A simheuristic algorithm for solving an integrated resource allocation and scheduling problem

Modern companies have to face challenging configuration issues in their manufacturing chains. One of these challenges is related to the integrated allocation and scheduling of resources such as machines, workers, energy, etc. These integrated optimization problems are difficult to solve, but they can be even more challenging when real-life uncertainty is considered. In this paper, we study an integrated allocation and scheduling optimization problem with stochastic processing times. A simheuristic algorithm is proposed in order to effectively solve this integrated and stochastic problem. Our approach relies on the hybridization of simulation with a metaheuristic to deal with the stochastic version of the allocation-scheduling problem. A series of numerical experiments contribute to illustrate the efficiency of our methodology as well as their potential applications in real-life enterprise settings

Problema de planeamento do projeto para biblioteca de desenvolvimento de software - PSPSWDLIB

Um Problema de Gestão de Projetos de Desenvolvimento de Software é uma variante do Problemas Gestão de Projetos onde o modelo de desenvolvimento de software pode ser apresentado como um conjunto de actividades de software a realizar, um conjunto de recursos humanos, um conjunto de recursos financeiros e o variável tempo dividida por actividade. Este artigo apresenta um exemplo do Problema de Gestão de Projetos de Desenvolvimento de Software para projectos de desenvolvimento de software.A Project Scheduling Problem for Software Development is a variant of Project Scheduling Problem where the software development model can be presented as a set of software activities, a set of developer skills and a set of resources specified on money and the total time divided on time per activity. This paper presents an instance set of Project Scheduling Problem for Software Development for projects of software development

Client-contractor bargaining on net present value in project scheduling with limited resources

The client-contractor bargaining problem addressed here is in the context of a multi-mode resource constrained project scheduling problem with discounted cash flows, which is formulated as a progress payments model. In this model, the contractor receives payments from the client at predetermined regular time intervals. The last payment is paid at the first predetermined payment point right after project completion. The second payment model considered in this paper is the one with payments at activity completions. The project is represented on an Activity-on-Node (AON) project network. Activity durations are assumed to be deterministic. The project duration is bounded from above by a deadline imposed by the client, which constitutes a hard constraint. The bargaining objective is to maximize the bargaining objective function comprised of the objectives of both the client and the contractor. The bargaining objective function is expected to reflect the two-party nature of the problem environment and seeks a compromise between the client and the contractor. The bargaining power concept is introduced into the problem by the bargaining power weights used in the bargaining objective function. Simulated annealing algorithm and genetic algorithm approaches are proposed as solution procedures. The proposed solution methods are tested with respect to solution quality and solution times. Sensitivity analyses are conducted among different parameters used in the model, namely the profit margin, the discount rate, and the bargaining power weights

Client-contractor bargaining on net present value in project scheduling with limited resources

The client-contractor bargaining problem addressed here is in the context of a multi-mode resource constrained project scheduling problem with discounted cash flows, which is formulated as a progress payments model. In this model, the contractor receives payments from the client at predetermined regular time intervals. The last payment is paid at the first predetermined payment point right after project completion. The second payment model considered in this paper is the one with payments at activity completions. The project is represented on an Activity-on-Node (AON) project network. Activity durations are assumed to be deterministic. The project duration is bounded from above by a deadline imposed by the client, which constitutes a hard constraint. The bargaining objective is to maximize the bargaining objective function comprised of the objectives of both the client and the contractor. The bargaining objective function is expected to reflect the two-party nature of the problem environment and seeks a compromise between the client and the contractor. The bargaining power concept is introduced into the problem by the bargaining power weights used in the bargaining objective function. Simulated annealing algorithm and genetic algorithm approaches are proposed as solution procedures. The proposed solution methods are tested with respect to solution quality and solution times. Sensitivity analyses are conducted among different parameters used in the model, namely the profit margin, the discount rate, and the bargaining power weights

Client-contractor bargaining problem in the context of multi-mode project scheduling with limited resources

This study focuses on the client-contractor bargaining problem in the context of multimode resource constrained project scheduling. The bargaining objective is to maximize the bargaining objective function comprised of the individual NPV maximizing objectives of both the client and the contractor. Although the well-known multi- mode resource constrained project scheduling problem has been under investigation from various dimensions in the literature, this thesis proposes a two-player setting to this problem. The solution procedure takes the objectives of both players into account. One other proposal we have in this thesis is the bargaining weights concept we have used in the model, which is used to determine the bargaining power of each player through the negotiation process. The effect of bargaining weights assigned to each player on the solution has also been analyzed. Different payment models have also been investigated in this thesis. We have used progress payments, payments at equal time intervals, and payments at activity completions in our tests. Simulated Annealing Algorithm and Genetic Algorithm are proposed as solution procedures. Also the solution set of the problem is investigated by further analyzing the nondominated solutions. We have conducted sensitivity analysis among different parameters we have used in the model. These parameters are profit margin, interest rate, and bargaining weights. The bargaining objective function we have used has been an important part of the model itself. We have investigated different solution approaches by using two different bargaining objective function formulations in our tests

Hybrid Fuzzy-Bayesian Dynamic Decision Support Tool for Resource-Based Scheduling of Construction Projects

Title from PDF of title page viewed September 7, 2017Dissertation advisor: Ceki HalmenVitaIncludes bibliographical references (pages 153-165)Thesis (Ph.D.)--School of Computing and Engineering and Bloch School of Management. University of Missouri--Kansas City, 2017This dissertation proposes a flexible and intelligent decision support tool for scheduling and resource allocation of construction projects. A hybrid Fuzzy-Bayesian scheduling network and a new optimization model and solution approach have been developed to assess the combinatory effect of different risk factors on scheduling and optimize the time-cost tradeoff. Developed decision support tool employs interval-valued fuzzy numbers and Bayesian networks to dynamically quantify uncertainty and predict project performance during its make span. Using interval-valued fuzzy numbers makes the model more flexible and intelligent comparing to conventional fuzzy risk assessment models through incorporating the decision makers` confidence degree. The linguistic assessments of experts regarding the likelihood and severity of increase or decrease in task duration and cost when influenced by different risk factors are used to generate a set of duration and cost prior-probability distributions. A learning dynamic Bayesian scheduling network is developed to probabilistically combine the prior-probability distributions with initial activity duration estimates and update them as new evidence in form of actual activity data feed into the network. This model also predicts project performance at any point of time during its execution. Optimization model explicitly considers variation of time-cost tradeoff relationship during project execution and complex payment terms to maximize the project net present value (NPV). A sequential solution approach is proposed to combine a procedure for updating time-cost tradeoff data, and mixed integer linear programming (MILP) methods to obtain optimal project crashing and scheduling solutions that is adaptive to the current project status and crew productivity. Capability of proposed model in quantifying uncertainty at initial phases of project where project performance data are scarce, learning from data and predicting project performance, considering financial aspects of scheduling through optimal resource allocation and providing useful and clear advice to managers are advantages of developed decision support tool over already existing approaches.Introduction -- Literature review -- Methodology -- Case study and model validation -- Conclusion and recommendations -- Appendix. Detailed Fuzzy Weighted Average Calculations for a-cut = 0 Based on the Max-Min Paired Elimination Algorit

Mathematical programming and financial objectives for scheduling projects

Mathematical Programming and Financial Objectives for Scheduling Projects focuses on decision problems where the performance is measured in terms of money. As the title suggests, special attention is paid to financial objectives and the relationship of financial objectives to project schedules and scheduling. In addition, how schedules relate to other decisions is treated in detail. The book demonstrates that scheduling must be combined with project selection and financing, and that scheduling helps to give an answer to the planning issue of the amount of resources required for a project. The author makes clear the relevance of scheduling to cutting budget costs. The book is divided into six parts. The first part gives a brief introduction to project management. Part two examines scheduling projects in order to maximize their net present value. Part three considers capital rationing. Many decisions on selecting or rejecting a project cannot be made in isolation and multiple projects must be taken fully into account. Since the requests for capital resources depend on the schedules of the projects, scheduling taken on more complexity. Part four studies the resource usage of a project in greater detail. Part five discusses cases where the processing time of an activity is a decision to be made. Part six summarizes the main results that have been accomplished