Resource dedication problem in a multi-project environment

There can be different approaches to the management of resources within the context of multi-project scheduling problems. In general, approaches to multiproject scheduling problems consider the resources as a pool shared by all projects. On the other hand, when projects are distributed geographically or sharing resources between projects is not preferred, then this resource sharing policy may not be feasible. In such cases, the resources must be dedicated to individual projects throughout the project durations. This multi-project problem environment is defined here as the resource dedication problem (RDP). RDP is defined as the optimal dedication of resource capacities to different projects within the overall limits of the resources and with the objective of minimizing a predetermined objective function. The projects involved are multi-mode resource constrained project scheduling problems with finish to start zero time lag and non-preemptive activities and limited renewable and nonrenewable resources. Here, the characterization of RDP, its mathematical formulation and two different solution methodologies are presented. The first solution approach is a genetic algorithm employing a new improvement move called combinatorial auction for RDP, which is based on preferences of projects for resources. Two different methods for calculating the projects’ preferences based on linear and Lagrangian relaxation are proposed. The second solution approach is a Lagrangian relaxation based heuristic employing subgradient optimization. Numerical studies demonstrate that the proposed approaches are powerful methods for solving this problem

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

An exploratory assessment of project duration in multiple-project schedules where resources are allocated by the Theory of Constraints method

The theory of constraints (TOC) method for managing multiple projects provides a relatively new and simple heuristic for allocating constrained resources to projects. While this method is increasingly being used, there is little information about its merit and it has not yet been subjected to extensive testing. In this paper literature relevant to the allocation of resources to multiple projects is first reviewed. Thereafter the TOC method for allocating resources to multiple projects is explained. Finally the paper reports on an exploratory study to evaluate the effect that the TOC method for assigning resources has on project duration.Die “Theory of Constraints” (TOC)-metode vir die bestuur van veelvuldige projekte bied ’n relatief nuwe en eenvoudige heuristiek om beperkte hulpbronne aan projekte toe te ken. In die praktyk raak hierdie metode toenemend gewild maar daar is weinig inligting beskikbaar oor die meriete daarvan. Die metode is ook nog nie aan uitgebreide toetsing onderwerp nie. In hierdie artikel word ’n oorsig gegee oor die literatuur van hulpbrontoedeling. Tweedens word die TOC metode vir hulpbrontoedeling bespreek. Daarna word verslag gedoen oor ’n eksploratiewe ondersoek na die effek wat die TOC metode op die tydsduur van projekte het.http://sajie.journals.ac.z

Resource allocation in multi-class dynamic PERT networks with finite capacity

In this paper, the resource allocation problem in multi-class dynamic PERT networks with finite capacity of concurrent projects (COnstant Number of Projects In Process (CONPIP)) is studied. The dynamic PERT network is modeled as a queuing network, where new projects from different classes (types) are generated according to independent Poisson processes with different rates over the time horizon. Each activity of a project is performed at a devoted service station with one server located in a node of the network, whereas activity durations for different classes in each service station are independent and exponentially distributed random variables with different service rates. Indeed, the projects from different classes may be different in their precedence networks and also the durations of the activities. For modeling the multi-class dynamic PERT . networks with CONPIP, we first consider every class separately and convert the queueing network of every class into a proper stochastic network. Then, by constructing a proper finite-state continuous-time Markov model, a system of differential equations is created to compute the project completion time distribution for any particular project. The problem is formulated as a multi-objective model with three objectives to optimally control the resources allocated to the service stations. Finally, we develop a simulated annealing (SA) algorithm to solve this multi-objective problem, using the goal attainment formulation.We also compare the SA results against the results of a discrete-time approximation of the original optimal control problem, to show the effectiveness of the proposed solution technique.N/

A survey of variants and extensions of the resource-constrained project scheduling problem

The resource-constrained project scheduling problem (RCPSP) consists of activities that must be scheduled subject to precedence and resource constraints such that the makespan is minimized. It has become a well-known standard problem in the context of project scheduling which has attracted numerous researchers who developed both exact and heuristic scheduling procedures. However, it is a rather basic model with assumptions that are too restrictive for many practical applications. Consequently, various extensions of the basic RCPSP have been developed. This paper gives an overview over these extensions. The extensions are classified according to the structure of the RCPSP. We summarize generalizations of the activity concept, of the precedence relations and of the resource constraints. Alternative objectives and approaches for scheduling multiple projects are discussed as well. In addition to popular variants and extensions such as multiple modes, minimal and maximal time lags, and net present value-based objectives, the paper also provides a survey of many less known concepts. --project scheduling,modeling,resource constraints,temporal constraints,networks

Proje planlamada kaynak kısıtlı çizelgeleme

Proje yönetimine 1950li ve 1960lı yıllarda gösterilen yoğun ilgi daha sonra azalmış ancak yaklaşık son on yılda yeniden hem araştırmacıların hem de uygulamacıların yakın ilgisini çekmeye başlamıştır. Konu ile ilgili yayımlanan makale ve kitapların sayısındaki büyük artıştan, bilgisayar yazılımlarının yaygınlaşmasından ve düzenlenen konferans ve seminer adedinin giderek artmasından proje yönetimine olan ilginin yoğunlaştığını anlayabiliyoruz. Ozellikle kaynak kısıtlı çizelgeleme alanında yogun bir arastırma ve yayın soz konusudur. Bu Bölümde, kaynak kısıtlı çizelgeleme alanındaki çalışma ve gelişmeler derlenme ve değerlendirilmeye çalışılacaktır

The trade off between diversity and quality for multi-objective workforce scheduling

In this paper we investigate and compare multi-objective and weighted single objective approaches to a real world workforce scheduling problem. For this difficult problem we consider the trade off in solution quality versus population diversity, for different sets of fixed objective weights. Our real-world workforce scheduling problem consists of assigning resources with the appropriate skills to geographically dispersed task locations while satisfying time window constraints. The problem is NP-Hard and contains the Resource Constrained Project Scheduling Problem (RCPSP) as a sub problem. We investigate a genetic algorithm and serial schedule generation scheme together with various multi-objective approaches. We show that multi-objective genetic algorithms can create solutions whose fitness is within 2% of genetic algorithms using weighted sum objectives even though the multi-objective approaches know nothing of the weights. The result is highly significant for complex real-world problems where objective weights are seldom known in advance since it suggests that a multi-objective approach can generate a solution close to the user preferred one without having knowledge of user preferences

Solving Multi-Mode Resource-Constrained Multi-Project Scheduling Problem with Combinatorial Auction Mechanisms

[[abstract]]This study solves a multi-project, multi-mode, and resource-constrained project scheduling problem. Multi-mode means that the activities in a project can be accomplished in one out of several execution modes, each of which represents an alternative combination of resource requirement of the activity. The present study considers the case that the resources need to be allocated first to individual projects by the upper-level manager, and then the project manager of each project schedules the project to optimize its outcome. In view of such a hierarchical decision-making structure, this study uses bi-level decentralized programming to model the problem. The proposed solution procedure employs combinatorial auction mechanisms to determine resource allocations to projects. A regular combinatorial auction and a fuzzy combinatorial auction are used, respectively, for cases of hard and soft capacity constraints. The proposed solution procedure is evaluated by comparison with the results reported in the literature.[[notice]]補正完

The effectiveness of resource levelling tools for Resource Constraint Project Scheduling Problem

Abstract The PERT/CPM network techniques are based on the assumption that all needed resources will be available. The scarcity of resources is a usual reason for project delays. For the solution of the Resource Constrained Project Scheduling Problem (RCPSP) other methods have been applied. The objective of these methods consists in minimizing the project's duration by considering both the precedence and resource constraints. Project Management software packages solve the resource conflicts using resource levelling. The paper evaluates the effectiveness of resource levelling tools of three popular packages by comparing the results when levelling two real construction projects as case studies

Multi-project scheduling with 2-stage decomposition

A non-preemptive, zero time lag multi-project scheduling problem with multiple modes and limited renewable and nonrenewable resources is considered. A 2-stage decomposition approach is adopted to formulate the problem as a hierarchy of 0-1 mathematical programming models. At stage one, each project is reduced to a macro-activity with macro-modes, which are systematically generated by utilizing artificial budgets. The resulting single project network problem is a Multi-Mode Resource Constrained Project Scheduling Problem (MRCPSP) with positive cash flows. MRCPSP with positive cash flows is solved to maximize NPV and to determine the starting times and resource allocations for the projects. Using the starting times and resource profiles obtained in stage one each project is solved at stage two for minimum makespan. Three different time horizon setting methods, namely, relaxed greedy approach, artificial budget and Lagrangian relaxation are developed for setting the time horizon for MRCPSP with positive cash flows. A genetic algorithm approach is adopted to generate good solutions, which is also employed as a starting solution for the exact solution procedure. The result of the second stage is subjected to a post-processing procedure to distribute the resource capacities that have not been utilized earlier in the procedure. Since currently there are no data instances with the required structure, four new test problem sets are generated with 81, 84, 27 and 4 problems each. Three different configurations of solution procedures are tested employing the first three problem sets. A new heuristic decision rule designated here as Resource Return factor is presented and tested employing the fourth problem set