An Auction-Based Method for Decentralized Train Scheduling

We present a computational study of an auction-based method for decentralized train scheduling. The method is well suited to the natural information and control structure of mod- ern railroads. We assume separate network territories, with an autonomous dispatch agent responsible for the ow of trains over each territory. Each train is represented by a self-interested agent that bids for the right to travel across the network from its source to destination, submitting bids to multiple dispatch agents along its route as necessary. The bidding language allows trains to bid for the right to enter and exit territories at particular times, and also to represent indifference over a range of times. Computational results on a simple network with straight-forward best-response bid- ding strategies demonstrate that the auction computes near- optimal system-wide schedules. In addition, the method appears to have useful scaling properties, both with the number of trains and with the number of dispatchers, and generates less extremal solutions than those obtained using traditional centralized optimization techniques.Engineering and Applied Science

An efficient memetic, permutation-based evolutionary algorithm for real-world train timetabling

Train timetabling is a difficult and very tightly constrained combinatorial problem that deals with the construction of train schedules. We focus on the particular problem of local reconstruction of the schedule following a small perturbation, seeking minimisation of the total accumulated delay by adapting times of departure and arrival for each train and allocation of resources (tracks, routing nodes, etc.). We describe a permutation-based evolutionary algorithm that relies on a semi-greedy heuristic to gradually reconstruct the schedule by inserting trains one after the other following the permutation. This algorithm can be hybridised with ILOG commercial MIP programming tool CPLEX in a coarse-grained manner: the evolutionary part is used to quickly obtain a good but suboptimal solution and this intermediate solution is refined using CPLEX. Experimental results are presented on a large real-world case involving more than one million variables and 2 million constraints. Results are surprisingly good as the evolutionary algorithm, alone or hybridised, produces excellent solutions much faster than CPLEX alone

On the Benefits of Inoculation, an Example in Train Scheduling

The local reconstruction of a railway schedule following a small perturbation of the traffic, seeking minimization of the total accumulated delay, is a very difficult and tightly constrained combinatorial problem. Notoriously enough, the railway company's public image degrades proportionally to the amount of daily delays, and the same goes for its profit! This paper describes an inoculation procedure which greatly enhances an evolutionary algorithm for train re-scheduling. The procedure consists in building the initial population around a pre-computed solution based on problem-related information available beforehand. The optimization is performed by adapting times of departure and arrival, as well as allocation of tracks, for each train at each station. This is achieved by a permutation-based evolutionary algorithm that relies on a semi-greedy heuristic scheduler to gradually reconstruct the schedule by inserting trains one after another. Experimental results are presented on various instances of a large real-world case involving around 500 trains and more than 1 million constraints. In terms of competition with commercial math ematical programming tool ILOG CPLEX, it appears that within a large class of instances, excluding trivial instances as well as too difficult ones, and with very few exceptions, a clever initialization turns an encouraging failure into a clear-cut success auguring of substantial financial savings

Economía artificial: métodos de inspiración social en la resolución de problemas complejos

La dimensión social de la Economía le confiere una complejidad que es muy difícil de formalizar en un conjunto de ecuaciones algebraicas. La aproximación de la Economía Experimental (EE) y la de su extensión de la Economía Artificial (EA) con modelos basados en agentes artificiales (ABM), permiten recoger parte de esa complejidad cuando el intercambio es impersonal. En este artículo analizamos desde la EA el paradigmático ejemplo de la subasta doble continua (CDA) y su dinámica social con diferentes tipos de agentes. Los resultados obtenidos con sociedades artificiales, no sólo son relevantes para explicar los mecanismos de la institución, sino que el propio mercado puede ser un vehículo para resolver problemas de gestión de la empresa y de elección y escasez de complejidad nphard. Para ilustrarlo empleamos un ejemplo basado en la aplicación de subastas combinatorias: mediante la programación basada en mercados se puede realizar la asignación de slots de recursos en problemas de gestión de carteras de proyectosMinisterio de Ciencia e Innovación con referencia CSD2010-00034 (SimulPast CONSOLIDER- INGENIO 2010) y el proyecto Application of Agent-Based Computational Economics to Strategic Slot Allocation cofinanciado por EUROCONTROL- SESAR Joint Undertaking (SJU) y la Unión Europea como parte del programa SESA

A decommitment strategy in a competitive multi-agent transportation setting

Decommitment is the action of foregoing of a contract for another (superior) offer. It has been shown that, using decommitment, agents can reach higher utility levels in case of negotiations with uncertainty about future prospects. In this paper, we study the decommitment concept for the novel setting of a large-scale logistics setting with multiple, competing companies. Orders for transportation of loads are acquired by agents of the (competing) companies by bidding in online auctions. We find significant increases in profit when the agents can decommit and postpone the transportation of a load to a more suitable time. Furthermore, we analyze the circumstances for which decommitment has a positive impact if agents are capable of handling multiple contracts simultaneously

Auctioning Airspace

The commercialization of air taxis and autonomous passenger drones will one day congest urban airspace. Operators expect that, once flights are autonomous and the cost of service falls, high-traffic urban “vertiports” could see hundreds of air taxi takeoffs and landings per hour. Low-altitude airspace—between 200 feet and 5,000 feet above ground level—offers a relatively blank slate to explore new regulatory models for air traffic management and avoid command-and-control mistakes made in the past in aviation. Regulators’ current proposals would centralize air taxi traffic management into a single system to coordinate air taxi traffic, but this approach likely creates technology lock-in and unduly benefits the initial operators at the expense of later innovators. To facilitate the development of the air taxi market, regulators should consider demarcating aerial travel corridors and auctioning exclusive-use licenses to operators for use of those corridors, much like regulators auction radio spectrum licenses and offshore wind energy sites. Exclusive rights to routes would allow transfer and sale to more efficient operators and would also give operators the certainty they need to finance the substantial capital investments

On the tradeoff between privacy and efficiency: A bidding mechanism for scheduling non-commercial services

Services providers, such as public healthcare systems and government agencies, are under tremendous pressure to reduce costs and improve service quality. Scheduling is an important managerial component which has considerable impact on both the costs and quality of services. Service providers need customers’ availability information to improve resource utilization. On the other hand, customers may be of “two minds” about communicating their private information. While communicating certain amount of availability might be necessary in order to obtain preferred schedules, too much communication place a potential cost due to privacy loss. In this paper, we present a bidding-based mechanism which aims at generating high quality schedules and, at the same time, protecting customers’ privacy. We show that, under the proposed bidding procedure, myopic bidding is the dominant strategy for customers. We also evaluate the privacy and efficiency performance of the proposed mechanism through a computational study

Real-time Train Rescheduling with Multi-agent System

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2019. 2. 홍성필.열차 운행 중 예상치 못한 상황이 발생하여 더 이상 기존의 운행 계획대로 운영하 지 못할 때, 열차들은 경합이 없는 새로운 운행 계획을 필요로 한다. 열차 재스케줄링 문제는 이와 같은 상황에서 조정된 운행 계획과 기존 운행 계획의 오차가 최소화 되는 스케줄을 찾는 것을 말한다. 한편 열차 재스케줄링에 관한 많은 기존 연구들은 일반 철도의 상황에서 고려되었으며 일반 철도와는 다른 도시 철도의 특징 때문에 이를 도시 철도에 그대로 적용하는 것은 어렵다. 본 연구에서는 열차 재스케줄링 문제를 마르코프 게임(Markov game)으로 모형화 하여 확률적이면서도 동적인 도시 철도의 특성을 반영한다. 또한, 모형화된 마르코프 게임의 균형을 찾기위해 리그렛 매칭(regret matching) 알고리즘을 심층 학습(deep learning)으로 근사하여 적용하는 방법을 제안한다. 더 나아가 리그렛 매칭 알고리즘과 동일하게 균형을 찾음을 보장하면서 실제 적용 상황에서 더 빠른 수렴 속도를 가질 수 있는 멱리그렛 매칭(power-regret matching) 알고리즘을 제안한다. 심층 학습으로 근 사한 멱리그렛 매칭 알고리즘은 기존의 리그렛 매칭이 적용 불가능한 대규모 마르코프 게임에서도 적용할 수 있으며 리그렛 매칭 알고리즘보다 더 빠른 학습 속도를 가질 수 있음을 실험적으로 보였다.When an unexpected situation arises during the train operation and the train can no longer operate according to the existing schedule, train system needs a new operation plan with no confliction. The problem of train rescheduling refers to finding a schedule that minimizes the error of the adjusted operation plan and the existing operation plan in such a situation. On the other hand, many previous studies on train rescheduling have been considered in the context of general railway and it is difficult to apply it to urban railway because feature of urban railway is different from general railway. In this study, urban train rescheduling problem is modeled as a Markov game, which reflects the features of urban railway which is both stochastic and dynamic. Also, we propose a method to approximate the regret matching algorithm with deep learning to find the equilibria of the modeled Markov game. In addition, we pro- pose a power-regret matching algorithm that guarantees the same equilibria as the regret-matching algorithm, but may have faster convergence speed in actual appli- cations. It has been experimentally shown that the power-regret matching algorithm approximated by deep learning can be applied to a large-scale Markov game in which conventional regret matching is not applicable and can have a learning speed faster than the regret matching algorithm.목차 초록 i 목차 iii 표 목차 iv 그림 목차 v 제 1 장 서론 1 1.1 연구 배경 및 연구 목적 . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 논문의 구성 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 제 2 장 기존 연구 4 2.1 열차재스케줄링문제........................... 4 2.1.1 일반철도재스케줄링....................... 4 2.1.2 도시철도재스케줄링....................... 5 2.2 다중에이전트심층강화학습....................... 6 2.2.1 다중에이전트강화학습 ..................... 6 2.2.2 심층강화학습........................... 7 제 3 장 모형 9 3.1 도시철도의특성 ............................. 9 3.2 마르코프게임모형화........................... 13 ii 3.2.1 상태의정의............................ 15 3.2.2 행동의정의............................ 17 3.2.3 보상함수의정의 ......................... 18 제 4 장 해법 20 4.1 리그렛매칭알고리즘........................... 20 4.1.1 불완전정보순차게임으로의환원................ 20 4.1.2 균형의종류와no-regretlearning알고리즘 . . . . . . . . . . . 23 4.1.3 리그렛매칭알고리즘적용의어려움 .............. 30 4.2 심층학습을이용한함수의근사 ..................... 32 4.3 멱리그렛매칭알고리즘.......................... 34 제 5 장 실험 39 5.1 실험환경 ................................. 39 5.2 실험의구성................................ 40 5.3 실험결과 ................................. 40 제 6 장 결론및추후연구 42 6.1 결론.................................... 42 6.2 추후연구 ................................. 42 참고문헌 Abstract 감사의 글 44 50 52Maste

AdSCHE: DESIGN OF AN AUCTION-BASED FRAMEWORK FOR DECENTRALIZED SCHEDULING

Decentralized scheduling is one of the newly emerged avenues in scheduling research. It is concerned with allocating resources to alternative possible uses over time, where competing uses are represented by autonomous agents. Compared with classical scheduling models, decentralized scheduling is characterized with the distribution of scheduling knowledge and control, which introduces new levels of complexities, namely the coordination complexity due to the interaction problems among agents and the mechanism design complexity due to the self-interested nature of agents. These complexities intertwine and need to be addressed concurrently. This paper presents an auction-based framework which tackles coordination and mechanism design complexities through integrating an iterative bidding protocol, a requirement-based bidding language, and a constraint-based winner determination approach. Without imposing a time window discretization on resources the requirement-based bidding language allows bidders to bid for the processing of a set of jobs with constraints. Prices can be attached to quality attributes of schedules. The winner determination algorithm uses a depth-first branch and bound search. A constraint directed scheduling procedure is used at each node to verify the feasibility of the allocation. The bidding procedure is implemented by an ascending auction protocol. Experimental results show that the proposed auction framework exhibits improved computational properties compared with the general combinatorial auctions. A case study of applying the framework to decentralized media content scheduling in narrowcasting is also presented

Combinatorial auctions for electronic business

Combinatorial auctions (CAs) have recently generated significant interest as an automated mechanism for buying and selling bundles of goods. They are proving to be extremely useful in numerous e-business applications such as e-selling, e-procurement, e-logistics, and B2B exchanges. In this article, we introduce combinatorial auctions and bring out important issues in the design of combinatorial auctions. We also highlight important contributions in current research in this area. This survey emphasizes combinatorial auctions as applied to electronic business situations