Stability of queueing-inventory systems with different priorities

We study a production-inventory system with two customer classes with different priorities which are admitted to the system following a flexible admission control scheme. The inventory management is according to a base stock policy and arriving demand which finds the inventory depleted is lost (lost sales). We analyse the global balance equations of the associated Markov process and derive structural properties of the steady state distribution which provide insights into the equilibrium behaviour of the system. We derive a sufficient condition for ergodicity using the Foster-Lyapunov stability criterion. For a special case we show that the condition is necessary as well

Admission control for a capacitated supply system with real-time replenishment information

Control towers can provide real-time information on logistic processes to support decision making. The question however, is how to make use of it and how much it may save. We consider this issue for a company supplying expensive spare parts and which has limited production capacity. Besides deciding on base stock levels, it can accept or reject customers. The real-time status information is captured by a k-Erlang distributed replenishment lead time. First we model the problem with patient customers as an infinite-horizon Markov decision process and minimize the total expected discounted cost. We prove that the optimal policy can be characterized using two thresholds: a base work storage level that determines when ordering takes place and an acceptance work storage level that determines when demand of customers should be accepted. In a numerical study, we show that using real-time status information on the replenishment item and adopting admission control can lead to significant cost savings. The cost savings are highest when the optimal admission threshold is a work storage level with a replenishment item halfway in process. This finding is different from the literature, where it is stated that the cost increase of ignoring real-time information is negligible under either the lost sales or the backordering case. Next we study the problem where customers are of limited patience. We find that the optimal admission policy is not always of threshold type. This is different from the literature which assumes an exponential production lead time.</p

On Production and Subcontracting Strategies for Manufacturers with Limited Capacity and Backlog-Dependent Demand

We study a manufacturing firm that builds a product to stock to meet a random demand. If there is a positive surplus of finished goods, the customers make their purchases without delay and leave. If there is a backlog, the customers are sensitive to the quoted lead time and some choose not to order if they feel that the lead time is excessive. A set of subcontractors, who have different costs and capacities, are available to supplement the firm's own production capacity. We derive a feedback policy that determines the production rate and the rate at which the subcontractors are requested to deliver products. The performance of the system when it is managed according to this policy is evaluated. The subcontractors represent a set of capacity options, and we calculate the values of these options

Essays on markets with frictions: applications to the housing, labour and financial markets

The classical treatment of market transactions in economics presumes that buyers and sellers engage in transactions instantly and at no cost. In a series of applications in the housing market, the labour market and the market for corporate bonds, this thesis shows that relaxing this assumption has important implications for Macroeconomics and Finance. The first chapter combines theory and empirical evidence to show that search frictions in the housing market imply a housing liquidity channel of monetary policy transmission. Expansionary monetary policy attracts buyers to the housing market, raising housing liquidity. Higher housing sale rates in turn allow lenders to threaten foreclosure more effectively, because the expected carrying costs on foreclosure inventory are lower. Ex-ante, this makes banks willing to offer larger loans, stimulating aggregate demand. The second chapter uses a heterogeneous firm industry model to explore how the macroeconomic response to a temporary employer payroll tax cut depends on the hiring and firing costs faced by firms. Controversially, the presence of non-convex labour adjustment costs suggests that tax cuts create fewer jobs in recessions. When firms hoard labour during downturns, they do not respond to marginal tax cuts by hiring additional workers. The third chapter develops a theory in which trader career concerns generate an endogenous transaction friction. Traders are reluctant to sell assets below historical purchase price, since realizing a loss signals to the employer that the trader is incompetent. The chapter documents empirically several properties of corporate bond transaction data consistent with this theory of career-concerned traders

Prioritizing Patients: Stochastic Dynamic Programming for Surgery Scheduling and Mass Casualty Incident Triage

The research presented in this dissertation contributes to the growing literature on applications of operations research models to problems in healthcare through the development and analysis of mathematical models for two fundamental problems facing nearly all hospitals: the single-day surgery scheduling problem and planning for triage in the event of a mass casualty incident. Both of these problems can be understood as sequential decision-making processes aimed at prioritizing between different classes of patients under significant uncertainty and are modeled using stochastic dynamic programming. Our study of the single-day surgery scheduling problem represents the first model to capture the sequential nature of the operating room (OR) manager's decisions during the transition between the generality of cyclical block schedules (which allocate OR time to surgical specialties) and the specificity of schedules for a particular day (which assign individual patients to specific ORs). A case study of the scheduling system at the University of Maryland Medical Center highlights the importance of the decision to release unused blocks of OR time and use them to schedule cases from the surgical request queue (RQ). Our results indicate that high quality block release and RQ decisions can be made using threshold-based policies that preserve a specific amount of OR time for late-arriving demand from the specialties on the block schedule. The development of mass casualty incident (MCI) response plans has become a priority for hospitals, and especially emergency departments and trauma centers, in recent years. Central to all MCI response plans is the triage process, which sorts casualties into different categories in order to facilitate the identification and prioritization of those who should receive immediate treatment. Our research relates MCI triage to the problem of scheduling impatient jobs in a clearing system and extends earlier research by incorporating the important trauma principle that patients' long-term (post-treatment) survival probabilities deteriorate the longer they wait for treatment. Our results indicate that the consideration of deteriorating survival probabilities during MCI triage decisions, in addition to previously studied patient characteristics and overall patient volume, increases the total number of expected survivors

전략적 고객 행동을 고려한 심층 강화학습 기반 항공사 동적 가격 결정 연구

학위논문(석사) -- 서울대학교대학원 : 공과대학 산업공학과, 2023. 2. 문일경.This thesis considers an airline dynamic pricing problem in the presence of patient customers. Nowadays, customers behave strategically to pay lower than their willingness to pay because they know airlines are implementing dynamic pricing strategies. To capture the non-myopic characteristic, we propose a Markov decision process (MDP) including a history of offered prices as a state variable. In contrast to previous studies, distributions of customers' properties are assumed to be unknown in advance. Deep reinforcement learning (DRL) algorithms are utilized to solve it, and the results of numerical experiments are presented to show that their performance can be improved with the proposed formulation. Comparisons between algorithms are also made to determine which can construct appropriate pricing structures for the patient and non-stationary demand. The structures of pricing policies generated from the bootstrapped deep Q-network algorithm imply that airlines should offer high and low prices alternately from the beginning of the sales period rather than increasing prices as time goes on. We also ascertain that more frequent consecutive high-priced periods can increase airlines' revenue in environments with higher customer patience levels.본 연구에서는 전략적 소비자가 존재하는 시장에서 항공사 동적 가격 결정 문제를 다루었다. 최근 소비자들은 항공사에서 동적 가격 정책을 시행하는 것을 인지하고 있기 때문에, 그들의 지불 용의보다 낮은 가격을 지불하기 위해 전략적으로 행동한다. 이러한 소비자 특성을 고려하여, 본 연구에서는 과거에 제시된 가격 기록을 상태 변수로 포함하는 마르코프 의사결정 과정 모델을 제안하였다. 이 때 고객 특성에 대한 확률 분포들은 사전에 알려져 있지 않다고 가정하였다. 문제 해결을 위해 심층 강화학습 방법론이 활용되었으며, 알고리즘 별 비교를 통해 전략적이고 동적인 수요 하에서 가장 적절한 가격 구조를 도출하는 알고리즘을 제시하였다. 또한 해당 가격 구조를 분석하여 전략적 수요로부터 추가적인 수익을 발생시키기 위한 경영적 통찰력을 제공하고자 하였다.Chapter 1 Introduction 1 Chapter 2 Problem description 9 2.1 Dynamics of patient customers 9 2.2 Markov decision process 11 2.3 Airline dynamic pricing 11 Chapter 3 Solution methods 15 3.1 Deep Q-network 17 3.2 Bootstrapped DQN 18 3.3 Optimistic learning for decreasing cyclic policies 21 Chapter 4 Numerical experiments 23 4.1 Comparison between MDP formulations in the presence of patient customers 24 4.2 Comparison between pricing algorithms for non-stationary demand and insufficient inventory 27 4.3 Structure of pricing policies from the BDQN algorithm 33 4.4 Non-stationary test for the distributions of reservation prices 34 Chapter 5 Conclusions 38 Bibliography 41 국문초록 47석

Lead-Time Quotation When Customers are Sensitive to Reputation

Firms consider a variety of factors when making lead-time promises, including current shop status and the size of the incoming order. The profit-maximising model presented in this paper is the first to include reputation effects explicitly in a lend-time optimisation model. Reputation is considered to be the lasting effect on the market of a firm\u27s delivery performance over time, and so it affects the future as well as the current profits. The model is complicated, and a counter-example demonstrates that qualitative monotonicity results are not obtainable. A computational study explores the relationships between shop status, order size, reputation, market characteristics and the lead-time decision. Regression analysis sheds light on these relationships and suggests three heuristics, which provide near-optimal solutions with relatively short running times

EUROPEAN CONFERENCE ON QUEUEING THEORY 2016

International audienceThis booklet contains the proceedings of the second European Conference in Queueing Theory (ECQT) that was held from the 18th to the 20th of July 2016 at the engineering school ENSEEIHT, Toulouse, France. ECQT is a biannual event where scientists and technicians in queueing theory and related areas get together to promote research, encourage interaction and exchange ideas. The spirit of the conference is to be a queueing event organized from within Europe, but open to participants from all over the world. The technical program of the 2016 edition consisted of 112 presentations organized in 29 sessions covering all trends in queueing theory, including the development of the theory, methodology advances, computational aspects and applications. Another exciting feature of ECQT2016 was the institution of the Takács Award for outstanding PhD thesis on "Queueing Theory and its Applications"