경영과학과 운영연구2

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경영과학과 운영연구2

한양대학교
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페이스북 트위터 카카오톡 네이버밴드 링크복사

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공학 >산업 >산업공학
강의학기

2016년 2학기

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강의계획서: 강의계획서

생산 및 서비스시스템 분석에 있어 확률적 요소 (예를 들어, 확률적인 수요)를 고려하는 것은 매우 중요하다. 본 과목에서는 확률적 요소가 내재된 시스템을 정량적으로 분석/설계/운영하는 방법을 주로 다루며, 대상 시스템은 생산/물류/서비스 시스템을 다룬다.

차시별 강의

1.	Newsvendor Problem 1	Design, analyze, and manage a manufacturing or service system with uncertainty. Solve a single period decision problem containing uncertainty or randomness.
2.	Newsvendor Problem 2	Design, analyze, and manage a manufacturing or service system with uncertainty. Solve a single period decision problem containing uncertainty or randomness.
3.	Newsvendor Problem 3	Design, analyze, and manage a manufacturing or service system with uncertainty. Solve a single period decision problem containing uncertainty or randomness.
4.	Discrete Time Markov Chain 1	How to manage uncertainty when you are selling perishable items. How should we Model di erently if we are running a business dealing with durable or non-perishable goods?
	Discrete Time Markov Chain 1	How to manage uncertainty when you are selling perishable items. How should we Model di erently if we are running a business dealing with durable or non-perishable goods?
	Discrete Time Markov Chain 1	How to manage uncertainty when you are selling perishable items. How should we Model di erently if we are running a business dealing with durable or non-perishable goods?
5.	Discrete Time Markov Chain 2	How to manage uncertainty when you are selling perishable items. How should we Model di erently if we are running a business dealing with durable or non-perishable goods?
	Discrete Time Markov Chain 2	How to manage uncertainty when you are selling perishable items. How should we Model di erently if we are running a business dealing with durable or non-perishable goods?
6.	Discrete Time Markov Chain 2	How to manage uncertainty when you are selling perishable items. How should we Model di erently if we are running a business dealing with durable or non-perishable goods?
	Discrete Time Markov Chain 2	How to manage uncertainty when you are selling perishable items. How should we Model di erently if we are running a business dealing with durable or non-perishable goods?
	Discrete Time Markov Chain 2	How to manage uncertainty when you are selling perishable items. How should we Model di erently if we are running a business dealing with durable or non-perishable goods?
7.	Discrete Time Markov Chain 3	How to manage uncertainty when you are selling perishable items. How should we Model di erently if we are running a business dealing with durable or non-perishable goods?
8.	Poisson Process 1	The way we modeled was to denote the inter-arrival times between customers by a random variable and assumed that the random variable has a exponential distribution.
	Poisson Process 1	The way we modeled was to denote the inter-arrival times between customers by a random variable and assumed that the random variable has a exponential distribution.
9.	Poisson Process 2	a special case: the case where the inter-arrival times follow iid exponential distribution. We will learn how it is different from other distributions.
	Poisson Process 2	a special case: the case where the inter-arrival times follow iid exponential distribution. We will learn how it is different from other distributions.
10.	Continuous Time Markov Chain 1	The time period is discretized so that time is denoted by integers. Consider discrete‐time stochastic process having discrete state space
	Continuous Time Markov Chain 1	The time period is discretized so that time is denoted by integers. Consider discrete‐time stochastic process having discrete state space
	Continuous Time Markov Chain 1	The time period is discretized so that time is denoted by integers. Consider discrete‐time stochastic process having discrete state space
	Continuous Time Markov Chain 1	The time period is discretized so that time is denoted by integers. Consider discrete‐time stochastic process having discrete state space
11.	Continuous Time Markov Chain 2	A stochastic process is a continuous time Markov chain with state space
	Continuous Time Markov Chain 2	A stochastic process is a continuous time Markov chain with state space
12.	Queueing basics 1	Queueing theory deals with a set of systems having waiting space. Analyzing a simple queue, a set of queues connected with each other will be covered as well in the end.
13.	Queueing basics 2	Design the system. How can we determine the number of server and customer, size of waiting capacity?
	Queueing basics 2	Design the system. How can we determine the number of server and customer, size of waiting capacity?
	Queueing basics 2	Design the system. How can we determine the number of server and customer, size of waiting capacity?
14.	Queueing basics 3	How does the system perform? For example that Utilization of servers, Average waiting time in queue, Average staying time in the system
15.	Queueing basics 4	Case study
	Queueing basics 4	Case study