Research Article
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Quality and pricing decisions for substitutable items under imperfect production process over a random planning horizon

Year 2018, Volume: 47 Issue: 1, 175 - 201, 01.02.2018

Abstract

The paper determines the optimum qualities and prices of two substitute products for a manufacturer cum retailer in an imperfect production process over a random planning horizon for maximum prot. In this Economic Production Lot-size (EPL) process, items are produced simultaneously, defective production commences during the out-of-control state after the passage of some time from the commencement of production and the defective units are partially reworked. The items are substitutable to each other depending on their prices and qualities jointly or either of these two. Unit production cost depends directly on raw-material, labour and quality improvement costs and inversely on the production rate. A part of it is spent against environment protection. Here learning effect is introduced in the set-up and maintenance costs. For the whole process, the planning horizon is random with normal distribution, which is treated as a chance constraint. The models are formulated as prot maximization problems subject to a chance constraint and solved using Genetic Algorithm with Variable Populations (GAVP). The models are demonstrated numerically and the near-optimum results are presented graphically. 

References

  • Abdel-Maleka, L. L., & Montanari, R. (2005). An analysis of the multi-product newsboy problem with a budget constraint. International Journal of Production Economics, 97, 296-307.
  • Absi, N., Dauz'ere-P'er'es, S., Kedad-Sidhoum, S., Penz, B., & Rapine, C. (2013). Lot sizing with carbon emission constraints. European Journal of Operational Research, 227 (1), 55-61.
  • Ahiska, S. S., & Kurtul, E. (2014). Modelling and analysis of a product substitution strategy for a stochastic manufacturing/re-manufacturing system. Computers & Industrial Engineering, 72, 111.
  • Bazan, E., Jaber, M.Y. & Zanoni, S. (2015). Supply chain models with greenhouse gases emissions, energy usage and dierent coordination decisions. Applied Mathematical Modelling, 39, 5131-5151.
  • Chand, S. (1989). Lot sizes and set up frequency with learning in set ups and process quality. European Journal of Operational Research, 42, 190-202.
  • Chen, Y. C. (2013). An optimal production and inspection strategy with preventive maintenance error and rework. Journal of Manufacturing Systems, 32, 99-106.
  • Chen, X., Benjaafar, S., & Elomri, A. (2013). The carbon-constrained EOQ. Operations Research Letters, 41 (2), 172 - 179.
  • Cheng, T. C. E., Wu, C. C., & Lee, W. C. (2010). Scheduling problems with deteriorating jobs and learning eects including proportional set up times. Computers & Industrial Engineering, 58, 326-331.
  • Chung, K. H., & Kim, Y. H. (1989). Economic Analysis of Inventory Systems: A rejoinder, Engineering Economics, 35, 75-80.
  • Das, B., & Maiti, M. (2007). An application of bi level newsboy problem in two substitutable items under capital cost. Applied Mathematics and Computation, 190, 410-422.
  • Ghosh, D., & Shah, J. (2015). Supply chain analysis under green sensitive consumer demand and cost sharing contract. International Journal of Production Economics, 164, 319-329.
  • Guria, A., Das, B., mandal, S., & Maiti, M. (2013). Inventory policy for an item with ination induced purchasing price, selling price and demand with immediate part payment. Applied Mathematical Modelling, 37, 240-257.
  • Gurler, U., & Yilmaz, A.(2010). Inventory and coordination issues with two substitutable products. Applied Mathematical Modelling, 34, 539-551.
  • Hu, J. S., Zheng, H., Guo, C. Y., & Ji, Y. P. (2010). Optimal production run length with imperfect production processes and backorder in fuzzy random environment. Computers & Industrial Engineering, 59(1), 915.
  • Jin, M., Granda-Marulanda, N.A., & Down, I. (2014). The impact of carbon policies on supply chain design and logistics of a major retailer. Journal of Cleaner Production, 85, 453-461.
  • Khouja, M., & Mehrez, A. (1994). An economic production lot size model with imperfect quality and variable production rate. Journal of the Operational Research Society, 45, 1405-1417.
  • Khouja, M., & Mehrez, A. (1998). A note on the eect of inventory costs on product quality levels. Production planning & Control, 9(7), 723-726.
  • Kim, S.W., Bell, & P.C., 2011. Optimal Pricing and Production Decisions in the Presence of Symmetrical and Asymmetrical Substitution. Omega-International Journal of Management Science, 39(5), 528-538.
  • Last, M., & Eyal, S. (2005). A fuzzy-based lifetime extension of genetic algorithms. Fuzzy Sets and Systems, 149, 131 - 147.
  • Liang, Y., & Zhou, F. (2011). A two-warehouse inventory model for deteriorating items under conditionally permissible delay in payment, Applied Mathematical Modelling, 35, 2221-2231.
  • Liu, B., & Iwamura, K. (1998). Chance constraint programming with fuzzy parameters. Fuzzy Sets and Systems, 94, 227-237.
  • Maiti A. K., Maiti M. K., & Maiti, M. (2009). Inventory model with stochastic lead-time and price dependent demand incorporating advance payment. Applied Mathematical Modelling, 33, 2433-2443.
  • Maiti, M. K. (2011). A fuzzy Genetic Algorithm with varying population size to solve an inventory model with credit-linked promotional demand in an imprecise planning horizon. European Journal of Operational Research, 213, 96-106.
  • Maity, K., & Maiti M. (2009). Optimal inventory policies for deteriorating complementary and substitute items. International Journal of Systems Science, 40(3), 267-276.
  • Mandal, S., Maity, K., Mondal, S., & Maiti, M. (2010). Optimal production inventory policy for defective items with fuzzy time period. Applied Mathematical Modelling, 34, 810-822.
  • Michalewicz, Z. (1996). Genetic Algorithm + Data Structure = Evolution Programs, 3rd Edition. New York, Springer-Verlag.
  • Mondal,B., Bhunia, A. K., & Maiti, M. (2003). An inventory system of ameliorating items for price dependent demand rate. Computers & Industrial Engineering, 45(3), 443-456.
  • Mondal, S & Maiti, M. (2002). Multi-item Fuzzy EOQ Models using Genetic Algorithm. Computers & Industrial Engineering, 44, 105-117.
  • Moon,J. & Yun, W. (1993). An economic order quantity model with a random planning horizon. Engineering Economics, 39, 77-86.
  • Pal, B., Sana, S. S., & Chaudhuri, K. S. (2013). A mathematical model on EPQ for stochastic demand in an imperfect production system. Journal of Manufacturing Systems, 32, 260-270.
  • Pal, S, Maiti, M.K. & Maiti, M. (2009). An EPQ model with price discounted promotional demand in an imprecise planning horizon via Genetic Algorithm. Computers & Industrial Engineering, 57, 181-187.
  • Paul, S., Wahab, M. I. M., & Ongkunaruk, P. (2014). Joint replenishment with imperfect items and price discount. Computers & Industrial Engineering, 74, 179-185.
  • Rad, M. A., Khoshalhan, F., & Glock, C. H. (2014). Optimizing inventory and sales decisions in a two-stage supply chain with imperfect production and backorders. Computers & Industrial Engineering, 74, 219-227.
  • Rao, S. S. (2009). Engineering Optimization: Theory and Practice, 4th Edition. John Wiley & Sons, Inc., Hoboken, New Jersey.
  • Rosenblatt, M. J., & Lee, H. L. (1986). Economic production cycle with imperfect production processes. IIE Transactions, 18, 48-55.
  • Roy, A., Pal, S., & Maiti, M. K. (2009). A production inventory model with stock dependent demand incorporating learning and inationary eect in a random planning horizon: A fuzzy genetic algorithm with varying population size approach. Computers & Industrial Engineering, 57(4), 1324- 1335.
  • Sana, S. S., (2010). An economic production lot size model in an imperfect production system. European Journal of Operational Research, 201, 158 - 170.
  • Sarkar, B., Sana, S., & Chaudhuri, K. S. (2010). Optimal reliability, production lot size and safety stock in an imperfect production system. International Journal of Mathematics in Operational Research, 2(4), 467-490.
  • Stavrulaki. E. (2011). Inventory decisions for substitutable products with stock-dependent demand. International Journal of Production Economics, 129, 65-78.
  • Swami, S., & Shah, J. (2013). Channel coordination in green supply chain management. Journal of the Operational Research Society, 64, 336 - 351.
  • Tarakci, H., Tang, K., & Teyarachakul, S. (2009). Learning eects on maintenance outsourcing. European Journal of Operational Research, 192, 138-150.
  • Yao, J. S., & Wu, K. (2000). The best prices of two mutually complements in fuzzy sense. Fuzzy Sets and Systems, 111, 433454.
  • Zakeri, A., Dehghanian, F., Fahimnia, B., & Sarkis, J. (2015). Carbon pricing versus emissions trading: A supply chain planning perspective. International Journal of Production Economics, 164, 197-205.
  • Zhang, B., & Xu, L.(2013). Multi-item production planning with carbon cap and trade mechanism. International Journal of Production Economics, 144, 1, 118-127.
  • Zhao, J., Tang, W., Zhao, R., & Wei, J. (2012). Pricing decisions for substitutable products with a common retailer in fuzzy environments. European Journal of Operational Research, 216, 409419.
Year 2018, Volume: 47 Issue: 1, 175 - 201, 01.02.2018

Abstract

References

  • Abdel-Maleka, L. L., & Montanari, R. (2005). An analysis of the multi-product newsboy problem with a budget constraint. International Journal of Production Economics, 97, 296-307.
  • Absi, N., Dauz'ere-P'er'es, S., Kedad-Sidhoum, S., Penz, B., & Rapine, C. (2013). Lot sizing with carbon emission constraints. European Journal of Operational Research, 227 (1), 55-61.
  • Ahiska, S. S., & Kurtul, E. (2014). Modelling and analysis of a product substitution strategy for a stochastic manufacturing/re-manufacturing system. Computers & Industrial Engineering, 72, 111.
  • Bazan, E., Jaber, M.Y. & Zanoni, S. (2015). Supply chain models with greenhouse gases emissions, energy usage and dierent coordination decisions. Applied Mathematical Modelling, 39, 5131-5151.
  • Chand, S. (1989). Lot sizes and set up frequency with learning in set ups and process quality. European Journal of Operational Research, 42, 190-202.
  • Chen, Y. C. (2013). An optimal production and inspection strategy with preventive maintenance error and rework. Journal of Manufacturing Systems, 32, 99-106.
  • Chen, X., Benjaafar, S., & Elomri, A. (2013). The carbon-constrained EOQ. Operations Research Letters, 41 (2), 172 - 179.
  • Cheng, T. C. E., Wu, C. C., & Lee, W. C. (2010). Scheduling problems with deteriorating jobs and learning eects including proportional set up times. Computers & Industrial Engineering, 58, 326-331.
  • Chung, K. H., & Kim, Y. H. (1989). Economic Analysis of Inventory Systems: A rejoinder, Engineering Economics, 35, 75-80.
  • Das, B., & Maiti, M. (2007). An application of bi level newsboy problem in two substitutable items under capital cost. Applied Mathematics and Computation, 190, 410-422.
  • Ghosh, D., & Shah, J. (2015). Supply chain analysis under green sensitive consumer demand and cost sharing contract. International Journal of Production Economics, 164, 319-329.
  • Guria, A., Das, B., mandal, S., & Maiti, M. (2013). Inventory policy for an item with ination induced purchasing price, selling price and demand with immediate part payment. Applied Mathematical Modelling, 37, 240-257.
  • Gurler, U., & Yilmaz, A.(2010). Inventory and coordination issues with two substitutable products. Applied Mathematical Modelling, 34, 539-551.
  • Hu, J. S., Zheng, H., Guo, C. Y., & Ji, Y. P. (2010). Optimal production run length with imperfect production processes and backorder in fuzzy random environment. Computers & Industrial Engineering, 59(1), 915.
  • Jin, M., Granda-Marulanda, N.A., & Down, I. (2014). The impact of carbon policies on supply chain design and logistics of a major retailer. Journal of Cleaner Production, 85, 453-461.
  • Khouja, M., & Mehrez, A. (1994). An economic production lot size model with imperfect quality and variable production rate. Journal of the Operational Research Society, 45, 1405-1417.
  • Khouja, M., & Mehrez, A. (1998). A note on the eect of inventory costs on product quality levels. Production planning & Control, 9(7), 723-726.
  • Kim, S.W., Bell, & P.C., 2011. Optimal Pricing and Production Decisions in the Presence of Symmetrical and Asymmetrical Substitution. Omega-International Journal of Management Science, 39(5), 528-538.
  • Last, M., & Eyal, S. (2005). A fuzzy-based lifetime extension of genetic algorithms. Fuzzy Sets and Systems, 149, 131 - 147.
  • Liang, Y., & Zhou, F. (2011). A two-warehouse inventory model for deteriorating items under conditionally permissible delay in payment, Applied Mathematical Modelling, 35, 2221-2231.
  • Liu, B., & Iwamura, K. (1998). Chance constraint programming with fuzzy parameters. Fuzzy Sets and Systems, 94, 227-237.
  • Maiti A. K., Maiti M. K., & Maiti, M. (2009). Inventory model with stochastic lead-time and price dependent demand incorporating advance payment. Applied Mathematical Modelling, 33, 2433-2443.
  • Maiti, M. K. (2011). A fuzzy Genetic Algorithm with varying population size to solve an inventory model with credit-linked promotional demand in an imprecise planning horizon. European Journal of Operational Research, 213, 96-106.
  • Maity, K., & Maiti M. (2009). Optimal inventory policies for deteriorating complementary and substitute items. International Journal of Systems Science, 40(3), 267-276.
  • Mandal, S., Maity, K., Mondal, S., & Maiti, M. (2010). Optimal production inventory policy for defective items with fuzzy time period. Applied Mathematical Modelling, 34, 810-822.
  • Michalewicz, Z. (1996). Genetic Algorithm + Data Structure = Evolution Programs, 3rd Edition. New York, Springer-Verlag.
  • Mondal,B., Bhunia, A. K., & Maiti, M. (2003). An inventory system of ameliorating items for price dependent demand rate. Computers & Industrial Engineering, 45(3), 443-456.
  • Mondal, S & Maiti, M. (2002). Multi-item Fuzzy EOQ Models using Genetic Algorithm. Computers & Industrial Engineering, 44, 105-117.
  • Moon,J. & Yun, W. (1993). An economic order quantity model with a random planning horizon. Engineering Economics, 39, 77-86.
  • Pal, B., Sana, S. S., & Chaudhuri, K. S. (2013). A mathematical model on EPQ for stochastic demand in an imperfect production system. Journal of Manufacturing Systems, 32, 260-270.
  • Pal, S, Maiti, M.K. & Maiti, M. (2009). An EPQ model with price discounted promotional demand in an imprecise planning horizon via Genetic Algorithm. Computers & Industrial Engineering, 57, 181-187.
  • Paul, S., Wahab, M. I. M., & Ongkunaruk, P. (2014). Joint replenishment with imperfect items and price discount. Computers & Industrial Engineering, 74, 179-185.
  • Rad, M. A., Khoshalhan, F., & Glock, C. H. (2014). Optimizing inventory and sales decisions in a two-stage supply chain with imperfect production and backorders. Computers & Industrial Engineering, 74, 219-227.
  • Rao, S. S. (2009). Engineering Optimization: Theory and Practice, 4th Edition. John Wiley & Sons, Inc., Hoboken, New Jersey.
  • Rosenblatt, M. J., & Lee, H. L. (1986). Economic production cycle with imperfect production processes. IIE Transactions, 18, 48-55.
  • Roy, A., Pal, S., & Maiti, M. K. (2009). A production inventory model with stock dependent demand incorporating learning and inationary eect in a random planning horizon: A fuzzy genetic algorithm with varying population size approach. Computers & Industrial Engineering, 57(4), 1324- 1335.
  • Sana, S. S., (2010). An economic production lot size model in an imperfect production system. European Journal of Operational Research, 201, 158 - 170.
  • Sarkar, B., Sana, S., & Chaudhuri, K. S. (2010). Optimal reliability, production lot size and safety stock in an imperfect production system. International Journal of Mathematics in Operational Research, 2(4), 467-490.
  • Stavrulaki. E. (2011). Inventory decisions for substitutable products with stock-dependent demand. International Journal of Production Economics, 129, 65-78.
  • Swami, S., & Shah, J. (2013). Channel coordination in green supply chain management. Journal of the Operational Research Society, 64, 336 - 351.
  • Tarakci, H., Tang, K., & Teyarachakul, S. (2009). Learning eects on maintenance outsourcing. European Journal of Operational Research, 192, 138-150.
  • Yao, J. S., & Wu, K. (2000). The best prices of two mutually complements in fuzzy sense. Fuzzy Sets and Systems, 111, 433454.
  • Zakeri, A., Dehghanian, F., Fahimnia, B., & Sarkis, J. (2015). Carbon pricing versus emissions trading: A supply chain planning perspective. International Journal of Production Economics, 164, 197-205.
  • Zhang, B., & Xu, L.(2013). Multi-item production planning with carbon cap and trade mechanism. International Journal of Production Economics, 144, 1, 118-127.
  • Zhao, J., Tang, W., Zhao, R., & Wei, J. (2012). Pricing decisions for substitutable products with a common retailer in fuzzy environments. European Journal of Operational Research, 216, 409419.
There are 45 citations in total.

Details

Primary Language English
Subjects Mathematical Sciences
Journal Section Statistics
Authors

Manoranjan De This is me

Barun Das This is me

Manoranjan Maiti This is me

Publication Date February 1, 2018
Published in Issue Year 2018 Volume: 47 Issue: 1

Cite

APA De, M., Das, B., & Maiti, M. (2018). Quality and pricing decisions for substitutable items under imperfect production process over a random planning horizon. Hacettepe Journal of Mathematics and Statistics, 47(1), 175-201.
AMA De M, Das B, Maiti M. Quality and pricing decisions for substitutable items under imperfect production process over a random planning horizon. Hacettepe Journal of Mathematics and Statistics. February 2018;47(1):175-201.
Chicago De, Manoranjan, Barun Das, and Manoranjan Maiti. “Quality and Pricing Decisions for Substitutable Items under Imperfect Production Process over a Random Planning Horizon”. Hacettepe Journal of Mathematics and Statistics 47, no. 1 (February 2018): 175-201.
EndNote De M, Das B, Maiti M (February 1, 2018) Quality and pricing decisions for substitutable items under imperfect production process over a random planning horizon. Hacettepe Journal of Mathematics and Statistics 47 1 175–201.
IEEE M. De, B. Das, and M. Maiti, “Quality and pricing decisions for substitutable items under imperfect production process over a random planning horizon”, Hacettepe Journal of Mathematics and Statistics, vol. 47, no. 1, pp. 175–201, 2018.
ISNAD De, Manoranjan et al. “Quality and Pricing Decisions for Substitutable Items under Imperfect Production Process over a Random Planning Horizon”. Hacettepe Journal of Mathematics and Statistics 47/1 (February 2018), 175-201.
JAMA De M, Das B, Maiti M. Quality and pricing decisions for substitutable items under imperfect production process over a random planning horizon. Hacettepe Journal of Mathematics and Statistics. 2018;47:175–201.
MLA De, Manoranjan et al. “Quality and Pricing Decisions for Substitutable Items under Imperfect Production Process over a Random Planning Horizon”. Hacettepe Journal of Mathematics and Statistics, vol. 47, no. 1, 2018, pp. 175-01.
Vancouver De M, Das B, Maiti M. Quality and pricing decisions for substitutable items under imperfect production process over a random planning horizon. Hacettepe Journal of Mathematics and Statistics. 2018;47(1):175-201.