Different resource management policies in multi-mode resource constrained multi-project scheduling

This study investigates different resource management policies in resource constrained multi-project problem environments. The problem environment under investigation has alternative modes for activities, a set of renewable and nonrenewable resources used by activities and further considerations such as general resource budget. The characterization of the way resources are used by individual projects in the multiproject environment is called resource management policy in this study. The solution approaches in the literature for multi-project problems generally defines the resources as a pool that can be shared by all the projects which in fact creates a general assumption for the resource usage characteristics. This resource management policy is referred as resource sharing policy in this study. Resource sharing policy can be invalid in some certain cases where sharing assumption is not feasible because of some characteristics of resources and/or projects which require different resource management policies for the multi-project environment. According to the characteristics of resources and projects, resource management policies such as resource dedication, relaxed resource dedication and generalized resource management policies can be defined. In this paper, these resource management policies will be defined and their mathematical formulations will be presented and discussed

Resource preference based improvement heuristics for resource portfolio problem

The multi-project problem environment under consideration involves multiple-projects with activities having alternative execution modes, a general resource budget and a resource management policy that does not allow sharing of resources among projects. The multi-project scheduling model for this problem environment is called Resource Portfolio Problem. There are three basic conceptual problems in RPP: (i) determining the general resource capacities from the given general resource budget (general resource capacities determination); (ii) dedication of the general resource capacities to projects (resource dedication) and finally (iii) scheduling of individual projects with the given resource dedications. In this study, different preference based improvement heuristics are proposed for general resource capacities determination and resource dedication conceptual problems. For general resource capacities determination, the current general resource capacity values are changed according to the resource preferences such that the resulting capacity state would be more preferable. Similarly for resource dedication, resource dedication values of projects are changed according to the preferences of projects for resources such that the resulting resource dedication state would be more preferable. These two improvement heuristics separates and couples the conceptual problems. Different preference calculation methods are proposed employing Lagrangian relaxation and linear relaxation of MRCPSP formulation

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

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

A modied branch and cut procedure for resource portfolio problem under relaxed resource dedication policy

Multi-project scheduling problems are characterized by the way resources are managed in the problem environment. The general approach in multi-project scheduling literature is to consider resource capacities as a common pool that can be shared among all projects without any restrictions or costs. The way the resources are used in a multi-project environment is called resource management policy and the aforementioned assumption is called Resource Sharing Policy in this study. The resource sharing policy is not a generalization for multi-project scheduling environments and different resource management policies maybe defined to identify characteristics of different problem environments. In this study, we present a resource management policy which prevents sharing of resources among projects but allows resource transfers when a project starts after the completion of another one. This policy is called the Relaxed Resource Dedication (RRD) Policy in this study. The general resource capacities might or might not be decision variables. We will treat here the case where the general available amounts of resources are decision variables to be determined subject to a limited budget. We call this problem as the Resource Portfolio Problem (RPP). In this study, RPP is investigated under RRD policy and a modified Branch and Cut (B&C)procedure based on CPLEX is proposed. The B&C procedure of CPLEX is modified with different branching strategies, heuristic solution approaches and valid inequalities. The computational studies presented demonstrate the effectiveness of the proposed solution approaches

A combination of different resource management policies in a multi-project environment

Multi-project problem environments are defined according to the way resources are managed in the problem environment, which is called the resource management policy (RMP) in this study. Different resource management policies can be defined according to the characteristics of the projects and/or resources in the problem environment. The most common RMP encountered in the multi-project scheduling literature is the resource sharing policy (RSP), where resources can be shared among projects without any costs or limitations. This policy can be seen as an extreme case since there is a strong assumption of unconstrained resource sharing. Another RMP can be defined as the other extreme such that resources cannot be shared among projects, which is called the resource dedication policy (RDP). The last RMP considered in this study is between these two policies where resources are dedicated but can be transferred among projects when a project finishes, the dedicated resources to this project can be transferred to another one starting after the finish of the corresponding project. This RPM is called the resource transfer policy (RTP). In this study we investigate a problem environment where all these three types of RPM are present. Additionally, the general resource capacities are taken as decision variables that are constrained by a given general budget. We call this multi-project environment as the Generalized Resource Portfolio Problem (GRPP). We have investigated this problem and proposed an iterative solution approach based on exact solution methods which determines the general resource capacities from the budget, resource dedications, resource sharing and resource transfer decisions and schedules the individual projects. Computational results for over forty test problems are reported

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; 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. Three issues arise when investigating this multi-project 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. The dedication of resources reduces the scheduling of the projects' activities to a multi-mode resource constrained project scheduling problem (MRCPSP)for each individual project. Finally, the last issue is the ecient 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. A computational study using test problems demonstrated the effectiveness of the solution approach proposed. Keywords: Project scheduling, resource portfolio problem, multi-project scheduling, resource dedication, resource preference

A Q-Learning-Based Approach for Simple and Multi-Agent Systems

This study proposes different machine learning-based solutions to both single and multi-agent systems, took place on a 2-D simulation platform, namely, Robocode. This dynamic and programmable platform allows agents to interact with the environment and each other by employing a variety of battling strategies. Q-Learning is one of the leading and popular machine learning-based solutions to be applied to such a problem. However, especially for continued spaces, the control problem gets deeper. Essentially, one of the main drawbacks of reinforcement learning (RL) is to design an appropriate reward function that the function can be described by only employing few parameters for simple tasks, whereas estimating the goal of the reward function may be a challenging problem. Recent studies prove that neural network-based approaches can handle these challenges and achieve to learn control strategies from 2-D or 1-D data. Besides those problems of RL algorithms for single robots, once the number of robots increases and the systems need to behave as multi-agent systems, the overall design requirements become more complex. Accordingly, the proposed system is validated by considering different battle scenarios. The performance of the Q-Learning-based system and the supervised learning techniques are compared by employing different scenarios for this problem. Results reveal the superiority of the ANN-based approach over other methods

Resource portfolio problem under relaxed resource dedication policy in multi-mode multi-project scheduling

The most common approach in the multi-project scheduling literature considers resources as a common pool shared among all projects. However, different resource management strategies may be required for different problem environments. We present the Relaxed Resource Dedication (RRD) policy, which prevents the sharing of resources among projects but allows resource transfers when a project starts after the completion of another one. We treat the case where the available amounts of resources -namely, the capacities- are decision variables subject to a limited budget. This capacity planning problem, called the Resource Portfolio Problem, is investigated under the RRD policy employing both renewable and nonrenewable resources with multiple modes of usage. A mixed integer linear programming model to minimize total weighted tardiness is proposed. To obtain some benchmark solutions for this hard problem, the branch and cut procedure of ILOG CPLEX is modified by customized branching strategies, feasible solution generation schemes and valid inequalities