Dynamic resource constrained multi-project scheduling problem with weighted earliness/tardiness costs

In this study, a conceptual framework is given for the dynamic multi-project scheduling problem with weighted earliness/tardiness costs (DRCMPSPWET) and a mathematical programming formulation of the problem is provided. In DRCMPSPWET, a project arrives on top of an existing project portfolio and a due date has to be quoted for the new project while minimizing the costs of schedule changes. The objective function consists of the weighted earliness tardiness costs of the activities of the existing projects in the current baseline schedule plus a term that increases linearly with the anticipated completion time of the new project. An iterated local search based approach is developed for large instances of this problem. In order to analyze the performance and behavior of the proposed method, a new multi-project data set is created by controlling the total number of activities, the due date tightness, the due date range, the number of resource types, and the completion time factor in an instance. A series of computational experiments are carried out to test the performance of the local search approach. Exact solutions are provided for the small instances. The results indicate that the local search heuristic performs well in terms of both solution quality and solution time

Exact and suboptimal reactive strategies for resource-constrained project scheduling with uncertain resource availabilities.

In order to cope with the uncertainty inherent in practical project management, proactive and/or reactive strategies can be used. Proactive strategies try to anticipate future disruptions by incorporating slack time or excess resource availability into the schedule, whereas reactive strategies react after a disruption happened and try to revert to a feasible schedule. Traditionally, reactive approaches have focused on obtaining a good schedule with respect to the original objective function or a schedule that deviates as little as possible from the baseline schedule. In this paper, we present various approaches, exact as well as heuristic, for optimizing the latter objective and thus encouraging schedule stability. Furthermore, in contrast to traditional rescheduling algorithms, we present a new heuristic that also takes future uncertainty into account when repairing the schedule. We consider a variant of the resource- constrained project scheduling problem in which the uncertainty is modeled by means of unexpected resource breakdowns. The results of an extensive computational experiment are given to compare the performance of the proposed strategies.Schedule stability; Stability; Algorithms; Heuristic; Uncertainty; Project scheduling; Scheduling; Performance; Strategy; Order; Project management; Management; Time;

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

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

Project scheduling under undertainty – survey and research potentials.

The vast majority of the research efforts in project scheduling assume complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. However, in the real world, project activities are subject to considerable uncertainty, that is gradually resolved during project execution. In this survey we review the fundamental approaches for scheduling under uncertainty: reactive scheduling, stochastic project scheduling, stochastic GERT network scheduling, fuzzy project scheduling, robust (proactive) scheduling and sensitivity analysis. We discuss the potentials of these approaches for scheduling projects under uncertainty.Management; Project management; Robustness; Scheduling; Stability;

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 classification of predictive-reactive project scheduling procedures.

The vast majority of the project scheduling research efforts over the past several years have concentrated on the development of workable predictive baseline schedules, assuming complete information and a static and deterministic environment. During execution, however, a project may be subject to numerous schedule disruptions. Proactive-reactive project scheduling procedures try to cope with these disruptions through the combination of a proactive scheduling procedure for generating predictive baseline schedules that are hopefully robust in that they incorporate safety time to absorb anticipated disruptions with a reactive procedure that is invoked when a schedule breakage occurs during project execution.proactive-reactive project scheduling; time uncertainty; stability; timely project completion; preselective strategies; resource constraints; trade-off; complexity; stability; management; makespan; networks; subject; job;

Multi-mode resource constrained project scheduling problem including multi-skill labor (MRCPSP-MS): model and a solution method

The problem that we address in this chapter is an extension of the Resource-Constrained Project Scheduling Problem (RCPSP). It belongs to the class of project scheduling problems with multi-level (or multi-mode) activities, that permit an activity to be processed by resources operating at appropriate modes, where each mode belongs to a different resource level and incurs different cost and duration. Each activity must be allocated exactly one unit of each required resource, and the resource unit may be used at any of its specified levels. The processing time of an activity is given by the maximum of the durations that would result from different resources allocated to that activity. The objective is to find an optimal solution that minimizes the overall project cost, given a delivery date. A penalty is incurred for tardiness beyond the specified delivery date, or a bonus is accrued for early completion. We present a mathematical programming formulation as an accurate problem definition. A Filtered Beam Search (FBS)-based method is used to solve the problem. It was implemented using the C# language. Results of our experimentations on the use of this method are also presented.(undefined

A bi-objective genetic algorithm approach to risk mitigation in project scheduling

A problem of risk mitigation in project scheduling is formulated as a bi-objective optimization problem, where the expected makespan and the expected total cost are both to be minimized. The expected total cost is the sum of four cost components: overhead cost, activity execution cost, cost of reducing risks and penalty cost for tardiness. Risks for activities are predefined. For each risk at an activity, various levels are defined, which correspond to the results of different preventive measures. Only those risks with a probable impact on the duration of the related activity are considered here. Impacts of risks are not only accounted for through the expected makespan but are also translated into cost and thus have an impact on the expected total cost. An MIP model and a heuristic solution approach based on genetic algorithms (GAs) is proposed. The experiments conducted indicate that GAs provide a fast and effective solution approach to the problem. For smaller problems, the results obtained by the GA are very good. For larger problems, there is room for improvement

Order Acceptance and Scheduling: A Taxonomy and Review

Over the past 20 years, the topic of order acceptance has attracted considerable attention from those who study scheduling and those who practice it. In a firm that strives to align its functions so that profit is maximized, the coordination of capacity with demand may require that business sometimes be turned away. In particular, there is a trade-off between the revenue brought in by a particular order, and all of its associated costs of processing. The present study focuses on the body of research that approaches this trade-off by considering two decisions: which orders to accept for processing, and how to schedule them. This paper presents a taxonomy and a review of this literature, catalogs its contributions and suggests opportunities for future research in this area