European J. of Industrial Engineering http://www.inderscience.com/browse/index.php?journalID=210&year=2012&vol=6&issue=1 Inderscience Publishers Ltd en-uk European J. of Industrial Engineering 1751-5254 1751-5262 © 2012 Inderscience Publishers Ltd editor@inderscience.com https://www.inderscience.com/images/files/coverImgs/ejie_scoverejie.jpg http://www.inderscience.com/browse/index.php?journalID=210&year=2012&vol=6&issue=1 http://www.inderscience.com/link.php?id=44808 Sports scheduling has become an important area of applied operations research in recent years, since satisfying fans' and teams' requests and revenues of a sports league and TV networks may be affected by quality of the league schedule. While this type of scheduling problem can be solved by mathematical methods and exact solutions are accessible, it computationally leads to hard problems. The travelling tournament problem (TTP) is defined as minimising total travelling distance for all teams in a league. In this study, a new mathematical model for the TTP with the no-repeater constraint is presented. In addition, a very fast hybrid metaheuristic algorithm is proposed, which combines particle swarm optimisation (PSO) and simulated annealing (SA). Our computational experiments on standard instances show that the hybrid approach results in comparable to or even better than current best known solutions, specifically in computational time. [Received 24 August 2009; Revised 16 January 2010, 9 June 2010; Accepted 12 June 2010] A hybrid PSO-SA algorithm for the travelling tournament problem
Alireza Tajbakhsh; Kourosh Eshghi; Azam Shamsi
European J. of Industrial Engineering, Vol. 6, No. 1 (2012) pp. 2 - 25
Sports scheduling has become an important area of applied operations research in recent years, since satisfying fans' and teams' requests and revenues of a sports league and TV networks may be affected by quality of the league schedule. While this type of scheduling problem can be solved by mathematical methods and exact solutions are accessible, it computationally leads to hard problems. The travelling tournament problem (TTP) is defined as minimising total travelling distance for all teams in a league. In this study, a new mathematical model for the TTP with the no-repeater constraint is presented. In addition, a very fast hybrid metaheuristic algorithm is proposed, which combines particle swarm optimisation (PSO) and simulated annealing (SA). Our computational experiments on standard instances show that the hybrid approach results in comparable to or even better than current best known solutions, specifically in computational time. [Received 24 August 2009; Revised 16 January 2010, 9 June 2010; Accepted 12 June 2010]

]]> 10.1504/EJIE.2012.044808 European J. of Industrial Engineering, Vol. 6, No. 1 (2012) pp. 2 - 25 Alireza Tajbakhsh; Kourosh Eshghi; Azam Shamsi Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran. ' Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran. ' Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran sports scheduling minimising total travelling distance travelling tournament problem TTP particle swarm optimisation PSO simulated annealing hybrid metaheuristics operations research league teams. 2012-01-09T23:20:50-05:00 6 1 2 25 2012-01-09T23:20:50-05:00 http://www.inderscience.com/link.php?id=44809 In this paper, we investigate the possible effects of a temporary price discount offered by the supplier on the retailer's replenishment policy under the premise that the capacity of the retailer-owned warehouse is limited. The purpose of this study is to develop a decision process for the retailer, which allows he/she to decide whether to adopt a special or regular order policy during a temporary sales period. For the case where a special order policy is selected, the optimal special order quantity by maximising the total cost saving between special and regular orders is determined. The model is developed when the temporary sales period coincides with one of the following: 1) the retailer's replenishment time; 2) the retailer's sales period. Furthermore, we present useful results to characterise the optimal solutions. Finally, several numerical examples are given to illustrate the theoretical results, and a sensitivity analysis of the optimal solution with respect to the main parameters is also conducted. [Received 26 September 2009; Accepted 11 July 2010]. Optimal ordering policy in response to a temporary sale price when retailer's warehouse capacity is limited
Chih-Te Yang; Liang-Yuh Ouyang; Kun-Shan Wu; Hsiu-Feng Yen
European J. of Industrial Engineering, Vol. 6, No. 1 (2012) pp. 26 - 49
In this paper, we investigate the possible effects of a temporary price discount offered by the supplier on the retailer's replenishment policy under the premise that the capacity of the retailer-owned warehouse is limited. The purpose of this study is to develop a decision process for the retailer, which allows he/she to decide whether to adopt a special or regular order policy during a temporary sales period. For the case where a special order policy is selected, the optimal special order quantity by maximising the total cost saving between special and regular orders is determined. The model is developed when the temporary sales period coincides with one of the following: 1) the retailer's replenishment time; 2) the retailer's sales period. Furthermore, we present useful results to characterise the optimal solutions. Finally, several numerical examples are given to illustrate the theoretical results, and a sensitivity analysis of the optimal solution with respect to the main parameters is also conducted. [Received 26 September 2009; Accepted 11 July 2010].

]]> 10.1504/EJIE.2012.044809 European J. of Industrial Engineering, Vol. 6, No. 1 (2012) pp. 26 - 49 Chih-Te Yang; Liang-Yuh Ouyang; Kun-Shan Wu; Hsiu-Feng Yen Department of Industrial Engineering and Management, Ching Yun Unversity, Jung-Li, Taoyuan 320, Taiwan. ' Department of Management Sciences and Decision Making, Tamkang University, Tamsui, Taipei 251, Taiwan. ' Department of Business Administration, Tamkang University, Tamsui, Taipei 251, Taiwan. ' Department of Accounting, Tamkang University, Tamsui, Taipei 251, Taiwan inventory management temporary sale price limited capacity warehouse capacity optimal ordering policy. 2012-01-09T23:20:50-05:00 6 1 26 49 2012-01-09T23:20:50-05:00 http://www.inderscience.com/link.php?id=44810 The fuzzy C-means (FCM) algorithm is a commonly used fuzzy clustering method which conducts data clustering by randomly selecting initial centroids. With larger data size or attribute dimensions, clustering results may be affected and more repetitive computations are required. To compensate the effect of random initial centroids on results, this study proposed a hybrid algorithm – immune genetic annealing fuzzy C-means algorithm (IGAFA). This algorithm obtains the proper initial cluster centroids to improve clustering efficiency and then tests them through three data sets: Hamberman's survival, iris, and liver disorders, and compares the results with the executed results of genetic fuzzy C-means algorithm (GFA), immune fuzzy C-means algorithm (IFA), and annealing fuzzy C-means algorithm (AFA). The results suggest that IGAFA could achieve better clustering results. [Received: November 18, 2009; Accepted: July 19, 2010] A hybrid algorithm for fuzzy clustering
Z.H. Che
European J. of Industrial Engineering, Vol. 6, No. 1 (2012) pp. 50 - 67
The fuzzy C-means (FCM) algorithm is a commonly used fuzzy clustering method which conducts data clustering by randomly selecting initial centroids. With larger data size or attribute dimensions, clustering results may be affected and more repetitive computations are required. To compensate the effect of random initial centroids on results, this study proposed a hybrid algorithm – immune genetic annealing fuzzy C-means algorithm (IGAFA). This algorithm obtains the proper initial cluster centroids to improve clustering efficiency and then tests them through three data sets: Hamberman's survival, iris, and liver disorders, and compares the results with the executed results of genetic fuzzy C-means algorithm (GFA), immune fuzzy C-means algorithm (IFA), and annealing fuzzy C-means algorithm (AFA). The results suggest that IGAFA could achieve better clustering results. [Received: November 18, 2009; Accepted: July 19, 2010]

]]> 10.1504/EJIE.2012.044810 European J. of Industrial Engineering, Vol. 6, No. 1 (2012) pp. 50 - 67 Z.H. Che Department of Industrial Engineering and Management, National Taipei University of Technology, 1, Sec. 3, Chung-Hsiao E. Rd., Taipei 10608, Taiwan fuzzy clustering fuzzy C-means FCM clustering efficiency genetic algorithms artificial immune systems simulated annealing liver disorders patient survival iris flowers. 2012-01-09T23:20:50-05:00 6 1 50 67 2012-01-09T23:20:50-05:00 http://www.inderscience.com/link.php?id=44811 Many complex production/inventory systems are characterised by uncertain capacities due to imperfect facilities and processes. Uncertainty in supply capacity may consist of variable supplier capacities and random yields. We extend a model with variable supplier capacity in several directions and analyse the effects of variable supplier capacity. First, we investigate a lot-sizing problem in an EOQ model with variable supplier capacity and random yield. Second, we develop an EOQ model with storage or investment constraints when multiple items are considered. Third, we apply a distribution-free approach (DFA) to the (Q, r) model with variable supplier capacity. [Received: 11 November 2009; Revised: 21 June 2010; Accepted: 19 July 2010] Inventory systems with variable capacity
Ilkyeong Moon; Byung-Hyun Ha; Jongchul Kim
European J. of Industrial Engineering, Vol. 6, No. 1 (2012) pp. 68 - 86
Many complex production/inventory systems are characterised by uncertain capacities due to imperfect facilities and processes. Uncertainty in supply capacity may consist of variable supplier capacities and random yields. We extend a model with variable supplier capacity in several directions and analyse the effects of variable supplier capacity. First, we investigate a lot-sizing problem in an EOQ model with variable supplier capacity and random yield. Second, we develop an EOQ model with storage or investment constraints when multiple items are considered. Third, we apply a distribution-free approach (DFA) to the (Q, r) model with variable supplier capacity. [Received: 11 November 2009; Revised: 21 June 2010; Accepted: 19 July 2010]

]]> 10.1504/EJIE.2012.044811 European J. of Industrial Engineering, Vol. 6, No. 1 (2012) pp. 68 - 86 Ilkyeong Moon; Byung-Hyun Ha; Jongchul Kim Department of Industrial Engineering, Pusan National University, Busan, 609-735, Korea. ' Department of Industrial Engineering, Pusan National University, Busan, 609-735, Korea. ' BS Process Innovation Group, LG Electronics, LG Twin Towers 20, Seoul, 150-721, Korea variable capacity economic order quantity EOQ (Q, r) policy distribution-free approach DFA uncertainty supplier capacity random yield storage constraints investment constraints. 2012-01-09T23:20:50-05:00 6 1 68 86 2012-01-09T23:20:50-05:00 http://www.inderscience.com/link.php?id=44812 This study focuses on a better understanding of user preferences based on perceived usability and perceived aesthetics. For this, previous studies on the relationships among perceived usability, perceived aesthetics, user performance and user preference are reviewed with the consideration of occurrence of actual use, and their findings are summarised and analysed in terms of relationships of interest, variable measurements, experimental domains, participant types, usability/aesthetics manipulations, and performance measures. The study also considers possible reasons of conflicting findings for the relationship between perceived usability and perceived aesthetics after actual use. A conceptual model for user preferences is constructed to show the processes of users' preference-making before and during/after actual use and to emphasise mutual influences and feedback loops in user experience. Finally, several suggestions for future research are made to confirm or clarify existing findings by considering various influential factors. [Received: 09 October 2009; Accepted: 21 July 2010] Users' perceptions of usability and aesthetics as criteria of pre- and post-use preferences
Sangwon Lee; Richard J. Koubek
European J. of Industrial Engineering, Vol. 6, No. 1 (2012) pp. 87 - 117
This study focuses on a better understanding of user preferences based on perceived usability and perceived aesthetics. For this, previous studies on the relationships among perceived usability, perceived aesthetics, user performance and user preference are reviewed with the consideration of occurrence of actual use, and their findings are summarised and analysed in terms of relationships of interest, variable measurements, experimental domains, participant types, usability/aesthetics manipulations, and performance measures. The study also considers possible reasons of conflicting findings for the relationship between perceived usability and perceived aesthetics after actual use. A conceptual model for user preferences is constructed to show the processes of users' preference-making before and during/after actual use and to emphasise mutual influences and feedback loops in user experience. Finally, several suggestions for future research are made to confirm or clarify existing findings by considering various influential factors. [Received: 09 October 2009; Accepted: 21 July 2010]

]]> 10.1504/EJIE.2012.044812 European J. of Industrial Engineering, Vol. 6, No. 1 (2012) pp. 87 - 117 Sangwon Lee; Richard J. Koubek The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, 343 Leonhard Building, University Park, PA 16802, USA. ' College of Engineering, The Louisiana State University, 3304 Patrick F. Taylor Hall, Baton Rouge, LA 70803, USA user preferences perceived usability perceived aesthetics user performance computer-based applications user perceptions. 2012-01-09T23:20:50-05:00 6 1 87 117 2012-01-09T23:20:50-05:00 http://www.inderscience.com/link.php?id=44813 The purpose of this study is to show how Lean and Six Sigma (LSS) improvement events were implemented to improve the performance of a home furnishing manufacturing operation. Each LSS improvement event was conducted at the targeted shop by an experienced facilitator with a team of workers from the shop. An improvement event consisted of a host of activities involving five phases which were completed over an eight-week period. The first four weeks included four phases: 1) planning and discovery; 2) training and opportunity finding; 3) refinement and preparation; 4) implementation and changes. The last four weeks included one phase: 5) enhancement and transfer of ownership. The facilitator was responsible for the first phase and the workers were responsible for all other phases, although the facilitator also played an important role in those phases. With some variation, the workers spent 100% of their time in activities related to phases 2 and 4, and 20% of their time in activities related to phases 3 and 5. Important to both practitioners and academicians, we discuss many implications of LSS event implementation and include directions for future research. [Received: 18 August 2009; Accepted: 01 August 2010] Lean Six Sigma (LSS): an implementation experience
Satya S. Chakravorty; Aakash D. Shah
European J. of Industrial Engineering, Vol. 6, No. 1 (2012) pp. 118 - 137
The purpose of this study is to show how Lean and Six Sigma (LSS) improvement events were implemented to improve the performance of a home furnishing manufacturing operation. Each LSS improvement event was conducted at the targeted shop by an experienced facilitator with a team of workers from the shop. An improvement event consisted of a host of activities involving five phases which were completed over an eight-week period. The first four weeks included four phases: 1) planning and discovery; 2) training and opportunity finding; 3) refinement and preparation; 4) implementation and changes. The last four weeks included one phase: 5) enhancement and transfer of ownership. The facilitator was responsible for the first phase and the workers were responsible for all other phases, although the facilitator also played an important role in those phases. With some variation, the workers spent 100% of their time in activities related to phases 2 and 4, and 20% of their time in activities related to phases 3 and 5. Important to both practitioners and academicians, we discuss many implications of LSS event implementation and include directions for future research. [Received: 18 August 2009; Accepted: 01 August 2010]

]]> 10.1504/EJIE.2012.044813 European J. of Industrial Engineering, Vol. 6, No. 1 (2012) pp. 118 - 137 Satya S. Chakravorty; Aakash D. Shah Department of Management and Entrepreneurship, Michael J. Coles College of Business, Kennesaw State University, 1000 Chastain Rd. Kennesaw GA 30144, USA. ' Shaw Industries, Inc., 1061 West Ave., P.O. Box 429 Cartersville, GA 30120, USA lean six sigma operational excellence home furnishing manufacturing six sigma implementation. 2012-01-09T23:20:50-05:00 6 1 118 137 2012-01-09T23:20:50-05:00