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The Resource-Constrained Project Scheduling Problem (RCPSP) plays a critical role in various management applications. Despite its importance, research efforts are still ongoing to improve lower bounds and reduce deviation values. This study aims to develop an innovative and straightforward algorithm for RCPSPs by integrating the “1+1” evolution strategy into a genetic algorithm framework. Unlike most existing studies, the proposed algorithm eliminates the need for parameter tuning and utilizes real-valued numbers and path representation as chromosomes. Consequently, it does not require priority rules to construct a feasible schedule. The algorithm's performance is evaluated using the RCPSP benchmark and compared to alternative algorithms, such as cWSA, Hybrid PSO, and EESHHO. The experimental results demonstrate that the proposed algorithm is competitive, while the exploration capability remains a challenge for further investigation.

International audience; The resource-constrained project scheduling problem (RCPSP), one of the most challenging combinatorial optimization scheduling problems, has been the focus of a great deal of research, resulting in numerous publications in the last decade. Previous publications focused on the RCPSP, including several extensions with different objectives to be minimized and constraints to be checked. The present work investigates the integration of the routing, i.e., the transport of the resources between activities into the RCPSP, and provides a new resolution scheme. The two subproblems are solved using an integrated approach that draws on both a disjunctive graph model and an explicit modeling of the routing. The resolution scheme takes advantage of an indirect representation of the solution to define both the schedule of activities and the routing of vehicles. The routing solution is modeled by a set of trips that define the loaded transport operations of vehicles that are induced by the flow in the graph. The numerical experiments prove that the models and the methods introduced in this paper are promising for solving the RCPSP with routing.

International audience; Several algorithms for finding a lower bound on the makespan for the Resource Constrained Project Scheduling Problem (RCPSP) were proposed in the literature. However, fast computable lower bounds usually do not provide the best estimations and the methods that obtain better bounds are mainly based on the cooperation between linear and constraint programming and therefore are time-consuming. In this paper, a new pseudo-polynomial algorithm is proposed to find a makespan lower bound for RCPSP with time-dependent resource capacities. Its idea is based on a consecutive evaluation of pairs of resources and their cumulated workload. Using the proposed algorithm, several bounds for the PSPLIB benchmark were improved. The results for industrial applications are also presented where the algorithm could provide good bounds even for very large problem instances.

The management of a project portfolio is a complex decision process because it encompasses the achievement of multiple objectives. A critical point that increases the complexity in the decision-making process of a portfolio manager is the allocation of human resources to manage the projects of the portfolio, project managers, which is crucial to the organization’s performance. In this case, the project manager can manage more than one project simultaneously and it is necessary to assign project managers to the projects, considering that project activities have an amount of work to be accomplished. The main objective of this work was to provide support for this capacity management problem, which aims to provide an easier decision-making process for the capacity management of an industrialization project portfolio. Therefore, it was developed: a hybrid model that creates a schedule respecting the resource constraints and the established due dates; a recommendation system that considers project managers’ allocation and projects requirements; and, an automatic status report that allows identifying the project portfolio capacity usage., This work is supported by: European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project nº 39479; Funding Reference: POCI-01-0247-FEDER-39479].

Priority rules are applied in many commercial software tools for scheduling projects under limited resources because of their known advantages such as the ease of implementation, their intuitive working, and their fast speed. Moreover, while numerous research papers present comparison studies between different priority rules, managers often do not know which rules should be used for their specific project, and therefore have no other choice than selecting a priority rule at random and hope for the best. This paper introduces a decision tree approach to classify and detect the best performing priority rule for the resource-constrained project scheduling problem (RCPSP). The research relies on two classification models to map project indicators onto the performance of the priority rule. Using such models, the performance of each priority rule can be predicted, and these predictions are then used to automatically select the best performing priority rule for a specific project with known network and resource indicator values. A set of computational experiment is set up to evaluate the performance of the newly proposed classification models using the most well-known priority rules from the literature. The experiments compare the performance of multi-label classification models with multi-class classification models, and show that these models can outperform the average performance of using any single priority rule. It will be argued that this approach can be easily extended to any extension of the RCPSP without changing the methodology used in this study. info:eu-repo/semantics/publishedVersion

In this paper we study the Resource Constrained Project Scheduling Problem (RCPSP) with “Feeding Precedence” (FP) constraints and minimum makespan objective. This problem typically arises in production planning environment, like make-to-order manufacturing, where the effort associated with the execution of an activity is not univocally related to its duration percentage and the traditional finish-to-start precedence constraints or the generalized precedence relations cannot completely represent the overlapping among activities. In this context, we need to introduce in the RCPSP the FP constraints. For this problem we propose a new mathematical formulation and define a lower bound based on the Lagrangian relaxation of the resource constraints. A computational experimentation on randomly generated instances of sizes of up to 100 activities shows a better performance of this lower bound when compared to other lower bounds. Moreover, for the optimally solved instances, its value is very close to the optimal one. Furthermore, in order to show the effectiveness of the proposed lower bound on large instances for which the optimal solution is known, we adapted our approach to solve the benchmarks of the basic RCPSP from the PSLIB with 120 activities.

In this paper the dynamic interaction strategy based on the Population Learning Algorithm (PLA) for the A-Team solving the Resource-Constrained Project Scheduling Problem (RCPSP) is proposed and experimentally validated. The RCPSP belongs to the NP-hard problem class. To solve this problem a team of asynchronous agents (A-Team) has been implemented using multiagent system. An A-Team is the set of objects including multiple agents and the common memory which through interactions produce solutions of optimization problems. These interactions are usually managed by some static strategy. In this paper the dynamic learning strategy based on PLA is suggested. The proposed strategy supervises interactions between optimization agents and the common memory. To validate the proposed approach computational experiment has been carried out.

The purpose of this paper is to propose models for project scheduling when there is considerable uncertainty in the activity durations, to the extent that the decision maker cannot with confidence associate probabilities with the possible outcomes of a decision. Our modeling techniques stem from robust optimization, which is a theoretical framework that enables the decision maker to produce solutions that will have a reasonably good objective value under any likely input data scenario. We develop and implement a scenario-relaxation algorithm and a scenario-relaxation-based heuristic. The first algorithm produces optimal solutions but requires excessive running times even for medium-sized instances; the second algorithm produces high-quality solutions for medium-sized instances and outperforms two benchmark heuristics. ispartof: FBE Research Report KBI_1034 pages:1-29 status: published

La programación de actividades con limitación de recursos (RCPSP: resource-constrained project scheduling problem) es un problema combinatorio clásico que aparece en las líneas de manufactura y producción. El problema consiste en establec

Due to the increasing competitiveness of businesses, project planning and scheduling have become a challenging theme in the last years. In this paper, we propose a two-stage stochastic programming model for the resource constrained project scheduling problem, taking into account the stochasticity of activity durations. In this formulation, assuming that some activity duration scenarios are known, resource allocations are taken in the first stage, while scheduling decisions are postponed in the second stage. The resulting problem is a mixed integer problem with recourse, where binary variables appear in the first stage. In order to efficiently solve the problem, a decomposition algorithm is developed, based on the well-known integer L-shaped method. Detailed computational results are presented for a set of benchmark instances taken from the literature.

The Resource Constrained Project Scheduling Problem (RCPSP) is NP-hard thus justifying the use meta-heuristics for its solution. This paper presents an evolutionary algorithm developed for the RCPSP problem. This evolutionary algorithm uses an alphabet based on random keys that makes easier its implementation while solving combinatorial optimization problems. Random keys allow the use of conventional genetic operators, what makes easier the adaptation of the evolutionary algorithm to new problems. To improve the method's performance, this evolutionary algorithm uses an initial population that is generated considering the information available for the instance. This paper studies the impact of using that information in the initial population. The computational experiments presented compare two types of initial population - the conventional one (generated randomly) and this new approach that considers the information of the instance., Fundação para a Ciência e a Tecnologia (FCT)

Project Management involves onetime endeavors that demand for getting it right the first time. On the other hand, project scheduling, being one of the most modeled project management process stages, still faces a wide gap from theory to practice. Demanding computational models and their consequent call for simplification, divert the implementation of such models in project management tools from the actual day to day project management process. Special focus is being made to the robustness of the generated project schedules facing the omnipresence of uncertainty. An "easy" way out is to add, more or less cleverly calculated, time buffers that always result in project duration increase and correspondingly, an increase in its cost. A better approach to deal with uncertainty seems to be to explore slack that might be present in a given project schedule especially when a non-optimal schedule is used. The combination of such approach to recent advances in modeling resource allocation and scheduling techniques to cope with the increasing flexibility in resources, as can be expressed in "Flexible Resource Constraint Project Scheduling Problem" (FRCPSP) formulations, should be a promising line of research to generate more adequate project management tools. In reality this approach is frequently used by project managers in an ad-hoc way.

Project management still faces a wide gap separating theory from practice, especially regarding the robustness of the generated project schedules facing the omnipresence of uncertainty. A new approach to deal with uncertainty is presented to explore slack that might exist in a given project schedule. We propose that renewable resources' capacity to perform work can be increased so that they can perform additional work in a time unit or can be decreased with the consequent reduction on the performed work. This possibility combined with the slack that some activities have in a specific schedule can be used to absorb deviations that might occur during a project's execution. When a critical activity is about to have its duration increased, slowing down other non-critical activities by putting their resources in a decreased work mode enables the activity to still be executed within time by using resources in an increased working mode. [Received: 14 February 2018; Revised: 2 January 2019; Revised: 8 June 2019; Accepted: 17 November 2019]