and control (1): Dynamic scheduling environment, discrete-event simulation (DES), and online job shop scheduling Dynamic scheduling and control (2): Discrete-time simulation (DTS), black-box optimization, and reinforcement learning Scheduling games and mechanisms (1): Game theoretical scheduling environment, and price of anarchy (POA) Scheduling games and mechanisms (2): Mechanism design, VCG mechanism, and scheduling auction Supply chain management (1): Bullwhip effect, and supply chain simulation Supply chain management (2): Double marginalization, and game theoretical analysis Summary and review
that each of the business entities corresponding to two different stages in a supply chain of a product applies a markup to its price. • As a result, the economic efficiency of the supply chain composed of two or more business entities will become lower than that of a vertically integrated one. • This phenomenon makes consumers face a price higher than (and the business entities in the chain face a demand lower than) that maximizes the profit of the whole chain. • Next, we will see a couple of mathematical models illustrating (an aspect of) the phenomenon. What is Double Marginalization?
product is a decreasing function of its retail price 𝑝, which can be approximated by: 𝑞 = 𝐷 𝑝 = 1000 − 𝑝 • Using (only) the variable cost per item 𝑐 = 200, the profit 𝑟 of selling this product is given by: 𝑟 = 𝑞 𝑝 − 𝑐 = 1000 − 𝑝 𝑝 − 200 = −𝑝! + 1200𝑝 − 200,000 • Thus, the retail price 𝑝∗ that maximizes the profit and the maximum profit 𝑟∗ achieved by this price are given by: 𝑝∗ = 600, 𝑟∗ = 160,000 • Let’s see what happens if the supply chain of this product is composed of two business entities, a maker and a seller. Model 1: Outline
is set as 𝑝, and the demand volume 𝑦 is forecasted as following a distribution 𝑓(𝑦) given this price. • Using the production volume 𝑞 and the variable cost per item 𝑐, the expected profit of selling this product is given by: 𝑟 = 𝑝 ∫ % & 𝑦𝑓(𝑦)𝑑𝑦 + 𝑝𝑞 ∫ & ' 𝑓(𝑦)𝑑𝑦 − 𝑐𝑞 • By taking 1st and 2nd order derivatives, we have: () (& = 𝑝𝑞𝑓 𝑞 − 𝑝𝑞𝑓 𝑞 + 𝑝 1 − 𝐹 𝑞 − 𝑐 = 𝑝 1 − 𝐹 𝑞 − 𝑐 ($) (&$ = −𝑝𝑓 𝑞 < 0 • So, we can maximize the expected profit by setting: () (& = 𝑝 1 − 𝐹 𝑞 − 𝑐 = 0 ⇒ 𝑞∗ = 𝐹*+ ,*- , Model 2: Outline #1
that 𝑝 = 600, 𝑐 = 200, and 𝑓(𝑦) is a uniform distribution in 𝑦. , 𝑦/ = 480, 720 . • Then, the optimal production volume 𝑞∗ is given by: 𝑞∗ = 𝐹*+ ⁄ (600 − 200) 600 = 𝑦. + ⁄ 2(𝑦/ − 𝑦. ) 3 = 640 • The maximum expected profit 𝑟∗ achieved by this volume is given by: 𝑟∗ = ? 0% &∗ 𝑝𝑦 𝑦/ − 𝑦. 𝑑𝑦 + ? &∗ 0& 𝑝𝑞∗ 𝑦/ − 𝑦. 𝑑𝑦 − 𝑐𝑞∗ = 𝑝 𝑞∗! − 𝑦. ! 2 𝑦/ − 𝑦. + 𝑝𝑞∗ 𝑦/ − 𝑞∗ 𝑦/ − 𝑦. − 𝑐𝑞∗ = 224,000 • Let’s see what happens if the supply chain of this product is composed of two business entities, a maker and a seller. Model 2: Outline #2
unsold, they will be bought back by the maker at a buy-back price 𝑏 < 𝑤. Effect of Buy-Back Contract • The expected profit of the seller under this contract is given by: 𝑟# = 𝑝 ∫ % & 𝑦𝑓(𝑦)𝑑𝑦 + 𝑏 ∫ % & (𝑞 − 𝑦)𝑓(𝑦)𝑑𝑦 + 𝑝𝑞 ∫ & ' 𝑓(𝑦)𝑑𝑦 − 𝑤𝑞 • By taking 1st and 2nd order derivatives, we have: ()' (& = 𝑏𝐹 𝑞 + 𝑝 1 − 𝐹 𝑞 − 𝑤 ($) (&$ = 𝑏 − 𝑝 𝑓 𝑞 < 0 Newsvendor Model with Buy-Back Contract #1