@conference{Wattanapornprom20091675,

title = {Multi-objective combinatorial optimisation with coincidence algorithm},

author = {Warin Wattanapornprom and Panuwat Olanviwitchai and Parames Chutima and Prabhas Chongstitvatana},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-70450015259&doi=10.1109%2fCEC.2009.4983143&partnerID=40&md5=370ff4b0cea33482f55a9f2af2efe249},

doi = {10.1109/CEC.2009.4983143},

isbn = {978-142442959-2},

year  = {2009},

date = {2009-01-01},

journal = {2009 IEEE Congress on Evolutionary Computation, CEC 2009},

pages = {1675 – 1682},

abstract = {Most optimization algorithms that use probabilistic models focus on extracting the information from good solutions found in the population. A selection method discards the below-average solutions. They do not contribute any information to be used to update the models. This work proposes a new algorithm, Combinatorial Optimization with Coincidence (COIN) that makes use of both good and not-good solutions. A Generator represents a probabilistic model of the required solution, is used to sample candidate solutions. Reward and punishment schemes are incorporated in updating the generator. The updating values are defined by selecting the good and not-good solutions. It has been observed that the notgood solutions contribute to avoid producing the bad solutions. The multi-objective version of COIN is also introduced. Several benchmarks of multi-objective problems of real world industrial applications are reported. © 2009 IEEE.},

note = {Cited by: 25; All Open Access, Green Open Access},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Chutima20081971,

title = {An investigation of local searches in memetic algorithms for multi-objective sequencing problems on mixed-model assembly lines},

author = {Parames Chutima and Penpak Pinkoompee},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84877790821&partnerID=40&md5=4ee4438a2943056ab116bff8e749102d},

isbn = {978-162748682-8},

year  = {2008},

date = {2008-01-01},

journal = {38th International Conference on Computers and Industrial Engineering 2008},

volume = {2},

pages = {1971 – 1980},

abstract = {Mixed model assembly lines are a type of production line where a variety of product models with similar product characteristics are assembled in a just-intime (JIT) production system. In this paper, we consider two objectives; which are setup times and production rates variation to be minimized simultaneously. The two objectives are inversely related to each other and, therefore, simultaneous optimization of both is challenging. This type of problem is also an NP-hard problem. We show in this study how the performance of such evolutionary multi-objective optimization algorithm as memetic algorithm can be improved by hybridization with local search. There are seven local search procedures applied in memetic algorithms to solve multi-objective sequencing problems on mixed-model assembly lines in JIT production systems. Experimental results show that the performance of local search in memetic algorithm is significantly better than highly meta-heuristics as Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Nondominated Sorting Genetic Algorithm II (NSGA-II) in terms of ability to find Pareto-optimal solution. Copyright© (2008) by Computers & Industrial Engineering.},

note = {Cited by: 3},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Chutima2007657,

title = {Application of Genetic Algorithm in purchasing strategy determination},

author = {Parames Chutima and Patanapong Sanghatawatana},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84885972205&partnerID=40&md5=f177c6a68f39b6376505e44cdede9528},

isbn = {978-162748681-1},

year  = {2007},

date = {2007-01-01},

journal = {37th International Conference on Computers and Industrial Engineering 2007},

volume = {1},

pages = {657 – 666},

abstract = {The purposes of this paper are to identify supplier selection criteria and proper strategy in ordering parts for automotive industry as well as analyzing learning ability of suppliers. The surveyed data obtained from conducting in-depth interviews of the experts in purchasing section of car assembly companies in Thailand are analyzed to get the correct and suitable methods of supplier selection and ordering strategy. The result is used as a guideline for the development of supplier selection and specifying proper ordering strategy which also consider suppliers' learning ability. Multi-objective Genetic Algorithm was applied to find suitable supplier and ordering strategy for this problem. The objective function consists of total cost, defect rate, and percentage on-time delivery. Learning equation, formulated by referring to the learning rate of supplier obtained from the survey and regression equation, consists of (1) supplier readiness (2) duration of contract (long term and short term) and (3) single and multi-sourcing strategy. The sensitivity analysis is also conducted. It is shown that the weight of each criteria and suppliers' learning rate affect the selection decision significantly. Although the supplier which has higher learning rate and better adaptable to uncertainties has worse property than others, in the long term, it can improve itself to have equal or better qualification than the others and would be selected by the buyer.},

note = {Cited by: 0},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Chutima20063562,

title = {Interaction analysis of dispatching and due-date assignment rules on assembly shop performances},

author = {Parames Chutima and Panitas Sureeyatanapas},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886871877&partnerID=40&md5=28aa38e491992194331caffb2a0e868d},

year  = {2006},

date = {2006-01-01},

journal = {36th International Conference on Computers and Industrial Engineering, ICC and IE 2006},

pages = {3562 – 3573},

abstract = {The purpose of this research is to analyze the interaction between dispatching rules and due date assignment rules on the performances of assembly shops with balanced and unbalanced workloads via computer simulation. The experiments are conducted under four different conditions including dispatching rules, due date assignment rules, product structures, and shop utilizations. Dispatching rules are chosen from the best rules found in literature, whereas due-date assignment rules can be internal or external setting. The internal setting assigns due dates to the jobs based on the information related to job characteristics and shop status. Constant interval and random due date assignments are considered as the external setting. Shop utilizations are experimented at 80% and 90% both in balanced and unbalanced shops. The performance measurements consist of mean flow time, mean tardiness, percent of tardy jobs, and mean absolute lateness. The simulation results indicate that assigning job due dates based on the number of jobs in the system (JIS) rule and earliness job due-date (JDD) rule give the best overall performances. In this research, a new due-date assignment rule, called "JISNL", is developed based on the number of jobs in the system and the number of levels exercised with an additional algorithm related to priority jumping of jobs. It is found that JISNL gives even better results especially in the tall structure products or the products with many assembly levels.},

note = {Cited by: 0},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Chutima20063574,

title = {A Study of impacts and solution guidelines for expedited and delayed jobs in thai plastic industry},

author = {Parames Chutima and Chana Yiangkamolsing and Chitlada Simcharoen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886870642&partnerID=40&md5=f13549e67ac64985a67b9605a944673d},

year  = {2006},

date = {2006-01-01},

journal = {36th International Conference on Computers and Industrial Engineering, ICC and IE 2006},

pages = {3574 – 3582},

abstract = {The objective of the research is to identify the main factors that cause expedited and delayed jobs in Thai plastic industry from the view points of customers, suppliers, and manufacturers. A real industrial survey was performed by mailing questionnaires and conducting direct interviews with production managers of plastic part manufacturers. The result indicates that many factors can cause expedited jobs including consumer, competitiveness, and engineering change. The factors that cause delayed jobs include limited storage area, capital shortage, and limited capacity of manufacturing process. Consequently, plastic manufacturers have to adapt the production plan to such changes, set new due dates, and higher or over utilization of machines and workers. This also results in cash flow shortage in the situation of delayed jobs. The research also highlights the linkage between dispatching rules that can be employed to manage expedited and delayed jobs. For examples, FASFS, EDD, OPNDD, MST, S/OPN, TWORK, LWKR, WINQ, NINQ, MOD, SLACK, and SLACK/TP can be used to alleviate the problem of due date tightness; COVERT can be used to manage the tardiness cost; and MOD and COVERT can be employed to satisfy average lateness.},

note = {Cited by: 0},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}