem of minimizing maximumlateness with release dates and presents four different mixed integer programming (MIP) models to solve thisproblem. These models have been formulated for the classical single machine problem, namely sequenceposition(SP), disjunctive (DJ), linear ordering (LO) and hybrid (HY). The main focus of this research is onstudying the structural properties of minimizing maximum lateness in a single machine using MIPformulations. This comparison helps us know the characteristics and priority of different models inminimizing maximum lateness. Regarding to these characteristics and priorities, while solving the latenessproblem in the procedure of solving a real-world problem, we apply the lateness model which yields insolution in shortest period of time and try not to use formulations which never lead to solution for large-scaleproblems. Beside single machine, these characteristics are applicable to more complicated machineenvironment. We generate a set of test problems in an attempt to solve the formulations, using CPLEXsoftware. According to the computational results, a detailed comparison between proposed MIP formulationsis reported and discussed in order to determine the best formulation which is computationally efficient andstructurally parsimonious to solve the considering problem. Among the four presented formulations,sequence-position (SP) has the most efficient computational time to find the optimal solution.
Single machine scheduling
Mixed Integer Programming
Maximum lateness
Release date
2015
02
26
33
42
http://jqepo.shahed.ac.ir/article_187_3f676765e00168e9e04dbfd9252521c4.pdf
Journal of Quality Engineering and Production Optimization
2015
1
1
Simultaneous Monitoring of Multivariate-Attribute Process Mean and Variability Using Artificial Neural Networks
Mohammad Reza Maleki
Maleki
Amirhossein
Amiri
In some statistical process control applications, the quality of the product is characterized by thecombination of both correlated variable and attributes quality characteristics. In this paper, we propose anovel control scheme based on the combination of two multi-layer perceptron neural networks forsimultaneous monitoring of mean vector as well as the covariance matrix in multivariate-attribute processeswhose quality characteristics are correlated. The proposed neural network-based methodology not onlydetects separate mean and variance shifts, but also can efficiently detect simultaneous changes in meanvector and covariance matrix of multivariate-attribute processes. The performance of the proposed neuralnetwork-based methodology in detecting separate as well as simultaneous changes in the process is evaluatedthorough a numerical example based on simulation in terms of average run length criterion and the resultsare compared with a statistical method based on the combination of two control charts that are developed formonitoring the mean vector and covariance matrix of multivariate-attribute processes, respectively. Theresults of model implementation on numerical example show the superior detection performance of theproposed NN-based methodology rather than the developed combined statistical control charts.
Average run length
Covariance matrix
Mean vector
Multi-layer perceptron neural network
Multivariate-attribute process
2015
02
26
43
54
http://jqepo.shahed.ac.ir/article_188_db89132e09a36c98cd375afffd946530.pdf
Journal of Quality Engineering and Production Optimization
2015
1
1
A Modified Benders Decomposition Algorithm for Supply Chain Network Design under Risk Consideration
Nima
Hamta
Mohammad
Fattahi
Mohsen
Akbarpour Shirazi
Behrooz
Karimi
In today’s competitive business environment, the design and management of supply chainnetwork is one of the most important challenges that managers encounter. The supply chain network shouldbe designed for satisfying of customer demands as well as minizing the total system costs. This paper presentsa multi-period multi-stage supply chain network design problem under demand uncertainty. The problem isformulated as a two-stage stochastic program. In the first-stage, strategic location decisions are made, whilethe second-stage contains the tactical decisions. In our developed model, conditional value-at-risk (CVaR) asan effective risk measure is used to produce first-stage decisions in which the loss cost in the second-stage isminimized. In addition, a modified Benders decomposition algorithm is developed to solve the model exactly.The computational results on a set of randomly generated problem instances demonstrate the effectiveness ofthe proposed algorithm in terms of the solution quality.
Benders decomposition
Conditional Value-at-Risk
Supply chain network design
Two-stage stochastic programming
Uncertain demand
2015
02
26
55
66
http://jqepo.shahed.ac.ir/article_189_9f12969da53b62bc3f6298ef62d6ae46.pdf