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Growing Science » International Journal of Industrial Engineering Computations

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Sort articles by: Volume | Date | Most Rates | Most Views | Reviews | Alphabet
1.

Robust simulation optimization using φ-divergence Pages 517-534 Right click to download the paper Download PDF

Authors: Samira Moghaddam, Mahlooji Mahlooji

DOI: 10.5267/j.ijiec.2016.5.003

Keywords: Simulation optimization, Kriging metamodel, Robust optimization, φ-divergence

Abstract:
We introduce a new robust simulation optimization method in which the probability of occurrence of uncertain parameters is considered. It is assumed that the probability distributions are unknown but historical data are on hand and using φ-divergence functionality the uncertainty region for the uncertain probability vector is defined. We propose two approaches to formulate the robust counterpart problem for the objective function estimated by Kriging. The first method is a minimax problem and the second method is based on the chance constraint definition. To illustrate the methods and assess their performance, numerical experiments are conducted. Results show that the second method obtains better robust solutions with less simulation runs.
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Journal: IJIEC | Year: 2016 | Volume: 7 | Issue: 4 | Views: 2363 | Reviews: 0

 
2.

A robust optimization model for blood supply chain in emergency situations Pages 535-554 Right click to download the paper Download PDF

Authors: Meysam Fereiduni, Kamran Shahanaghi

DOI: 10.5267/j.ijiec.2016.5.002

Keywords: Blood supply chain, Humanitarian logistics, Robust optimization, P-robust approach, Uncertainty programing

Abstract:
In this paper, a multi-period model for blood supply chain in emergency situation is presented to optimize decisions related to locate blood facilities and distribute blood products after natural disasters. In disastrous situations, uncertainty is an inseparable part of humanitarian logistics and blood supply chain as well. This paper proposes a robust network to capture the uncertain nature of blood supply chain during and after disasters. This study considers donor points, blood facilities, processing and testing labs, and hospitals as the components of blood supply chain. In addition, this paper makes location and allocation decisions for multiple post disaster periods through real data. The study compares the performances of “p-robust optimization” approach and “robust optimization” approach and the results are discussed.
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Journal: IJIEC | Year: 2016 | Volume: 7 | Issue: 4 | Views: 3353 | Reviews: 0

 
3.

Buffer clustering policy for sequential production lines with deterministic processing times Pages 555-572 Right click to download the paper Download PDF

Authors: Francesca Schuler, Hoshang Darabi

DOI: 10.5267/j.ijiec.2016.5.001

Keywords: Sequential, Production, Buffer, Cluster, Deterministic, Configuration

Abstract:
A sequential production line is defined as a set of sequential operations within a factory or distribution center whereby entities undergo one or more processes to produce a final product. Sequential production lines may gain efficiencies such as increased throughput or reduced work in progress by utilizing specific configurations while maintaining the chronological order of operations. One problem identified by the authors via a case study is that, some of the configurations, such as work cell or U-shaped production lines that have groups of buffers, often increase the space utilization. Therefore, many facilities do not take advantage of the configuration efficiencies that a work cell or U-shaped production line provide. To solve this problem, the authors introduce the concept of a buffer cluster. The production line implemented with one or more buffer clusters maintains the throughput of the line, identical to that with dedicated buffers, but with the clusters reduces the buffer storage space. The paper derives a time based parametric model that determines the sizing of the buffer cluster, provides a reduced time space for which to search for the buffer cluster sizing, and determines an optimal buffer clustering policy that can be applied to any N-server, N+1 buffer sequential line configuration with deterministic processing time. This solution reduces the buffer storage space utilized while ensuring no overflows or underflows occur in the buffer. Furthermore, the paper demonstrates how the buffer clustering policy serves as an input into a facility layout tool that provides the optimal production line layout.
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Journal: IJIEC | Year: 2016 | Volume: 7 | Issue: 4 | Views: 2759 | Reviews: 0

 
4.

Integrated approach in solving parallel machine scheduling and location (ScheLoc) problem Pages 573-584 Right click to download the paper Download PDF

Authors: Mohsen Rajabzadeh, Mohsen Ziaee, Ali Bozorgi-Amiri

DOI: 10.5267/j.ijiec.2016.4.003

Keywords: Scheduling, Layout planning, Parallel machines

Abstract:
Scheduling and layout planning are two important areas of operations research, which are used in the areas of production planning, logistics and supply chain management. In many industries locations of machines are not specified, previously, therefore, it is necessary to consider both location and scheduling, simultaneously. This paper presents a mathematical model to consider both scheduling and layout planning for parallel machines in discrete and continuous spaces, concurrently. The preliminary results have indicated that the integrated model is capable of handling problems more efficiently.
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Journal: IJIEC | Year: 2016 | Volume: 7 | Issue: 4 | Views: 2024 | Reviews: 0

 
5.

An alternative hybrid evolutionary technique focused on allocating machines and sequencing operations Pages 585-596 Right click to download the paper Download PDF

Authors: Mariano Frutos, Fernando Tohmé, Fernando Delbianco, Fabio Miguel

DOI: 10.5267/j.ijiec.2016.4.002

Keywords: Flexible job-shop scheduling problem, Optimization, Multi-objective hybrid Evolutionary algorithm, Production

Abstract:
We present here a hybrid algorithm for the Flexible Job-Shop Scheduling Problem (FJSSP). This problem involves the optimal use of resources in a flexible production environment in which each operation can be carried out by more than a single machine. Our algorithm allocates, in a first step, the machines to operations and in a second stage it sequences them by integrating a Multi-Objective Evolutionary Algorithm (MOEA) and a path-dependent search algorithm (Multi-Objective Simulated Annealing), which is enacted at the genetic phase of the procedure. The joint interaction of those two components yields a very efficient procedure for solving the FJSSP. An important step in the development of the algorithm was the selection of the right MOEA. Candidates were tested on problems of low, medium and high complexity. Further analyses showed the relevance of the search algorithm in the hybrid structure. Finally, comparisons with other algorithms in the literature indicate that the performance of our alternative is good.
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Journal: IJIEC | Year: 2016 | Volume: 7 | Issue: 4 | Views: 1735 | Reviews: 0

 
6.

A GRASP-based approach to the multi activity combined timetabling and crew scheduling problem considering a heterogeneous workforce Pages 597-606 Right click to download the paper Download PDF

Authors: Diego Novoa, Camilo Olarte, David Barrera, Eliana María González-Neira

DOI: 10.5267/j.ijiec.2016.4.001

Keywords: Workforce Scheduling, Multi-activity Combined Timetabling and Crew Scheduling Problem (MCTCSP), Heterogeneous workforce, Categorical Skills, GRASP

Abstract:
This paper tackles an extension to the Multi-activity Combined Timetabling and Crew Scheduling Problem (MCTCSP). The goal of the original problem is to schedule the minimum number of homogenous workers required, in order to visit a set of customers characterized by services needed against schedule availability. However, since in home services it is common to have specialized workers, a mathematical model considering a heterogeneous workforce is proposed. As a solution, a GRASP-based algorithm is designed. In order to test the metaheuristic performance, 110 instances from the literature are adapted to include categorical skills. In addition, another 10 instances are randomly generated to consider large problems. The results show that the proposed GRASP finds optimal solutions in 46% of the cases and saves 40–96% computational time.
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Journal: IJIEC | Year: 2016 | Volume: 7 | Issue: 4 | Views: 1958 | Reviews: 0

 
7.

A genetic algorithm for preemptive scheduling of a single machine Pages 607-614 Right click to download the paper Download PDF

Authors: Amir-Mohammad Golmohammadi, Hamid Bani-Asadi, Hamed Jafar Zanjani, Hamid Tikani

DOI: 10.5267/j.ijiec.2016.3.004

Keywords: Preemption, Single machine scheduling, Work in process, Genetic algorithm

Abstract:
This paper presents a mathematical model for scheduling of a single machine when there are preemptions in jobs. The primary objective of the study is to minimize different objectives such as earliness, tardiness and work in process. The proposed mathematical problem is considered as NP-Hard and the optimal solution is available for small scale problems. Therefore, a genetic algorithm (GA) is developed to solve the problem for large-scale problems. The implementation of the proposed model is compared with GA for problems with up to 50 jobs using three methods of roulette wheel sampling, random sampling and competition sampling. The results have indicated that competition sampling has reached optimal solutions for small scale problems and it could obtain better near-optimal solutions in relatively lower running time compared with other sampling methods.
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Journal: IJIEC | Year: 2016 | Volume: 7 | Issue: 4 | Views: 2355 | Reviews: 0

 
8.

A multi-objective location-inventory model for 3PL providers with sustainable considerations under uncertainty Pages 615-634 Right click to download the paper Download PDF

Authors: R. Daghigh, M.S. Jabalameli, A. Bozorgi Amiri, M.S. Pishvaee

DOI: 10.5267/j.ijiec.2016.3.003

Keywords: Sustainable Development, Supply chain network design, Multi-objective optimization Possibilistic programming

Abstract:
In recent years, logistics development is considered as an important aspect of any country’s development. Outsourcing logistics activities to third party logistics (3PL) providers is a common way to achieve logistics development. On the other hand, globalization and increasing customers’ concern about the environmental impact of activities as well as the appearance of the issue of social responsibility have led companies employ sustainable supply chain management, which considers economic, environmental and social benefits, simultaneously. This paper proposes a multi-objective model to design logistics network for 3PL providers by considering sustainable objectives under uncertainty. Objective functions include minimizing the total cost, minimizing greenhouse gas emission and maximizing social responsibility subject to fair access to products, number of created job opportunities and local community development. It is worth mentioning that in the present paper the perishability of products is also considered. A numerical example is provided to solve and validate model using augmented Epsilon-Constraint method. The results show that three sustainable objectives were in conflict and as the one receives more desirable values, the others fall into more undesirable values. In addition, by increasing maximum perishable time periods and by considering lateral transshipment among facilities of a level one can improve sustainability indices of the problem, which indicates the necessity of such policy in improving network sustainability.
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Journal: IJIEC | Year: 2016 | Volume: 7 | Issue: 4 | Views: 2572 | Reviews: 0

 
9.

Locating distribution/service centers based on multi objective decision making using set covering and proximity to stock market Pages 635-648 Right click to download the paper Download PDF

Authors: Mazyar Dabibi, Babak Farhang Moghaddam, Mohammad Ali Afshar Kazemi

DOI: 10.5267/j.ijiec.2016.3.002

Keywords: Marketing mix, Set covering problem, GA, Customer satisfaction, Facility location, Multi objective Optimization

Abstract:
In the present competitive world, facility location is an important aspect of the supply chain (sc) optimization. It involves selecting specific locations for facility construction and allocation of the distribution channel among different SC levels. In fact, it is a strategic issue which directly affects many operational/tactical decisions. Besides the accessibility, which results in customer satisfaction, the present paper optimizes the establishment costs of a number of distribution channels by considering their proximity to the stock market of the goods they distribute, and proposes mathematical models for two objective functions using the set covering problem. Then, two objective functions are proposed into one through the ε-constraint method and solved by the metaheuristic Genetic Algorithm (GA). To test the resulted model, a smaller scale problem is solved. Results from running the algorithm with different ε-values show that, on average, a 10% increase in ε, which increases the value of the second objective function - distance covered by customers will cause a 2% decrease in the value of the first objective function including the costs of establishing distribution centers). The repeatability and solution convergence of the two-objective model presented by the GA are other results obtained in this study.
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Journal: IJIEC | Year: 2016 | Volume: 7 | Issue: 4 | Views: 2457 | Reviews: 0

 
10.

A novel robust chance constrained possibilistic programming model for disaster relief logistics under uncertainty Pages 649-670 Right click to download the paper Download PDF

Authors: Maryam Rahafrooz, Mahdi Alinaghian

DOI: 10.5267/j.ijiec.2016.3.001

Keywords: Disaster relief Logistics, Relief facility location, Uncertainty, Chance constrained possibilistic programming, Robust optimization, Multi-objective optimization

Abstract:
In this paper, a novel multi-objective robust possibilistic programming model is proposed, which simultaneously considers maximizing the distributive justice in relief distribution, minimizing the risk of relief distribution, and minimizing the total logistics costs. To effectively cope with the uncertainties of the after-disaster environment, the uncertain parameters of the proposed model are considered in the form of fuzzy trapezoidal numbers. The proposed model not only considers relief commodities priority and demand points priority in relief distribution, but also considers the difference between the pre-disaster and post-disaster supply abilities of the suppliers. In order to solve the proposed model, the LP-metric and the improved augmented ε-constraint methods are used. Second, a set of test problems are designed to evaluate the effectiveness of the proposed robust model against its equivalent deterministic form, which reveales the capabilities of the robust model. Finally, to illustrate the performance of the proposed robust model, a seismic region of northwestern Iran (East Azerbaijan) is selected as a case study to model its relief logistics in the face of future earthquakes. This investigation indicates the usefulness of the proposed model in the field of crisis.
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Journal: IJIEC | Year: 2016 | Volume: 7 | Issue: 4 | Views: 2678 | Reviews: 0

 
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