April - June 2007 - Kasetsart University

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392 From the statistical tests, the qualities of solutions found from both methods were not significantly different for test problem sets 1, 2 and 3. However, Algorithm 2 became superior to the pattern search for larger problem sets. Notice on the test results, the total cost obtained from the pattern search could be as poor as 68% deviating from the MINLP lower bounds in large problem instances, while those from Algorithm 2 would not be worse than 20% from the lower bounds. Algorithm 2 was hence the most efficient method for solving this safety stock determination problem, in terms of both solution quality and computation time. DISCUSSION It had been shown in the previous section that Algorithm 2, which was based on a basic NLP theorem, could provide high quality solutions in short computation times for the safety stock level determination problem in the considered two-stage manufacturing system. The algorithm utilized only a set of simple decision rules, in contrast to the pattern search heuristic, which required the users to comprehend its mechanisms. The decision rules for finding optimum safety stocks also matched the common managerial logics that when the opportunity cost was high, the safety stocks should be held to prevent the product deficiency, but they should not be stocked when the inventory costs were high. Moreover, the search heuristic such as pattern search would terminate the search after some stopping criteria had been satisfied. Therefore, in some cases, it might not thoroughly search the solution space for the solutions. CONCLUSION In this research, two algorithms for determining safety stocks in a two-stage manufacturing system were proposed by analyzing the derivatives of cost function and the cost Kasetsart J. (Nat. Sci.) 41(2) comparisons. The algorithms were found to work very efficiently on the test problems. They could provide high-quality solutions for every test instance in less than 1 second. The deviations from the known integer solutions or the lower bounds were less than 3% on average. The algorithms also outperformed the pattern search algorithm, which was presented in the previous research. ACKNOWLEDGEMENT This project was funded by faculty of Agro-Industry, Kasetsart University. The author gratefully acknowledges this support. LITURATURE CITED Inderfurth, K. and S. Minner. 1998. Safety Stocks in Multi-Stage Inventory Systems under Different Service Measures. Eur. J. Oper. Res. 106: 57-73. Krupp, J.A.G. 1997. Safety Stock Management. Prod. Inventory Manag. J. 38(3): 11-18. Kolda, T.G., R.M. Lewis and V. Torczon. 2003. Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods. Siam Rev. 45(3): 385–482 Maia, L.O.A. and R.Y. Qassim. 1999. Minimum Cost Safety Stocks for Frequent Delivery Manufacturing. Int. J. Prod. Econ. 62: 233- 236. Marsden, J.E. and A. Tromba. 1981. Vector Calculus. 2 nd ed. W.H. Freeman. San Francisco. 591 p. Montgomery, D.C. and G.C. Runger. 2004. Applied Statistics and Probability for Engineers, 3 rd ed. John Wiley & Sons. New Jersey. 706 p. Silver, E.A., D.F. Pyke and R. Peterson. 1998. Inventory Management and Production Planning and Scheduling. 3 rd ed. John Wiley & Sons. New Jersey. 754 p. Siribanluoewut, Y. 2006. Determining Safety
Stock Quantities Using Heuristic Optimization. M.S. Thesis. Kasetsart University, Bangkok. Talluri, S., K. Cetin and A.J. Gardner. 2004. Integrating Demand and Supply Variability into Safety Stock Evaluations. Int. J. Phys. Distrib. Logist. Manag. 34: 62-69. Kasetsart J. (Nat. Sci.) 41(2) 393 Vollmann, T.E., W.L. Berry and D.C. Whybark. 1997. Manufacturing Planning and Control Systems, 4 th ed. McGraw-Hills. New York. 836 p. Zeng, A.Z. 2000. Efficiency of Using Fill-Rate Criterion to Determine Safety Stock: A Theoretical Perspective and a Case Study. Prod. Inventory Manag. J. 41(2): 41-44.
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April - June 2007 Volume 41 Number
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KASETSART JOURNAL NATURAL SCIENCE T
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Kasetsart J. (Nat. Sci.) 41 : 205 -
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harvesting time which started from
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y Chen and Paull (2000). Figure 1 a
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pineapple fruit allowed the accumul
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Kasetsart J. (Nat. Sci.) 41(2) 215
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Cluster analysis Cluster analysis u
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Table 3 (Continued) 1 2 3 4 5 6 7 8
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CONCLUSION AFLP markers classified
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difference of soil texture used in
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under wet soil condition planting.
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tropics. Following the expansion, w
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sealed in a plastic box with 1 cm w
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moisture content increment after so
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Frequency Frequency 30 20 10 0 20.0
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School, Kasetsart University. The a
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for combining ability of lines (Lam
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selective mass sibbing within indiv
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Although most of S 2-interfamily hy
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composite sets as used in this stud
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days. The numbers of calli producin
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the report of Ching (1982) showing
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clearly amplified bands generated b
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caused by either the recombination
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Kuhlmann, V. and B. Foroughi-Wehr.
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Xanthomonas fuscans subsp. aurantif
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was collected and washed with 70% e
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expected size was amplified with 35
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eaction technique has been used for
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Schaad, N.W., D. Opgenorth and P. G
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MATERIALS AND METHODS Model descrip
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calculated TF value calculated TF v
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sincere gratitude and deep apprecia
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1989). In monogastrics, the inclusi
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of all LLS samples in this study we
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Melbourne, Parkville, Victoria. Goe
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considered responsible for low prod
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mineral supplementation suggested b
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Body weight chane (kg/head) 32 30 2
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CONCLUSION AND RECOMMENDATION With
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SAS. (Statistical Analysis System).
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genes covering a variety of desirab
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mg/l NAA and 0.5 mg/l zeatin) incub
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5. Purification by various sucrose
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as the culture period increased. So
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culture, pp. 1-20. In L.C. Fowke an
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GC. Analytical methods Total carboh
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ash. The crude hot water polysaccha
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spirulan (H-SP), and a desulfated c
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cells often displayed an increased
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LITERATURE CITED Bell, T.A. and D.V
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for alternative sources of supply.
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BR2.1.2 formed the same clade with
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supplement with glutamate (Iida et
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optimal for S. limacinum BR2.1.2 fo
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fluorescent lamp at 1.5 kLux develo
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Wisconsin, USA). Total extracted RN
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RESULTS Cloning and sequencing of m
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Page 157 and 158: MATERIALS AND METHODS 1. Materials
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Page 163 and 164: Kasetsart J. (Nat. Sci.) 41 : 363 -
Page 165 and 166: the cultures at 10,200 × g for 15
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Page 175 and 176: As the 25 companies were divided in
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Page 181 and 182: et al. (1998); Zeng (2000); and Tal
Page 183 and 184: p 1, i * Kasetsart J. (Nat. Sci.) 4
Page 185 and 186: c ≤ c2 jq2 j n q p k p * 2, j , ,
Page 187 and 188: algorithm would consider RM 5, prio
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Page 191: the maximum deviation of about 10%.
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Page 201 and 202: Throughput (job/sec) 7 6 5 4 3 2 1
Page 203 and 204: Speed up 40 35 30 25 20 15 10 5 0 F
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April - June 2007 - Kasetsart University

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