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"project scheduling"
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Project scheduling in a lean environment to maximize value and minimize overruns
Motivated by the recent trend in delivering projects with value or benefit to stakeholders and seeking to reduce the significant fraction of projects plagued by schedule and budget overruns, researchers are looking at lean project management (LPM) as a possible solution. This paper outlines a new approach to project scheduling in an LPM framework. We develop and solve a math program for balancing project time, cost, value, and risk, seeking to maximize the project value subject to schedule and budget constraints in multimode stochastic projects. Each activity mode contains fixed and resource cost information and duration data, and may be associated with one or more value attributes, thereby integrating project and product scope. By selecting a mode for each activity, the value of the project is determined, and stability is achieved by complying with on-schedule and on-budget probability thresholds. We solve the problem by applying a reinforcement learning-based heuristic, a tool known for obtaining fast solutions in a variety of applications in uncertain environments. We validate the method by comparing the results to two benchmarks—those obtained by solving a mixed-integer program, and the values obtained by adapting a recently published genetic algorithm. Our method generates competitive values faster than the benchmarks, making this approach interesting for the planning stage of a project, when multiple project tradespace alternatives are explored and solved, and runtime is limited. Our approach can be applied by decision-makers to calculate an efficient frontier with the best project plans for given on-schedule and on-budget probabilities.
Journal Article
Investigating constraint programming and hybrid methods for real world industrial test laboratory scheduling
In this paper we deal with a complex real world scheduling problem closely related to the well-known Resource-Constrained Project Scheduling Problem (RCPSP). The problem concerns industrial test laboratories in which a large number of tests are performed by qualified personnel using specialised equipment, while respecting deadlines and other constraints. We present different constraint programming models and search strategies for this problem. Furthermore, we propose a Very Large Neighborhood Search approach based on our CP methods. Our models are evaluated using CP solvers and a MIP solver both on real-world test laboratory data and on a set of generated instances of different sizes based on the real-world data. Further, we compare the exact approaches with VLNS and a Simulated Annealing heuristic. We could find feasible solutions for all instances and several optimal solutions and we show that using VLNS we can improve upon the results of the other approaches.
Journal Article
A generic heuristic for multi-project scheduling problems with global and local resource constraints (RCMPSP)
This paper presents a novel algorithm to solve the multi-project scheduling problem with resource constraints (RCMPSP). The algorithm was tested with all the problems proposed in the multi-project scheduling problem library, which is the main reference to benchmark RCMPSP algorithms. Our analysis of the results demonstrates that this algorithm, in spite of its simplicity, outperforms other algorithms published in the library in 16% of the cases and holds the best result in 27% of the cases. These results, along with the fact that this is a general-purpose algorithm, make it a good choice to deal with limited time and resources in portfolio management.
Journal Article
Decomposition based extended project scheduling for make-to-order production
Project scheduling has a critical function for many companies. Although there is a vast literature dedicated to the development of solution methodologies, the real-life applications of those techniques are few and often require over-simplification of the real-life problem. This study deals with the implementation of a project scheduling routine for a “make-to-order” machinery manufacturer. Although the problem is very similar to a resource-constrained project scheduling problem (RCPSP), which is a well-known and well-studied problem in the literature, it has multi-project and multi-mode components which make the project planning more challenging. Because of the multi-dimensional complexity of the problem, it is called a “Rich Project Scheduling Problem”. Our approach employs two techniques to deal with the issue of complexity. The first one is regarding calculation of the latest start time for each activity in the project. The second one is the decomposition method that includes breaking down the big problem into small manageable pieces. The approach was tested on real-life problems of a machine-building company. We also built more complex test cases from the real projects for testing purposes. Our approach produced high-quality solutions to the complex project scheduling problems within reasonable time limits. This work contributes to current research in several ways. An extension to the RCPSP formulation to include multi-project and multi-mode problems is introduced and successfully used for solving real-life problems. Additionally, activities of an RCPSP can be split into multiple sub-activities. Doing so is called preemption. The proposed formulation permits mode-switching during the execution of an activity, and it uses an alternative methodology to calculate latest activity start times.
Journal Article
Bi-objective optimization of a multi-mode, multi-site resource-constrained project scheduling problem
Purpose
This study aims to deal with the multi-mode resource-constrained project scheduling problem (MRCPSP) with the ability to transport resources among multiple sites, aiming to minimize the total completion time and the total cost of the project simultaneously.
Design/methodology/approach
To deal with the problem under consideration, a bi-objective optimization model is developed. All activities are interconnected by finish-start precedence relations, and pre-emption is not allowed. Then, the ɛ-constraint optimization method is used to solve 24 different-sized instances, ranging from 5 to 120 activities, and report the makespan, total cost and CPU time. A set of Pareto-optimal solutions are determined for some instances, and sensitivity analyses are performed to find the impact of changing parameters on objective values.
Findings
Results highlight the importance of resource transportability assumption on project completion time and cost, providing useful insights for decision makers and practitioners.
Originality/value
A novel bi-objective optimization model is proposed to deal with the multi-site MRCPSP, considering both the cost and time of resource transportation between multiple sites. To the best of the authors’ knowledge, none of the studies in the project scheduling area has yet addressed this problem.
Journal Article
A branch-and-bound procedure for the resource-constrained project scheduling problem with partially renewable resources and general temporal constraints
In this paper, we consider the resource-constrained project scheduling problem with partially renewable resources and general temporal constraints. For the first time, the concept of partially renewable resources is embedded in the context of projects with general temporal constraints. While partially renewable resources have already broadened the area of applications for project scheduling, the extension by general temporal constraints allows to consider even more relevant aspects of real projects. We present a branch-and-bound procedure for the problem with the objective to minimize the project duration. To improve the performance of the solution procedure, new consistency tests, lower bounds, and dominance rules are developed. Furthermore, new temporal planning procedures, based on forbidden start times of activities, are presented which can be used for any project scheduling problem with general temporal constraints independent on the considered resource type. In a performance analysis, we compare our branch-and-bound procedure with the mixed-integer linear programming solver IBM CPLEX 12.8.0 on adaptations of benchmark instances from the literature. In addition, we compare our solution procedure with the only available branch-and-bound procedure for partially renewable resources. The results of the computational experiments prove the efficiency of our branch-and-bound procedure.
Journal Article
Multi-Project Scheduling with Uncertainty and Resource Flexibility: A Narrative Review and Exploration of Future Landscapes
This paper presents a narrative review on the Resource-Constrained Multi-Project Scheduling Problem (RCMPSP) under uncertainty and resource flexibility. Traditional project scheduling assumes complete information and a deterministic environment where a pre-computed baseline schedule is executed. However, real-world projects frequently face uncertainty, such as variable task durations and fluctuating resource availability. Analyzing studies from 2013 to 2024, this review examines optimization models addressing multiple objectives, including minimizing project duration, cost, and resource leveling. It categorizes solution approaches, from exact algorithms to heuristic and metaheuristic methods, while reviewing the primary instance sets and benchmarks used in the field. Additionally, it highlights the value of flexible resource management approaches that enable adaptive responses to real-time project demands, thereby enhancing scheduling robustness. By systematically addressing RCMPSP under uncertainty, this paper provides a valuable framework for researchers and practitioners seeking to develop resilient, adaptive scheduling solutions for complex, dynamic project environments.
Journal Article
Multi-project scheduling under uncertainty and resource flexibility: a systematic literature review
A Systematic Literature Review (SLR) on the Resource-Constrained Multi-Project Scheduling Problem (RCMPSP), Uncertainty, and Resource Flexibility (human resource) is presented in this study. The main purpose is to help scholars with an overview of existing techniques and to identify new research directions. After applying exclusion criteria, 107 papers were analysed (2013-2023). The methodology adopted for this SRL is PRISMA. Based on the results, the approaches proposed to solve the RCMPSP were classified and the main findings were presented. The results show that the main focus of the existing research has been devoted to approximate algorithms. Genetic algorithms (GAs) and priority rules (PRs) are the most representative approximate algorithms, with 39% and 18%, respectively. At the same time, mixed integer programming (MIP) (9%) and branch & bound (B&B) algorithms (4%) are the most used exact algorithms. This analysis provides a vivid roadmap for future research based on the collected papers.
Journal Article
Integrating Schedule Risk Analysis with Multi-Skilled Resource Scheduling to Improve Resource-Constrained Project Scheduling Problems
Construction projects are planned in a complex and dynamic environment characterized by high risks and uncertainties amidst resource constraints. Assessing construction schedule risk facilitates informed decision-making, especially in a resource-constrained situation, and allows proactive actions to be taken so that project objectives are not jeopardized. This study presents a stochastic multiskilled resource scheduling (SMSRS) model for resource-constrained project scheduling problems (RCSPSP) considering the impact of risk and uncertainty on activity durations. The SMSRS model was developed by integrating a schedule risk analysis (SRA) model (developed in MS Excel) with an existing multiskilled resource scheduling (MSRS) algorithm for the development of a feasible and realistic schedule. The computational experiment carried out on three case projects using the proposed SMSRS model revealed an average percentage deviation of 10.50%, indicating the inherent risk and uncertainty in activity durations of the project schedule. The core contribution of the proposed SMSRS model is that it: (1) presents project practitioners with a simple tool for assessing the risks and uncertainty associated with resource-constrained project schedules so that necessary response actions can be taken to ensure project success; (2) provides the small-scale construction businesses with an affordable tool for evaluating schedule risk and developing a feasible and realistic project schedule.
Journal Article