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Solving an aggregate production planning problem by using multi-objective genetic algorithm (MOGA) approach
, Pages 1-12 Ripon Kumar Chakrabortty and Md. A. Akhtar Hasin PDF (151 K) |
Abstract: In hierarchical production planning system, Aggregate Production Planning (APP) falls between the broad decisions of long-range planning and the highly specific and detailed short-range planning decisions. This study develops an interactive Multi-Objective Genetic Algorithm (MOGA) approach for solving the multi-product, multi-period aggregate production planning (APP) with forecasted demand, related operating costs, and capacity. The proposed approach attempts to minimize total costs with reference to inventory levels, labor levels, overtime, subcontracting and backordering levels, and labor, machine and warehouse capacity. Here several genetic algorithm parameters are considered for solving NP-hard problem (APP problem) and their relative comparisons are focused to choose the most auspicious combination for solving multiple objective problems. An industrial case demonstrates the feasibility of applying the proposed approach to real APP decision problems. Consequently, the proposed MOGA approach yields an efficient APP compromise solution for large-scale problems. DOI: 10.5267/j.ijiec.2012.09.003 Keywords: Multi-objective optimization, Genetic algorithm, Aggregate production planning |
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Network design and operational modelling for construction green supply chain management
, Pages 13-28 Pengfei Zhou Dong Chen and Qiuliang Wang PDF (467 K) |
Abstract: Based on studying organizational structure of Construction Green Supply Chain Management (CGSCM), a mathematical programming model of CGSCM was proposed. The model aimed to maximize the aggregate profits of normalized construction logistics, the reverse logistics and the environmental performance. Numerical experiments show that the proposed approach can improve the aggregate profit effectively. In addition, return ratio, subsidies from governmental organizations, and environmental performance were analyzed for CGSCM performance. Herein, the proper return, subsidy and control strategy could optimize construction green supply chain. DOI: 10.5267/j.ijiec.2012.11.001 Keywords: Green Supply Chain Management, Mathematical Programming, Reverse Logistics, Environmental Performance |
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Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems
, Pages 29-50 R. Venkata Rao and Vivek Patel PDF (710 K) |
Abstract: Teaching-Learning-based optimization (TLBO) is a recently proposed population based algorithm, which simulates the teaching-learning process of the class room. This algorithm requires only the common control parameters and does not require any algorithm-specific control parameters. In this paper, the effect of elitism on the performance of the TLBO algorithm is investigated while solving unconstrained benchmark problems. The effects of common control parameters such as the population size and the number of generations on the performance of the algorithm are also investigated. The proposed algorithm is tested on 76 unconstrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms. A statistical test is also performed to investigate the results obtained using different algorithms. The results have proved the effectiveness of the proposed elitist TLBO algorithm. DOI: 10.5267/j.ijiec.2012.09.001 Keywords: Teaching-learning-based optimization; Elitism; Population size; Number of generations; Unconstrained optimization problems |
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4. |
Optimization of machining parameters of turning operations based on multi performance criteria
, Pages 51-60 Abhijit Saha and N.K.Mandal PDF (467 K) |
Abstract: The selection of optimum machining parameters plays a significant role to ensure quality of product, to reduce the manufacturing cost and to increase productivity in computer controlled manufacturing process. For many years, multi-objective optimization of turning based on inherent complexity of process is a competitive engineering issue. This study investigates multi-response optimization of turning process for an optimal parametric combination to yield the minimum power consumption, surface roughness and frequency of tool vibration using a combination of a Grey relational analysis (GRA). Confirmation test is conducted for the optimal machining parameters to validate the test result. Various turning parameters, such as spindle speed, feed and depth of cut are considered. Experiments are designed and conducted based on full factorial design of experiment. DOI: 10.5267/j.ijiec.2012.11.004 Keywords: Turning, Power consumption, Surface roughness, Grey relational analysis, Frequency of tool vibration |
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A multi supplier lot sizing strategy using dynamic programming
, Pages 61-70 Iman Parsa, Mohsen Emadi Khiav, Mohammad Mahdavi Mazdeh and Saharnaz Mehrani PDF (467 K) |
Abstract: In this paper, the problem of lot sizing for the case of a single item is considered along with supplier selection in a two-stage supply chain. The suppliers are able to offer quantity discounts, which can be either all-unit or incremental discount policies. A mathematical modeling formulation for the proposed problem is presented and a dynamic programming methodology is provided to solve it. Computational experiments are performed in order to examine the accuracy and the performance of the proposed method in terms of running time. The preliminary results indicate that the proposed algorithm is capable of providing optimal solutions within low computational times, high accuracy solutions. DOI: 10.5267/j.ijiec.2012.11.003 Keywords: Supply chain, Lot sizing, Supplier selection, Quantity discounts, Dynamic programming |
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Optimization of process parameters for friction Stir welding of dissimilar Aluminum alloys (AA2024 -T6 and AA6351-T6) by using Taguchi method
, Pages 71-80 P. Murali Krishna, N. Ramanaiah and K. Prasada Rao PDF (467 K) |
Abstract: The present study focused on the Taguchi experimental design technique of Friction Stir Welds of dissimilar aluminum alloys (AA2024-T6 and AA6351-T6) for tensile properties. Effect of process parameters, rotational speed, Traverse speed and axial force, on tensile strength was evaluated. Optimized welding conditions for maximize tensile strength were estimated in order to improve the productivity, weld quality. Non-linear regression mathematical model was developed to correlate the process parameters to tensile strength. The results were verified by conducting the confirmation tests at identified optimum conditions. DOI: 10.5267/j.ijiec.2012.11.002 Keywords: Friction stir welding, Taguchi, Optimization, Process Parameters |
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7. |
Three stage supply chain model with two warehouse, imperfect production, variable demand rate and inflation
, Pages 81-92 S.R. Singh, Vandana Gupta Preety Gupta PDF (467 K) |
Abstract: This study develops an integrated production inventory model from the perspectives of vendor, supplier and buyer. The demand rate is time dependent for the vendor and supplier and buyer assumes the stock dependent demand rate. As per the demand, supplier uses two warehouses (rented and owned) for the storage of excess quantities. Shortages are allowed at the buyer’s part only and the unfulfilled demand is partially backlogged. The effect of imperfect production processes on lot sizing is also considered. This complete model is studied under the effect of inflation. The objective is to minimize the total cost for the system. A solution procedure is developed to find a near optimal solution for the model. A numerical example along with sensitivity analysis is given to illustrate the model. DOI: 10.5267/j.ijiec.2012.10.005 Keywords: Supply chain model, Two warehouse, Partially backlogging, Imperfect items, Variable demand rate and inflation |
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A location-inventory model for distribution centers in a three-level supply chain under uncertainty
, Pages 93-110 Sara Gharegozloo Hamedani, M. Saeed Jabalameli and Ali Bozorgi-Amiri PDF (467 K) |
Abstract: We study a location-inventory problem in a three level supply chain network under uncertainty, which leads to risk. The (r,Q) inventory control policy is applied for this problem. Besides, uncertainty exists in different parameters such as procurement, transportation costs, supply, demand and the capacity of different facilities (due to disaster, man-made events and etc). We present a robust optimization model, which concurrently specifies: locations of distribution centers to be opened, inventory control parameters (r,Q), and allocation of supply chain components. The model is formulated as a multi-objective mixed-integer nonlinear programming in order to minimize the expected total cost of such a supply chain network comprising location, procurement, transportation, holding, ordering, and shortage costs. Moreover, we develop an effective solution approach on the basis of multi-objective particle swarm optimization for solving the proposed model. Eventually, computational results of different examples of the problem and sensitivity analysis are exhibited to show the model and algorithm's feasibility and efficiency. DOI: 10.5267/j.ijiec.2012.10.004 Keywords: Location-inventory, Facility location, Uncertainty, Supply chain network design, Multi-objective particle swarm Optimization |
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9. |
Designing reliable supply chain network with disruption risk
, Pages 111-126 Fateme Bozorgi Atoei, Ebrahim Teimory, Ali Bozorgi Amiri PDF (670K) |
Abstract: Although supply chains disruptions rarely occur, their negative effects are prolonged and severe. In this paper, we propose a reliable capacitated supply chain network design (RSCND) model by considering random disruptions in both distribution centers and suppliers. The proposed model determines the optimal location of distribution centers (DC) with the highest reliability, the best plan to assign customers to opened DCs and assigns opened DCs to suitable suppliers with lowest transportation cost. In this study, random disruption occurs at the location, capacity of the distribution centers (DCs) and suppliers. It is assumed that a disrupted DC and a disrupted supplier may lose a portion of their capacities, and the rest of the disrupted DC's demand can be supplied by other DCs. In addition, we consider shortage in DCs, which can occur in either normal or disruption conditions and DCs, can support each other in such circumstances. Unlike other studies in the extent of literature, we use new approach to model the reliability of DCs; we consider a range of reliability instead of using binary variables. In order to solve the proposed model for real-world instances, a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied. Preliminary results of testing the proposed model of this paper on several problems with different sizes provide seem to be promising. DOI: 10.5267/j.ijiec.2012.10.003 Keywords: Disruption risk, Reliability, Supply chain, Network design |
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Differential evolution algorithm for multi-commodity and multi-level of service hub covering location problem
, Pages 127-138 M. EghbaliZarch M. Abedzadeh and M. Setak PDF (151 K) |
Abstract: The hub location problem involves a network of origins and destinations over which transportation takes place. There are many studies associated with finding the location of hub nodes and the allocation of demand nodes to these located hub nodes to transfer the only one kind of commodity under one level of service. However, in this study, carrying different commodity types from origin to destination under various levels of services (e.g. price, punctuality, reliability or transit time) is studied. Quality of services experienced by users such as speed, convenience, comfort and security of transportation facilities and services is considered as the level of service. In each system, different kinds of commodities with various levels of services can be transmitted. The appropriate level of service that a commodity can be transmitted through is chosen by customer preferences and the specification of the commodity. So, a mixed integer programming formulation for single allocation hub covering location problem, which is based on the idea of transferring multi commodity flows under multi levels of service is presented. These two are applied concepts, multi-commodity and multi-level of service, which make the model's assumptions closer to the real world problems. In addition, a differential evolution algorithm is designed to find near-optimal solutions. The obtained solutions using differential evolution (DE) algorithm (upper bound), where its parameters are tuned by response surface methodology, are compared with exact solutions and computed lower bounds by linear relaxation technique to prove the efficiency of proposed DE algorithm. DOI: 10.5267/j.ijiec.2012.10.001 Keywords: Hub covering location, Multi-commodity, Multi-level of service, Differential evolution algorithm, Response surface methodology, Lower and upper bounds |
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A fuzzy-random programming for integrated closed-loop logistics network design by using priority-based genetic algorithm
, Pages 139-154 Emad Roghanian and Keyvan Kamandanipour PDF (357 K) |
Abstract: Recovery of used products has steadily become interesting issue for research due to economic reasons and growing environmental or legislative concern. This paper presents a closed-loop logistics network design based on reverse logistics models. A mixed integer linear programming model is implemented to integrate logistics network design in order to prevent the sub-optimality caused by the separate design of the forward and reverse networks. The study presents a single product and multi-stage logistics network problem for the new and return products not only to determine subsets of logistics centers to be opened, but also to determine transportation strategy, which satisfies demand imposed by facilities and minimizes fixed opening and total shipping costs. Since the deterministic estimation of some parameters such as demand and rate of return of used products in closed loop logistics models is impractical, an uncertain programming is proposed. In this case, we assume there are several economic conditions with predefined probabilities calculated from historical data. Then by means of expert's opinion, a fuzzy variable is offered as customer's demand under each economic condition. In addition, demand and rate of return of products for each customer zone is presented by fuzzy-random variables, similarly. Therefore, a fuzzy-random programming is used and a priority-based genetic algorithm is proposed to solve large-scale problems. DOI: 10.5267/j.ijiec.2012.09.002 Keywords: Integrated logistics network, Closed-loop logistics, Genetic algorithm (GA), Priority-based encoding, Fuzzy-random programming |
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12. |
Determination of the optimal investment portfolio using CAPM in Tehran Stock Exchange industries: A VAR-Multivariate GARCH approach
, Pages 155-164 Seyed Ahmad Hosseini Ahmad Moradifard and Kobra Sabzzadeh PDF (151 K) |
Abstract: This study determines the optimal investment portfolio in Tehran Stock Exchange (TSE) industries. For this purpose, a conditional capital asset pricing model (CAPM) with time-varying covariance, according to a Multivariate GARCH approach has been formulated. According to this conditional CAPM, the conditional variance-covariance matrix and mean of returns are calculated for some industries. By using the Mean-Value at Risk portfolio selection model, the optimum proportion is detected. Results showed that the Pharmaceutical Industry, Financial Group and Cement Industry have the most quotas in portfolio since they maintain the minimum variance and maximum return among all other industries. DOI: 10.5267/j.ijiec.2012.10.002 Keywords: Capital asset pricing model, Multivariate GARCH model, Value at Risk, Portfolio selection model |
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Do MENA stock market returns follow a random walk process?
, Pages 165-172 Salim Lahmiri PDF (95 K) |
Abstract: In this research, three variance ratio tests: the standard variance ratio test, the wild bootstrap multiple variance ratio test, and the non-parametric rank scores test are adopted to test the random walk hypothesis (RWH) of stock markets in Middle East and North Africa (MENA) region using most recent data from January 2010 to September 2012. The empirical results obtained by all three econometric tests show that the RWH is strongly rejected for Kuwait, Tunisia, and Morocco. However, the standard variance ratio test and the wild bootstrap multiple variance ratio test reject the null hypothesis of random walk in Jordan and KSA, while non-parametric rank scores test do not. We may conclude that Jordan and KSA stock market are weak efficient. In sum, the empirical results suggest that return series in Kuwait, Tunisia, and Morocco are predictable. In other words, predictable patterns that can be exploited in these markets still exit. Therefore, investors may make profits in such less efficient markets. DOI: 10.5267/j.ijiec.2012.11.005 Keywords: Stock Markets, Random Walk Hypothesis, Variance Ratio Test, MENA Region |
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14. |
Comments on “An economic order quantity (EOQ) for items with imperfect quality and inspection errors”
, Pages 173-176 Ping-Hui Hsu Hui-Ming Teng and Hui Ming Wee PDF (237 K) |
Abstract: The purpose of these comments is to serve as a revision to the article by Khan, Jaber, & Bonney [2011, An economic order quantity (EOQ) for items with imperfect quality and inspection errors, International Journal of Production Economics, 133: 113–118]. This commenting paper first suggests that the revenue function derived in Khan et al. (2011) is unrealistic, and then offers revisions to complement the shortcomings. DOI: 10.5267/j.ijiec.2012.09.004 Keywords: Imperfect process, Misclassification errors, EOQ |
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