International Journal of Operational Research http://www.inderscience.com/browse/index.php?journalID=170&year=2012&vol=13&issue=2 Inderscience Publishers Ltd en-uk International Journal of Operational Research 1745-7645 1745-7653 © 2012 Inderscience Publishers Ltd editor@inderscience.com https://www.inderscience.com/images/files/coverImgs/ijor_scoverijor.jpg http://www.inderscience.com/browse/index.php?journalID=170&year=2012&vol=13&issue=2 http://www.inderscience.com/link.php?id=45183 This paper deals with the usage of heuristics for the optimisation of the control of chaotic system. The main aim of this paper is to show a new approach of solving this problem and constructing new cost functions (CFs) operating in 'black box mode' without a previous exact mathematical analysis of the system, thus without knowledge of the stabilising target state. Three proposals of 'black box' mode CFs were tested in this paper. As a model of deterministic chaotic system, the two-dimensional Hénon map was used. The optimisations were realised in several ways, each one for another desired state of system. Evolutionary algorithms (EAs) self-organising migrating algorithm and differential evolution were used. For each version, repeated simulations were conducted to outline the effectiveness and robustness of the used method and CF. Evolutionary optimisation of Hénon map control: a black box approach
Roman Senkerik; Ivan Zelinka; Donald Davendra; Zuzana Oplatkova
International Journal of Operational Research, Vol. 13, No. 2 (2012) pp. 129 - 146
This paper deals with the usage of heuristics for the optimisation of the control of chaotic system. The main aim of this paper is to show a new approach of solving this problem and constructing new cost functions (CFs) operating in 'black box mode' without a previous exact mathematical analysis of the system, thus without knowledge of the stabilising target state. Three proposals of 'black box' mode CFs were tested in this paper. As a model of deterministic chaotic system, the two-dimensional Hénon map was used. The optimisations were realised in several ways, each one for another desired state of system. Evolutionary algorithms (EAs) self-organising migrating algorithm and differential evolution were used. For each version, repeated simulations were conducted to outline the effectiveness and robustness of the used method and CF.

]]> 10.1504/IJOR.2012.045183 International Journal of Operational Research, Vol. 13, No. 2 (2012) pp. 129 - 146 Roman Senkerik; Ivan Zelinka; Donald Davendra; Zuzana Oplatkova Faculty of Applied Informatics, Department of Informatics and Artificial Intelligence, Tomas Bata University in Zlin, Nad Stranemi 4511, Zlin 76005, Czech Republic ' Faculty of Electrical Engineering and Computer Science, Department of Computer Science, VSB – Technical University of Ostrava, 17. listopadu 15, Ostrava-Poruba 708 33, Czech Republic ' Faculty of Applied Informatics, Department of Informatics and Artificial Intelligence, Tomas Bata University in Zlin, Nad Stranemi 4511, Zlin 76005, Czech Republic ' Faculty of Applied Informatics, Department of Informatics and Artificial Intelligence, Tomas Bata University in Zlin, Nad Stranemi 4511, Zlin 76005, Czech Republic optimisation chaos control EAs evolutionary algorithms differential evolution SOMA self-organising migrating algorithm heuristics simulation. 2012-01-31T23:20:50-05:00 13 2 129 146 2012-01-31T23:20:50-05:00 http://www.inderscience.com/link.php?id=45184 This paper presents a genetic algorithm (GA)-principal component analysis (PCA) for long-term natural gas (NG) consumption prediction and improvement. Six models are proposed to forecast the annual gas demand. Around 27 GAs have been constructed and tested in order to find the best GA for gas consumption. The proposed models consist of input variables such as gross domestic product (GDP) and population (POP). All of trained GAs are then compared with each other respect to the mean absolute percentage error (MAPE). The GA model is capable of dealing both complexity and uncertainty in the data set. To show the applicability and superiority of the GA, actual gas consumptions in Finland, Hungary, Ireland, Japan and Malaysia from 1980 to 2007 are considered. With the aid of an autoregressive model, GDP and population are projected till 2015, and then with the projected GDP and population as inputs to the best GA model, gas consumption is predicted till 2015. Finally, we use the multivariate method of PCA in behaviour analysis of gas consumption in the selected countries. This method normalises the gas consumption by both population and GDP, and then the PCA procedure is run for efficiency assessment of the selected countries. PCA is used to examine the behaviour of gas consumption in the past and also to make insights for the forthcoming years. An integrated genetic algorithm-principal component analysis for improvement and estimation of gas consumption in Finland, Hungary, Ireland, Japan and Malaysia
Ali Azadeh; Sayed Mohammad Asadzadeh; Morteza Saberi; Saman Khoshmagham
International Journal of Operational Research, Vol. 13, No. 2 (2012) pp. 147 - 173
This paper presents a genetic algorithm (GA)-principal component analysis (PCA) for long-term natural gas (NG) consumption prediction and improvement. Six models are proposed to forecast the annual gas demand. Around 27 GAs have been constructed and tested in order to find the best GA for gas consumption. The proposed models consist of input variables such as gross domestic product (GDP) and population (POP). All of trained GAs are then compared with each other respect to the mean absolute percentage error (MAPE). The GA model is capable of dealing both complexity and uncertainty in the data set. To show the applicability and superiority of the GA, actual gas consumptions in Finland, Hungary, Ireland, Japan and Malaysia from 1980 to 2007 are considered. With the aid of an autoregressive model, GDP and population are projected till 2015, and then with the projected GDP and population as inputs to the best GA model, gas consumption is predicted till 2015. Finally, we use the multivariate method of PCA in behaviour analysis of gas consumption in the selected countries. This method normalises the gas consumption by both population and GDP, and then the PCA procedure is run for efficiency assessment of the selected countries. PCA is used to examine the behaviour of gas consumption in the past and also to make insights for the forthcoming years.

]]> 10.1504/IJOR.2012.045184 International Journal of Operational Research, Vol. 13, No. 2 (2012) pp. 147 - 173 Ali Azadeh; Sayed Mohammad Asadzadeh; Morteza Saberi; Saman Khoshmagham Department of Industrial Engineering, Center of Excellence for Intelligent-Based Experimental Mechanics, College of Engineering, University of Tehran, Tehran, Iran ' Department of Industrial Engineering, Center of Excellence for Intelligent-Based Experimental Mechanics, College of Engineering, University of Tehran, Tehran, Iran ' Department of Industrial Engineering, University of Tafresh, Tafresh, Iran and Institute for Digital Ecosystems and Business Intelligence, Curtin University of Technology, Perth, Australia ' Department of Industrial Engineering, Center of Excellence for Intelligent-Based Experimental Mechanics, College of Engineering, University of Tehran, Tehran, Iran gas consumption forecasting behavioural analysis genetic algorithms GAs principal component analysis PCA Finland Hungary Ireland Japan Malaysia natural gas modelling efficiency assessment. 2012-01-31T23:20:50-05:00 13 2 147 173 2012-01-31T23:20:50-05:00 http://www.inderscience.com/link.php?id=45185 This paper considers the integration of production scheduling and preventive maintenance (PM) scheduling on a single machine that is subject to random failures. PM that restores the machine to an 'as good as new' condition is to be performed before the start of job processing, if needed. The objective is to determine the job schedule as well as PM schedule that minimise the total weighted expected jobs completion times. The problem is formulated to jointly model production scheduling and maintenance decisions as a mixed integer programme. Solving the model results in the optimum schedule of maintenance as well as production, which is usually determined independently. Joint job scheduling and preventive maintenance on a single machine
Laith A. Hadidi; Umar M. Al-Turki; M. Abdur Rahim
International Journal of Operational Research, Vol. 13, No. 2 (2012) pp. 174 - 184
This paper considers the integration of production scheduling and preventive maintenance (PM) scheduling on a single machine that is subject to random failures. PM that restores the machine to an 'as good as new' condition is to be performed before the start of job processing, if needed. The objective is to determine the job schedule as well as PM schedule that minimise the total weighted expected jobs completion times. The problem is formulated to jointly model production scheduling and maintenance decisions as a mixed integer programme. Solving the model results in the optimum schedule of maintenance as well as production, which is usually determined independently.

]]> 10.1504/IJOR.2012.045185 International Journal of Operational Research, Vol. 13, No. 2 (2012) pp. 174 - 184 Laith A. Hadidi; Umar M. Al-Turki; M. Abdur Rahim Department of Systems Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia ' Department of Systems Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia ' Faculty of Business Administration, University of New Brunswick, Fredericton, NB E3B 5A3, Canada; Department of Systems Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia production scheduling preventive maintenance integrated modelling machine breakdowns single machine scheduling random failure job schedules. 2012-01-31T23:20:50-05:00 13 2 174 184 2012-01-31T23:20:50-05:00 http://www.inderscience.com/link.php?id=45186 In this paper, a general model has been formulated for facility location. The proposed model allows for locating several facilities in a single location, and as such, it is therefore a generalisation of the well-known quadratic assignment problem (QAP) and other variants. The general model has a wide range of applications in facility location, operations research and business. A branch-and-bound algorithm is developed for the generalised quadratic semi-assignment problem (GQSAP), which is a special form of the general model. The branch-and-bound algorithm is based on a modified version of the well-known Gilmore bound. Computational results are presented to corroborate the theoretical model developed here. General forms of the quadratic assignment problem
Salih O. Duffuaa; Chawki A. Fedjki
International Journal of Operational Research, Vol. 13, No. 2 (2012) pp. 185 - 199
In this paper, a general model has been formulated for facility location. The proposed model allows for locating several facilities in a single location, and as such, it is therefore a generalisation of the well-known quadratic assignment problem (QAP) and other variants. The general model has a wide range of applications in facility location, operations research and business. A branch-and-bound algorithm is developed for the generalised quadratic semi-assignment problem (GQSAP), which is a special form of the general model. The branch-and-bound algorithm is based on a modified version of the well-known Gilmore bound. Computational results are presented to corroborate the theoretical model developed here.

]]> 10.1504/IJOR.2012.045186 International Journal of Operational Research, Vol. 13, No. 2 (2012) pp. 185 - 199 Salih O. Duffuaa; Chawki A. Fedjki Department of Systems Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia ' Department of Systems Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia QAP quadratic assignment problem facility location clustering problem QSAP quadratic semi-assignment problem operational research branch and bound. 2012-01-31T23:20:50-05:00 13 2 185 199 2012-01-31T23:20:50-05:00 http://www.inderscience.com/link.php?id=45187 In this paper, we develop and analyse an inventory model for deteriorating items with the assumption that the lifetime of the commodity is random and follows an exponential distribution and the demand is inversely proportional to the stock on-hand, having variable cycle lengths declining in arithmetic progression. It is further assumed that the shortages are allowed and fully backlogged. Using the differential equations, the instantaneous level of inventory is derived. With suitable cost consideration, the total cost function is obtained. By minimising the total cost, the optimal cycle length and ordering quantities are derived. The sensitivity of the model with respect to the parameters and costs is also performed. This model is extended to the case of without shortages. This model is useful in practical situations arising at places such as textile markets, fruit and vegetable markets. Optimal ordering policies of an inventory model for deteriorating items with demand inversely proportional to the on-hand inventory
V.V.S.S.V. Prasad Rao Patnaik; K. Srinivasa Rao
International Journal of Operational Research, Vol. 13, No. 2 (2012) pp. 200 - 218
In this paper, we develop and analyse an inventory model for deteriorating items with the assumption that the lifetime of the commodity is random and follows an exponential distribution and the demand is inversely proportional to the stock on-hand, having variable cycle lengths declining in arithmetic progression. It is further assumed that the shortages are allowed and fully backlogged. Using the differential equations, the instantaneous level of inventory is derived. With suitable cost consideration, the total cost function is obtained. By minimising the total cost, the optimal cycle length and ordering quantities are derived. The sensitivity of the model with respect to the parameters and costs is also performed. This model is extended to the case of without shortages. This model is useful in practical situations arising at places such as textile markets, fruit and vegetable markets.

]]> 10.1504/IJOR.2012.045187 International Journal of Operational Research, Vol. 13, No. 2 (2012) pp. 200 - 218 V.V.S.S.V. Prasad Rao Patnaik; K. Srinivasa Rao Department of Mathematics and Statistics, M.R. College (Autonomous), Vizianagaram 535 002, Andhra Pradesh, India ' Department of Statistics, Andhra University, Visakhapatnam 530 003, Andhra Pradesh, India inventory modelling deteriorating items stock-dependent demand variable cycle lengths sensitivity analysis ordering policies optimisation total cost function textile markets fruit and vegetable markets fruit and veg. 2012-01-31T23:20:50-05:00 13 2 200 218 2012-01-31T23:20:50-05:00 http://www.inderscience.com/link.php?id=45188 Classical warehouse design is commonly done in two steps by first determining the aisle layout and dimension followed by the assignment of items to storage. The design process is performed iteratively until a design with appropriate performance criterion is found. This paper proposes an approach for warehouse design in one step by determining the aisle layout and dimension while simultaneously assigning shelf spaces for storing the items based on item classes. A mathematical model is formulated to determine the number of aisles, the length of aisle and the length of each pick aisle to allocate to each product class that will minimise the average travel distance for a warehouse that operates under a class-based storage policy. A particle swarm optimisation algorithm was developed to determine the optimal warehouse design. The proposed method not only accomplishes the task in one step but also can identify multiple alternative designs. A case study is used to illustrate the proposed algorithm. A class-based storage warehouse design using a particle swarm optimisation algorithm
Natanaree Sooksaksun; Voratas Kachitvichyanukul; Dah-Chuan Gong
International Journal of Operational Research, Vol. 13, No. 2 (2012) pp. 219 - 237
Classical warehouse design is commonly done in two steps by first determining the aisle layout and dimension followed by the assignment of items to storage. The design process is performed iteratively until a design with appropriate performance criterion is found. This paper proposes an approach for warehouse design in one step by determining the aisle layout and dimension while simultaneously assigning shelf spaces for storing the items based on item classes. A mathematical model is formulated to determine the number of aisles, the length of aisle and the length of each pick aisle to allocate to each product class that will minimise the average travel distance for a warehouse that operates under a class-based storage policy. A particle swarm optimisation algorithm was developed to determine the optimal warehouse design. The proposed method not only accomplishes the task in one step but also can identify multiple alternative designs. A case study is used to illustrate the proposed algorithm.

]]> 10.1504/IJOR.2012.045188 International Journal of Operational Research, Vol. 13, No. 2 (2012) pp. 219 - 237 Natanaree Sooksaksun; Voratas Kachitvichyanukul; Dah-Chuan Gong Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand ' Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand ' Chung Yuan Christian University, 200, Chung Pei Road, Chung Li 32023, Taiwan travel distance class-based storage warehouse design PSO particle swarm optimisation aisle layout shelf spaces item classes mathematical modelling. 2012-01-31T23:20:50-05:00 13 2 219 237 2012-01-31T23:20:50-05:00