Processing, Please wait...

  • Home
  • About Us
  • Search:
  • Advanced Search

Growing Science

Journals

  • IJIEC (396)
  • MSL (1547)
  • DSL (251)
  • CCL (148)
  • USCM (175)
  • ESM (141)
  • AC (69)
  • JPM (38)

Keywords

Tehran Stock Exchange(92)
Factor analysis(74)
Banking industry(55)
Supply chain management(49)
Genetic Algorithm(47)
TOPSIS(47)
Efficiency(42)
Knowledge Management(41)
Customer satisfaction(40)
Inventory(38)
Data envelopment analysis(37)
Supply chain(34)
optimization(33)
AHP(32)
Performance measurement(30)
Emotional intelligence(28)
Supplier selection(27)
Information technology(27)
Productivity(27)
Organizational Culture(27)


» Show all keywords

Authors

Naser Azad(82)
Mohammad Reza Iravani(64)
Hassan Ghodrati(31)
Mohammad Khodaei Valahzaghard(30)
Ali Harounabadi(26)
Shankar Chakraborty(26)
Ahmad Makui(25)
Masoud Rabbani(20)
Abolfazl Danaei(20)
Somayeh Hozouri(17)
S.R. Singh(16)
Seyed Shahab Mousavi(16)
Reza Tavakkoli-Moghaddam(15)
Hassan Javanshir(15)
Allahyar Arabmomeni(13)
Hajar Jannesari(13)
Mohammad Mohammadi(12)
Ashkan Faraji(12)
Seyed Foad Zarifi(12)
Younos Vakil alroaia(12)


» Show all authors

Countries

Iran(1924)
India(429)
USA(53)
Malaysia(49)
Canada(29)
Italy(26)
Pakistan(24)
Nigeria(22)
Algeria(21)
Poland(20)
Ghana(16)
China(15)
Bangladesh(15)
Indonesia(15)
Columbia(14)
Tunisia(14)
Turkey(12)
Thailand(12)
Tunesia(12)
Taiwan(12)


» Show all countries
Sort articles by: Volume | Date | Most Rates | Most Views | Reviews | Alphabet
1.

Flow-shop scheduling problem under uncertainties: Review and trends Pages 399-426 Right click to download the paper Download PDF

Authors: Eliana María González-Neira, Jairo R. Montoya-Torres, David Barrera

DOI: 10.5267/j.ijiec.2017.2.001

Keywords: Flow shop, Flexible flow shop, Uncertainties, Stochastic, Fuzzy, Production logistics, Review

Abstract:
Among the different tasks in production logistics, job scheduling is one of the most important at the operational decision-making level to enable organizations to achieve competiveness. Scheduling consists in the allocation of limited resources to activities over time in order to achieve one or more optimization objectives. Flow-shop (FS) scheduling problems encompass the sequencing processes in environments in which the activities or operations are performed in a serial flow. This type of configuration includes assembly lines and the chemical, electronic, food, and metallurgical industries, among others. Scheduling has been mostly investigated for the deterministic cases, in which all parameters are known in advance and do not vary over time. Nevertheless, in real-world situations, events are frequently subject to uncertainties that can affect the decision-making process. Thus, it is important to study scheduling and sequencing activities under uncertainties since they can cause infeasibilities and disturbances. The purpose of this paper is to provide a general overview of the FS scheduling problem under uncertainties and its role in production logistics and to draw up opportunities for further research. To this end, 100 papers about FS and flexible flow-shop scheduling problems published from 2001 to October 2016 were analyzed and classified. Trends in the reviewed literature are presented and finally some research opportunities in the field are proposed.
Details
  • 34
  • 1
  • 2
  • 3
  • 4
  • 5

Journal: IJIEC | Year: 2017 | Volume: 8 | Issue: 4 | Views: 843 | Reviews: 0

 
2.

Heterogeneous workers with learning ability assignment in a cellular manufacturing system Pages 427-440 Right click to download the paper Download PDF

Authors: Sergio Fichera, Antonio Costa, Fulvio Antonio Cappadonna

DOI: 10.5267/j.ijiec.2017.3.005

Keywords: Flow-shop, Group scheduling, Workforce assignment, Learning effect, Skills, Evolutionary algorithm

Abstract:
This paper deals with Flow-shop Sequence-Dependent Group Scheduling and worker assignment problem. Flow-shop allows the process of a set of families of products applying the group technology concept to reduce setup costs, lead times, and work-in-process inventory costs. The worker assignment problem deals with assigning workers to workstations considering their different abilities and learning effect. The proposed model in this paper considers different objectives. The decision problems in this cellular manufacturing system are the jobs scheduling within of own group, the group scheduling and the workers assignment to the machines. The aim of this paper is to consider a more realistic profile of heterogeneous workers introducing the learning effect in the joint group scheduling and workers assignment problem. A mathematical model and an evolutionary procedure has been developed to solve this problem. A benchmark of test cases having different numbers of machines, groups, jobs, worker skills and learning index, has been taken into account to compare the efficiency of the proposed algorithm with two well known procedures.
Details
  • 0
  • 1
  • 2
  • 3
  • 4
  • 5

Journal: IJIEC | Year: 2017 | Volume: 8 | Issue: 4 | Views: 302 | Reviews: 0

 
3.

Introducing radiality constraints in capacitated location-routing problems Pages 441-452 Right click to download the paper Download PDF

Authors: Eliana Mirledy Toro Ocampo, Frederico G. Guimarães, Ramón Alfonso Gallego Rendón

DOI: 10.5267/j.ijiec.2017.3.004

Keywords: Capacitated vehicle routing problem, Capacitated location-routing problem, Combinatorial optimization, Radiality constraints, Spanning tree, vehicle routing problem

Abstract:
In this paper, we introduce a unified mathematical formulation for the Capacitated Vehicle Routing Problem (CVRP) and for the Capacitated Location Routing Problem (CLRP), adopting radiality constraints in order to guarantee valid routes and eliminate subtours. This idea is inspired by formulations already employed in electric power distribution networks, which requires a radial topology in its operation. The results show that the proposed formulation greatly improves the convergence of the solver.

Details
  • 0
  • 1
  • 2
  • 3
  • 4
  • 5

Journal: IJIEC | Year: 2017 | Volume: 8 | Issue: 4 | Views: 296 | Reviews: 0

 
4.

Greenhouse gas penalty and incentive policies for a joint economic lot size model with industrial and transport emissions Pages 453-480 Right click to download the paper Download PDF

Authors: Ivan Darma Wangsa

DOI: 10.5267/j.ijiec.2017.3.003

Keywords: A joint economic lot size model, Greenhouse gas emission, Direct and indirect emissions, Penalty and incentive policies and stochastic demand

Abstract:
This paper presents a joint economic lot size model for a single manufacturer-a single buyer. The purposed model involves the greenhouse gas emission from industrial and transport sectors. We divide the emission into two types, namely the direct and indirect emissions. In this paper, we consider the Government’s penalty and incentive policies to reduce the emission. We assume that the demand of the buyer is normally distributed and partially backordered. The objective is to minimize joint total cost incurred by a single manufacturer-a single buyer and involves the transportation costs of the freight forwarder. Transportation costs are the function of shipping weight, distance, fuel price and consumption with two transportation modes: truckload and less-than-truckload shipments. Finally, an algorithm procedure is proposed to determine the optimal order quantity, safety factor, actual shipping weight, total emission and frequency of deliveries. Numerical examples and analyses are given to illustrate the results.
Details
  • 68
  • 1
  • 2
  • 3
  • 4
  • 5

Journal: IJIEC | Year: 2017 | Volume: 8 | Issue: 4 | Views: 386 | Reviews: 0

 
5.

Flexibility configurations and preventive maintenance impact on job-shop manufacturing systems Pages 481-492 Right click to download the paper Download PDF

Authors: Paolo Renna

DOI: 10.5267/j.ijiec.2017.3.002

Keywords: Machine flexibility, Routing flexibility, Corrective maintenance, Preventive maintenance, Simulation

Abstract:
Manufacturing systems need to be able to work under the dynamic and uncertain production environment. Machine and routing flexibility combined with preventive maintenance actions can improve the performance of the manufacturing systems under dynamic conditions. This paper evaluates different levels of machine and routing flexibility combined with different degrees of preventive maintenance policy. The performance measures considered are throughput, work in process and throughput. The performance measures are compared with a system without any flexibility and no preventive maintenance actions. Different levels of flexibility and preventive maintenance actions are examined under a simulation environment. The simulation results highlight more important factors for the performance measures and the best combination of the factors to improve the performance.

Details
  • 0
  • 1
  • 2
  • 3
  • 4
  • 5

Journal: IJIEC | Year: 2017 | Volume: 8 | Issue: 4 | Views: 383 | Reviews: 0

 
6.

Modeling and optimization of surface roughness and productivity thru RSM in face milling of AISI 1040 steel using coated carbide inserts Pages 493-512 Right click to download the paper Download PDF

Authors: Mohamed Fnides, Mohamed Athmane Yallese, Riad Khattabi, Tarek Mabrouki, François Girardin

DOI: 10.5267/j.ijiec.2017.3.001

Keywords: Face milling, RSM, Optimization, Flank wear, Surface roughness and productivity

Abstract:
The aim of this study is to evaluate the impact of factors such as cutting speed, feed rate, and depth of cut on surface roughness and Material Removed Rate (MRR) when machining in dry face milling AISI 1040 steel with coated carbide inserts GC1030 using the response surface methodology (RSM). For this purpose, a number of machining experiments based on statistical three-factor and three-level factorial experiment designs, completed (L27) with a statistical analysis of variance (ANOVA), were performed in order to develop mathematical models and to identify the significant factors of these technological parameters. Multi-objective optimization procedure for minimizing Ra, Ry and Rz and maximizing MRR using desirability approach has been also implementented. The current study was also carried out to investigate the tool life of the inserts. The models found the relationship between the cutting parameters (Vc, fz and ap) and the studied technological parameters. It has been found that the cutting speed was the most affecting surface roughness which is due to the geometry of the insert which has a scraping edge and enables to obtain low roughness even at important feed rate, followed by the feed rate and the depth of cut at the end. The optimal combination of cutting parameters were cutting speed of 314 m/min, feed rate of 0.16 mm/tooth and depth of cut of 0.6 mm with a composite desirability of 0.924.
Details
  • 51
  • 1
  • 2
  • 3
  • 4
  • 5

Journal: IJIEC | Year: 2017 | Volume: 8 | Issue: 4 | Views: 537 | Reviews: 0

 
7.

Solving a bi-objective vehicle routing problem under uncertainty by a revised multi-choice goal programming approach Pages 283-302 Right click to download the paper Download PDF

Authors: Hossein Yousefi, Reza Tavakkoli-Moghaddam, Mahyar Taheri Bavil Oliaei, Mohammad Mohammadi, Ali Mozaffari

DOI: 10.5267/j.ijiec.2017.1.003

Keywords: Vehicle routing problem, Multi-choice goal programming, Customer priority, Customer satisfaction

Abstract:
A vehicle routing problem with time windows (VRPTW) is an important problem with many real applications in a transportation problem. The optimum set of routes with the minimum distance and vehicles used is determined to deliver goods from a central depot, using a vehicle with capacity constraint. In the real cases, there are other objective functions that should be considered. This paper considers not only the minimum distance and the number of vehicles used as the objective function, the customer’s satisfaction with the priority of customers is also considered. Additionally, it presents a new model for a bi-objective VRPTW solved by a revised multi-choice goal programming approach, in which the decision maker determines optimistic aspiration levels for each objective function. Two meta-heuristic methods, namely simulated annealing (SA) and genetic algorithm (GA), are proposed to solve large-sized problems. Moreover, the experimental design is used to tune the parameters of the proposed algorithms. The presented model is verified by a real-world case study and a number of test problems. The computational results verify the efficiency of the proposed SA and GA.
Details
  • 0
  • 1
  • 2
  • 3
  • 4
  • 5

Journal: IJIEC | Year: 2017 | Volume: 8 | Issue: 3 | Views: 649 | Reviews: 0

 
8.

Performance analysis and optimization for CSDGB filling system of a beverage plant using particle swarm optimization Pages 303-314 Right click to download the paper Download PDF

Authors: Parveen Kumar, P.C. Tewari

DOI: 10.5267/j.ijiec.2017.1.002

Keywords: Performance optimization, PSO, Bottling system, Markov approach

Abstract:
The paper deals with the performance analysis and optimization for Carbonated Soft Drink Glass Bottle (CSDGB) filling system of a beverage plant using Particle Swarm Optimization (PSO) approach. The CSDGB system consists of seven main subsystems arranged in series namely Uncaser, Bottle Washer, Electronic Inspection Station, Filling Machine, Crowner, Coding Machine and Case Packer. Considering exponential distribution for probable failures and repairs, mathematical modeling is performed using Markov Approach (MA). The differential equations have been derived on the basis of probabilistic approach using transition diagram. These equations are solved using normalizing condition and recursive method to drive out the steady state availability expression of the system i.e. system’s performance criterion. The performance optimization of system has been carried out by varying the number of particles and number of generations. It has been observed that the maximum availability of 90.27% is achieved at flock size of 55 and 90.84% at 300th generation. Thus, findings of the paper will be useful to the plant management for execution of proper maintenance decisions.
Details
  • 0
  • 1
  • 2
  • 3
  • 4
  • 5

Journal: IJIEC | Year: 2017 | Volume: 8 | Issue: 3 | Views: 482 | Reviews: 0

 
9.

A two-layer genetic algorithm for the design of reliable cellular manufacturing systems Pages 315-332 Right click to download the paper Download PDF

Authors: Hassan Rezazadeh, Amin Khiali-Miab

DOI: 10.5267/j.ijiec.2017.1.001

Keywords: Cell Formation, Reliability, Mathematical Model, Two-Layer Genetic Algorithm

Abstract:
This study presents a new mathematical model for the design of reliable cellular manufacturing systems, which leads to reduced manufacturing costs, improved product quality and improved total reliability of the manufacturing system. This model is expected to provide a more noticeable improvement in time and solution quality in comparison with other existing models. Each part to be manufactured may select each of the predefined manufacturing routes, such that the total reliability of the system is increased. On the other hand, the model adopts to categorize the machines to determine the manufacturing cells (cell formation) and reduce the transportation costs. Thereby, both criteria of system reliability and manufacturing costs will be simultaneously improved. Due to the complexity of cell formation problems, a two-layer genetic algorithm is applied on the problem in order to achieve near optimal solutions. Furthermore, the performance of the proposed algorithm is shown for solving some computational experiments. Finally, the results of a practical study for designing a cellular manufacturing system as a case study in Iranian Diesel Engine Manufacturing Co., Tabriz, Iran are present.
Details
  • 0
  • 1
  • 2
  • 3
  • 4
  • 5

Journal: IJIEC | Year: 2017 | Volume: 8 | Issue: 3 | Views: 598 | Reviews: 0

 
10.

Hybridized genetic-immune based strategy to obtain optimal feasible assembly sequences Pages 333-346 Right click to download the paper Download PDF

Authors: Bala Murali Gunji, B. B. V. L. Deepak, M. V. A. Raju Bahubalendruni, Bibhuti Bhusan Biswal

DOI: 10.5267/j.ijiec.2016.12.004

Keywords: Assembly sequence planning, Artificial immune system, Genetic algorithm, Assembly automation, Feasible assembly sequence, Assembly automation

Abstract:
An appropriate sequence of assembly operations increases the productivity and enhances product quality there by decrease the overall cost and manufacturing lead time. Achieving such assembly sequence is a complex combinatorial optimization problem with huge search space and multiple assembly qualifying criteria. The purpose of the current research work is to develop an intelligent strategy to obtain an optimal assembly sequence subjected to the assembly predicates. This paper presents a novel hybrid artificial intelligent technique, which executes Artificial Immune System (AIS) in combination with the Genetic Algorithm (GA) to find out an optimal feasible assembly sequence from the possible assembly sequence. Two immune models are introduced in the current research work: (1) Bone marrow model for generating possible assembly sequence and reduce the system redundancy and (2) Negative selection model for obtaining feasible assembly sequence. Later, these two models are integrated with GA in order to obtain an optimal assembly sequence. The proposed AIS-GA algorithm aims at enhancing the performance of AIS by incorporating GA as a local search strategy to achieve global optimum solution for assemblies with large number of parts. The proposed algorithm is implemented on a mechanical assembly composed of eleven parts joined by several connectors. The method is found to be successful in achieving global optimum solution with less computational time compared to traditional artificial intelligent techniques.
Details
  • 0
  • 1
  • 2
  • 3
  • 4
  • 5

Journal: IJIEC | Year: 2017 | Volume: 8 | Issue: 3 | Views: 550 | Reviews: 0

 
1 2 3 4 5 6 7 8 9 10 ... 22
Previous Next

® 2010-2018 GrowingScience.Com