QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
11
1
2018
03
01
An Integrated Approach of Fuzzy Quality Function Deployment and Fuzzy Multi-Objective Programming Tosustainable Supplier Selection and Order Allocation
1
22
EN
Amir Hossein
Azadnia
Assistant Professor, Department of Industrial Engineering, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran
azadnia.ie@gmail.com
Pezhman
Ghadimi
Assistant Professor, School of Mechanical and Materials Engineering, University College Dublin, Ireland
pezhman.ghadimi@ul.ie
10.22094/joie.2017.629.1405
The emergence of sustainability paradigm has influenced many research disciplines including supply chain management. It has drawn the attention of manufacturing companies’ CEOs to incorporate sustainability in their supply chain and manufacturing activities. Supplier selection problem, as one of the main problems in supply chain activities, is also combined with sustainable development where traditional procedures are now transformed to sustainable initiatives. Moreover, allocating optimal order quantities to sustainable suppliers has also attracted attention of many scholars and industrial practitioners, which has not been comprehensively addressed. Therefore, a practical model of supplier selection and order allocation based on the sustainability Triple Bottom Line (TBL) approach is presented in this research article. The proposed approach utilizes Fuzzy Analytical Hierarchy Process combined with Quality Function Deployment (FAHP-QFD) for reflecting buyer’s sustainability requirements into the preference weights that are then exerted by an efficient Fuzzy Assessment Method (FAM) to assess the suppliers to obtain their sustainability scores. Thereupon, these scores are utilized in a fuzzy multi-objective mix-integer non-linear programming model (MINLP) for allocating orders to suppliers based on the manufacturer’s sustainability preference. A real-world application of food industry is presented to show the practicality of the proposed approach.
Sustainability,Sustainable supplier selection,Fuzzy inference system,Order allocation,Fuzzy multi-objective non-linear programming
http://www.qjie.ir/article_535488.html
http://www.qjie.ir/article_535488_485946d8c85a137b6aa9d0d3d2ced02c.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
11
1
2018
03
01
Service Performance Improvement Model: The Case of Teklehaymanot General Hospital
23
33
EN
Eshetie
Berhan
Associate Professor, Institute of Technology, School of Mechanical and Industrial Engineering, Addis Ababa University, Addis Ababa, Ethiopia
berhan.eshetie@gmail.com
Selam
Yibeltal
MSc, Student, Institute of Technology, School of Mechanical and Industrial Engineering, Addis Ababa University, Addis Ababa, Ethiopia
yebltal@gmail.com
Sisay
Geremew
Assistant Professor, Institute of Technology, School of Mechanical and Industrial Engineering, Addis Ababa University, Addis Ababa, Ethiopia
sisayg78@yahoo.com
10.22094/joie.2017.716.1452
In service sector, there are challenges in keeping an optimum balance between customers' demand and availability of resources. This problem is going to be more intense in the health sector due to the fact that both arrival and service times are random. Therefore, designing the service environment by keeping the optimum balance between customers’ demand and available resources is becoming a series problem in Teklehaymanot General Hospital. This paper tries to develop a model that investigates the performances of Teklehaymanot General Hospital and determines the optimum number of specialist doctors based on their respective workload. To address this objective, the study develops a model using Arena Simulation Software that considers the real working environment and scenario of Teklehaymanot General Hospital. For the purpose of this research, three years’ secondary data that include the type of services and number of specialized doctors under each service channel are collected from the hospital records and fitted to the model. The findings of the study show that there are unbalanced distributions on the daily workload among specialist doctors and extended long waiting time of patients in Teklehaymanot General Hospital. It reveals that specialist doctors who are working in pre-breast center, Hematology oncology imaging, neurology, obstetrics & gynecology, ophthalmology, pulmonology, urgent care, urology and women’s imaging are relatively overloaded, whereas those who are working in ENT Allergy Audiology, gastroenterology, Nuclear Medicine, orthopedics, physical therapy, and surgery are relatively underloaded. Moreover, from the scenario analysis, the result shows thatadditional specialized doctors in the fifteen areas are required so as to reduce the waiting time of patients by 54.41%. Therefore, the hospital is recommended to have a balanced workload distribution among specialist doctors and increase the number of specialist doctors by one or two in the fifteen service areas.
Arena Model,Hospital,Scheduling,Specialized Doctor,Service Performances
http://www.qjie.ir/article_535405.html
http://www.qjie.ir/article_535405_03da165baa9c22b468ff1d84acdff858.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
11
1
2018
03
01
Design of a Hybrid Genetic Algorithm for Parallel Machines Scheduling to Minimize Job Tardiness and Machine Deteriorating Costs with Deteriorating Jobs in a Batched Delivery System
35
50
EN
Mohammad
Saidi-Mehrabad
Professor, Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
mehrabad@iust.ac.ir
Samira
Bairamzadeh
Ph.D. Student, department of industrial engineering, Iran University of Science and Technology, Tehran, Iran
samira_bairamzadeh@yahoo.com
10.22094/joie.2018.272
This paper studies the parallel machine scheduling problem subject to machine and job deterioration in a batched delivery system. By the machine deterioration effect, we mean that each machine deteriorates over time, at a different rate. Moreover, job processing times are increasing functions of their starting times and follow a simple linear deterioration. The objective functions are minimizing total tardiness, delivery, holding and machine deteriorating costs. The problem of total tardiness on identical parallel machines is NP-hard, thus the under investigation problem, which is more complicated, is NP-hard too. In this study, a mixed-integer programming (MILP) model is presented and an efficient hybrid genetic algorithm (HGA) is proposed to solve the concerned problem. A new crossover and mutation operator and a heuristic algorithm have also been proposed depending on the type of problem. In order to evaluate the performance of the proposed model and solution procedure, a set of small to large test problems are generated and results are discussed. The related results show the effectiveness of the proposed model and GA for test problems.
Parallel machine scheduling,Machine deterioration,Job deterioration,Batched delivery system,Genetic algorithm
http://www.qjie.ir/article_272.html
http://www.qjie.ir/article_272_f2aa32fa1d321e365c85f08fb189012f.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
11
1
2018
03
01
Location-Allocation and Scheduling of Inbound and Outbound Trucks in Multiple Cross-Dockings Considering Breakdown Trucks
51
65
EN
javad
Behnamian
Assistant Professor, Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
behnamian@basu.ac.ir
Seyed Mohammad Taghi
Fatemi Ghomi
Professor, Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran.
fatemi@aut.ac.ir
Fariborz
Jolai
Professor, Department of Industrial Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran
fjolai@ut.ac.ir
Pooya
Heidary
MSc, Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran.
pooya.heydari1365@yahoo.com
10.22094/joie.2017.594.1382
This paper studies multiple cross-dockings where the loads are transferred from origins (suppliers) to destinations (customers) through cross-docking facilities. Products are no longer stored in intermediate depots and incoming shipments are consolidated based on customer demands and immediately delivered to them to their destinations. In this paper, each cross-docking has a covering radius that customers can be served by at least one cross-docking provided. In addition, this paper considers the breakdown of trucks. We present a two-stage model for the location of cross-docking centers and scheduling inbound and outbound trucks in multiple cross-dockings.We work on minimizing the transportation cost in a network by loading trucks in the supplier locations and route them to the customers via cross-docking facilities. The objective, in the first stage, is to minimize transportation cost of delivering products from suppliers to open cross-docks and cross-docks to the customers; in the second-stage, the objective is to minimize the makespans of open cross-dockings and the total weighted summation of completion time. Due to the difficulty of obtaining the optimum solution tomedium- and large-scale problems, we propose four types of metaheuristic algorithms, i.e., genetic, simulated annealing, differential evolution, and hybrid algorithms.The result showed that simulated annealing is the best algorithm between the four algorithms.
Cross-docking,Transhipment,Location of cross-docking centers,Metaheuristic
http://www.qjie.ir/article_535407.html
http://www.qjie.ir/article_535407_d04dd37a471117fa316763b445ac8d56.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
11
1
2018
03
01
Effects of Probability Function on the Performance of Stochastic Programming
67
76
EN
Mohammad Ebrahim
Karbaschi
Ph.D. Candidate in Structural Eengineering, Shiraz University, Shiraz, Iran.
ebrahim_karbaschi@shirazu.ac.ir
Mohammad Reza
Banan
Associate Professor, Department of Civil and Environment Engineering, Shiraz University, Shiraz, Iran.
banan@shirazu.ac.ir
10.22094/joie.2017.567.63
Stochastic programming is a valuable optimization tool where used when some or all of the design parameters of an optimization problem are defined by stochastic variables rather than by deterministic quantities. Depending on the nature of equations involved in the problem, a stochastic optimization problem is called a stochastic linear or nonlinear programming problem. In this paper,a stochastic optimization problem is transformed intoan equivalent deterministic problem,which can be solved byany known classical methods (interior penalty method is applied here).The paper mainly focuseson investigatingthe effect of applying various probability functions distributions(normal, gamma, and exponential) for design variables. The following basic required equations to solve nonlinear stochastic problems with various probability functionsfor random variables are derived and sensitivity analyses to studythe effects of distribution function typesand input parameterson the optimum solution are presented as graphs and in tables by studyingtwoconsidered test problems. It is concluded that thedifference between probabilistic and deterministic solutions toa problem, when the normal distribution ofrandom variables isused, is very different fromthe results when gamma and exponential distribution functions are used. Finally, it is shownthat the rate of solution convergence tothe normal distribution is faster than the other distributions.
Stochastic programming,Sensitivity Analysis,Linear programming,Nonlinear Programming,Exponential, Gamma and normal probability functions
http://www.qjie.ir/article_535410.html
http://www.qjie.ir/article_535410_168ee764165d8bfa1a9586260e6892f5.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
11
1
2018
03
01
A Bi-Objective Airport Gate Scheduling with Controllable Processing Times Using Harmony Search and NSGA-II Algorithms
77
90
EN
Morteza
khakzar Bafruei
Assistant Professor, Department of Industrial Engineering, Technology Development Institute (ACECR), Tehran, Iran
khakzar@acecr.ac.ir
Sananz
khatibi
Ph.D. Student, Department of Industrial Engineering, Technology Development Institute (ACECR), Tehran, Iran
khatibi.sanaz@gmail.com
Morteza
Rahmani
Associate Professor, Department of Industrial Engineering, Technology Development Institute (ACECR), Tehran, Iran
rahmanimr@yahoo.com
10.22094/joie.2018.234
Optimizing gate scheduling at airports is an old, but also a broad problem. The main purpose of this problem is to find an assignment for the flights arriving at and departing from an airport, while satisfying a set of constraints.A closer look at the literature in this research line shows thatin almost all studies airport gate processing time has been considered as a fix parameter. In this research, however, we investigate a more realistic situation in which airport gate processing time is a controllable. It is also assumed that the possible compression/expansion processing time of a flight can be continuously controlled, i.e. it can be any number in a given interval.Doing sohas some positive effectswhich lead to increasing the total performance at airports’ terminals. Depending on the situation, different objectives become important.. Therefore, a model which simultaneously (1) minimize the total cost of tardiness, earliness, delay andthe compression as well as the expansion costs of job processing time, and (2) minimize passengers overcrowding on gate is presented. In this study, we first propose a mixed-integer programming model for the formulated problem. Due to complexity of problem, two multi-objective meta-heuristic algorithms, i.e. multi-objective harmony search algorithm (MOHSA) and non-dominated sorting genetic algorithm II (NSGA-II) are applied in order to generate Pareto solutions. For calibrating the parameter of the algorithms, Taguchi method is used and three optimal levels of the algorithm’s performance are selected. The algorithms are tested with real-life data from Mehrabad International Airport for nine medium size test problems. The experimental results show that NSGA-II has better convergence near the true Pareto-optimal front as compared to MOHSA; however, MOHSA finds a better spread in the entire Pareto-optimal region.Finally, it is possible to apply some practical constraints into the model and also test them with even large real-life problems instances.
Gate scheduling problem,Multi-objective Decision Making,Harmony search algorithm,NSGA-II,Controllable processing times
http://www.qjie.ir/article_234.html
http://www.qjie.ir/article_234_0678dc15ee705bdde3f8f1a7a0239957.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
11
1
2018
03
01
Presentation and Solving Non-Linear Quad-Level Programming Problem Utilizing a Heuristic Approach Based on Taylor Theorem
91
101
EN
Eghbal
Hosseini
Department of Mathematics, University of Raparin, Ranya, Kurdistan Region, Iraq
eghbal.hosseini@uor.edu.krd
10.22094/joie.2017.282
The multi-level programming problems are attractive for many researchers because of their application in several areas such as economic, traffic, finance, management, transportation, information technology, engineering and so on. It has been proven that even the general bi-level programming problem is an NP-hard problem, so the multi-level problems are practical and complicated problems therefore solving these problems would be significant. The literature shows several algorithms to solve different forms of the bi-level programming problems (BLPP).Not only there is no any algorithm for solving quad-level programming problem, but also it has not been studied by any researcher. The most important part of this paper is presentation and studying of a new model of non-linear multi-level problems.Then we attempt to develop an effective approach based on Taylor theorem for solving the non-linear quad-level programming problem. In this approach, by using aproposedsmoothing method the quad-level programming problem is converted to a linear single problem. Finally, the single level problem is solved using the algorithm based on Taylor algorithm. The presented approach achieves an efficient and feasible solution in an appropriate time which has been evaluated by solving test problems.
Non-Linear quad-level programming problem,Smoothing method,Taylor algorithm
http://www.qjie.ir/article_282.html
http://www.qjie.ir/article_282_8d159dc9429445882410f713549b7c48.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
11
1
2018
03
01
A Stochastic Optimization Approach to a Location-Allocation Problem of Organ Transplant Centers
103
111
EN
Mahshid
Ghane
MSc, Department of Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
m_ghane_ie@yahoo.com
Reza
Tavakkoli-Moghaddam
Professor, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
tavakoli@ut.ac.ir
10.22094/joie.2018.266
Decision-making concerning thelocation of critical resource on the geographical network is important in many industries.In the healthcare system,these decisions include location of emergency and preventive care. The decisions of location play a crucial role due to determining the travel time between supply and de//////mand points and response time in emergencies.Organs are considered as highly perishable products,whosevarietyof each product has a specific perish time. Despite the importance of this field,only a small proportion of healthcare sector is dedicated to this field. Matching and finding the best recipient for a donated organ is one of the major problems in this field, which is also crucial for the overall organ transplantation process.Balancing the demand and supply in a transplant organ supply chain in order to decrease the waiting list needs certain scheduling and management.The main contribution of this paper consists of considering recipient regionsas another component of the supply chain;in addition,importance of transportation time and waiting lists hasled us to consider a bi-objective model. In addition, uncertainty of input data has led us to consider a stochastic approach.
Organ transplant supply chain,Location-allocation,Health care,Stochastic optimization approach
http://www.qjie.ir/article_266.html
http://www.qjie.ir/article_266_15c2fc6daea45d3e4dbbf474b62367e8.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
11
1
2018
03
01
Optimizing a Fuzzy Green p-hub Centre Problem Using Opposition Biogeography Based Optimization
113
132
EN
Marzieh
Karimi
M.sc, Faculty of Engineering, Department of Industrial Engineering, Kharazmi University, Tehran, Iran
marziyeh.karimi65@gmail.com
Seyed Hamid Reza
Pasandideh
Associate Professor, Faculty of Engineering, Department of Industrial Engineering, Kharazmi University, Tehran, Iran
shr_pasandideh@khu.ac.ir
10.22094/joie.2017.670.1432
Hub networks have always been acriticalissue in locating health facilities. Recently, a study has been investigated by Cocking et al. (2006)in Nouna health district in Burkina Faso, Africa, with a population of approximately 275,000 people living in 290 villages served by 23 health facilities. The travel times of the population to health services become extremely high during the rainy season, since many roads are unusable. In this regard, for many people, travelling to a health facility is a deterrent to seeking proper medical care. Furthermore, in real applications of hub networks, the travel times may vary due to traffic, climate conditions, and land or road type.To handle this challenge this paper considers the travel times are assumed to be characterized by trapezoidal fuzzy variables in order to present a fuzzy green capacitated single allocation p-hub center system (FGCSApHCP) with uncertain information. The proposed FGCSApHCP is redefined into its equivalent parametric integer nonlinear programming problem using credibility constraints. The aim is to determine the location of pcapacitated hubs and the allocation of center nodes to them in order to minimize the maximum travel time in a hub-and-center network in such uncertain environment. As the FGCSApHCP is NP-hard, a novel algorithmcalledoppositionbiogeography based optimizationis developed to solve that. This algorithm utilizes a binary oppositionbased learning mechanism to generate a diversity mechanism. At the end, both the applicability of the proposed approach and the solution methodologies are demonstrated using GAMS/BARON Software under severalkind of problems. Sensitivity analyses on the number of hubs and center nodes are conducted toprovide more insights as well.
Capacitated p-hub centre system,Single allocation,Fuzzy travel time,Opposition based learning,Biogeography Based Optimization,Uncertain information
http://www.qjie.ir/article_535413.html
http://www.qjie.ir/article_535413_dd2ef7509183e986cde6f5332abf86d1.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
11
1
2018
03
01
Fuzzy Mathematical Model For A Lot-Sizing Problem In Closed-Loop Supply Chain
133
141
EN
Amir
Fatehi Kivi
Instructor, Young researchers and elite clud, Khalkhal branch, Islamic azad university, khalkhal, iran,
amir.fatehi.g@gmail.com
Amir aydin
Atashi Abkenar
MSc, Young researchers and elite clud, Khalkhal branch, Islamic azad university, khalkhal, iran,
a.atashi@live.com
Hossin
alipour
Assistant Professor, Industrial Eng. Dept., Khalkhal Branch, Islamic Azad University, Khalkhal, Iran
alipour82@gmail.com
10.22094/joie.2018.268
The aim of lot sizing problems is to determine the periods where production takes place and the quantities to be produced in order to satisfy the customer demand while minimizing the total cost. Due to its importance on the efficiency of the production and inventory systems, Lot sizing problems are one of the most challenging production planning problems and have been studied for many years with different modeling features. In this paper, we propose a fuzzy mathematical model for the single-item capacitated lot-sizing problem in closed-loop supply chain. The possibility approach is chosen to convert the fuzzy mathematical model to crisp mathematical model. The obtained crisp model is in the form of mixed integer linear programming (MILP), which can be solved by existing solver in crisp environment to find optimal solution. Due to the complexity of the problems harmony search (HS) algorithm and genetic algorithm (GA) have been used to solve the model for fifteen problem. To verify the performance of the algorithm, we computationally compared the results obtained by the algorithms with the results of the branch-and-bound method. Additionally, Taguchi method was used to calibrate the parameters of the meta-heuristic algorithms. The computational results show that, the objective values obtained by HS are better from GA results for large dimensions test problems, also CPU time obtained by HS are better than GA for Large dimensions.
Lot-sizing,Harmony Search,returned products
http://www.qjie.ir/article_268.html
http://www.qjie.ir/article_268_602b7880f4cf1e71deb053a82bcb9f98.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
11
1
2018
03
01
Hub Covering Location Problem Considering Queuing and Capacity Constraints
143
156
EN
Mehdi
Seifbarghy
Associate Professor, Department of Industrial Engineering, Alzahra University, Tehran, Iran
seifbar@yahoo.com
Mojtaba
Hemmati
MSc, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
m.hemmati84@yahoo.com
Sepideh
Soltan Karimi
MSc, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
sepidehsoltankarimi@yahoo.com
10.22094/joie.2017.351.0
In this paper, a hub covering location problem is considered. Hubs, which are the most congested part of a network, are modeled as M/M/C queuing system and located in placeswhere the entrance flows are more than a predetermined value.A fuzzy constraint is considered in order to limit the transportation time between all origin-destination pairs in the network.On modeling, a nonlinear mathematical program is presented.Then, the nonlinear constraints are convertedto linear ones.Due to the computational complexity of the problem,genetic algorithm (GA),particle swarm optimization (PSO)based heuristics, and improved hybrid PSO are developedto solve the problem. Since the performance of the given heuristics is affected by the corresponding parameters of each, Taguchi method is appliedin order to tune the parameters. Finally,the efficiency ofthe proposed heuristicsis studied while designing a number of test problems with different sizes.The computational results indicated the greater efficiency of the heuristic GA compared to the other methods for solving the problem
Hub covering location,Queuing system,Congestion,Genetic algorithm,Hybrid particle swarm optimization algorithm
http://www.qjie.ir/article_535414.html
http://www.qjie.ir/article_535414_df4e78fdb4d93a9889c69308e03791a8.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
11
1
2018
03
01
A Multi-Objective Mixed-Model Assembly Line Sequencing Problem With Stochastic Operation Time
157
167
EN
Parviz
Fattahi
Associate Professor, Department of Industrial Engineering, Alzahra University, Tehran, Iran
p.fattahi@alzahra.ac.ir
Arezoo
Askari
MSc, Department of Industrial Engineering, Bu-Ali Sina University, Hamedan, Iran
arezoo.a116@yahoo.com
10.22094/joie.2017.568.62
In today’s competitive market, those producers who can quickly adapt themselves todiverse demands of customers are successful. Therefore, in order to satisfy these demands of market, Mixed-model assembly line (MMAL) has an increasing growth in industry. A mixed-model assembly line (MMAL) is a type of production line in which varieties of products with common base characteristics are assembled on. This paper focuses on this type of production line in a stochastic environment with three objective functions: 1) total utility work cost, 2) total idle cost, and 3) total production rate variation cost that are simultaneously considered. In real life, especially in manual assembly lines, because of some inevitable human mistakes, breakdown of machines, lack of motivation in workers and the things alike, events are notdeterministic, sowe consideroperation time as a stochastic variable independently distributed with normal distributions; for dealing with it, chance constraint optimization is used to model the problem. At first, because of NP-hard nature of the problem, multi-objective harmony search (MOHS) algorithm is proposed to solve it. Then, for evaluating the performance of the proposed algorithm, it is compared with NSGA-II that is a powerful and famous algorithm in this area. At last, numerical examples for comparing these two algorithms with some comparing metrics are presented. The results have shown that MOHS algorithm has a good performance in our proposed model.
Mixed-Model assembly line sequencing,Stochastic operation time,Chance constraint
http://www.qjie.ir/article_535416.html
http://www.qjie.ir/article_535416_1daf384fcf1da0ef04ba457f9285e24c.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
11
1
2018
03
01
Modeling and Solution Procedure for a Preemptive Multi-Objective Multi-Mode Project Scheduling Model in Resource Investment Problems
169
183
EN
Mostafa
Salimi
MSc, Department of Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
mostafa_salimi777@yahoo.com
Amir Abbas
Najafi
Associate Professor, Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran
aanajafi@kntu.ac.ir
10.22094/joie.2017.592.1381
In this paper, a preemptive multi-objective multi-mode project scheduling model for resource investment problem is proposed. The first objective function is to minimize the completion time of project (makespan);the second objective function is to minimize the cost of using renewable resources. Non-renewable resources are also considered as parameters in this model. The preemption of activities is allowed at any integer time units, and for each activity, the best execution mode is selected according to the duration and resource. Since this bi-objective problem is the extension of the resource-constrained project scheduling problem (RCPSP), it is NP-hard problem, and therefore, heuristic and metaheuristic methods are required to solve it. In this study, Non-dominated Sorting Genetic AlgorithmII (NSGA-II) and Non-dominated Ranking Genetic Algorithm (NRGA) are used based on results of Pareto solution set.We also present a heuristic method for two approaches of serial schedule generation scheme (S-SGS) and parallel schedule generation scheme (P-SGS) in the developed algorithm in order to optimize the scheduling of the activities.The input parameters of the algorithm are tuned with Response Surface Methodology (RSM). Finally, the algorithms are implemented on some numerical test problems, and their effectiveness is evaluated.
Resource Investment Problem,Preemption,Serial and Parallel Schedule Generation Scheme,Response surface methodology
http://www.qjie.ir/article_535423.html
http://www.qjie.ir/article_535423_7cdf1d9af10a8a69d78e466137ea8f53.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
11
1
2018
03
01
Scheduling of Multiple Autonomous Guided Vehicles for an Assembly Line Using Minimum Cost Network Flow
185
193
EN
Hamed
Fazlollahtabar
Assistant Professor , Iran University of Science and Technology
hfazl@iust.ac.ir
10.22094/joie.2017.587.1378
This paper proposed a parallel automated assembly line system to produce multiple products having multiple autonomous guided vehicles (AGVs). Several assembly lines are configured to produce multiple products in which the technologies of machines are shared among the assembly lines when required. The transportation between the stations in an assembly line (intra assembly line) and among stations in different assembly lines (inter assembly line) are performed using AGVs. Scheduling of AGVs to service the assembly lines and the corresponding stations are purposed. In the proposed problem the assignment of multiple AGVs to different assembly lines and the stations are performed using minimum-cost network flow (MCF). It optimizes weighted completion time of tasks for each short-term window by formulating the task and resource assignment problem as MCF problem during each short-term scheduling window.
Parallel assembly line,Autonomous guided vehicle (AGV),Scheduling,Minimum cost network flow
http://www.qjie.ir/article_537064.html
http://www.qjie.ir/article_537064_48cf3589b31e4cf817ec906b4116e899.pdf
QIAU
Journal of Optimization in Industrial Engineering
2251-9904
2423-3935
11
1
2018
03
01
A Ratio-Based Efficiency Measurement for Ranking Multi-Stage Production Systems in DEA
195
202
EN
Roza
Azizi
PhD, Department of Mathematics, Karaj Branch, Islamic Azad University, Karaj, Iran
aziziroza@kiau.ac.ir
Reza
Kazemi Matin
Associate Professor, Department of Mathematics, Karaj Branch, Islamic Azad University, Karaj, Iran.
rkmatin@gmail.com
10.22094/joie.2017.479.0
Conventional data envelopment analysis (DEA) models are used to measure efficiency score of production systems when they are considered as black boxes and their internal relationship is ignored. This paper deals with a common special case of network systems which is called multi-stage production system and can be generalized to many organizations. A multi stage production system has some stages in which the outputs of each stage are used as the inputs of the next stage to produce the final outputs of the system. Most of the approaches handling multi-stage systems in DEA evaluate efficiency measure of a production system considering the interrelationship between its stages; however, they do not present their ranking or impact of each stage in ranking of a special multi-stage system through comparison with the others. In this paper, considering the series internal structure of the multi-stage systems and their efficiency measure, we propose some new ratio-based DEA models to determine the best and the worst rank of the multi-stage systems over all sets of feasible weights. In order to improve the performance of the whole system, the proposed models are used to recognize the stages with the most important role in the system’s inefficiency. Some numerical examples are presented to illustrate the approach.
Data Envelopment Analysis,Multi-stage production systems,The best and worst ranks,Inefficiency sources
http://www.qjie.ir/article_537065.html
http://www.qjie.ir/article_537065_531ce174c322b8f04ca3a5dc12ad4b04.pdf