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Research Article Supply Chain Model with Stochastic Lead Time, Trade-Credit Financing, and Transportation Discounts Sung Jun Kim and Biswajit Sarkar Department of Industrial & Management Engineering, Hanyang University, Ansan, Gyeonggi-do 15588, Republic of Korea Correspondence should be addressed to Biswajit Sarkar; [email protected] Received 26 October 2016; Revised 22 January 2017; Accepted 14 February 2017; Published 18 May 2017 Academic Editor: Mohammad D. Aliyu Copyright Β© 2017 Sung Jun Kim and Biswajit Sarkar. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. is model extends a two-echelon supply chain model by considering the trade-credit policy, transportations discount to make a coordination mechanism between transportation discounts, trade-credit financing, number of shipments, quality improvement of products, and reduced setup cost in such a way that the total cost of the whole system can be reduced, where the supplier offers trade-credit-period to the buyer. For buyer, the backorder rate is considered as variable. ere are two investments to reduce setup cost and to improve quality of products. e model assumes lead time-dependent backorder rate, where the lead time is stochastic in nature. By using the trade-credit policy, the model gives how the credit-period would be determined to achieve the win-win outcome. An iterative algorithm is designed to obtain the global optimum results. Numerical example and sensitivity analysis are given to illustrate the model. 1. Introduction Supply chain indicates that the relation among the supply chain players is forever to obtain maximum profit together and individual profit always. e aim of this model is to reduce the total supply chain cost. Supply chain management (SCM) is the collaboration among suppliers, manufacturers, retailers, and customers. Practically, the aim of the SCM model is to minimize the total cost or to maximize the total profit throughout the channel. In this direction, the idea of integrated vendor-buyer inventory management has been successfully considered since last few decades. In some practical situations, lead time and setup cost can be controlled and reduced in various ways. It is a trend by shortening the lead time and reducing setup cost; the safety stock can be minimized. us, the target is always to decrease the stockout loss and improve the service level for the customer as to increase the competitive edge in business within the SCM environment. us, the controllable lead time and setup cost reduction are the key concepts to obtain successful business and have attracted extensive research attention [1]. Reduced setup in the basic inventory model was investigated by Porteus [2] which is the key research idea for cost reduction policy in a supply chain. Ouyang et al. [3] developed a continuous review inven- tory model for lead time and ordering cost reductions with partial backorders. is model initiated single-vendor multi- buyer with ordering cost reduction. In the same direction, Woo et al. [4] and Chang et al. [5] developed several models with cost reduction policies. Zhang et al. [6] developed the integrated vendor-managed inventory (VMI) model for a two-echelon system with ordering cost reduction. Recently, Shin et al. [7] discussed a continuous review inventory model with transportation cost discount and a service level constraint, whereas Huang [8] introduced another new cost reduction policy through order processing. Recently, Sarkar [9] introduced another cost reduction policy with variable demand under imperfect production process. e above- mentioned models are several major contributions in this field. In reality, transportation cost is not always constant. But, many papers used the concept of constant transportation costs. us, it is too much important to consider the cost as variable. By using the single-setup-multidelivery (SSMD), Hindawi Mathematical Problems in Engineering Volume 2017, Article ID 6465912, 14 pages https://doi.org/10.1155/2017/6465912

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  • Research ArticleSupply Chain Model with Stochastic Lead Time, Trade-CreditFinancing, and Transportation Discounts

    Sung Jun Kim and Biswajit Sarkar

    Department of Industrial & Management Engineering, Hanyang University, Ansan, Gyeonggi-do 15588, Republic of Korea

    Correspondence should be addressed to Biswajit Sarkar; [email protected]

    Received 26 October 2016; Revised 22 January 2017; Accepted 14 February 2017; Published 18 May 2017

    Academic Editor: Mohammad D. Aliyu

    Copyright Β© 2017 Sung Jun Kim and Biswajit Sarkar. This is an open access article distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

    This model extends a two-echelon supply chain model by considering the trade-credit policy, transportations discount to make acoordination mechanism between transportation discounts, trade-credit financing, number of shipments, quality improvement ofproducts, and reduced setup cost in such a way that the total cost of the whole system can be reduced, where the supplier offerstrade-credit-period to the buyer. For buyer, the backorder rate is considered as variable. There are two investments to reduce setupcost and to improve quality of products. The model assumes lead time-dependent backorder rate, where the lead time is stochasticin nature. By using the trade-credit policy, the model gives how the credit-period would be determined to achieve the win-winoutcome. An iterative algorithm is designed to obtain the global optimum results. Numerical example and sensitivity analysis aregiven to illustrate the model.

    1. Introduction

    Supply chain indicates that the relation among the supplychain players is forever to obtain maximum profit togetherand individual profit always. The aim of this model is toreduce the total supply chain cost.

    Supply chain management (SCM) is the collaborationamong suppliers, manufacturers, retailers, and customers.Practically, the aim of the SCMmodel is tominimize the totalcost or tomaximize the total profit throughout the channel. Inthis direction, the idea of integrated vendor-buyer inventorymanagement has been successfully considered since last fewdecades. In some practical situations, lead time and setupcost can be controlled and reduced in various ways. It is atrend by shortening the lead time and reducing setup cost;the safety stock can be minimized. Thus, the target is alwaysto decrease the stockout loss and improve the service level forthe customer as to increase the competitive edge in businesswithin the SCM environment. Thus, the controllable leadtime and setup cost reduction are the key concepts to obtainsuccessful business and have attracted extensive researchattention [1]. Reduced setup in the basic inventorymodel was

    investigated by Porteus [2] which is the key research idea forcost reduction policy in a supply chain.

    Ouyang et al. [3] developed a continuous review inven-tory model for lead time and ordering cost reductions withpartial backorders. This model initiated single-vendor multi-buyer with ordering cost reduction. In the same direction,Woo et al. [4] and Chang et al. [5] developed several modelswith cost reduction policies. Zhang et al. [6] developed theintegrated vendor-managed inventory (VMI) model for atwo-echelon system with ordering cost reduction. Recently,Shin et al. [7] discussed a continuous review inventorymodel with transportation cost discount and a service levelconstraint, whereas Huang [8] introduced another new costreduction policy through order processing. Recently, Sarkar[9] introduced another cost reduction policy with variabledemand under imperfect production process. The above-mentioned models are several major contributions in thisfield.

    In reality, transportation cost is not always constant. But,many papers used the concept of constant transportationcosts. Thus, it is too much important to consider the costas variable. By using the single-setup-multidelivery (SSMD),

    HindawiMathematical Problems in EngineeringVolume 2017, Article ID 6465912, 14 pageshttps://doi.org/10.1155/2017/6465912

    https://doi.org/10.1155/2017/6465912

  • 2 Mathematical Problems in Engineering

    the number of types of transportation increases always.The primary aim of using SSMD policy is to reduce theholding cost of buyer, but as a result, the transportationcost increases. Therefore, there will be a trade-off betweenthem to control the cost of the whole system. Ganeshan [10]developed amodel formanaging supply chain inventory, withmultiretailer, single-warehouse, and multisupplier. Recently,Sarkar et al. [11, 12] developed two SCMmodels with constantand variable transportation costs.

    Nowadays transportation mode selection with positivemanufacturing lead time is more effective in SCM system[13]. Ertogral et al. [14] developed a vendor-buyer supplychain model for production and shipment issues with trans-portation cost. Kang and Kim [15] discussed a coordinationof inventory holding and transportation management ina two-echelon supply chain model. Chung [16] proposedan integrated-inventory model with transportation cost andtwo-level trade-credit policy.

    In real life situation, everybody prefers the best quality ofproducts with the cheapest price. As a result, all industrieshave to make good quality products with at least cheaperprice. That is why, in many cases, some investment can bedone to reduce the setup cost and improve the quality ofproducts. In this direction, Ouyang et al. [17] developed anintegrated-inventory model with quality improvement, setupcost reduction, and stochastic lead time in an imperfectproduction process. Basically, they used the concept ofPorteus [2] regarding the investment to reduce setup cost andimprove the product’s quality.They simultaneously optimizedlot size, quality improvement parameter, setup cost, safetystock, and lead time to obtain the minimum cost of theintegrated model. Sarkar and Moon [18] extended Ouyang etal.’s [17] model with variable backorder rate. Yoo et al. [19]introduced inspection process with commercial return andrework during imperfect production for quality investmentand quality cost analyses.

    In this highly competitive business environment, compa-nies always desire for trade-credit policy for the entire cus-tomers.Thus, trade-credit plays an important role in modernbusiness system. Vendors offer trade-credit-period to buyersto encourage sales, promote market shares, and reduce on-hand stock. Goyal [20] proposed an economic order quantitymodel under conditions of permissible delay-in-payments.Aggarwal and Jaggi [21] discussed about ordering policiesof deteriorating items under permissible delay-in-payments.Jamal et al. [22] extended the permissible delay-in-paymentsconcepts with allowable shortage and deterioration. Teng [23]discussed an economic order quantity under the conditionsof permissible delay-in-payments. Chang [24]wrote a note onpermissible delay-in-payments for (𝑄, π‘Ÿ) inventory systemswith ordering cost reduction.

    Jaber and Osman [25] explained about the coordinationof delay-in-payments and profit sharing in a two-echelonsupply chain model. Luo [26] examined a buyer-vendorintegrated-inventory model with credit-period incentives.Huang [8] proposed an integrated-inventorymodel under theconditions of order processing cost reduction andpermissibledelay-in-payments. Sarkar et al. [27] extended an integrated-inventory model with variable lead time, defective units,

    and delay-in-payments. They assumed stochastic lead timein combination with delay-in-payments to reduce total costof the system. Recently, Sarkar [28] discussed some conceptof discount policies from vendor to buyer with variablebackorder for buyer and multi-inspection for vendor byconsidering the fixed lifetime constrains of products. Thismodel emphasized a coordination policy within the supplychain by some special discounts if the buyer agrees to buysome order quantities which are decided by vendor.

    In some inventory system, such as fashionable items, thelength of the waiting time for the next replenishment woulddetermine whether the backorder is accepted or not. There-fore, backorder rate is variable and dependent on the waitingtime for the next replenishment [29]. Pan et al. [30] optimizedan inventory model with reorder point, variable lead time,and backorder discount considerations. Pan and Hsiao [31]formulated an integrated-inventory model with controllablelead time and backorder discount considerations. Lee et al.[32] developed a computational algorithmic procedure foroptimal inventory policy involving ordering cost reductionand backorder discounts when the lead time demand iscontrollable. Lo et al. [33] introduced lead time and safetyfactor in mixed inventory models with backorder discounts.Lin [34] discussed an integrated vendor-buyer inventorymodel with a backorder price-discount and an effectiveinvestment to reduce ordering cost. Huang [8] designed asimple and an efficient algorithm involving ordering costreduction and backorder price-discount on inventory systemunder variable lead time.

    1.1. Problem Description. This paper illustrates a chan-nel coordination mechanism between transportation dis-counts, trade-credit financing, number of shipments, qualityimprovement of products, and reduced setup cost in a two-echelon supply chain model. Table 1 shows the distinctionbetween existing model and this model.The work is differentfrom [18, 35] for the purpose of transportation discountsand trade-credit financing. It is differing from [36] fromtransportation discount and setup cost reduction and qualityimprovement of products. It is totally differing from [37] asit is a supply chain model and [37] is a basic EPQ model. Toshow this all, we have added Table 1 for the same. The aimof this model is to minimize the total cost throughout thesupply chain network under single-supplier and single-buyerfor a single type of product and single-setup-multidelivery(SSMD) policy. The supplier offers trade-credit-period to thebuyer and the buyer uses the delay time to increase his/herprofit. A continuous review inventory model is consideredfor both supplier and buyer. For buyer, the backorder rate isconsidered as variable. An investment is used to reduce setupcost and another investment is used to improve the qualityof products. To reduce the total supply chain cost, the modelassumes lead time-dependent backorder rate, where the leadtime is stochastic in nature. By using the trade-credit policy,the model gives how the credit-period would be benefited forthe whole system.The paper is designed as follows: Section 2

  • Mathematical Problems in Engineering 3

    introduces the mathematical model. In Section 3, numericalexample is given. Section 4 gives the conclusions of themodel.

    2. Mathematical Model

    2.1. Notation. The following notation are used to develop themodel.(i) Decision Variables

    𝑄: buyer’s order quantity (units).𝐴 𝑠: supplier’s setup cost per setup ($/setup).𝐿: length of lead time in unit time (days).πœƒ: probability of the production process whichmay goto out-of-control state during producing a lot.π‘˜: safety factor for reorder point.π‘š: number of lots delivered from the supplier to thebuyer in one production cycle, a positive integer.

    (ii) Parameters

    𝐴𝑏: buyer’s ordering cost per order ($/order).𝐷: average demand per unit time (units/year).β„Žπ‘: buyer’s inventory holding cost per unit per unittime ($/unit/year).β„Žπ‘ : supplier’s inventory holding cost per unit per unittime ($/unit/year).πœ‹: stockout cost per unit short ($/unit short).π‘Ÿ: reorder point (units).𝜎: standard deviation of the lead time demand.πœƒ0: initial probability of the production process whichmay go to out-of-control state during producing a lot.𝛼: annual fractional cost of capital investment toreduce setup cost ($/year).𝐼(𝐴, πœƒ): total investment for setup cost reduction from𝐴0 to 𝐴 and quality improvement from πœƒ0 to πœƒ.𝐿 𝑖: length of the lead time components for 𝑖 =1, 2, . . . , 𝑛.𝑐𝑖: crashing cost per unit time 𝐿 𝑖 ($/unit time).𝑋: lead time demand which has distribution function𝐹 (units).𝐸(𝑋): mathematical expectation of𝑋.𝑧+: max{𝑧, 0}, where 𝑧 is any random variable.𝐸(𝑋 βˆ’ π‘Ÿ)+: expected shortage quantity at the end ofthe cycle.𝑃: production rate (unit/unit time).𝑖𝑏: buyer’s interest or opportunity cost in annualpercentage.𝑖𝑠: supplier’s interest or opportunity cost in annualpercentage.𝑑𝑐𝑖: transportation cost for 𝑖th unit, 𝑖 = 1, 2, . . . , 𝑛($/unit).

    𝑁: length of credit-period (unit time).𝑠: rework cost per unit defective item ($/unit defectiveitem).𝑝𝑐: purchasing cost per unit ($/unit).

    2.2. Assumptions. The following assumptions are consideredto formulate this model. These assumptions are mainlyadopted from Sarkar and Moon [18] and Arkan and Hejazi[36].

    (i) The study considers a supply chain model for a singletype of products with the single-setup-multidelivery(SSMD) policy and controllable lead time.

    (ii) The lead time 𝐿 has 𝑛 mutually independent compo-nents. The 𝑖th component has a normal duration 𝑇𝑖and the minimum duration 𝑑𝑖 with crashing cost perunit time 𝑐𝑖 with 𝑐1 ≀ 𝑐2 ≀ 𝑐3 ≀ β‹… β‹… β‹… ≀ 𝑐𝑛. The lead timedemand 𝑋 follows a normal distribution with mean𝐷𝐿 and standard deviation 𝜎√𝐿 (Ouyang et al. [35]).

    (iii) Let 𝐿0 = βˆ‘π‘›π‘—=1 𝑇𝑗 and 𝐿 𝑖 be the length of the leadtime with components 1, 2, 3, . . . , 𝑖 crashed to theirminimum duration. Then, 𝐿 𝑖 can be considered as𝐿 𝑖 = 𝐿0 βˆ’ βˆ‘π‘–π‘—=1(𝑇𝑗 βˆ’ 𝑑𝑗) and the lead time crashingcost per cycle 𝑅(𝐿) can be expressed as 𝑅(𝐿) = 𝑐𝑖(𝐿 𝑖 βˆ’πΏ) +βˆ‘π‘–βˆ’1𝑗=1 𝑐𝑗(𝑇𝑗 βˆ’ 𝑑𝑗) for = 1, 2, . . . , 𝑛 (see for referenceOuyang et al. [35]).

    (iv) This model considers the variable backorder rate 𝛽with respect to lead time (Sarkar and Moon [18]).

    (v) Logarithmic expressions are assumed for both qual-ity improvement and setup cost reduction (Porteus,[37]).

    (vi) The trade-credit financing is considered to make it acost-reduced supply chain.

    (vii) The supplier provides a transportation cost discount,when the buyer places the order of 𝑄 units.

    2.3. Model Formulation. The model considers the single-setup-multidelivery (SSMD) policy in a single-suppliersingle-buyer supply chain model. If the buyer orders quantity𝑄 when the on-hand inventory reaches the reorder point π‘Ÿ,that is, it considers (𝑄, π‘Ÿ) continuous review inventorymodel,to save holding cost of the buyer, the supplier produces π‘šπ‘„quantities, which will be delivered to the buyer π‘š times inone production cycle. Thus, the expected cycle length forthe supplier is π‘šπ‘„/𝐷 and for the buyer is 𝑄/𝐷, respectively.Therefore, the ordering cost per unit time for the buyer is𝐴𝑏𝐷/𝑄.

    If the inventory level reaches the reorder point π‘Ÿ, whereπ‘Ÿ = 𝐷𝐿 + π‘˜πœŽβˆšπΏ, 𝐷𝐿 = the expected demand during the leadtime, π‘˜πœŽβˆšπΏ = safety stock (SS), and π‘˜ = safety factor, thebuyer places an order of quantity𝑄.Thus, before receiving anorder, the inventory is π‘Ÿβˆ’π·πΏ and after receiving the order, theinventory is 𝑄 + (π‘Ÿ βˆ’ 𝐷𝐿). Hence, the average inventory overa cycle can be written as𝑄/2+ π‘Ÿβˆ’π·πΏ. Therefore, the holdingcost per unit per unit time of the buyer is β„Žπ‘(𝑄/2 + π‘Ÿ βˆ’ 𝐷𝐿).

  • 4 Mathematical Problems in Engineering

    Table 1: Distinction between previous and this model.

    Author(s) Supply chainmodelVariable lead

    timeVariablebackorder

    Setup costreduction

    Qualityimprovement

    Transportationdiscounts

    Trade-creditfinancing

    Porteus, 1986 √ √Ouyang et al., 1996 √Moon and Choi, 1998 √Hariga and Ben-Daya, 1999 √Ouyang and Chuang, 2001 √ √Ouyang et al., 2002 √ √ √Lee, 2005 √ √Lin, 2008 √ √Sarkar and Majumder, 2013 √ √ √Sarkar and Moon, 2014 √ √ √ √Sarkar et al., 2014 √ √ √Sarkar et al., 2015 √ √This research √ √ √ √ √ √ √

    Themodel assumes that the lead time demand𝑋 follows anormal distribution with mean𝐷𝐿, standard deviation 𝜎√𝐿,and safety factor π‘˜. Thus, the reorder point π‘Ÿ = 𝐷𝐿+π‘˜πœŽβˆšπΏ. If𝑋 < π‘Ÿ, then shortage occurs. Hence, the expected shortage atthe end of the cycle is 𝐸(𝑋 βˆ’ π‘Ÿ)+, and then expected shortagecost per unit time is (πœ‹π·/𝑄)𝐸(𝑋 βˆ’ π‘Ÿ)+.

    The concept of Ouyang et al. [35] for lead time crashingcost is used in this model. The lead time 𝐿 has 𝑛 mutuallyindependent components. The 𝑖th component has a normalduration 𝑇𝑖 and the minimum duration 𝑑𝑖 with crashing costper unit time 𝑐𝑖 with 𝑐1 ≀ 𝑐2 ≀ 𝑐3 ≀ β‹… β‹… β‹… ≀ 𝑐𝑛. Let𝐿0 = βˆ‘π‘›π‘—=1 𝑇𝑗 and 𝐿 𝑖 be the length of the lead time withcomponents 1, 2, 3, . . . , 𝑖 crashed to their minimum duration.Then, 𝐿 𝑖 can be written as 𝐿 𝑖 = 𝐿0 βˆ’ βˆ‘π‘–π‘—=1(𝑇𝑗 βˆ’ 𝑑𝑗) andthe lead time crashing cost per cycle 𝑅(𝐿) can be expressedas 𝑅(𝐿) = 𝑐𝑖(𝐿 𝑖 βˆ’ 𝐿) + βˆ‘π‘–βˆ’1𝑗=1 𝑐𝑖(𝑇𝑖 βˆ’ 𝑑𝑗) for 𝑖 = 1, 2, . . . , 𝑛.Thus, the lead time crashing cost per unit time is (𝐷/𝑄)𝑅(𝐿).Therefore, the total expected cost per unit time to the buyercan be expressed as follows:

    𝑇𝐢𝑏 (𝑄, 𝐿) = 𝐴𝑏𝐷𝑄 + β„Žπ‘ (𝑄2 + π‘Ÿ βˆ’ 𝐷𝐿)+ πœ‹π·π‘„ 𝐸 (𝑋 βˆ’ π‘Ÿ)+ + 𝐷𝑄𝑅 (𝐿) .

    (1)

    In reality, the fixed or constant backorder rate is veryrare and it is found only in case of life saving drugs, costlyproducts, or others. But for any low-cost products, it isgenerally variable.Thus, based on lead time of this model, weuse the concept of Sarkar and Moon [18] for the backorderrate as a function of the lead time as follows:

    𝛽 = 11 + πœŒπœŽβˆšπΏπœ“ (π‘˜) ,𝜌 being a constant, 0 < 𝜌 < ∞. (2)

    Thus, total expected cost per unit time for the buyer,considering the partial backorder, can be expressed as

    𝑇𝐢𝑏 (𝑄, 𝐿, π‘˜)= 𝐴𝑏𝐷𝑄 + β„Žπ‘ (𝑄2 + π‘Ÿ βˆ’ 𝐷𝐿 + (1 βˆ’ 𝛽) 𝐸 (𝑋 βˆ’ π‘Ÿ)+)+ [πœ‹ + πœ‹0 (1 βˆ’ 𝛽)]𝐷𝑄 𝐸 (𝑋 βˆ’ π‘Ÿ)+ + 𝐷𝑄𝑅 (𝐿) .

    (3)

    Using the above, the expected shortage at the end of thecycle can be expressed as

    𝐸 (𝑋 βˆ’ π‘Ÿ)+ = βˆ«βˆžπ‘Ÿ(𝑋 βˆ’ π‘Ÿ) 𝑑𝐹 (π‘₯)

    = 𝜎√𝐿 {πœ™ (π‘˜) βˆ’ π‘˜ (1 βˆ’ Ξ¦ (π‘˜))}= πœŽβˆšπΏπœ“ (π‘˜) ,

    (4)

    where πœ“(π‘˜) = πœ™(π‘˜) βˆ’ π‘˜(1 βˆ’ Ξ¦(π‘˜)), πœ™(π‘˜) and Ξ¦(π‘˜) are thestandard normal distribution function and the cumulativedistribution function of the normal distribution, respectively.Thus, the safety factor π‘˜ can be treated as a decision variableinstead of π‘Ÿ. Therefore, total expected cost per unit time forthe buyer considering the partial backorder can be written as

    𝑇𝐢𝑏 (𝑄, 𝐿, π‘˜) = 𝐴𝑏𝐷𝑄 + β„Žπ‘ (𝑄2 + π‘˜πœŽβˆšπΏ) + πœŽβˆšπΏπœ“ (π‘˜)β‹… [β„Žπ‘ πœŒπœŽβˆšπΏπœ“ (π‘˜)1 + πœŒπœŽβˆšπΏπœ“ (π‘˜)+ 𝐷𝑄 (πœ‹ + πœ‹0 πœŒπœŽβˆšπΏπœ“ (π‘˜)1 + πœŒπœŽβˆšπΏπœ“ (π‘˜))] + 𝐷𝑄𝑅 (𝐿) .

    (5)

    In thismodel, under the SSMDpolicy, the cycle length forsupplier is π‘šπ‘„/𝐷. Thus, the setup cost per unit time for thesupplier is 𝐴 𝑠𝐷/π‘šπ‘„ (see for instance Figure 1). The average

  • Mathematical Problems in Engineering 5

    Quantity mQ/P

    Time

    Q/P

    mQ

    Q/D

    Q

    Accumulated inventory for the buyer

    Accumulated inventory for the supplier

    Q

    (m βˆ’ 1)Q/D

    Figure 1: Inventory pattern under the SSMD policy (see for reference Ouyang et al. [38]).

    inventory of the supplier can be written as [{π‘šπ‘„(𝑄/𝑃 +(π‘š βˆ’ 1)(𝑄/𝐷)) βˆ’ π‘š2𝑄2/2𝑃} βˆ’ {(𝑄2/𝐷)(1 + 2 + β‹… β‹… β‹… + (π‘š βˆ’1))}](𝐷/π‘šπ‘„) = (𝑄/2)[π‘š(1 βˆ’ 𝐷/𝑃) βˆ’ 1 + 2𝐷/𝑃]. Hence, theholding cost per unit per unit time for the supplier becomesβ„Žπ‘ (𝐷/2)[π‘š(1 βˆ’ 𝐷/𝑃) βˆ’ 1 + 2𝐷/𝑃].

    In this model, there are two investments to reduce thetotal supply chain cost to make the supply chain moreprofitable. An investment is used to improve the quality ofproducts and another investment is used to reduce setupcost. We consider the concept of Porteus [37] for qualityimprovement πΌπœƒ(πœƒ) = 𝑏 ln(πœƒ0/πœƒ) for 0 < πœƒ ≀ πœƒ0 and for setupcost reduction 𝐼𝐴(𝐴) = 𝐡 ln(𝐴0/𝐴) for 0 < 𝐴 ≀ 𝐴0. Hence,the supplier’s the total investment for quality improvementand setup cost reduction becomes as follows:

    𝐼 (𝐴, πœƒ) = πΌπœƒ (πœƒ) + 𝐼𝐴 (𝐴) = 𝐺 βˆ’ 𝑏 ln πœƒ βˆ’ 𝐡 ln𝐴, (6)where 𝐺 = 𝑏 ln(πœƒ0) + 𝐡 ln(𝐴0).

    Using the concept of defective items, the expected annualtotal cost is

    𝑇𝐢𝑠 (π‘š,𝑄, π‘Ÿ, 𝐿) = 𝐢 (π‘š,𝑄, π‘Ÿ, 𝐿) + π‘ π·π‘šπ‘„πœƒ2 . (7)Therefore, the total expected cost per unit time for

    supplier can be expressed as follows:

    𝑇𝐢𝑠 (𝑄, πœƒ, 𝐴 𝑠, π‘š) = 𝐴 π‘ π·π‘šπ‘„+ β„Žπ‘ π·2 [π‘š(1 βˆ’ 𝐷𝑃 ) βˆ’ 1 + 2𝐷𝑃 ]+ 𝛼 (𝐺 βˆ’ 𝑏 ln πœƒ βˆ’ 𝐡 ln𝐴 𝑠)+ π‘ π·π‘šπ‘„πœƒ2

    (8)

    for 0 < πœƒ ≀ πœƒ0 and 0 < 𝐴 ≀ 𝐴0.

    To make the profitable supply chain, an attempt of trade-credit policy is used. By using the trade-credit policy, buyersaves his/her total interest during the credit-period and thesupplier lost opportunity cost.We define the trade-credit costfor buyer offered by the supplier as follows:

    𝑝𝑐 (𝑄 βˆ’ 𝐷𝑁)2 𝑖𝑠2𝐷 βˆ’ 𝐷2𝑝𝑐𝑁2𝑖𝑏2𝐷

    βˆ’ 𝑝𝑐𝑁𝑖𝑏𝐷 πœŽβˆšπΏπœ“ (π‘˜)1 + πœŒπœŽβˆšπΏπœ“ (π‘˜) .(9)

    Nowadays, for highly competitive business market, trans-portation cost is a major issue of the total operational costin SCM. For appropriate incorporation of transportation costinto the total annual cost function, it should identify the exacttransportation cost which relates the reality. In many SCMmodels, the transportation cost is only considered implicitlyas a part of fixed setup or ordering cost and thus, it is assumedto be the independent of the size of the shipment. In thissection, we address the case, where the transportation cost isexplicitly considered in the model. The structure of all-unit-discount transportation cost is adopted, which is similar toErtogral et al. [14] (see Table 2 for it).

    Another attempt of transportation cost discount is con-sidered to make a SCM forever. For selling large quantities,the supplier offers a transportation cost discount to the buyer.In this model, the transportation cost is dependent on𝑄. Weconsider that the supplier offers the discount once the buyerplaces the order 𝑄 units. Thus, the buyer orders quantity𝑄 for the transportation cost discounts from the supplier.Besides, the supplier carries quantity π‘Œ instead of 𝑄 due tovarious reasons. However, this imperfect quantity π‘Œ does notaffect the transportation cost discount condition. For a givenshipment of lot size 𝑄 ∈ [𝑀𝑖,𝑀𝑖+1), transportation cost perunit time is equal to 𝐢𝑖𝑄/(𝑄/𝐷) = 𝐢𝑖𝐷, which can be found

  • 6 Mathematical Problems in Engineering

    Table 2: Structure of all-unit-discount transportation cost.

    Range Unit transportation cost0 ≀ 𝑄 < 𝑀1 𝐢0𝑀1 ≀ 𝑄 < 𝑀2 𝐢1𝑀2 ≀ 𝑄 < 𝑀3 𝐢2... ...π‘€π‘βˆ’1 ≀ 𝑄 < 𝑀𝑏 πΆπ‘βˆ’1𝑀𝑏 ≀ 𝑄 𝐢𝑏where 𝐢1 > 𝐢2 > β‹… β‹… β‹… > 𝐢𝑏

    by dividing the transportation cost per order cycle by theduration of the order cycle. The transportation cost can berepresented as

    𝑇𝐷 (𝑄) =

    {{{{{{{{{{{{{{{{{{{{{{{{{{{{{

    𝐢0𝐷, 𝑄 ∈ [0,𝑀1) ,𝐢1𝐷, 𝑄 ∈ [𝑀1,𝑀2) ,𝐢2𝐷, 𝑄 ∈ [𝑀2,𝑀3) ,... ...𝐢𝑏𝐷, 𝑄 ∈ [𝑀𝑏,∞) .

    (10)

    Hence, the expected annual total cost per unit timeincludes the receiving of uncertain quantity and the trans-portation cost for the SCM model with partial backorder,setup cost, quality improvement, and trade-credit. Therefore,this problem reduces to

    min 𝑇𝐢𝑠𝑐 (𝑄, π‘˜, πœƒ, 𝐴 𝑠, π‘š, 𝐿)= 𝐴𝑏𝐷𝑄 + β„Žπ‘ (𝑄2 + π‘˜πœŽβˆšπΏ) + πœŽβˆšπΏπœ“ (π‘˜) [β„Žπ‘ πœŒπœŽβˆšπΏπœ“ (π‘˜)1 + πœŒπœŽβˆšπΏπœ“ (π‘˜) + 𝐷𝑄 (πœ‹ + πœ‹0 πœŒπœŽβˆšπΏπœ“ (π‘˜)1 + πœŒπœŽβˆšπΏπœ“ (π‘˜))]

    + 𝐷𝑄 [[𝑐𝑖 (𝐿 𝑖 βˆ’ 𝐿) +π‘–βˆ’1βˆ‘π‘—=1

    𝑐𝑖 (𝑇𝑖 βˆ’ 𝑑𝑗)]] βˆ’π·2𝑝𝑐𝑁2𝑖𝑏2𝑄 βˆ’ 𝐷𝑝𝑐𝑁𝑖𝑏𝑄 πœŽβˆšπΏπœ“ (π‘˜)1 + πœŒπœŽβˆšπΏπœ“ (π‘˜) + 𝐴 π‘ π·π‘šπ‘„

    + β„Žπ‘ π‘„2 [π‘š(1 βˆ’ 𝐷𝑃 ) βˆ’ 1 + 2𝐷𝑃 ] + 𝛼 (𝐺 βˆ’ 𝑏 ln πœƒ βˆ’ 𝐡 ln𝐴 𝑠) + π‘ π·π‘šπ‘„πœƒ2 + 𝑝𝑐 (𝑄 βˆ’ 𝐷𝑁)2 𝑖𝑠2𝑄 + 𝑇𝐷 (𝑄)

    subject to 0 < πœƒ ≀ πœƒ0,0 < 𝐴 ≀ 𝐴0.

    (11)

    2.4. Solution Procedure. Now the optimum cost of the wholesupply chain model is calculated. To do that optimization,we initially ignore all constraints and calculate all the partialderivatives which are necessary for the optimization; thenall restrictions are applied on it. The values of all the partialderivatives are as follows:

    πœ•π‘‡πΆπ‘ π‘πœ•π‘„ = 1𝑄2 [βˆ’π΄π‘π· βˆ’ π·πœ‹πœ‰πœŒ βˆ’ 𝐷𝑅 (𝐿) + 𝑝𝑐𝑖𝑏𝐷2𝑁22

    + 𝑝𝑐𝑖𝑏𝐷𝑁2πœ‰2 (1 + πœ‰) βˆ’ 𝑝𝑐𝑖𝑠𝐷2𝑁22 βˆ’ 𝐴 π‘ π·π‘š ] + β„Žπ‘2

    + π‘ π·πœƒπ‘š2 + β„Žπ‘ 2 [π‘š(1 βˆ’ 𝐷𝑃 ) βˆ’ 1 + 2𝐷𝑃 ] + 𝑝𝑐𝑖𝑠2 ,πœ•π‘‡πΆπ‘ π‘πœ•π‘˜ = β„Žπ‘πœŽβˆšπΏ + πœŽβˆšπΏπœ‰3 [ β„Žπ‘πœ‰(1 + πœ‰) + π·πœ‹π‘„ ]+ 𝜎√𝐿[ β„Žπ‘πœ‰πœ‰3(1 + πœ‰)2 + π·πœ‹0πœ‰πœ‰3𝑄 (1 + πœ‰)2] βˆ’ π·π‘π‘π‘π‘–π‘πœŒβˆšπΏπœ‰3𝑄 (1 + πœ‰)2 ,

    πœ•π‘‡πΆπ‘ π‘πœ•πœƒ = βˆ’π›Όπ‘πœƒ + π‘ π·π‘šπ‘„2 ,πœ•π‘‡πΆπ‘ π‘πœ•π΄ 𝑠 = π·π‘šπ‘„ βˆ’ 𝛼𝐡𝐴 𝑠 ,πœ•π‘‡πΆπ‘ π‘πœ•π‘š = βˆ’π΄ π‘ π·π‘š2𝑄 + β„Žπ‘ π‘„2 (1 βˆ’ 𝐷𝑃 ) + π‘ π·π‘„πœƒ2 ,πœ•π‘‡πΆπ‘ π‘πœ•πΏ = 12β„Žπ‘π‘˜πœŽπΏβˆ’1/2 βˆ’ 𝐷𝑄𝑐𝑖 + (β„Žπ‘πœ‰

    2πΏβˆ’1/2𝜌 + π·πœ‹0πœ‰2

    π‘„πœŒπΏ2βˆ’ π·π‘π‘π‘π‘–π‘πœ‰2π‘„πœŒπΏ ) βˆ’ πœ‰2 (1 + πœ‰)2 [β„Žπ‘πœŒπœ“ (π‘˜)2𝜎2

    + π·πœ‹0πœŒπœ“ (π‘˜)2𝜎2𝑄 βˆ’ π·π‘π‘π‘π‘–π‘πœŽπœ“ (π‘˜)𝑄 ] ,(12)

    where πœ‹ = πœ‹ + πœ‹0πœ‰/(1 + πœ‰), πœ‰ = πœŒπœŽβˆšπΏπœ“(π‘˜), and πœ‰3 =Ξ¦(π‘˜) βˆ’ 1.

  • Mathematical Problems in Engineering 7

    To obtain the global minimum solution of the supplychain model, the following second-order partial derivativesare used to calculate all minors:

    πœ•2π‘‡πΆπ‘ π‘πœ•π‘„2 = 2𝑄3 [𝐴𝑏𝐷 + π·πœ‹πœ‰πœŒ + 𝐷𝑅 (𝐿) βˆ’ 𝑝𝑐𝑖𝑏𝐷2𝑁22

    βˆ’ 𝑝𝑐𝑖𝑏𝐷𝑁2πœ‰2 (1 + πœ‰) + 𝑝𝑐𝑖𝑠𝐷2𝑁22 + 𝐴 π‘ π·π‘š ] ,

    πœ•2π‘‡πΆπ‘ π‘πœ•π‘˜2 = πœŽβˆšπΏπœ‘ (π‘˜) [ β„Žπ‘πœ‰(1 + πœ‰) + π·πœ‹π‘„ ] + 𝜎√𝐿 (Ξ¦ (π‘˜)βˆ’ 1) [β„Žπ‘πœŒπœŽβˆšπΏ (Ξ¦ (π‘˜) βˆ’ 1)(1 + πœ‰)βˆ’ β„Žπ‘πœŒ2𝜎2𝐿 (Ξ¦ (π‘˜) βˆ’ 1) πœ“ (π‘˜)(1 + πœ‰)2 ] + 𝜎√𝐿 (Ξ¦ (π‘˜) βˆ’ 1)β‹… [(β„Žπ‘πœŒπœŽβˆšπΏ + π·πœ‹0πœŽπœŒβˆšπΏπ‘„ )((Ξ¦ (π‘˜) βˆ’ 1)(1 + πœ‰)2 )]+ πœŽβˆšπΏπœ“ (π‘˜) [(β„Žπ‘πœŒπœŽβˆšπΏ + π·πœ‹0πœŽπœŒβˆšπΏπ‘„ )( πœ‘ (π‘˜)(1 + πœ‰)2βˆ’ 2𝜌𝜎√𝐿 ((Ξ¦ (π‘˜) βˆ’ 1))2(1 + πœ‰)2 )] βˆ’ π·π‘π‘π‘π‘–π‘πœŒβˆšπΏπœ‘ (π‘˜)𝑄 (1 + πœ‰)2+ 2π·π‘π‘π‘π‘–π‘πœŒ2𝜎2𝐿 ((Ξ¦ (π‘˜) βˆ’ 1))2𝑄 (1 + πœ‰)3 ,

    πœ•2π‘‡πΆπ‘ π‘πœ•πœƒ2 = π›Όπ‘πœƒ2 ,πœ•2π‘‡πΆπ‘ π‘πœ•π΄ 𝑠2 = 𝛼𝐡𝐴 𝑠2 ,πœ•2π‘‡πΆπ‘ π‘πœ•π‘š2 = 2𝐴 π‘ π·π‘š3𝑄 ,

    πœ•2π‘‡πΆπ‘ π‘πœ•πΏ2 = βˆ’[14β„Žπ‘π‘˜πœŽπΏβˆ’3/2 + 12β‹… πœ‰2𝐿5/2 (1 + πœ‰)3 {π·π‘π‘π‘π‘–π‘πœŽπœ“ (π‘˜)2𝑄 βˆ’ β„Žπ‘πœ‰

    2πΏβˆ’3/2πœŒβˆ’ π·πœ‹0πœ‰2πΏβˆ’3/2πœŒπ‘„ } + πœ‰2𝐿 ((1 + πœ‰)2) (2β„Žπ‘πœ‰

    2

    𝜌𝐿2+ 32 π·πœ‹0πœ‰

    2

    πœŒπ‘„πΏ2 βˆ’ 𝐷𝑝𝑐𝑁𝑖𝑏2𝑄 (πœ‰ + (1 + πœ‰) 𝜌𝐿𝜌 ))] ,(13)

    where πœ‹ = πœ‹+πœ‹0πœ‰/(1+πœ‰), πœ‰ = πœŒπœŽβˆšπΏπœ“(π‘˜), and πœ‰3 = Ξ¦(π‘˜)βˆ’1.It is found that 𝑇𝐢𝑠𝑐(𝑄, π‘˜, πœƒ, 𝐴 𝑠, π‘š, 𝐿) is concave with

    respect to 𝐿 as the second-order partial derivative of𝑇𝐢𝑠𝑐(𝑄, π‘˜, πœƒ, 𝐴 𝑠, π‘š, 𝐿) with respect to 𝐿 which is negative asthe 2nd term is very smaller than the 1st term within theparenthesis; that is,

    πœ•2π‘‡πΆπ‘ π‘πœ•πΏ2 = βˆ’[14β„Žπ‘π‘˜πœŽπΏβˆ’3/2 + 12β‹… πœ‰2𝐿5/2 (1 + πœ‰)3 {π·π‘π‘π‘π‘–π‘πœŽπœ“ (π‘˜)2𝑄 βˆ’ β„Žπ‘πœ‰

    2πΏβˆ’3/2πœŒβˆ’ π·πœ‹0πœ‰2πΏβˆ’3/2πœŒπ‘„ } + πœ‰2𝐿 ((1 + πœ‰)2) (2β„Žπ‘πœ‰

    2

    𝜌𝐿2+ 32 π·πœ‹0πœ‰

    2

    πœŒπ‘„πΏ2 βˆ’ 𝐷𝑝𝑐𝑁𝑖𝑏2𝑄 (πœ‰ + (1 + πœ‰) 𝜌𝐿𝜌 ))] < 0.

    (14)

    Thus, by taking the values of 𝑄, π‘˜, πœƒ, 𝐴 𝑠, and π‘š asconstant, 𝑇𝐢𝑠𝑐(𝑄, π‘˜, πœƒ, 𝐴 𝑠, π‘š, 𝐿) is concave with respect to 𝐿.Hence, for constant values of𝑄, π‘˜, πœƒ, 𝐴 𝑠, andπ‘š, theminimumexpected cost can be obtained from the end point of [𝐿 𝑖, 𝐿 π‘–βˆ’1].Thus, the optimal values of𝑄, π‘˜, πœƒ, 𝐴 𝑠, andπ‘š can be obtainedfor given 𝐿 ∈ [𝐿 𝑖, 𝐿 π‘–βˆ’1].Therefore, equating other four partialderivatives to zero, we can find the optimum values as

    𝑄 = √ [𝐴𝑏𝐷 + π·πœ‹πœ‰/𝜌 + 𝐷𝑅 (𝐿) βˆ’ 𝑝𝑐𝑖𝑏𝐷2𝑁2/2 βˆ’ 𝑝𝑐𝑖𝑏𝐷𝑁2πœ‰/2 (1 + πœ‰) + 𝑝𝑐𝑖𝑠𝐷2𝑁2/2 + 𝐴 𝑠𝐷/π‘š]β„Žπ‘/2 + π‘ π·πœƒπ‘š/2 + (β„Žπ‘ /2) [π‘š (1 βˆ’ 𝐷/𝑃) βˆ’ 1 + 2𝐷/𝑃] + 𝑝𝑐𝑖𝑠/2 , (15)

    Ξ¦ (π‘˜) = 1 βˆ’ (1 + πœ‰)2 β„Žπ‘π‘„β„Žπ‘πœ‰π‘„ (1 + πœ‰) + π·πœ‹π‘„ (1 + πœ‰)2 + β„Žπ‘πœ‰π‘„ + π·πœ‹0πœ‰ βˆ’ π·π‘π‘π‘π‘–π‘πœŒ , (16)πœƒ = 2π›Όπ‘π‘ π·π‘šπ‘„, (17)𝐴 𝑠 = π›Όπ΅π‘šπ‘„π· . (18)

  • 8 Mathematical Problems in Engineering

    Lemma 1. For a given 𝐿 ∈ [𝐿 𝑖, 𝐿 π‘–βˆ’1], 𝑇𝐢𝑠𝑐(𝑄, π‘˜, πœƒ, 𝐴 𝑠, π‘š, 𝐿)has the global minimum solution at the optimal values(π‘„βˆ—, π‘˜βˆ—, πœƒβˆ—, 𝐴 π‘ βˆ—).Proof. See Appendix.

    It is a nonlinear program. Thus, the following algorithmis employed to obtain the optimum results.

    Algorithm 2.Step 1. Setπ‘š = 1 and input all parametric values.Step 2. For each 𝐿 𝑖, 𝑖 = 1, 2, . . . , 𝑛, perform Steps 2(a)–2(f).

    Step 2(a). Set 𝐴 𝑠𝑖1 = 0, πœƒπ‘ π‘–1 = 0, and π‘˜π‘–1 = 0 (impliesπœ“(π‘˜π‘–1) = 0.39894).Step 2(b). Substitute πœ“(π‘˜π‘–1) into (15) and evaluate𝑄𝑖1.Step 2(c). Utilize 𝑄𝑖1 to calculate the value of Ξ¦(π‘˜π‘–2)from (16).

    Step 2(d). For the value of Ξ¦(π‘˜π‘–2), find π‘˜π‘–2 from thenormal table and hence evaluate πœ“(π‘˜π‘–2).Step 2(e). Utilize 𝑄𝑖1 to obtain πœƒπ‘ π‘–2 and 𝐴 𝑠𝑖2 from (17)and (18).

    Step 2(f). Repeat 2(b)–2(e) until no changes occur inthe values of 𝑄𝑖, π‘˜π‘–, πœƒπ‘ π‘–, and 𝐴 𝑠𝑖; denote these valuesby (𝑄𝑖, π‘˜π‘–, πœƒπ‘–, 𝐴 𝑠𝑖).

    Step 3. Compare πœƒπ‘ π‘– and πœƒ0 and 𝐴 𝑠𝑖 and 𝐴 𝑠0, respectively.Step 3(a). If πœƒπ‘– < πœƒ0 and 𝐴 𝑠𝑖 < 𝐴 𝑠0, then the solutionfound in Step 1 is optimal for the given 𝐿 𝑖. We denotethe optimal solution by (π‘„βˆ—π‘– , π‘˜βˆ—π‘– , πœƒβˆ—π‘– , 𝐴 π‘ βˆ—π‘– ); that is, if(π‘„βˆ—π‘– , π‘˜βˆ—π‘– , πœƒβˆ—π‘– , 𝐴 π‘ βˆ—π‘– ) = (𝑄𝑖, π‘˜π‘–, πœƒπ‘–, 𝐴 𝑠𝑖), go to Step 4.Step 3(b). If πœƒπ‘– β‰₯ πœƒ0 and 𝐴 𝑠𝑖 < 𝐴 𝑠0, then for given𝐿 𝑖, assume πœƒβˆ—π‘– = πœƒ0 and utilize (15) (replace πœƒ by πœƒ0),(16), and (18) to obtain the new (𝑄𝑖, π‘˜π‘–, 𝐴 𝑠𝑖) by similarprocedure like Step 1 (the solution is denoted by(𝑄𝑖, π‘˜π‘–, 𝐴 𝑠𝑖)). If 𝐴 𝑠𝑖 < 𝐴 𝑠0, then the optimal solutionis found; that is, if (π‘„βˆ—π‘– , π‘˜βˆ—π‘– , πœƒβˆ—π‘– , 𝐴 π‘ βˆ—π‘– ) = (𝑄𝑖, π‘˜π‘–, πœƒ0, 𝐴 𝑠𝑖),go to Step 4; otherwise, go to Step 3.

    Step 3(c). If πœƒπ‘– < πœƒ0 and 𝐴 𝑠𝑖 β‰₯ 𝐴 𝑠0, then for given𝐿 𝑖, let 𝐴 π‘ βˆ—π‘– = 𝐴 𝑠0 and utilize (15) (replace 𝐴 𝑠 by𝐴 𝑠0), (16), and (17) to obtain the new (𝑄𝑖, π‘˜π‘–, πœƒπ‘–) bysimilar procedure like Step 1 (the solution is denotedby (𝑄𝑖, π‘˜π‘–, πœƒπ‘–)). If πœƒπ‘– < πœƒ0, then the optimal solution isfound; that is, if (π‘„βˆ—π‘– , π‘˜βˆ—π‘– , πœƒβˆ—π‘– , 𝐴 π‘ βˆ—π‘– ) = (𝑄𝑖, π‘˜π‘–, πœƒπ‘–, 𝐴 𝑠𝑖), goto Step 4; otherwise, go to Step 3.

    Step 3(d). If πœƒπ‘– β‰₯ πœƒ0 and 𝐴 𝑠𝑖 β‰₯ 𝐴 𝑠0, go to Step 4.

    Step 4. Find𝑇𝐢𝑠𝑐(π‘„βˆ—π‘– , π‘˜βˆ—π‘– , πœƒπ‘ βˆ—π‘– ,π΄π‘ βˆ—π‘– , 𝐿𝑖,π‘š) andmin𝑖=1,2,...,𝑛𝑇𝐢𝑠𝑐(π‘„βˆ—π‘– ,π‘˜βˆ—π‘– , πœƒπ‘ βˆ—π‘– , 𝐴 π‘ βˆ—π‘– , 𝐿 𝑖, π‘š).Step 4(a). If 𝑇𝐢𝑠𝑐(π‘„βˆ—π‘– , π‘˜βˆ—π‘– , πœƒπ‘ βˆ—π‘– , π΄π‘ βˆ—π‘– , 𝐿𝑖 , π‘š) =min𝑖=1,2,...,𝑛𝑇𝐢𝑠𝑐(π‘„βˆ—π‘– , π‘˜βˆ—π‘– , πœƒπ‘ βˆ—π‘– , 𝐴 π‘ βˆ—π‘– , 𝐿 𝑖, π‘š), then 𝑇𝐢𝑠𝑐(π‘„βˆ—π‘– ,π‘˜βˆ—π‘– , πœƒπ‘ βˆ—π‘– , 𝐴 π‘ βˆ—π‘– , 𝐿 𝑖, π‘š) is the optimal solution for fixedπ‘š.

    Step 5. Set π‘š = π‘š + 1. If 𝑇𝐢𝑠𝑐(π‘„βˆ—π‘š, π‘˜βˆ—π‘š, πœƒπ‘ βˆ—π‘š, 𝐴 π‘ βˆ—π‘š, πΏπ‘š, π‘š) ≀𝑇𝐢𝑠𝑐(π‘„βˆ—π‘šβˆ’1, π‘˜βˆ—π‘šβˆ’1, πœƒπ‘ βˆ—π‘šβˆ’1, 𝐴 π‘ βˆ—π‘šβˆ’1, πΏπ‘šβˆ’1, π‘š βˆ’ 1), repeat Step 2.Otherwise go to Step 6.Step 6. Set 𝑇𝐢𝑠𝑐(π‘„βˆ—π‘š, π‘˜βˆ—π‘š, πœƒπ‘ βˆ—π‘š, 𝐴 π‘ βˆ—π‘š, πΏπ‘š, π‘š) = 𝑇𝐢𝑠𝑐(π‘„βˆ—π‘šβˆ’1,π‘˜βˆ—π‘šβˆ’1, πœƒπ‘ βˆ—π‘šβˆ’1, 𝐴 π‘ βˆ—π‘šβˆ’1, πΏπ‘šβˆ’1, π‘š βˆ’ 1). Then (π‘„βˆ—, π‘˜βˆ—, πΏβˆ—, πœƒπ‘ βˆ—,𝐴 π‘ βˆ—, π‘šβˆ—) is the optimal solution and the optimal reorderpoint can be calculated from π‘Ÿβˆ— = π·πΏβˆ— + π‘˜βˆ—πœŽβˆšπΏβˆ—, where π‘Ÿβˆ—denotes the optimal reorder point.

    3. Numerical Experiments

    The input parameters are taken from Sarkar and Moon [18]and the rest of the values are taken fromSarkar andMajumder[39] (see Tables 3 and 4 for it) as follows:

    𝐷 = 600 units/year.𝐴0 = $1500/setup.𝐴𝑏 = $200/order.β„Žπ‘ = $100/unit/year.β„Žπ‘  = $80/unit/year.πœ‹ = $5/unit.πœ‹0 = $10/unit.𝑃 = 1500 unit/year.𝑠 = $75/unit.πœƒ0 = 0.0002.𝐡 = 5800.𝛼 = 0.5 dollar/unit.𝑏 = 400.𝜎 = 7 units.𝜌 = 0.2 dollar/unit.𝑑𝑐𝑖 = $0.1/unit.𝑝𝑐 = $2/unit.𝑁 = 3.

    The optimal cost 𝑇𝐢𝑠𝑐 = $1961.21/year, and the optimaldecision variable is π‘„βˆ— = 37.11, π‘˜βˆ— = 1.89, πœƒβˆ— =.000004, 𝐴 𝑠 = $1076.35, π‘š = 2, 𝐿 = 21days. It isclearly found that the optimum lot size belongs to themaximum range of transportation discount, which indicatesthat the supply chain is profitable forever for the purpose oftransportation discount with the trade-credit financing.

  • Mathematical Problems in Engineering 9

    Table 3: Lead time data.

    Lead timecomponent 𝑖 Normal duration𝑇𝑖 (days) Minimumduration 𝑑𝑖 (days) Unit crashingcost 𝑐𝑖 ($/day)1 20 6 0.42 20 6 1.23 20 9 5.0

    Table 4: Transportation cost structure.

    Range Unit transportation cost0 ≀ 𝑄 < 100 0.4100 ≀ 𝑄 < 200 0.25200 ≀ 𝑄 < 300 0.173000 ≀ 𝑄 0.01Table 5: Sensitivity analysis.

    Parameters Changes of parameters (in %) 𝑇𝐢𝑠𝑐 (in %)𝐴0

    βˆ’10% βˆ’15.58βˆ’5% βˆ’7.58+5% 7.21+10% 14.09β„Žπ‘

    βˆ’10% βˆ’32.40βˆ’5% βˆ’16.10+5% 15.83+10% 23.71β„Žπ‘ 

    βˆ’10% βˆ’65.91βˆ’5% βˆ’32.15+5% 30.70+10% 60.07𝐴𝑏

    βˆ’10% βˆ’7.45βˆ’5% βˆ’3.68+5% 3.58+10% 7.08𝑠

    βˆ’10% βˆ’16.58βˆ’5% βˆ’16.00+5% 15.18+10% 14.67𝛼

    βˆ’10% βˆ’1.72βˆ’5% βˆ’8.36+5% 7.78+10% 2.123.1. Sensitivity Analysis. Sensitivity analysis for the total costof supply chain is executed with changing parameters byβˆ’10%, βˆ’5%, +5%, and +10% in (Table 5). From the sensitivityanalysis results, the following can be concluded:

    (i) The holding cost for supplier is themost sensitive costin the supply chain. Negative changes are more thanpositive changes; that is, when supplier’s holding costincreases total cost increases and vice versa. Its effectsare more in supply chain than any other parameters.

    (ii) Theholing cost of buyer is 2ndmost sensitive compar-ing other costs of the supply chain. Negative changesare more than positive changes. Decreasing value of

    βˆ’15 βˆ’10 βˆ’5 0(%)

    5 10 15

    TC

    TCsc

    βˆ’25

    βˆ’20

    βˆ’15

    βˆ’10

    βˆ’5

    0

    5

    10

    15

    20

    Figure 2: 𝑠 versus total cost.

    βˆ’15 βˆ’10 βˆ’5 0(%)

    5 10 15βˆ’1111111

    TC

    TCsc

    βˆ’10βˆ’8βˆ’6βˆ’4βˆ’2

    02468

    10

    Figure 3: 𝛼 versus total cost.buyer’s holding cost affects more than the increasingvalue of buyer’s holding cost in the total supply chaincost.

    (iii) From the sensitivity analysis, it is found that if initialsetup cost increases, total cost also increases. It followsthat negative and positive changes are almost similarfor two changes. Negative changes are slightly morethan positive change. Thus, this model consideredthe reduction of this setup cost by some investmentfunction and by the numerical study, the obtainedreduced setup cost with reduced total supply chaincost.

    (iv) The increasing value of the buyer’s ordering costindicates the increasing value of the total cost. Bycomparing the changes within positive and negativedirection, two changes are similar. Positive and nega-tive percentage changes are almost same.

    (v) If rework cost increases or decreases, then the totalcost increases or decreases and negative percentagechange and positive percentage change are almost thesame (see Figure 2).

    (vi) The percentage changes for annual fractional costare less sensitive than rework cost. Total supply cost

  • 10 Mathematical Problems in Engineering

    change increases for the increase of this parameter.This is the least sensitive parameter among all param-eters (see Figure 3).

    4. Conclusions

    The paper developed a supply chain model with a stochasticlead time demand, trade-credit policy, quality improvementof products, setup cost reduction of supplier, and variablebackorder rate. The backorder rate was lead time-dependent.The aim was to minimize the total supply chain cost withsimultaneous optimization of six decision variables as num-ber of shipments, lot size, lead time, setup cost of supplier,quality improvement parameters, and safety stock. Sarkarand Moon [18] did not consider supply chain model andwe extended their model with supplier-buyer supply chainmodel and trade-credit policy. Due to highly nonlinear costequation, we cannot obtain closed form solutions. We usedan improved algorithm to obtain the numerical results.Our results indicated that the cost was minimized basedon the existing literature. The limitation of the model wasthat we used constant demand for both buyer and supplier.The managers can use our suggested policy and can savemore funds. This model can be extended with the uncertaindemand along with multiechelon sustainable supply chainmodel. Several sustainability issues like water resources andenergy consumption can be added to make a new andimproved sustainable supply chain.

    Appendix

    Proof of Lemma 1. For given concave function 𝐿 ∈ [𝐿 𝑖, 𝐿 π‘–βˆ’1]and π‘š is integer, thus Hessian matrix𝐻 is calculated for thevariables π‘„βˆ—, π‘˜βˆ—, πœƒβˆ—, 𝐴 π‘ βˆ— as follows:𝐻

    =[[[[[[[[[[[[[[[

    πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘„βˆ—2 πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘„βˆ—πœ•π‘˜βˆ— πœ•

    2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘„βˆ—πœ•πœƒβˆ— πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘„βˆ—πœ•π΄ π‘ βˆ—πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘˜βˆ—πœ•π‘„βˆ— πœ•

    2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘˜βˆ—2 πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘˜βˆ—πœ•πœƒβˆ— πœ•

    2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘˜βˆ—πœ•π΄ π‘ βˆ—πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•πœƒβˆ—πœ•π‘„βˆ— πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•πœƒβˆ—πœ•π‘˜βˆ— πœ•

    2𝑇𝐢𝑠𝑐 (β‹…)πœ•πœƒβˆ—2 πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•πœƒβˆ—πœ•π΄ π‘ βˆ—πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π΄ π‘ βˆ—πœ•π‘„βˆ— πœ•

    2𝑇𝐢𝑠𝑐 (β‹…)πœ•π΄ π‘ βˆ—πœ•π‘˜βˆ— πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π΄ π‘ βˆ—πœ•πœƒβˆ— πœ•

    2𝑇𝐢𝑠𝑐 (β‹…)πœ•π΄ π‘ βˆ—2

    ]]]]]]]]]]]]]]]

    , (A.1)

    where 𝑇𝐢𝑠𝑐(β‹…) = 𝑇𝐢𝑠𝑐(π‘„βˆ—, π‘˜βˆ—, πœƒβˆ—, 𝐴 π‘ βˆ—, π‘š, 𝐿). The partialderivatives with respect to decision variables are obtained asfollows:

    πœ•2𝑇𝐢𝑠𝑐 (π‘„βˆ—, π‘˜βˆ—, πœƒβˆ—, 𝐴 π‘ βˆ—, π‘š, 𝐿)πœ•π‘„βˆ—2 = 2π‘„βˆ—3 [𝐴𝑏𝐷+ π·πœ‹πœ‰πœŒ + 𝐷𝑅 (𝐿) βˆ’ 𝑝𝑐𝑖𝑏𝐷

    2𝑁22 βˆ’ 𝑝𝑐𝑖𝑏𝐷𝑁2πœ‰2 (1 + πœ‰)

    + 𝑝𝑐𝑖𝑠𝐷2𝑁22 + 𝐴 π‘ π·π‘š ] ,

    πœ•2𝑇𝐢𝑠𝑐 (π‘„βˆ—, π‘˜βˆ—, πœƒβˆ—, 𝐴 π‘ βˆ—, π‘š, 𝐿)πœ•π‘˜βˆ—2 = πœŽβˆšπΏπœ‘ (π‘˜) [ β„Žπ‘πœ‰(1 + πœ‰)+ π·πœ‹π‘„βˆ— ] + πœŽβˆšπΏπœ‰3 [β„Žπ‘πœŒπœŽβˆšπΏπœ‰3(1 + πœ‰)βˆ’ β„Žπ‘πœŒ2𝜎2πΏπœ‰3πœ“ (π‘˜)(1 + πœ‰)2 ]+ πœŽβˆšπΏπœ‰3 [(β„Žπ‘πœŒπœŽβˆšπΏ + π·πœ‹0πœŽπœŒβˆšπΏπ‘„βˆ— )β‹… ( πœ‰3(1 + πœ‰)2)] + πœŽβˆšπΏπœ“ (π‘˜)β‹… [(β„Žπ‘πœŒπœŽβˆšπΏ + π·πœ‹0πœŽπœŒβˆšπΏπ‘„βˆ— )β‹… ( πœ‘ (π‘˜)(1 + πœ‰)2 βˆ’ 2𝜌𝜎

    √𝐿 (πœ‰3)2(1 + πœ‰)2 )]βˆ’ π·π‘π‘π‘π‘–π‘πœŒβˆšπΏπœ‘ (π‘˜)π‘„βˆ— (1 + πœ‰)2 + 2π·π‘π‘π‘π‘–π‘πœŒ

    2𝜎2𝐿 (πœ‰3)2π‘„βˆ— (1 + πœ‰)3 ,πœ•2𝑇𝐢𝑠𝑐 (π‘„βˆ—, π‘˜βˆ—, πœƒβˆ—, 𝐴 π‘ βˆ—, π‘š, 𝐿)πœ•πœƒβˆ—2 = π›Όπ‘πœƒβˆ—2 ,πœ•2𝑇𝐢𝑠𝑐 (π‘„βˆ—, π‘˜βˆ—, πœƒβˆ—, 𝐴 π‘ βˆ—, π‘š, 𝐿)πœ•π΄ π‘ βˆ—2 =

    𝛼𝐡𝐴 π‘ βˆ—2 ,πœ•2𝑇𝐢𝑠𝑐 (π‘„βˆ—, π‘˜βˆ—, πœƒβˆ—, 𝐴 π‘ βˆ—, π‘š, 𝐿)πœ•π‘„βˆ—πœ•π‘˜βˆ—= πœ•2𝑇𝐢𝑠𝑐 (π‘„βˆ—, π‘˜βˆ—, πœƒβˆ—, 𝐴 π‘ βˆ—, π‘š, 𝐿)πœ•π‘˜βˆ—πœ•π‘„βˆ—= 1π‘„βˆ—2 [βˆ’π·πœŽβˆšπΏπœ‹πœ‰3 βˆ’ 2π·πœ‹0πœŽβˆšπΏπœ‰3πœ‰(1 + πœ‰)+ π·πœ‹0πœŽβˆšπΏπœ‰3πœ‰2(1 + πœ‰)2 + 𝐷𝑁

    2π‘π‘π‘–π‘πœŒπœŽβˆšπΏπœ‰32 (1 + πœ‰)βˆ’ 𝐷𝑁2π‘π‘π‘–π‘πœŒπœŽβˆšπΏπœ‰3πœ‰2 (1 + πœ‰)2 ] ,

    πœ•2𝑇𝐢𝑠𝑐 (π‘„βˆ—, π‘˜βˆ—, πœƒβˆ—, 𝐴 π‘ βˆ—, π‘š, 𝐿)πœ•π‘„βˆ—πœ•πœƒβˆ—= πœ•2𝑇𝐢𝑠𝑐 (π‘„βˆ—, π‘˜βˆ—, πœƒβˆ—, 𝐴 π‘ βˆ—, π‘š, 𝐿)πœ•πœƒβˆ—πœ•π‘„βˆ— = π‘ π·π‘š2 ,

    πœ•2𝑇𝐢𝑠𝑐 (π‘„βˆ—, π‘˜βˆ—, πœƒβˆ—, 𝐴 π‘ βˆ—, π‘š, 𝐿)πœ•π‘„βˆ—πœ•π΄ π‘ βˆ—= πœ•2𝑇𝐢𝑠𝑐 (π‘„βˆ—, π‘˜βˆ—, πœƒβˆ—, 𝐴 π‘ βˆ—, π‘š, 𝐿)πœ•π΄ π‘ βˆ—πœ•π‘„βˆ— = βˆ’ π·π‘šπ‘„βˆ—2 ,

  • Mathematical Problems in Engineering 11

    πœ•2𝑇𝐢𝑠𝑐 (π‘„βˆ—, π‘˜βˆ—, πœƒβˆ—, 𝐴 π‘ βˆ—, π‘š, 𝐿)πœ•π‘˜βˆ—πœ•πœƒβˆ—= πœ•2𝑇𝐢𝑠𝑐 (π‘„βˆ—, π‘˜βˆ—, πœƒβˆ—, 𝐴 π‘ βˆ—, π‘š, 𝐿)πœ•πœƒβˆ—πœ•π‘˜βˆ— = 0,

    πœ•2𝑇𝐢𝑠𝑐 (π‘„βˆ—, π‘˜βˆ—, πœƒβˆ—, 𝐴 π‘ βˆ—, π‘š, 𝐿)πœ•π‘˜βˆ—πœ•π΄ π‘ βˆ—= πœ•2𝑇𝐢𝑠𝑐 (π‘„βˆ—, π‘˜βˆ—, πœƒβˆ—, 𝐴 π‘ βˆ—, π‘š, 𝐿)πœ•π΄ π‘ βˆ—πœ•π‘˜βˆ— = 0,

    πœ•2𝑇𝐢𝑠𝑐 (π‘„βˆ—, π‘˜βˆ—, πœƒβˆ—, 𝐴 π‘ βˆ—, π‘š, 𝐿)πœ•πœƒβˆ—πœ•π΄ π‘ βˆ—= πœ•2𝑇𝐢𝑠𝑐 (π‘„βˆ—, π‘˜βˆ—, πœƒβˆ—, 𝐴 π‘ βˆ—, π‘š, 𝐿)πœ•π΄ π‘ βˆ—πœ•πœƒβˆ— = 0.

    (A.2)

    At the optimum values of the decision variables, the principalminors are calculated to confirm their positivity as follows.

    For the 1st minor, one can obtain easily as

    det (𝐻11) = det(πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘„βˆ—2 ) = 2π‘„βˆ—3 [𝐴𝑏𝐷 + π·πœ‹πœ‰πœŒ+ 𝐷𝑅 (𝐿) βˆ’ 𝑝𝑐𝑖𝑏𝐷2𝑁22 βˆ’ 𝑝𝑐𝑖𝑏𝐷𝑁

    2πœ‰2 (1 + πœ‰)+ 𝑝𝑐𝑖𝑠𝐷2𝑁22 + 𝐴 π‘ π·π‘š ] > 0.

    (A.3)

    For 2nd minor, it is found as

    det (𝐻22) = det[[[[πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘„βˆ—2 πœ•

    2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘„βˆ—πœ•π‘˜βˆ—πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘˜βˆ—πœ•π‘„βˆ— πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘˜βˆ—2

    ]]]]= πœ”πœ βˆ’ 𝜐2,

    (A.4)

    where

    πœ” = 2π‘„βˆ—3 [𝐴𝑏𝐷 + π·πœ‹πœ‰πœŒ + 𝐷𝑅 (𝐿) βˆ’ 𝑝𝑐𝑖𝑏𝐷2𝑁22

    βˆ’ 𝑝𝑐𝑖𝑏𝐷𝑁2πœ‰2 (1 + πœ‰) + 𝑝𝑐𝑖𝑠𝐷2𝑁22 + 𝐴 π‘ π·π‘š ] ,

    𝜏 = πœŽβˆšπΏπœ‘ (π‘˜) [ β„Žπ‘πœ‰(1 + πœ‰) + π·πœ‹π‘„βˆ— ]+ πœŽβˆšπΏπœ‰3 [β„Žπ‘πœŒπœŽβˆšπΏπœ‰3(1 + πœ‰) βˆ’ β„Žπ‘πœŒ

    2𝜎2πΏπœ‰3πœ“ (π‘˜)(1 + πœ‰)2 ]

    + πœŽβˆšπΏπœ‰3 [(β„Žπ‘πœŒπœŽβˆšπΏ + π·πœ‹0πœŽπœŒβˆšπΏπ‘„βˆ— )β‹… ( πœ‰3(1 + πœ‰)2)] + πœŽβˆšπΏπœ“ (π‘˜)β‹… [(β„Žπ‘πœŒπœŽβˆšπΏ + π·πœ‹0πœŽπœŒβˆšπΏπ‘„βˆ— )β‹… ( πœ‘ (π‘˜)(1 + πœ‰)2 βˆ’ 2𝜌𝜎

    √𝐿 (πœ‰3)2(1 + πœ‰)2 )]βˆ’ π·π‘π‘π‘π‘–π‘πœŒβˆšπΏπœ‘ (π‘˜)π‘„βˆ— (1 + πœ‰)2 + 2π·π‘π‘π‘π‘–π‘πœŒ

    2𝜎2𝐿 (πœ‰3)2π‘„βˆ— (1 + πœ‰)3 ,𝜐 = 1π‘„βˆ—2 [βˆ’π·πœŽβˆšπΏπœ‹πœ‰3 βˆ’ 2π·πœ‹0πœŽβˆšπΏπœ‰3πœ‰(1 + πœ‰)+ π·πœ‹0πœŽβˆšπΏπœ‰3πœ‰2(1 + πœ‰)2 + 𝐷𝑁

    2π‘π‘π‘–π‘πœŒπœŽβˆšπΏπœ‰32 (1 + πœ‰)βˆ’ 𝐷𝑁2π‘π‘π‘–π‘πœŒπœŽβˆšπΏπœ‰3πœ‰2 (1 + πœ‰)2 ] .

    (A.5)

    Now,

    βˆ’ π·πœŽβˆšπΏπœ‹πœ‰3 βˆ’ 2π·πœ‹0πœŽβˆšπΏπœ‰3πœ‰(1 + πœ‰) + π·πœ‹0πœŽβˆšπΏπœ‰3πœ‰2

    (1 + πœ‰)2+ 𝐷𝑁2π‘π‘π‘–π‘πœŒπœŽβˆšπΏπœ‰32 (1 + πœ‰) βˆ’ 𝐷𝑁

    2π‘π‘π‘–π‘πœŒπœŽβˆšπΏπœ‰3πœ‰2 (1 + πœ‰)2= 2π·πœ‹0πœŽβˆšπΏπœ‰3πœ‰2 βˆ’ 𝐷𝑁2π‘π‘π‘–π‘πœŒπœŽβˆšπΏπœ‰3πœ‰2 (1 + πœ‰)2+ 𝐷𝑁2π‘π‘π‘–π‘πœŒπœŽβˆšπΏπœ‰3 βˆ’ 4π·πœ‹0πœŽβˆšπΏπœ‰3πœ‰ βˆ’ 2 (1 + πœ‰)π·πœŽβˆšπΏπœ‹πœ‰32 (1 + πœ‰) .

    (A.6)

    Again

    𝜏 = πœŽβˆšπΏπœ‘ (π‘˜) [ β„Žπ‘πœ‰(1 + πœ‰) + π·πœ‹π‘„βˆ— ] + πœŽβˆšπΏπœ‰3 [β„Žπ‘πœŒπœŽβˆšπΏπœ‰3(1 + πœ‰)βˆ’ β„Žπ‘πœŒ2𝜎2πΏπœ‰3πœ“ (π‘˜)(1 + πœ‰)2 ] + πœŽβˆšπΏπœ‰3 [(β„Žπ‘πœŒπœŽβˆšπΏ + π·πœ‹0πœŽπœŒβˆšπΏπ‘„βˆ— )β‹… ( πœ‰3(1 + πœ‰)2)] + πœŽβˆšπΏπœ“ (π‘˜) [(β„Žπ‘πœŒπœŽβˆšπΏ + π·πœ‹0πœŽπœŒβˆšπΏπ‘„βˆ— )β‹… ( πœ‘ (π‘˜)(1 + πœ‰)2 βˆ’ 2𝜌𝜎

    √𝐿 (πœ‰3)2(1 + πœ‰)2 )] βˆ’ π·π‘π‘π‘π‘–π‘πœŒβˆšπΏπœ‘ (π‘˜)π‘„βˆ— (1 + πœ‰)2+ 2π·π‘π‘π‘π‘–π‘πœŒ2𝜎2𝐿 (πœ‰3)2π‘„βˆ— (1 + πœ‰)3 = πœŽβˆšπΏπœ‘ (π‘˜) [ β„Žπ‘πœ‰(1 + πœ‰) + π·πœ‹π‘„βˆ— ]+ πœŽβˆšπΏπœ‰3 [β„Žπ‘πœŒπœŽβˆšπΏπœ‰3(1 + πœ‰) βˆ’ β„Žπ‘πœŒ

    2𝜎2πΏπœ‰3πœ“ (π‘˜)(1 + πœ‰)2 ]+ 2π·π‘π‘π‘π‘–π‘πœŒ2𝜎2𝐿 (πœ‰3)2π‘„βˆ— (1 + πœ‰)3 βˆ’ π·π‘π‘π‘π‘–π‘πœŒβˆšπΏπœ‘ (π‘˜)π‘„βˆ— (1 + πœ‰)2

  • 12 Mathematical Problems in Engineering

    + [(β„Žπ‘πœŒπœŽβˆšπΏ + π·πœ‹0πœŽπœŒβˆšπΏπ‘„βˆ— )β‹… (𝜎√𝐿 (πœ‰3)2 + πœŽβˆšπΏπœ“ (π‘˜) πœ‘ (π‘˜) βˆ’ 2𝜌𝜎√𝐿 (πœ‰3)2 πœŽβˆšπΏπœ“ (π‘˜)(1 + πœ‰)2 )]= 𝜐 + 𝜐,

    (A.7)

    where

    𝜐 = πœŽβˆšπΏπœ‘ (π‘˜) [ β„Žπ‘πœ‰(1 + πœ‰) + π·πœ‹π‘„βˆ— ]+ πœŽβˆšπΏπœ‰3 [β„Žπ‘πœŒπœŽβˆšπΏπœ‰3(1 + πœ‰) βˆ’ β„Žπ‘πœŒ

    2𝜎2πΏπœ‰3πœ“ (π‘˜)(1 + πœ‰)2 ]+ 2π·π‘π‘π‘π‘–π‘πœŒ2𝜎2𝐿 (πœ‰3)2π‘„βˆ— (1 + πœ‰)3 βˆ’ π·π‘π‘π‘π‘–π‘πœŒβˆšπΏπœ‘ (π‘˜)π‘„βˆ— (1 + πœ‰)2 ,

    𝜐 = (β„Žπ‘πœŒπœŽβˆšπΏ + π·πœ‹0πœŽπœŒβˆšπΏπ‘„βˆ— )β‹… (𝜎√𝐿 (πœ‰3)2 + πœŽβˆšπΏπœ“ (π‘˜) πœ‘ (π‘˜) βˆ’ 2𝜌𝜎√𝐿 (πœ‰3)2 πœŽβˆšπΏπœ“ (π‘˜)(1 + πœ‰)2 ) .

    (A.8)

    Thus, πœ”πœ > 𝜐2 whereπœ” = 2π‘„βˆ—3 [𝐴𝑏𝐷 + π·πœ‹πœ‰πœŒ + 𝐷𝑅 (𝐿) βˆ’ 𝑝𝑐𝑖𝑏𝐷

    2𝑁22βˆ’ 𝑝𝑐𝑖𝑏𝐷𝑁2πœ‰2 (1 + πœ‰) + 𝑝𝑐𝑖𝑠𝐷

    2𝑁22 + 𝐴 π‘ π·π‘š ] > 𝜐,𝜏 = 𝜐 + 𝜐 > 𝜐.

    (A.9)

    Hence, det(𝐻22) > 0.For 3rd minor, the value is obtained as

    𝐻33 =[[[[[[[[[

    πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘„βˆ—2 πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘„βˆ—πœ•π‘˜βˆ— πœ•

    2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘„βˆ—πœ•πœƒβˆ—πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘˜βˆ—πœ•π‘„βˆ— πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘˜βˆ—2 πœ•

    2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘˜βˆ—πœ•πœƒβˆ—πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•πœƒβˆ—πœ•π‘„βˆ— πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•πœƒβˆ—πœ•π‘˜βˆ— πœ•

    2𝑇𝐢𝑠𝑐 (β‹…)πœ•πœƒβˆ—2

    ]]]]]]]]]= [[[[[[

    πœ” 𝜐 π‘ π·π‘š2𝜐 𝜏 0π‘ π·π‘š2 0 π›Όπ‘πœƒβˆ—2]]]]]]

    = π‘ π·π‘š2 [βˆ’π‘ π·π‘š2 𝜏] + π›Όπ‘πœƒβˆ—2𝐻22= 𝜏( π›Όπ‘πœƒβˆ—2πœ” βˆ’ 𝑠

    2𝐷2π‘š24 ) βˆ’ π›Όπ‘πœƒβˆ—2 𝜐2.

    (A.10)

    It is already proved that 𝜏 > 𝜐; thus it is enough to show(𝛼𝑏/πœƒβˆ—2)πœ” βˆ’ 𝑠2𝐷2π‘š2/4 > (𝛼𝑏/πœƒβˆ—2)𝜐; that is,π›Όπ‘πœƒβˆ—2πœ” βˆ’ π›Όπ‘πœƒβˆ—2 𝜐 > 𝑠

    2𝐷2π‘š24 β‡’π›Όπ‘πœƒβˆ—2 (πœ” βˆ’ 𝜐) > 𝑠

    2𝐷2π‘š24 β‡’πœ” βˆ’ 𝜐 > 𝑠2𝐷2π‘š2πœƒβˆ—24𝛼𝑏 β‡’

    πœ” βˆ’ 𝜐 βˆ’ 𝑠2𝐷2π‘š2πœƒβˆ—24𝛼𝑏 > 0;

    (A.11)

    that is, det(𝐻33) > 0.Finally, for 4th minor, the optimum value is obtained as

    𝐻44

    =[[[[[[[[[[[[[

    πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘„βˆ—2 πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘„βˆ—πœ•π‘˜βˆ— πœ•

    2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘„βˆ—πœ•πœƒβˆ— πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘„βˆ—πœ•π΄ π‘ βˆ—πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘˜βˆ—πœ•π‘„βˆ— πœ•

    2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘˜βˆ—2 πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘˜βˆ—πœ•πœƒβˆ— πœ•

    2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘˜βˆ—πœ•π΄ π‘ βˆ—πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•πœƒβˆ—πœ•π‘„βˆ— πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•πœƒβˆ—πœ•π‘˜βˆ— πœ•

    2𝑇𝐢𝑠𝑐 (β‹…)πœ•πœƒβˆ—2 πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•πœƒβˆ—πœ•π΄ π‘ βˆ—πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π΄ π‘ βˆ—πœ•π‘„βˆ— πœ•

    2𝑇𝐢𝑠𝑐 (β‹…)πœ•π΄ π‘ βˆ—πœ•π‘˜βˆ— πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π΄ π‘ βˆ—πœ•πœƒβˆ— πœ•

    2𝑇𝐢𝑠𝑐 (β‹…)πœ•π΄ π‘ βˆ—2

    ]]]]]]]]]]]]]

    = βˆ’πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘„βˆ—πœ•π΄ π‘ βˆ—[[[[[[[[

    πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘˜βˆ—πœ•π‘„βˆ— πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘˜βˆ—2 πœ•

    2𝑇𝐢𝑠𝑐 (β‹…)πœ•π‘˜βˆ—πœ•πœƒβˆ—πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•πœƒβˆ—πœ•π‘„βˆ— πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•πœƒβˆ—πœ•π‘˜βˆ— πœ•

    2𝑇𝐢𝑠𝑐 (β‹…)πœ•πœƒβˆ—2πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π΄ π‘ βˆ—πœ•π‘„βˆ— πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π΄ π‘ βˆ—πœ•π‘˜βˆ— πœ•

    2𝑇𝐢𝑠𝑐 (β‹…)πœ•π΄ π‘ βˆ—πœ•πœƒβˆ—

    ]]]]]]]]+ πœ•2𝑇𝐢𝑠𝑐 (β‹…)πœ•π΄ π‘ βˆ—2 𝐻33

    = π·π‘šπ‘„βˆ—2[[[[[[

    𝜐 𝜏 0π‘ π·π‘š2 0 π›Όπ‘πœƒβˆ—2βˆ’ π·π‘šπ‘„βˆ—2 0 0]]]]]]+ 𝛼𝐡𝐴 π‘ βˆ—2𝐻33

    = 𝛼𝑏𝐷2πœπ‘š2π‘„βˆ—4πœƒβˆ—2 + 𝛼𝐡𝐴 π‘ βˆ—2𝐻33.

    (A.12)

    First part is positive and 𝐻33 is already shown greater thanzero.

    Hence, det(𝐻44) > 0.From the above calculations, all principal minors of the

    Hessian matrix are positive. Therefore, the Hessian matrix𝐻 is positively definite at (π‘„βˆ—, π‘˜βˆ—, πœƒβˆ—, 𝐴 π‘ βˆ—). Thus, total costfunction has a global minimum.

    Conflicts of Interest

    The authors declare that there are no conflicts of interestregarding the publication of this paper.

  • Mathematical Problems in Engineering 13

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