Resource Constraint Project Scheduling Problem Research Articles

A New Hybrid Algorithm for the Resource Constraint Project Scheduling Problem

In today’s competitive environment, projects must be implemented at a lower cost and in less time, and resources must be used optimally. Therefore, project management and scheduling using an efficient tool is a necessity. Resource constraint project scheduling (RCPSP) is one of the most widely used project planning issues. One of the most difficult non-polynomial problems is that innovative and meta-heuristic methods are more effective than exact solutions. The current study mainly aims to introduce a new meta-innovative algorithm with an imperialist competitive algorithm and genetic algorithm (ICA-GA). Next, by proposing a two-objective project scheduling problem, it is attempted to minimize the project execution time and its cost simultaneously with the proposed algorithm. Finally, to assess the validity of the ICA-GA hybrid algorithm, the famous MOPSO algorithm in solving the proposed model is utilized. The evaluated data is extracted from the PSPLIB standard library. The study’s results demonstrate that the imperialist-genetic competition algorithm is superior to the MOPSO algorithm and holds high efficiency in solving the proposed model.

A new approach for material ordering and multi-mode resource constraint project scheduling problem in a multi-site context under interval-valued fuzzy uncertainty

Taking both material procurement and project scheduling problem simultaneously has been proved the most noteworthy areas in project management. The ordering amount is a key factor due to the fact that through keeping a full stock of required materials, the probability of material shortage and corresponding inefficiency decline but the costs of inventory holding increase significantly. Besides, the delay in individual activities and consequently in project completion increase if a small amount of needed material is purchased. The multi-site context as one of the new resource constraint project scheduling problem (RCPSP) extensions has been widely utilized in some applications in real-world cases, especially in construction projects. In this paper, a new mathematical model is proposed for the combination of ordering problems considering all-unit discount policy and multi-mode RCPSP (MRCPSP) in the multi-site context. The developed model aims to minimize the project total cost as well as the completion time of the project. In the novel proposed mathematical model, the transportation time is crucial and depends on the site that activities are assigned. With respect to the inherent uncertainties in project management, the duration of activities, transportation time between sites, and lead time are regarded as triangular interval-valued fuzzy numbers. In addition, a new hybrid multi-objective uncertainty approach is presented to handle interval-valued fuzzy mathematical model. The results obtained by solving the presented model employing an application example from MPSPLIB data set and a real case study about seawater intake systems are appraised considering key parameters to validate the proposed model and provide some managerial insights.

Project management under uncertainty: resource flexibility visualization in the schedule

Going from an idea to a result, using a series of well-defined techniques with deadlines and limited resources, is a skill that a project manager must have. In a rapidly changing environment like the present one, there are many factors that contribute to budget overruns and delays that occur in most major projects. To be successful, one must rely on techniques and tools that assist the project manager in the various stages of the project. Therefore, this paper presents an Add-In for MS Project®, based on a previously developed model for solving the Resource Constraint Project Scheduling Problem with Flexible Resource Management (RCPSP-FRM), which can help the project manager with regard to the scheduling of resources using flexibility, giving him relevant information to the decision making process in case of delays during the execution phase. The flexible chart and table format view allows project managers to easily and objectively identify the activities which can have their durations increased or shortened, impacting significantly on project execution time. This research contributes not only to the academic environment, but also to professional project management environments.

Non-dominated Sorting Genetic Algorithms for a Multi-objective Resource Constraint Project Scheduling Problem

Abstract The resource constraint project scheduling problem (RCPSP) has attracted growing attention since the last decades. Precedence constraints are considered as well as resources with limited capacities. During the project, the same resource can be required by several in-process jobs and it is compulsory to ensure that the consumptions do not exceed the limited capacities. In this paper, several criteria are involved, namely makespan, total job tardiness, and workload balancing level. Our problem is firstly solved by the non-dominated sorting genetic algorithm-II (NSGAII) as well as the recently proposed NSGAIII. Giving emphasis to the selection procedure, we apply both the traditional Pareto dominance and the less documented Lorenz dominance into the niching mechanism of NSGAIII. Hence, we adopt and modify L-NSGAII to our problem and propose L-NSGAIII by integrating the notion of Lorenz dominance. Our methods are tested by 1350 randomly generated instances, considering problems with 30–150 jobs and different configurations of resources and due dates. Hypervolume and C-metric are considered to evaluate the results. The Lorenz dominance leads the population more toward the ideal point. As experiments show, it allows improving the original NSGA approach.

A Robust Solution to Resource-Constraint Project Scheduling Problem

A Robust Solution to Resource-Constraint Project Scheduling Problem

Fuzzy Resource Constraint Project Scheduling Problem Using CBO and CSS Algorithms

Resource allocation project scheduling problem (RCPSP) has been one of the challenging subjects amongst researchers in the past decades. Most of the researchers in this area have used deterministic variables; however, in a real project, activities are exposed to risks and uncertainties that cause delay in project’s duration. There are some researchers that have considered the risks for scheduling; however, new metahuristics are available to solve this problem for finding better solution with less computational time. In this paper, two new metahuristic algorithms are applied for solving fuzzy resource allocation project scheduling problem (FRCPSP), known as charged system search (CSS) and colliding body optimization (CBO). The results show that both of these algorithms find reasonable solutions; however, CBO finds the results in a less computational time, with a better quality. A case study is conducted to evaluate the performance and applicability of the proposed algorithms.

An exact method to solve a Bi-objective Resource Constraint Project Scheduling Problem

The Resource Constraint Project Scheduling Problem (RCPSP) is one of the most challenging and interesting problems within the scheduling topic. It brings more and more attention to the researchers since the recent decades. In the RCPSP, we consider a project scheduling problem with a set of jobs submitted to precedence relationships as well as a set of resources with limited capacities. During each period, it is compulsory to guarantee enough resources since one resource could be required by several jobs in process. In this paper, we tackle a bi-objective RCPSP with minimization of the makespan and the total job tardiness. In order to find optimal Pareto fronts, we have introduced two bi-objective mathematical formulations and solved our problem with the two phase method (TPM). Our method is tested with instances randomly generated considering 10, 15, 20 jobs and 4 types of resources. In this paper, we report the experimental results within 1800s and more tests will be launched to explore the limits of the exact method.

Comparison of Knowledge Acquisition Methods for Dynamic Scheduling of Wafer Test Processes with Unpredictable Testing Errors

The scheduling of semiconductor wafer testing processes may be seen as a resource constraint project scheduling problem (RCPSP), but it includes uncertainties caused by wafer error, human factors, etc. Because uncertainties are not simply quantitative, estimating the range of the parameters is not useful. Considering such uncertainties, finding a good situationdependent dispatching rule is more suitable than solving an RCPSP under uncertainties. In this paper we apply machine learning approaches to acquiring situation-dependent dispatching rule. We compare obtained rules and examine their effectiveness and usefulness in problems with unpredictable wafer testing errors.

Dynamic Scheduling Approaches to Wafer Test Scheduling with Unpredictable Error

Scheduling of semiconductor wafer testing processes can be seen as a resource constraint project scheduling problem (RCPSP). However, it includes uncertainties caused by human factors, wafer errors and so on. Because some uncertainties are not simply quantitative, range estimation of the parameters would not be very useful. Considering such uncertainties, finding a good situation-dependent dispatching rule would be more suitable than solving the RCPSP under uncertainties. In this paper we apply the Pitts approach, one of the genetic algorithms, to the situation-dependent dispatching rule acquisition. We compare the obtained rule with the simple dispatching rules and examine the effectiveness and usefulness of the obtained rule in the problems with unpredictable wafer errors.

Fuzzy uncertainty modelling for project planning: application to helicopter maintenance

Maintenance is an activity of growing interest, especially for critical systems. In particular, aircraft maintenance costs are becoming an important issue in the aeronautical industry. Managing an aircraft maintenance centre is a complex activity. One of the difficulties comes from the numerous uncertainties that affect the activity and disturb the plans in the short and medium term. Based on a helicopter maintenance planning and scheduling problem, we study in this paper the integration of uncertainties into tactical and operational multi-resource, multi-project planning (respectively Rough Cut Capacity Planning and the Resource Constraint Project Scheduling Problem). Our main contributions are in modelling the periodic workload on a tactical level considering uncertainties in macro-task work content, and modelling the continuous workload on the operational level considering uncertainties in task duration. We model uncertainties using a fuzzy/possibilistic approach instead of a stochastic approach since very limited data are available. We refer to the problems as the Fuzzy Rough Cut Capacity Problem (FRCCP) and the Fuzzy Resource Constraint Project Scheduling Problem (RCPSP). We apply our models to helicopter maintenance activity within the frame of the Helimaintenance project, an industrial project approved by the French Aerospace Valley cluster that aims at building a centre for civil helicopter maintenance.

Solving Resource-Constrained Project Scheduling Problems with Time-Windows Using Iterative Improvement Algorithms

This paper proposes an iterative improvement approach for solving the Resource Constraint Project Scheduling Problem with Time-Windows (RCPSP/max), a well-known and challenging NP-hard scheduling problem. The algorithm is based on Iterative Flattening Search (IFS), an effective heuristic strategy for solving multi-capacity optimization scheduling problems. Given an initial solution, IFS iteratively performs two-steps: a relaxation-step, that randomly removes a subset of solution constraints and a solving-step, that incrementally recomputes a new solution. At the end, the best solution found is returned. The main contribution of this paper is the extension to RCPSP/max of the IFS optimization procedures developed for solving scheduling problems without time-windows. An experimental evaluation performed on medium-large size and web-available benchmark sets confirms the effectiveness of the proposed procedures. In particular, we have improved the average quality w.r.t. the current bests, while discovering three new optimal solutions, thus demonstrating the general efficacy of IFS.

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

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.

Improving iterative repair strategies for scheduling with the SVM

The resource constraint project scheduling problem (RCPSP) is an NP-hard benchmark problem in scheduling which takes into account the limitation of resources' availabilities in real life production processes and subsumes open-shop, job-shop, and flow-shop scheduling as special cases. We present here an application of machine learning to adapt simple greedy strategies for the RCPSP. Iterative repair steps are applied to an initial schedule which neglects resource constraints. The rout-algorithm of reinforcement learning is used to learn an appropriate value function which guides the search. We propose three different ways to define the value function and we use the support vector machine (SVM) for its approximation. The specific properties of the SVM allow to reduce the size of the training set and SVM shows very good generalization behavior also after short training. We compare the learned strategies to the initial greedy strategy for different benchmark instances of the RCPSP.