Multi-mode resource constrained multi-project scheduling and resource portfolio problem

This paper introduces a multi-project problem environment which involves multiple projects with assigned due dates; with activities that have alternative resource usage modes; a resource dedication policy that does not allow sharing of resources among projects throughout the planning horizon; and a total budget. There are three issues to face when investigating this multiproject environment. First, the total budget should be distributed among different resource types to determine the general resource capacities which correspond to the total amount for each renewable resource to be dedicated to the projects. With the general resource capacities at hand, the next issue is to determine the amounts of resources to be dedicated to the individual projects. With the dedication of resources accomplished, the scheduling of the projects' activities reduces to the multi-mode resource constrained project scheduling problem (MRCPSP) for each individual project. Finally the last issue is the effcient solution of the resulting MRCPSPs. In this paper, this multi-project environment is modeled in an integrated fashion and designated as the Resource Portfolio Problem. A two-phase and a monolithic genetic algorithm are proposed as two solution approaches each of which employs a new improvement move designated as the combinatorial auction for resource portfolio and the combinatorial auction for resource dedication. Computational study using test problems demonstrated the effectiveness of the solution approach proposed

Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services

The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms – a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms

Resource dedication problem in a multi-project environment

Resource dedication problem (RDP) in a multi-project environment is defined as the optimal dedication of resource capacities to dierent projects within the overall limits of the resources with the objective of minimizing the sum of the weighted tardinesses of all projects. The projects involved are in general multi-mode resource constrained project scheduling problems (MRCPSP) with nish to start zero time lag and nonpreemtive activities. In general, approaches to multi-project scheduling consider the resources as a pool shared by all projects. When projects are distributed geographically or sharing resources between projects is too costly, then the resource sharing policy may not be appropriate and hence the resources are dedicated to individual projects throughout project durations. To the best of our knowledge, this point of view for resources is not considered in multi-project literature. In the following, we propose a solution methodology for RDP with a new local improvement heuristic by determining the resource dedications to individual projects and solving scheduling problems with the given resource limits

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