Simulation and Optimization of Scheduling Policies in Dynamic Stochastic Resource-Constrained Multi-Project Environments

The goal of the Project Management is to organise project schedules to complete projects before their completion dates, specified in their contract. When a project is beyond its completion date, organisations may lose the rewards from project completion as well as their organisational prestige. Project Management involves many uncertain factors such as unknown new project arrival dates and unreliable task duration predictions, which may affect project schedules that lead to delivery overruns. Successful Project Management could be done by considering these uncertainties. In this PhD study, we aim to create a more comprehensive model which considers a system where projects (of multiple types) arrive at random to the resource-constrained environment for which rewards for project delivery are impacted by fees for late project completion and tasks may complete sooner or later than expected task duration. In this thesis, we considered two extensions of the resource-constrained multi-project scheduling problem (RCMPSP) in dynamic environments. RCMPSP requires scheduling tasks of multiple projects simultaneously using a pool of limited renewable resources, and its goal usually is the shortest make-span or the highest profit. The first extension of RCMPSP is the dynamic resource-constrained multi-project scheduling problem. Dynamic in this problem refers that new projects arrive randomly during the ongoing project execution, which disturbs the existing project scheduling plan. The second extension of RCMPSP is the dynamic and stochastic resource-constrained multi-project scheduling problem. Dynamic and stochastic represent that both random new projects arrivals and stochastic task durations. In these problems, we assumed that projects generate rewards at their completion; completions later than a due date cause tardiness costs, and we seek to maximise average profits per unit time or the expected discounted long-run profit. We model these problems as infinite-horizon discrete-time Markov decision processes

Heuristic algorithm for single resource constrained project scheduling problem based on the dynamic programming

We introduce a heuristic method for the single resource constrained project scheduling problem, based on the dynamic programming solution of the knapsack problem. This method schedules projects with one type of resources, in the non-preemptive case: once started an activity is not interrupted and runs to completion. We compare the implementation of this method with well-known heuristic scheduling method, called Minimum Slack First (known also as Gray-Kidd algorithm), as well as with Microsoft Project

Effective Project Scheduling Under Workspace Congestion and Workflow Disturbance Factors

Effective project management implies the use of advanced planning and scheduling methods that allow to determine feasible sequences of activities and to complete a project on time and on budget. Traditional scheduling tools like fundamental Critical Path Method (CPM) and various methods for Resource Constrained Project Scheduling Problem (RCPSP) and Time Constrained Project Scheduling Problem (TCPSP) have many shortcomings for construction projects where spatial factor plays a critically important role. Previous attempts to interpret space as a specific resource were successful for particular problems of line-of-balance scheduling, space scheduling, dynamic layout planning, horizontal and vertical logic scheduling, workspace congestion mitigating, scheduling multiple projects with movable resources, spatial scheduling of repeated and grouped activities and motion planning. However, none of these methods considers the spatio-temporal requirements in a holistic framework of generic RCPSP problem and provides feasible results accounting for workspace and workflow factors. In this paper we start with the classical RCPSP statement and then present mathematically strong formalisation of the extended generalised problem, taking into account workspace congestion and workflow disturbance constraints specified in practically meaningful and computationally constructive ways. For the generalised RCPSP problem an effective scheduling method is proposed. The method tends to minimise the project makespan while satisfying timing constraints and precedence relations, not exceeding resource utilisation limits, avoiding workspace congestions and keeping workflows continuous. The method reuses so-called serial scheduling scheme and provides for additional computational routines and heuristic priority rules to generate feasible schedules satisfying all the imposed requirements. Advantages of the method and prospects for its application to industrial needs are outlined in the paper too

Proactive project scheduling in an R&D department: a bi-objective genetic algorithm

In this paper, we present part of a study on stochastic, dynamic project scheduling in an R&D Department of a leading home appliances company in Turkey. The problem under consideration is the preemptive resource constrained multi-project scheduling problem with generalized precedence relations in a stochastic and dynamic environment. The model consists of three phases. Phase I of the model provides a systematic approach to assess uncertainty resulting in activity deviation distributions. In Phase II, proactive project scheduling is accomplished through two different scheduling approaches,which employ a bi-objective genetic algorithm. Phase III is the reactive project scheduling phase aiming at rescheduling the disrupted project activities. Here, we will limit our presentation to Phase II – the proactive project scheduling phase. The procedure is demonstrated through an implementation with real data covering 37 R&D projects. Computational study is performed to compare the two different scheduling approaches called single and multi-project scheduling approaches, as well as two different chromosome evaluation heuristics. Results are presented and discussed

Real-Time Optimizations in Industrial Production

Tato diplomová práce se zabývá rozvrhováním výrobních operací v průmyslové výrobě. Tento problém je formálně popsán jako Resource-Constrained Project Scheduling Problem, jehož cílem je nalezení optimálního přiřazení množiny operací na omezené zdroje. V této práci byl nejprve vytvořen základní optimalizátor výrobních operací založený na genetickém algoritmu. Následně byl navržen model poruch a byl vytvořen systém zahrnující real-time optimalizátor, který je schopen plynule reagovat na vznikající problémy ve výrobě. V real-time optimalizátoru bylo implementováno několik metod řešení a byly s nimi prováděny četné experimenty. Zmíněný systém rovněž umožňuje simulovat provádění výrobních operací a vykreslovat Ganttův diagram.The thesis deals with the scheduling problem of manufacturing operations in industrial production. This problem is described as the well-known the Resource-Constrained Project Scheduling Problem. The objective of this problem is to find an optimal assignment of operations to limited resources. Optimizer created for the thesis uses a genetic algorithm to solve the scheduling problem. For the purpose of a dynamic scheduling, a failures model was designed and a system with real-time optimizer, that is able to repair the original schedule fluently, was created. In the real-time optimizer, several solution methods were implemented and these solution methods underwent a number of experiments. The system thus created is also able to simulate manufacturing operations and draw a Gantt chart.

Short-Term Resource Allocation and Management

Almost all sectors of the economy, such as, government, healthcare, education, ship repair, construction, and manufacturing require project management. A key component of project management deals with scheduling of tasks such that limited resources are utilized in an effective manner. Current research on resource constrained project-scheduling has been classified as: a) Single project with single mode for various tasks, b) Single project with multiple task modes, c) Multiple projects with single task mode, and d) Multiple projects with multiple task modes.;This work extends the current multi-project, multi-mode scheduling techniques. The resources can be renewable, and non-renewable. In addition, it focuses on short term scheduling, that is, scheduling on an hourly, daily, or weekly basis. Long term scheduling assumes a stable system, that is, resources, priorities, and other constraints do no change during the scheduling period. In this research, short term scheduling assumes a dynamic system, that is, resources, priorities, and other constraints change over time.;A hybrid approach is proposed to address the dynamic nature of the problem. It is based on discrete event simulation and a set of empirical rules provided by the project manager. The project manager is assumed to be highly knowledgeable about the project. He/she is regarded as an integral part of the system. Such an approach is better suited to deal with real world scheduling. The proposed approach does not seek to provide a single optimum solution, instead, it generates a series of feasible solutions, along with the impact of each solution on schedule and cost.;Two project case studies dealing with finding an optimum solution were selected from the literature. The proposed technique was applied to the data set in these studies. In both cases the proposed approach found the optimum solution. The model was then applied to two additional problems to test the features that could not be tested on the dataset from the literature.;As for practical implications, the proposed approach enhances the decision making process, by providing more resource allocation flexibility, and results in improved solutions in terms of total project duration and cost. From an academic viewpoint, this research enriches the existing literature, as it provides an extension of the resource constrained project scheduling problems, a discrete event simulation and four cases studies which highlights relevant issues to model properly the complexity of real-life projects

Uncertainty assessment in project scheduling with data mining

During project execution, especially in a multi-project environment project activities are subject to risks that may cause delays or interruptions in the baseline schedules. This paper considers the resource constrained multi-project scheduling problem with generalized activity precedence relations requiring multi-skilled resources in a stochastic and dynamic environment present in the R&D department of a home appliances company and introduces a two-phase model incorporating data mining and project scheduling techniques. This paper presents the details of Phase I, uncertainty assessment phase, where Phase II corresponds to proactive project scheduling module. In the proposed uncertainty assessment approach models are developed to classify the projects and their activities with respect to resource usage deviation levels. In doing so, the proposed approach enables the project managers not only to predict the deviation level of projects before they actually start, but also to take needed precautions by detecting the most risky projects. Moreover, Phase I generates one of the main inputs of Phase II to obtain robust baseline project schedules and identifies the risky activities that need close monitoring. Details of the proposed approach are illustrated using R&D project data of a leading home appliances company. The results support the efficiency of the proposed approach

Two Heuristics for Scheduling Multiple Projects with Resource Constraints

[[abstract]]The purpose of this paper is to develop two efficient heuristic priority rules for the resource-constrained multiproject scheduling problem. The aptness of the two heuristic rules is analysed in terms of several dynamic characteristics of the scheduling problem. Fifteen heuristic rules presented in previous studies are used for comparison with the two heuristic rules on 4941 test problems which were generated by combining two, three or four projects from seven typical networks. The results indicate that the two proposed heuristics are superior to the other scheduling rules under the performance criteria of the minimum total project delay and the maximum number of times that a scheduling rule can obtain the best solution. Encouragingly, the two heuristic rules are proven to be adaptive and stable enough for scheduling under different problem sizes, network structures and degrees of resource tightness. As a result, the two proposed rules are the best representatives of the single priority rule method and the weighted combination search method, respectively. This study also includes a categorization process on which a project summary measure is based and then provides project schedulers with a convenient scheme to adopt appropriate scheduling rules

Performance evaluation of scheduling policies for the DRCMPSP

In this study, we consider the dynamic resource-constrained multi-project scheduling problem (DRCMPSP) where projects generate rewards at their completion, completions later than a due date cause tardiness costs and new projects arrive randomly during the ongoing project execution which disturbs the existing project scheduling plan. We model this problem as a discrete Markov decision process and explore the computational limitations of solving the problem by dynamic programming. We run and compare four different solution approaches on small size problems. These solution approaches are: a dynamic programming algorithm to determine a policy that maximises the average profit per unit time net of charges for late project completion, a genetic algorithm which generates a schedule to maximise the total reward of ongoing projects and updates the schedule with each new project arrival, a rule-based algorithm which prioritise processing of tasks with the highest processing durations, and a worst decision algorithm to seek a non-idling policy to minimise the average profit per unit time. Average profits per unit time of generated policies of the solution algorithms are evaluated and compared. The performance of the genetic algorithm is the closest to the optimal policies of the dynamic programming algorithm, but its results are notably suboptimal, up to 67.2\%. Alternative scheduling algorithms are close to optimal with low project arrival probability but quickly deteriorate their performance as the probability increases

The Project Scheduling Problem with Non-Deterministic Activities Duration: A Literature Review

Purpose: The goal of this article is to provide an extensive literature review of the models and solution procedures proposed by many researchers interested on the Project Scheduling Problem with nondeterministic activities duration. Design/methodology/approach: This paper presents an exhaustive literature review, identifying the existing models where the activities duration were taken as uncertain or random parameters. In order to get published articles since 1996, was employed the Scopus database. The articles were selected on the basis of reviews of abstracts, methodologies, and conclusions. The results were classified according to following characteristics: year of publication, mathematical representation of the activities duration, solution techniques applied, and type of problem solved. Findings: Genetic Algorithms (GA) was pointed out as the main solution technique employed by researchers, and the Resource-Constrained Project Scheduling Problem (RCPSP) as the most studied type of problem. On the other hand, the application of new solution techniques, and the possibility of incorporating traditional methods into new PSP variants was presented as research trends. Originality/value: This literature review contents not only a descriptive analysis of the published articles but also a statistical information section in order to examine the state of the research activity carried out in relation to the Project Scheduling Problem with non-deterministic activities duration.Peer Reviewe