Minimum Cardinality Matrix Decomposition into Consecutive-Ones Matrices: CP and IP Approaches.- Connections in Networks: Hardness of Feasibility Versus Optimality.- Modeling the Regular Constraint with Integer Programming.- Hybrid Local Search for Constrained Financial Portfolio Selection Problems.- The "Not-Too-Heavy Spanning Tree" Constraint.- Eliminating Redundant Clauses in SAT Instances.- Cost-Bounded Binary Decision Diagrams for 0-1 Programming.- YIELDS: A Yet Improved Limited Discrepancy Search for CSPs.- A Global Constraint for Total Weighted Completion Time.- Computing Tight Time Windows for RCPSPWET with the Primal-Dual Method.- Necessary Condition for Path Partitioning Constraints.- A Constraint Programming Approach to the Hospitals / Residents Problem.- Best-First AND/OR Search for 0/1 Integer Programming.- A Position-Based Propagator for the Open-Shop Problem.- Directional Interchangeability for Enhancing CSP Solving.- A Continuous Multi-resources cumulative Constraint with Positive-Negative Resource Consumption-Production.- Replenishment Planning for Stochastic Inventory Systems with Shortage Cost.- Preprocessing Expression-Based Constraint Satisfaction Problems for Stochastic Local Search.- The Deviation Constraint.- The Linear Programming Polytope of Binary Constraint Problems with Bounded Tree-Width.- On Boolean Functions Encodable as a Single Linear Pseudo-Boolean Constraint.- Solving a Stochastic Queueing Control Problem with Constraint Programming.- Constrained Clustering Via Concavity Cuts.- Bender's Cuts Guided Large Neighborhood Search for the Traveling Umpire Problem.- A Large Neighborhood Search Heuristic for Graph Coloring.- Generalizations of the Global Cardinality Constraint for Hierarchical Resources.- A Column Generation Based Destructive Lower Bound for Resource Constrained Project Scheduling Problems.

Integration of AI and OR techniques in constraint programming for combinatorial optimization problems : 4th International Conference, CPAIOR 2007, Brussels, Belgium, May 23-26, 2007 : proceedings

We consider heuristic algorithms for the resource-constrained project scheduling problem. Starting with a literature survey, we summarize the basic components of heuristic approaches. We briefly describe so-called X -pass methods which are based on priority rules as well as metaheuristic algorithms. Subsequently, we present the results of our in-depth computational study. Here, we evaluate the performance of several state-of-the-art heuristics from the literature on the basis of a standard set of test instances and point out to the most promising procedures. Moreover, we analyze the behavior of the heuristics with respect to their components such as priority rules and metaheuristic strategy. Finally, we examine the impact of problem characteristics such as project size and resource scarceness on the performance.

Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem

A follow-up survey of berth allocation and quay crane scheduling problems in container terminals

In this paper, we review the main concepts and methods to perform capacity analyses, and we present an automated tool that is able to perform several capacity analyses. Capacity is extremely dependent on infrastructure, tra‐c, and operating parameters. Therefore, an in-depth study of the main factors that in∞uence railway capacity is performed on several Spanish railway infrastructures. The results show how the capacity varies according to factors such as train speed, commercial stops, train heterogeneity, distance between railway signals, and timetable robustness. One consequence of the globalization of the economy and the increasing integration of the international economies is a considerable growth in the entire transport sector. During the 1990’s, many countries began to sufier from congestion in certain areas and on certain routes. Nowadays, there is no doubt about the congestion of the transport situation in some countries. The problem is now beginning to threaten economic competitiveness. Greater economic development cannot take place in the current transport scenario unless ambitious measures are taken. Revitalizing the railways is one of the principal measures

/pdf/an-assessment-of-railway-capacity-14qwt0gaps.pdf

An Assessment of Railway Capacity

This paper reports the outcome of the third in the series of biennial international planning competitions, held in association with the International Conference on AI Planning and Scheduling (AIPS) in 2002. In addition to describing the domains, the planners and the objectives of the competition, the paper includes analysis of the results. The results are analysed from several perspectives, in order to address the questions of comparative performance between planners, comparative difficulty of domains, the degree of agreement between planners about the relative difficulty of individual problem instances and the question of how well planners scale relative to one another over increasingly difficult problems. The paper addresses these questions through statistical analysis of the raw results of the competition, in order to determine which results can be considered to be adequately supported by the data. The paper concludes with a discussion of some challenges for the future of the competition series.

/pdf/the-3rd-international-planning-competition-results-and-4t8y5hqdjw.pdf

The 3rd international planning competition: results and analysis

An efficient hybrid genetic algorithm for scheduling projects with resource constraints and multiple execution modes

Many real-world scheduling problems are solved to obtain optimal solutions in term of processing time, cost, and quality as optimization objectives. Currently, energy-efficiency is also taken into consideration in these problems. However, this problem is NP-hard, so many search techniques are not able to obtain a solution in a reasonable time. In this paper, a genetic algorithm is developed to solve an extended version of the Job-shop Scheduling Problem in which machines can consume different amounts of energy to process tasks at different rates (speed scaling). This problem represents an extension of the classical job-shop scheduling problem, where each operation has to be executed by one machine and this machine can work at different speeds. The evaluation section shows that a powerful commercial tool for solving scheduling problems was not able to solve large instances in a reasonable time, meanwhile our genetic algorithm was able to solve all instances with a good solution quality.

A genetic algorithm for energy-efficiency in job-shop scheduling

This work deals with the multi-mode resource-constrained project scheduling problem. In this problem, activities of the project may be executed in more than one operating mode and renewable resource constraints exist. Each activity operation mode has a different duration and requires different amount of renewable resources. The objective function considered is the minimisation of the project completion time. Heuristics based on priority rule are considered as solution procedures for this problem. Both, parallel and serial scheduling generation schemes are described in the multi-mode context. On the basis of a well-known set of project instances, the new heuristics based on priority rules are evaluated against the best ones published. Finally, multi-pass heuristics based on priority rules that outperforms the deterministic multi-pass heuristic previously published are designed.

Multi-mode resource constrained project scheduling: scheduling schemes, priority rules and mode selection rules

This work is focused on the application of evolutionary algorithms to solve very complex real-world problems. For this purpose a Genetic Algorithm is designed to solve the Train Timetabling Problem. Optimizing train timetables on a single line track is known to be NP-hard with respect to the number of conflicts in the schedule. This makes it difficult to obtain good solutions to real life problems in a reasonable computational time and raises the need for good heuristic scheduling techniques. The railway scheduling problem considered in this work implies the optimization of trains on a railway line that is occupied (or not) by other trains with fixed timetables. The timetable for the new trains is obtained with a Genetic Algorithm (GA) that includes a guided process to build the initial population. The proposed GA is tested using real instances obtained from the Spanish Manager of Railway Infrastructure (ADIF). The results of the computational experience, point out that GA is an appropriate method to explore the search space of this complex problems and able to lead to good solutions in a short amount of time.

Federico Barber

Papers

An Assessment of Railway Capacity

An efficient hybrid genetic algorithm for scheduling projects with resource constraints and multiple execution modes

A genetic algorithm for energy-efficiency in job-shop scheduling

Multi-mode resource constrained project scheduling: scheduling schemes, priority rules and mode selection rules

A Genetic Algorithm for Railway Scheduling Problems