Statics and dynamics of selfish interactions in distributed service systems

We study a class of games which model the competition among agents to access some service provided by distributed service units and which exhibit congestion and frustration phenomena when service units have limited capacity. We propose a technique, based on the cavity method of statistical physics, to characterize the full spectrum of Nash equilibria of the game. The analysis reveals a large variety of equilibria, with very different statistical properties. Natural selfish dynamics, such as best-response, usually tend to large-utility equilibria, even though those of smaller utility are exponentially more numerous. Interestingly, the latter actually can be reached by selecting the initial conditions of the best-response dynamics close to the saturation limit of the service unit capacities. We also study a more realistic stochastic variant of the game by means of a simple and effective approximation of the average over the random parameters, showing that the properties of the average-case Nash equilibria are qualitatively similar to the deterministic ones.Comment: 30 pages, 10 figure

REKABETÇİ TESİS YER SEÇİMİ PROBLEMLERİNE İLİŞKİN BİR TARAMA ÇALIŞMASI

Amaç: Bu çalışmanın amacı, son yıllarda Rekabetçi Tesis Yer Seçimi (RTYS) problemlerinin ve problem bileşenlerinin literatürde ele alınış biçimlerine ilişkin bir bilimsel yayın taraması sunmaktadır. Yöntem: İlk olarak literatürde problemin temel bileşenlerinin ele alınış biçimlerine yer verilmiştir. Daha sonra RTYS problemi için literatürdeki en temel sınıflandırma kriteri olan rekabet tiplerine göre problem türleri incelenmiştir. Son olarak genişletilmiş RTYS problem türlerini ve çok amaçlı RTYS problemlerini ele alan çalışmalara yer verilmiş ve tarama çalışmasının sonuçları sunulmuştur. Bulgular: Tarama çalışması sonucu RTYS alanında gelecek vadeden çalışma konuları; birden fazla firmanın pazar paylarının enbüyüklenmesi amaçlarının çok-amaçlı olarak ele alındığı RTYS problemleri, müşterilerin tesis seçimlerinin çok amaçlı eniyileme kullanılarak yapıldığı RTYS problemleri, ikiden fazla rakip firma içeren RTYS problemleri olarak belirlenmiştir. Özgünlük: RTYS, hem tedarik zinciri için en önemli stratejik kararlardan biri olması hem de gerçek hayat problemlerine uygulanabilirliğinin yüksek olması sebebiyle araştırmacıların üzerinde durdukları bir alan olmuştur. Özellikle son yıllarda RTYS problemleri ve varyasyonları üzerinde önemli gelişmeler kaydedilmiştir. RTYS literatürüne ilişkin son çalışma Ashtiani (2016) tarafından yapılmıştır ve 2015 yılına kadar yapılan çalışmaları içermektedir. Bu çalışmada 2010 – 2020 arasında yapılan bilimsel çalışmaları içeren özgün bir tarama çalışması sunulmaktadır

Supplier Choice: Market Selection under Uncertainty.

Suppliers and Manufacturers generally have some say in which subset of all possible demand they will meet. In some cases that choice is implicit through pricing decisions and feature selection. Other times it is made explicitly by choosing only specific regions to stock a product in. This thesis includes models using both approaches and incorporates random demands. We present several methods for choosing a subset of all candidate customers given uncertain demands. In this thesis we consider four models of demand selection. The first two research problems consider market selection, which has been studied in the literature. The Selective Newsvendor Problem (SNP) looks at a decision maker choosing a subset of candidate markets to serve, and then receiving revenues and paying newsvendor-type costs based on the selected collection. In this thesis we consider a generalization with normally distributed demands which includes a multi-period problem as a special case and develop both exact and heuristic algorithms to solve it. When demands are not normally distributed, the problem is considerably more complex and is in general NP-hard. We develop an approximation algorithm using sample average approximation and a rounding approach to efficiently solve the problem. In addition to the work on market selection, we propose two other models for demand selection. We study auctions as a tool for a supplier with a fixed capacity to allocate the limited supply to retailers with newsvendor-type costs. Finally, we present a model for a supplier who must ensure demand is met in all markets, but has the option to work with subsidiary suppliers to meet that demand.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120864/1/zstrinka_1.pd

Strategic Network Design for Delivery by Drones under Service-based Competition

In today’s world, E-commerce is a fast growing industry and e-retailers are looking for innovative ways to deliver customer orders within short delivery times at a low cost. Currently, the use of drone technology for last-mile delivery is being developed by such companies as Amazon, FedEx, and UPS. Drones are relatively cheaper and faster than trucks but are limited in range and may be restricted in landing and takeoff. Most of the work in the Operations Research literature focusses on the operational challenges of integrating drones with truck delivery. The more strategic questions of whether it is economically feasible to use drones and the effects on distribution network design are rarely addressed. These questions are the focus of this work. We consider an e-retailer offering multiple same day services using both existing vehicles and drones, and develop a facility location problem under service-based competition where the services offered by the e-retailer not only compete with the stores (convenience, grocery, etc.), but also with each other. The competition in the market is incorporated using the Multinomial Logit (MNL) market share model. To solve the resulting nonlinear mathematical formulation we develop a novel logic-based Benders decomposition approach. We also show that the nonlinear model can be transformed into a linear mixed integer formulation. Computational experiments show that our algorithm outperforms direct solution of the linear formulation. We carry out extensive numerical testing of the model and perform sensitivity analyses over pricing, delivery time, government regulations, technological limitations, customer behavior, and market size. The results show that government regulations play a vital role in determining the future of drone delivery

Inventory and Service Optimization for Self-serve Kiosks

In the retail industry, labor costs constitute a big chunk of total operating costs and retailers are advancing towards process automation to minimize their operating costs and to provide reliable services to their customers. One such example of technological advancement is self-service kiosks that are becoming an integral part of our life, whether it be for cashing a cheque, self-checkout at retail stores, airports, hospitals, or checkout-free stores. Although self-serve kiosks are cost-effective due to low setup and operating costs, the technology is relatively new and poses new research questions that have not been studied before. This thesis explores and addresses strategic and operational challenges associated with self-serve kiosk technology. The first part of the thesis is based on a collaboration with MedAvail Technologies Inc., a Canada-based healthcare technology company, developing self-serve pharmacy kiosk technology to dispense over-the-counter and prescription drugs. MedAvail faces several challenges related to assortment and stocking decisions of medications in the kiosk due to its limited capacity and the thousands of drugs being ordered in various quantities. We address these challenges by analyzing pharmaceutical sales data and developing a data-driven stochastic optimization approach to determine optimized kiosk storage capacity and service levels and recommend assortment and stocking decisions under supplier-driven product substitution. A column-generation based heuristic approach is also proposed to solve the models efficiently. The second part of the thesis extends the self-serve kiosk inventory planning problem to a robust optimization (RO) framework under fill rate maximization objective. We propose a data-driven approach to generate polyhedral uncertainty sets from hierarchical clustering and the resulting RO model is solved using column-and-constraint generation and conservative approximation solution methodologies. The proposed robust framework is tested on actual pharmacy sales data and randomly generated instances with 1600 products. The robust solutions outperform stochastic solutions with an increase in out-of-sample fill rate of 5.8%, on average, and of up to 17%. Finally, the third part of the thesis deals with an application of pharmacy kiosks to improve healthcare access in rural regions. We present a mathematical function to model customer healthcare accessibility as the expected travel distance when multiple pharmacy location (store and kiosks) choices are available to customers. Customer choice behavior is modelled using a multinomial logit (MNL) model where customer utility for a pharmacy location depends on travel distance which is not exactly known but rather depends on kiosk fill rate. We model the problem as a newsvendor problem with fill-rate dependent demand to decide on kiosk stock level (or capacity) to minimize the weighted sum of expected travel distance and total cost. Sensitivity analysis over modelling parameters is carried out to derive insights and to determine problem settings under which pharmacy kiosks improve customer accessibility

Location of an agribusiness enterprise with respect to economic viability: a risk analysis

This study analyzes the economic and geographic effects of alternative locations on risky investment decisions in a probabilistic framework. Historically, alternative locations for multi-million dollar investments are often evaluated with deterministic models that rely on expected values or best case/worst case scenarios. Stochastic simulation was used to estimate the probability distribution for select key output variables, including net present value (NPV), of a proposed biomass to ethanol production facility in three alternative regions in Texas. The simulated NPV probability distributions were compared using Stochastic Efficiency with Respect to a Function (SERF) to predict the location preference of decision makers with alternative levels of risk aversion. Risk associated with input availability and costs were analyzed for the proposed plant locations so each location resulted in different levels of economic viability and risk that would not have been observed with a traditional deterministic analysis. For all analyzed scenarios, the projected financial feasibility results show a positive NPV over the 16 year planning horizon with a small probability of being negative. The SERF results indicate the Central Region of Texas is preferred for risk averse decision makers compared to the Panhandle and Coastal Bend Regions. Risk premiums were calculated for the alternative locations and are consistent for all risk averse decision makers, indicating the ranking of alternative locations are robust. Positive community impacts and sensitivity elasticities for key variables were estimated in the model. The estimated positive economic gains for the local economy are quite large and indicate locating a production facility in the region could substantially impact the local economy. The calculated sensitivity elasticities show ethanol price, ethanol yield, and hydrogen price are the three variables that have the greatest affect on the feasibility of a biomass to ethanol production facility