物件導向軟體之架構(Object-Oriented Software Construction)探討

Constraint integer programming (CIP) is a novel paradigm which integrates constraint programming (CP), mixed integer programming (MIP), and satisfiability (SAT) modeling and solving techniques. In this paper we discuss the software framework and solver SCIP (Solving Constraint Integer Programs), which is free for academic and non-commercial use and can be downloaded in source code. This paper gives an overview of the main design concepts of SCIP and how it can be used to solve constraint integer programs. To illustrate the performance and flexibility of SCIP, we apply it to two different problem classes. First, we consider mixed integer programming and show by computational experiments that SCIP is almost competitive to specialized commercial MIP solvers, even though SCIP supports the more general constraint integer programming paradigm. We develop new ingredients that improve current MIP solving technology. As a second application, we employ SCIP to solve chip design verification problems as they arise in the logic design of integrated circuits. This application goes far beyond traditional MIP solving, as it includes several highly non-linear constraints, which can be handled nicely within the constraint integer programming framework. We show anecdotally how the different solving techniques from MIP, CP, and SAT work together inside SCIP to deal with such constraint classes. Finally, experimental results show that our approach outperforms current state-of-the-art techniques for proving the validity of properties on circuits containing arithmetic.

/pdf/scip-solving-constraint-integer-programs-21j9hcniww.pdf

SCIP: solving constraint integer programs

The following three articles are full versions of extended abstracts that were presented at the Twenty-Third ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS) held in Paris, France, June 14–16, 2004. After the conference, the authors were invited to submit full versions of their papers for publication in JACM. The articles have been reviewed according to the standard JACM refereeing process. We wish to thank the PODS Program Committee for their help in selecting these papers.

Journal of the ACM

Railway timetables are developed to make operations robust and resilient to small delays. However, disturbances perturb the daily plan, and dispatchers adjust the plan to keep operations feasible and to limit delay propagation. Rescheduling approaches aim at updating the offline timetable at best, in the presence of delays. We present a survey of the recent approaches on online railway traffic rescheduling problems, which exhibit dynamic and stochastic (or, at least, not completely deterministic) aspects. In fact, while online static rescheduling has reached a wide degree of dissemination, much is still to be done with regard to the probabilistic nature of the railway traffic rescheduling problems, and also how to best take uncertainty into account for future states. Open challenges for the future research are finally outlined.

A Review of Online Dynamic Models and Algorithms for Railway Traffic Management

Loading, unloading and premarshalling of stacks in storage areas: Survey and classification

In this survey we present a selection of commonly used and new train classification methods from an algorithmic perspective.

Shunting for Dummies: An Introductory Algorithmic Survey

Delay management for public transport consists of deciding whether vehicles should wait for delayed transferring passengers, with the objective of minimizing the overall passenger discomfort.

This paper classifies the computational complexity of delay management problems with respect to various structural parameters, such as the maximum number of passenger transfers, the graph topology, and the capability of trains to reduce delays. Our focus is to distinguish between polynomially solvable and nP-complete problem variants. To that end, we show that even fairly restricted versions of the delay management problem are hard to solve.

/pdf/the-computational-complexity-of-delay-management-4sg634v5jj.pdf

The computational complexity of delay management

We consider delay management in railway systems. Given delayed trains, we want to find a waiting policy for the connecting trains minimizing the weighted total passenger delay. If there is a single delayed train and passengers transfer at most twice along fixed routes, or if the railway network has a tree structure, the problem can be solved by reduction to min-cut problems. For delayed passenger flows on a railway network with a path structure, the problem can be solved to optimality by dynamic programming. If passengers are allowed to adapt their route to the waiting policy, the decision problem is strongly \(\mathcal{NP}\)-complete.

/pdf/railway-delay-management-exploring-its-algorithmic-3p1h1wp2z5.pdf

Railway Delay Management: Exploring Its Algorithmic Complexity

We provide competitive analyses for the online delay management problem on a single train line. The passengers that want to connect to the train line might arrive delayed at the connecting stations, and these delays happen in an online setting. Our objective is to minimize the total passenger delay on the train line.

We relate this problem to the Ski-Rental problem and present a family of 2-competitive online algorithms. Further, we show that no online algorithm for this problem can be better than Golden Ratio competitive, and that no online algorithm can be competitive if the objective accounts only for the optimizable passenger delay.

Online delay management on a single train line

The Cargo Express service of Swiss Federal Railways (SBB Cargo) offers fast overnight transportation of goods between selected train stations in Switzerland and is operated as a hub-and-spoke system with two hubs. We present three different models for planning the operation of this service as a whole. All models capture the underlying optimization problem with a high level of detail: Traffic routing, train routing, makeup, scheduling, and locomotive assignment are all addressed. At the same time we respect hard constraints like tight service time windows and train capacities, and we avoid hub overloading. We describe our approaches for obtaining provably good quality solutions. Our algorithmic techniques involve branch-and-cut, branch-and-price, and problem-specific exact and heuristic acceleration methods. We conclude our study with computational results on realistic data.

/pdf/optimizing-the-cargo-express-service-of-swiss-federal-e2c9zzi6u2.pdf

Optimizing the Cargo Express Service of Swiss Federal Railways

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