Chapter 10: Modeling and Simulation for Project Management

Sometimes, monitoring resources, budgets, and milestones for various projects and divisions can present a challenge. Simulation tools help us improve planning and coordination in the various phases of the project so that we always keep control of it. In addition, the preventive simulation of a project can highlight the critical issues related to a specific task. This helps us evaluate the cost of any actions to be taken. Through the preventive evaluation of the development of a project, errors that increase the costs of a project can be avoided.

In this chapter, we will deal with practical cases of project management using the tools we learned about in the previous chapters. We will learn how to evaluate the results of the actions we take when managing a forest using Markov processes, and then move on and learn how to evaluate a project using the Monte Carlo simulation.

In this chapter, we're going to cover the following...

Technical requirements

In this chapter, we will address modeling examples of project management. To deal with these topics, it is necessary that you have a basic knowledge of algebra and mathematical modeling.

To work with the Python code in this chapter, you'll need the following files (available on GitHub at the following URL: https://github.com/PacktPublishing/Hands-On-Simulation-Modeling-with-Python):

• TinyForestManagement.py
• TinyForestManagementModified.py
• MonteCarloTasksScheduling.py

Introducing project management

To assess the consequences of a strategic or tactical move in advance, companies need reliable predictive systems. Predictive analysis systems are based on data collection and the projection of reliable scenarios in the medium- and long-term. In this way, we can provide indications and guidelines for complex strategies, especially those that must consider numerous factors from different entities.

This allows us to examine the results of the evaluation in a more complete and coordinated way since we can simultaneously consider a range of values and, consequently, a range of possible scenarios. Finally, when managing complex projects, the use of artificial intelligence to interpret data has increased, thus giving these projects meaning. This is because we can perform a sophisticated analysis of the information in order to improve the strategic decision-making process we will undertake. This methodology allows us to search and analyze data from different...

Managing a tiny forest problem

As we mentioned in Chapter 5, Simulation-Based Markov Decision Processes, a stochastic process is called Markovian if it starts from an instant t in which an observation of the system is made. The evolution of this process will depend only on t, so it will not be influenced by the previous instants. So, a process is called Markovian when the future evolution of the process depends only on the instant of observing the system and does not depend in any way on the past. MDP is characterized by five elements: decision epochs, states, actions, transition probability, and reward.

Summarizing the Markov decision process

The crucial elements of a Markovian process are the states in which the system finds itself, and the available actions that the decision maker can carry out on that state. These elements identify two sets: the set of states in which the system can be found, and the set of actions available for each specific state. The action chosen by...

Scheduling project time using Monte Carlo simulation

Each project requires a time of realization, and the beginning of some activities can be independent or dependent on previous activities ending. Scheduling a project means determining the times of realization of the project itself. A project is a temporary effort undertaken to create a unique product, service, or result. The term project management refers to the application of knowledge, skills, tools, and techniques for the purpose of planning, managing, and controlling a project and the activities of which it is composed.

The key figure in this area is the project manager, who has the task and responsibility of coordinating and controlling the various components and actors involved, with the aim of reducing the probability of project failure. The main difficulty in this series of activities is to achieve the objectives set in compliance with constraints such as the scope of the project, time, costs, quality, and resources. In...

Summary

In this chapter, we addressed several practical model simulation applications based on project management-related models. To start, we looked at the essential elements of project management and how these factors can be simulated to retrieve useful information.

Next, we tackled the problem of running a tiny forest for the wood trade. We treated the problem as an MDP, summarizing the basic characteristics of these processes and then moved on to a practical discussion of them. We defined the elements of the problem and then we saw how to use the policy evaluation and policy improvement algorithms to obtain the optimal forest management policy. This problem was addressed using the MDPtoolbox package, which is available from Python.

Subsequently, we addressed the problem of evaluating the execution times of a project using Monte Carlo simulation. To start, we defined the task execution diagram by specifying which tasks are performed in series and which are performed in parallel...