- Open Access
Solving multi-customer FPR model with quality assurance and discontinuous deliveries using a two-phase algebraic approach
© Chiu et al. 2016
Received: 15 October 2015
Accepted: 12 April 2016
Published: 21 April 2016
A multi-customer finite production rate (FPR) model with quality assurance and discontinuous delivery policy was investigated in a recent paper (Chiu et al. in J Appl Res Technol 12(1):5–13, 2014) using differential calculus approach. This study employs mathematical modeling along with a two-phase algebraic method to resolve such a specific multi-customer FPR model. As a result, the optimal replenishment lot size and number of shipments can be derived without using the differential calculus. Such a straightforward method may assist practitioners who with insufficient knowledge of calculus in learning and managing the real multi-customer FPR systems more effectively.
Mathematical modeling along with a two-phase algebraic approach is used to reexamine a multi-customer FPR model with quality assurance and discontinuous deliveries (Chiu et al. 2014). The classic FPR model derived the most economic production lotfor a single product production system with perfect quality in production and a continuous end products issuing policy (Taft 1918; Nahmias 2009). However, in real-life supply chains management, we often see vendor who fabricates products and supplies them to multiple customers. Managing such an integrated supply chains system needs to determine the best production–shipment policy in order to minimize the total system costs. Goyal and Gupta (1989) reviewed buyer–vendor integrated inventory models and presented a scheme to classify these models, and identified some future directions. Lu (1995) examined a one-vendor multi-buyer integrated inventory model with the objective of minimizing a vendor’s total annual cost. As a result, an optimal solution for the one-vendor one-buyer case was obtained, and a heuristic approach for the one-vendor multi-buyer case was provided. Woo et al. (2001) studied an integrated inventory system where a single vendor purchases and processes raw materials in order to deliver finished products to multiple buyers. The vendor and all buyers are willing to invest in reducing the ordering cost so as to decrease their joint total cost. An analytical model is developed and the optimal investment amount and replenishment decisions for both vendor and buyers are derived accordingly. Khouja (2003) studied a three-stage supply chain model where a firm can supply many customers. Three different inventory coordination mechanisms between chain members are investigated based on total costs minimization. Many studies that focused on various aspects of supply chain issues have also been extensively carried out (e.g. Benjaafar and Elhafsi 2006; Hoque 2008; Chiu et al. 2013, 2015a; Tseng et al. 2014; Hishamuddin et al. 2014).
Also, in real-life production systems due to various unpredictable factors, generation of nonconforming items in any given production run is inevitable. Mak (1985) utilized mathematical modeling approach to investigate an inventory system where the number of units of acceptable quality in a replenishment lot is uncertain, and the demand is partially captive. His assumptions included backordering of a fraction of the demand during the stock-out period. The optimal replenishment policy was derived along with a numerical example illustrating his theory. He also indicated that optimal replenishment policy is sensitive to the nature of the demand during the stock-out period. Gopalan and Kannan (1994) treated the manufacturing, inspections and rework activities as a two-stage transfer-line production system. They analyzed some transient state characteristics of such a system subject to an initial buffer of infinite capacity, inspection at both inter- and end-stages, and rework. A stochastic model was developed to investigate their system. Explicit analytical expressions for some of the system characteristics were obtained using the state-space method and regeneration point technique. Inderfurth et al. (2006) studied a deterministic problem of planning the production of new and recovering defective items of the same product manufactured on the same facility. Deterioration of defective items is assumed while waiting to be reworked. The objective of their study was to find batch sizes and positions of items to be reworked such that overall production–inventory costs are minimized. A polynomial dynamic programming algorithm was presented to solve this problem. Other studies that addressed different aspects of imperfect production systems and quality assurance issues in production can also be found in (Chelbi and Rezg 2006; Sarkar and Sarkar 2013; Lin et al. 2014; Safaei 2014; Khedlekar et al. 2014; Pal et al. 2015; Ocampo 2015; Chiu et al. 2015b).
Continuous inventory issuing policy is another unrealistic assumption in the classic FPR model. In real vendor–buyer integrated systems it is common for vendors to adopt multiple or periodic delivery policy for transporting finished goods to buyers. Hahm and Yano (1992) determined the frequency of production and delivery of a single component with the objective of minimizing total production–inventory–transportation costs per unit time. They proved that the ratio between the production interval and delivery interval must be an integer in an optimal solution. They used these results to characterize situations in which it is optimal to have synchronized production and delivery, and discussed the ramifications of these conditions on strategies for setup cost and time reductions. Sarker and Khan (1999) considered a manufacturing system that procures raw materials from suppliers in a lot and processes them into finished products which are then delivered to outside buyers at fixed points in time. Accordingly, a general cost model was formulated, and the solution procedure was developed to derive the optimal ordering policy for raw materials and the production lot-size. Abdul-Jalbar et al. (2008) examined a multi-echelon inventory system in which one vendor supplies an item to multiple buyers. The goal is to determine the order quantities at the buyers and the production and shipment schedule at the vendor in order to minimize the total cost per unit time. The problem was formulated in terms of integer-ratio policies and a heuristic procedure was developed to solve the problem. Chiu et al. (2014) examined a multi-customer FPR model with quality assurance and discontinuous deliveries. They consider that a product is made by a producer and all items are screened for quality control purpose. Nonconforming items are either scrap or repairable items, the latter is reworked immediately after regular production ends in each production cycle. After the entire lot is quality assured, multiple shipments are synchronously delivered to multi-customer. Each customer has its own annual product demand, unit stock holding cost, and fixed and variable product delivery costs. Mathematical modeling along with Hessian matrix equations is employed to solve their model and a closed-form optimal replenishment–shipment policy is obtained. Many other studies (e.g.: Sana 2012; Glock 2012; Wu et al. 2014; Chiu et al. 2015c) also addressed various aspects of periodic or multiple delivery issues in vendor–buyer integrated systems.
Grubbström and Erdem (1999) presented algebraic approach to the economic order quantity (EOQ) model with backlogging without reference to the use of derivatives, neither applying the first-order nor second-order differentiations. A few papers extended the same or similar approach to deal with various specific production lot sizing and vendor–buyer integrated problems (Lin et al. 2008; Chen et al. 2012). This study extends such an algebraic approach to the problem of Chiu et al. (2014) and demonstrates that the optimal production-shipment policy can be obtained without using the differential calculus.
Problem statement and formulations
Reconsider the problem of a multi-customer FPR model with quality assurance and discontinuous deliveries as studied in Chiu et al. (2014) as follows: A product has a total demand λ items per year from m different customers. This product can be made by a producer at an annual production rate P. All items made are screened and inspection cost is included in the unit production cost C. It is assumed that during the production process, an x portion of defective items may randomly be produced at a rate d. Defective items are categorized as scrap or repairable items. The latter are reworked right after regular production ends in each cycle at a rate of P 1.
- λ i :
individual demand rate of customer i where i = 1, 2, …, m
- m :
number of customers
- Q :
production lot size per cycle, a decision variable
- n :
number of fixed quantity installments of finished lot to be delivered to customers in each cycle, a decision variable
- T :
production cycle length
- θ :
the portion of defective items that is scrap
- t 1 :
production uptime of the proposed system
- t 2 :
reworking time in each cycle
- t 3 :
time required for delivering all quality assured finished products to customers
- t n :
a fixed interval of time in t 3 between each installment of finished products delivered
- H 1 :
level of on-hand inventory in units when regular production process ends
- H :
maximum level of on-hand inventory in units when the rework process ends
producer’s on-hand inventory of perfect quality items at time t
- I d(t):
producer’s on-hand inventory of defective items at time t
- I c(t):
customers’ on-hand inventory at time t
- D i :
number of fixed quantity finished items distributed to customer i per delivery
- I i :
left over items per delivery after the depletion in t n for customer i
- TC(Q, n):
total production–inventory–delivery costs per cycle
- E[TCU(Q, n)]:
total expected system cost per unit time
Two-phase algebraic approach
Phase 1: deriving n*
In real life situation, the number of delivery takes on integer value only. To find the integer value of n* that minimizes the long-run expected system costs, two adjacent integers to n must be examined, respectively (see Chiu et al. 2013). Let n + denote the smallest integer greater than or equal to n [from Eq. (24)] and n − denote the largest integer less than or equal to n. Because n* is either n + or n −, we can first consider E[TCU(Q, n)] [Eq. (19)] as a cost function with a single decision variable Q and enter the phase 2 as follows.
Phase 2: deriving the optimal Q*
Finally, the solution procedure to the proposed study is summarized as: (1) in phase 1, apply Eq. (24) and find n − and n + first. (2) In phase 2, Eq. (30): substitute n + and n − and find Qs, respectively. (3) Substitute the resulting (Q, n +) and (Q, n −) in Eq. (13), respectively, and select the one that gives minimum cost as optimal replenishment–delivery policy (Q*, n*).
This section is to verify the aforementioned results. To ease the comparison efforts for readers, we use the same numerical example as in Chiu et al. (2014). Consider a producer can manufacture a product at an annual production rate P = 60,000. This product has experienced a steady annual demand from five different industrial clients, where λ i is 400, 500, 600, 700, and 800 respectively (i.e., the sum λ = 3000 per year). The producer has experienced a random defective rate during production that follows a Uniform distribution over the range of [0, 0.3]. Among the nonconforming items a portion θ = 0.2 is determined to be scrap and the other portion can be reworked and repaired at an annual rate P 1 = 3600. Additional values of system variables used in this study, include: include K = $35,000; C = $100; h = $25; C S = $20; h 1 = $60; C R = $60; and for i = 1, 2, …, and 5, K 1i = $100, $200, $300, $400 and $500; C Ti = $0.5, $0.4, $0.3, $0.2, and $0.1; h 2i = $75, $70, $65, $60, and $55, respectively.
Applying Eqs. (22) and (24) we have n = 4.47 and Q = 2428 as our initial solutions (they are real numbers). However, in real application the number of deliveries n can only take on integer values. So, by examining two adjacent integers to n and applying Eq. (30), one obtains (Q, n +) = (2472, 5) and (Q, n −) = (2385, 4). Then, substituting (Q, n +) and (Q, n −) in Eq. (13), respectively, we have E[TCU(2472, 5)] = $440,533 and E[TCU(2385, 4)] = $440,531. Finally, selecting the one that gives the minimum system cost, one obtains the optimal number of delivery n* = 4, the optimal replenishment lot size Q* = 2385, and the expected system cost E[TCU(Q*, n*)] = $440,531. These results are identical to that obtained in Chiu et al. (2014).
Alternative scenario: suppose we examining two adjacent integers to n, but not applying Eq. (30) to obtain a new value of Q accordingly (i.e., to keep initial solution of Q = 2428 unchanged). Then, substituting (Q, n +) and (Q, n −) in Eq. (13), respectively, we have E[TCU(2428, 5)] = $440,551 and E[TCU(2428, 4)] = $440,548. It is noted that both system costs obtained in this scenario are higher than our previous optimal costs $440,531. These additional analytical results reconfirm our optimal solutions.
In this study, a multi-customer FPR model with quality assurance and discontinuous deliveries (Chiu et al. 2014) is reexamined using the mathematical modelling along with a two-phase algebraic approach. Such a simplified solution procedure does not need to refer to the differential calculus. As a result, we successfully demonstrate that the optimal replenishment lot size and number of shipments can be derived without derivatives. This straightforward approach may assist practitioners who with insufficient knowledge of calculus in understanding and managing the real multi-customer FPR systems more effectively.
All authors have contributed to the manuscript equally. All authors read and approved the final manuscript.
Authors deeply appreciate the National Science Council of Taiwan for supporting this research under Grant No. MOST 102-2410-H-324-015-MY2.
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Abdul-Jalbar B, Gutiérrez JM, Sicilia J (2008) Policies for a single-vendor multi-buyer system with finite production rate. Decis Support Syst 46(1):84–100View ArticleGoogle Scholar
- Benjaafar S, Elhafsi M (2006) Production and inventory control of a single product assemble-to-order system with multiple customer classes. Manag Sci 52(12):1896–1912View ArticleGoogle Scholar
- Chelbi A, Rezg N (2006) Analysis of a production/inventory system with randomly failing production unit subjected to a minimum required availability level. Int J Prod Econ 99(1–2):131–143View ArticleGoogle Scholar
- Chen K-K, Wu M-F, Chiu SW, Lee C-H (2012) Alternative approach for solving replenishment lot size problem with discontinuous issuing policy and rework. Expert Syst Appl 39(2):2232–2235View ArticleGoogle Scholar
- Chiu SW, Chou C-L, Wu W-K (2013) Optimizing replenishment policy in an EPQ-based inventory model with nonconforming items and breakdown. Econ Model 35:330–337View ArticleGoogle Scholar
- Chiu Y-SP, Lin L-W, Pai F-Y, Chiu SW (2014) Finite production rate model with quality assurance, multi-customer and discontinuous deliveries. J Appl Res Technol 12(1):5–13View ArticleGoogle Scholar
- Chiu SW, Huang C-C, Chiang K-W, Wu M-F (2015a) On intra-supply chain system with an improved distribution plan, multiple sales locations and quality assurance. SpringerPlus 4:687View ArticleGoogle Scholar
- Chiu Y-SP, Sung P-C, Chiu SW, Chou C-L (2015b) Mathematical modeling of a multi-product EMQ model with an enhanced end items issuing policy and failures in rework. SpringerPlus 4:679View ArticleGoogle Scholar
- Chiu SW, Sung P-C, Tseng C-T, Chiu Y-SP (2015c) Multi-product FPR model with rework and multi-shipment policy resolved by algebraic approach. J Sci Ind Res India 74(10):555–559Google Scholar
- Glock CH (2012) Coordination of a production network with a single buyer and multiple vendors. Int J Prod Econ 135(2):771–780View ArticleGoogle Scholar
- Gopalan MN, Kannan S (1994) Expected duration analysis of a two-stage transfer-line production system subject to inspection and rework. J Oper Res Soc 45(7):797–805View ArticleGoogle Scholar
- Goyal SK, Gupta YP (1989) Integrated inventory models: the buyer–vendor coordination. Eur J Oper Res 41(3):261–269View ArticleGoogle Scholar
- Grubbström RW, Erdem A (1999) The EOQ with backlogging derived without derivatives. Int J Prod Econ 59:529–530View ArticleGoogle Scholar
- Hahm J, Yano CA (1992) The economic lot and delivery scheduling problem: the single item case. Int J Prod Econ 28:235–252View ArticleGoogle Scholar
- Hishamuddin H, Sarker RA, Essam D (2014) A recovery mechanism for a two echelon supply chain system under supply disruption. Econ Model 38:555–563View ArticleGoogle Scholar
- Hoque MA (2008) Synchronization in the single-manufacturer multi-buyer integrated inventory supply chain. Eur J Oper Res 188(3):811–825View ArticleGoogle Scholar
- Inderfurth K, Janiak A, Kovalyov MY, Werner F (2006) Batching work and rework processes with limited deterioration of reworkables. Comput Oper Res 33(6):1595–1605View ArticleGoogle Scholar
- Khedlekar UK, Shukla D, Chandel RPS (2014) Computational study for disrupted production system with time dependent demand. J Sci Ind Res India 73:294–301Google Scholar
- Khouja M (2003) Optimizing inventory decisions in a multi-stage multi-customer supply chain. Transp Res E Log 39(3):193–208View ArticleGoogle Scholar
- Lin H-D, Chiu Y-SP, Ting C-K (2008) A note on optimal replenishment policy for imperfect quality EMQ model with rework and backlogging. Comput Math Appl 56(11):2819–2824View ArticleGoogle Scholar
- Lin GC, Gong D-C, Chang C-C (2014) On an economic production quantity model with two unreliable key components subject to random failures. J Sci Ind Res India 73(3):149–152Google Scholar
- Lu L (1995) A one-vendor multi-buyer integrated inventory model. Eur J Oper Res 81(2):312–323View ArticleGoogle Scholar
- Mak KL (1985) Inventory control of defective products when the demand is partially captive. Int J Prod Res 23(3):533–542View ArticleGoogle Scholar
- Nahmias S (2009) Production and operations analysis. McGraw-Hill Co., Inc., New YorkGoogle Scholar
- Ocampo LA (2015) A hierarchical framework for index computation in sustainable manufacturing. Adv Prod Eng Manag 10:40–50Google Scholar
- Pal S, Mahapatra GS, Samanta GP (2015) A production inventory model for deteriorating item with ramp type demand allowing inflation and shortages under fuzziness. Econ Model 46:334–345View ArticleGoogle Scholar
- Safaei M (2014) An integrated multi-objective model for allocating the limited sources in a multiple multi-stage lean supply chain. Econ Model 37:224–237View ArticleGoogle Scholar
- Sana SS (2012) A collaborating inventory model in a supply chain. Econ Model 29(5):2016–2023View ArticleGoogle Scholar
- Sarkar B, Sarkar S (2013) An improved inventory model with partial backlogging, time varying deterioration and stock-dependent demand. Econ Model 30(1):924–932View ArticleGoogle Scholar
- Sarker RA, Khan LR (1999) Optimal batch size for a production system operating under periodic delivery policy. Comput Ind Eng 37(4):711–730View ArticleGoogle Scholar
- Taft EW (1918) The most economical production lot. Iron Age 101:1410–1412Google Scholar
- Tseng C-T, Wu M-F, Lin H-D, Chiu Y-SP (2014) Solving a vendor–buyer integrated problem with rework and a specific multi-delivery policy by a two-phase algebraic approach. Econ Model 36:30–36View ArticleGoogle Scholar
- Woo YY, Hsu S-L, Wu S (2001) An integrated inventory model for a single vendor and multiple buyers with ordering cost reduction. Int J Prod Econ 73(3):203–215View ArticleGoogle Scholar
- Wu M-F, Chiu Y-SP, Sung P-C (2014) Optimization of a multi-product EPQ model with scrap and an improved multi-delivery policy. J Eng Res 2:51–65View ArticleGoogle Scholar