Automatic Clustering and Multi-Directional Scheduling Scheme for Multi-Mode Scheduling Projects

An efficient hybrid genetic algorithm for scheduling projects with resource constraints and multiple execution modes

In recent years, the resource-constrained project scheduling problem RCPSP with multiple execution modes is becoming more and more popular. In this paper, a new cooperative coevolutionary algorithm based on the concept of organizations, namely Organizational Cooperative Coevolutionary Algorithm for MRCPSPs OCCA-MRCPSPs, is proposed for solving this problem. The objective is to find a schedule of activities together with their execution modes so that the makespan is minimized. In the OCCA-MRCPSPs, the population is divided into two subpopulations, for activities execution modes, respectively. The two subpopulations evolve independently, and each subpopulation is composed of organizations. During the evolutionary process, the global searching and the local searching are combined efficiently by conducting different operators. At first, each subpopulation searches the whole space of its domain through the splitting operator, the annexing operator, and the cooperation operator. Afterwards, the two subpopulations are combined to form complete solutions, and a local search operator is performed. In the experiments, the performance of OCCA-MRCPSPs is validated on benchmark problem sets J10, J12, J14, and J16 from the PLPSIB, and the experimental results show that the OCCA-MRCPSPs obtains a good performance not only in terms of the optimal solutions found but also in terms of the average deviations from optimal solutions.

The Multi-mode Resource Constrained Project Scheduling Problem (MRCPSP) is composed of two interacting sub- problems which are the mode assignment problem and the scheduling problem. Solving these sub-problems in isola- tion pose a challenge due to the interaction that exists be- tween them. We present a unified approach for applying a combination of algorithms to the sub-problems of the MR- CPSP. The use of Genetic algorithms and Estimation of Distribution Algorithms aims to exploit efficiently and syn- chronously the distinct search spaces presented by the two sub-problems.

In this paper we present a genetic algorithm for the multi-mode resource-constrained project scheduling problem (MRCPSP), in which multiple execution modes are available for each of the activities of the project. To solve the problem, we apply a hybrid genetic algorithm, which makes use of nonrenewable resource feasibility checking procedure, local search based mutation and topological sort procedure. We present detailed computational results for the MRCPSP, which reveal that our procedure is effective in solving the problem.

This paper provides a fast near‐optimum solution to the Multi‐Mode Resource Constrained Project Scheduling Problem (MRCPSP), for projects with activities that have known deterministic renewable and non‐renewable resource requirements. Multi‐modes allows any activity to execute one of many alternative combinations of resource usages and durations, while each activity needs to pick the most feasible one. This type of setting is more prevalent for real‐life project schedules, which necessitates systematic methodologies and advanced solution approaches in harmony with real‐world implementations. This study proposes a Modified Variable Neighbourhood Search Heuristic (MVNSH) algorithm as an advanced optimization technique for scheduling projects. To validate our approach, exhaustive computational tests were carried out by using relevant benchmark instances. The MVNSH algorithm has been shown to be faster and more accurate than many existing heuristic methods. This research also contributes to developing a practical decision support system for resolving real‐life constraints in multi‐mode resource‐constrained projects.

The Multi-mode Resource Constrained Project Scheduling Problem (MRCPSP) has been of research interest for over two decades. The problem is composed of two interacting sub problems: mode assignment and activity scheduling. These problems cannot be solved in isolation because of the interaction that exists between them. Many evolutionary algorithms have been applied to this problem most commonly the Genetic Algorithm (GA). It has been common practice to improve the performance of the GA with some local search techniques. The Bi-population Genetic Algorithm (BPGA) is one of the most competitive GAs for solving the MRCPSP. In this paper, we improve the BPGA by hybridising it with an Estimation of Distribution Algorithm that focuses on improving how modes are generated. We also suggest improvement to the existing experimental methodology.

A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem

In this paper the dynamic interaction strategy based on the Population Learning Algorithm (PLA) for the A-Team solving the Multi-mode Resource-Constrained Project Scheduling Problem (MRCPSP) is proposed and experimentally validated. The MRCPSP 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.

Multi-objective algorithms to multi-mode resource-constrained projects under mode change disruption

In this paper a multi‐start iterated local search (MS‐ILS) algorithm is presented as a new and effective approach to solve the multi‐mode resource‐constrained project scheduling problem (MRCPSP). The MRCPSP is a well‐known project scheduling NP‐Hard optimization problem, in which there is a trade‐off between the duration of each project activity and the amount of resources they require to be completed. The proposed algorithm generates an initial solution, performs a local search to obtain a local optimum, subsequently, for a certain number of iterations, makes a perturbation to that local optimum and performs a new local search on the perturbed solution. This whole process then restarts with a different initial solution for a certain number of restarts. The algorithm was tested on benchmark instances of projects with 30, 50 and 100 activities from well‐known libraries. The obtained results were compared to recent benchmark results from the literature. The proposed algorithm outperforms other solution methods found in related literature for the largest tested instances (100 activities), while for smaller instances it shows to be quite competitive, in terms of the average deviation against known lower bounds.

In this paper, a new evolutionary algorithm, namely organizational evolutionary algorithm for multimode resource-constrained project scheduling problems (OEA-MRCPSPs), is proposed. In OEA-MRCPSPs, the population is composed of organizations, and the number of organizations is adjusted by the splitting operator and annexing operator. In the evolutionary process of OEA-MRCPSPs, the global and local searches are combined efficiently. In the experiments, the performance of OEA-MRCPSPs is validated on benchmarks. The results show that OEA-MRCPSPs obtains a good performance in terms of both optimal solutions found and average deviations from optimal solutions or critical path lower bounds. The comparison results show that OEA-MRCPSPs outperforms six other existing algorithms.

This paper aims at providing a fast near‐optimum solution to the multi‐mode resource‐constrained project scheduling problems (MRCPSPs), for projects with activities that have known deterministic renewable and nonrenewable resource requirements. The MRCPSP is known to be nondeterministic polynomial‐time hard and has been solved using various exact, heuristic, and meta‐heuristic procedures. In this paper, a modified variable neighborhood search heuristic algorithm is used as an advanced optimization technique that suits scheduling problems. For the experimental study, we have considered a standard set of 3929 multi‐mode benchmark instances from the project scheduling library with a range of projects comprising 10–30 activities. Moreover, for a better comparison, this research also considers a standard set of 4320 newly developed multi‐mode instances from MMLIB50, MMLIB100, and MMLIB+ datasets. With the limit of 50,000 schedules on these datasets, our proposed algorithm provides better makespan for 106, 34, and 1601 instances, respectively, which justifies the efficiency of the proposed algorithm, particularly for projects with a larger number of activities. The results reported in this paper can be used as a benchmark for other researchers to compare and improve.

The reduction of waste through the establishment of closed-loop material flows has an important role in creating a more sustainable built environment. Especially in deconstruction projects, participants could take advantage of the application of waste management and recovery strategies while adhering to the principle of ‘sustainable construction’. The opportunity for materials recovery depends on the type of deconstruction technique applied at a building's end-of-life. An integrated deconstruction–recovery planning approach for deconstruction projects is developed to include ecological as well as economic benefits. This planning approach is an extension of the multi-mode resource-constrained project-scheduling problem (MMRCPSP) from operations research. Although the general MMRCPSP considers time– and resource–resource trade-offs, the proposed extension incorporates energy–time–resource trade-offs. The energy–time–resource trade-offs reflect the realization of energy savings or costs over the life cycle of a building. Based on a life cycle energy analysis, recovery options can be selected for deconstructed components and materials dependent on the chosen deconstruction techniques (e.g. demolition, selective deconstruction, manual deconstruction, and deconstruction using various resources). La réduction des déchets par l'établissement de flux de matériaux en boucles fermées a un rôle important dans la création d'un milieu bâti plus durable. Dans les projets de démolition, en particulier, les participants pourraient tirer parti de l'application de stratégies de gestion des déchets et de récupération tout en respectant les principes de la construction durable. L'opportunité de la récupération de matériaux dépend du type de technique de démolition utilisée à la fin de la vie utile d'un bâtiment. Une méthode de planification intégrée de la démolition-récupération pour les projets de démolition est développée pour inclure des avantages écologiques et économiques. Cette méthode de planning est une prolongation du problème d'ordonnancement de projet à réaliser à l'aide de ressources en nombre limité à plusieurs modes (MMRCPSP) inspiré de la recherche opérationnelle. Bien que le MMRCPSP général considère les arbitrages concernant le temps et les ressources, l'extension proposée inclut des arbitrages portant sur l'énergie, le temps et les ressources. De tels arbitrages traduisent la réalisation d'économies d'énergie ou de coût sur le cycle de vie d'un bâtiment. En se basant sur l'analyse énergétique du cycle de vie, on peut choisir des options de récupération portant sur des éléments démolis et des matériaux en fonction de la technique de démolition choisie (par exemple, démolition, démolition sélective, démolition manuelle et démolition utilisant diverses ressources). déchets de la construction et de la démolition; démolition; cycle de vie; récupération de matériaux; planning de projet; recyclage

Solving resource constrained project scheduling problem (RCPSP) has important role in the context of project scheduling.Considering a single objective RCPSP, the goal is to find a schedule that minimizes the makespan.This is NP-hard problem (Blazewicz et al., 1983) and one may use meta-heuristics to obtain a global optimum solution or at least a near-optimal one.Recently, various meta-heuristics such as ACO, PSO, GA, SA etc have been applied on RCPSP.Bee algorithms are among most recently introduced meta-heuristics.This study aims at adapting artificial bee colony as an alternative and efficient optimization strategy for solving RCPSP and investigating its performance on the RCPSP.To evaluate the artificial bee colony, its performance is investigated against other meta-heuristics for solving case studies in the PSPLIB library.Simulation results show that the artificial bee colony presents an efficient way for solving resource constrained project scheduling problem.

This paper is concerned with an overview of the Resource-Constrained Project Scheduling Problem (RCPSP) and the conventional meta-heuristic solution techniques that have attracted the attention of many researchers in the field. Therefore, researchers have developed algorithms and methods to solve the problem. This paper addresses the single-mode RCPSP where the objective is to optimize and minimize the project duration while the quantities of resources are constrained during the project execution. In this problem, resource constraints and precedence relationships between activities are known to be the most important constraints for project scheduling. In this context, the standard RCPSP is presented. Then, the classifications of the collected papers according to the year of publication and the different meta-heuristic approaches applied are presented. Five weighted articles and their meta-heuristic techniques developed for RCPSP are described in detail and their results are summarized in the corresponding tables. In addition, researchers have developed various conventional meta-heuristic algorithms such as genetic algorithms, particle swarm optimization, ant colony optimization, bee colony optimization, simulated annealing, evolutionary algorithms, and so on. It is stated that genetic algorithms are more popular among researchers than other meta-heuristics. For this reason, the various conventional meta-heuristics and their corresponding articles are also presented to give an overview of the conventional meta-heuristic optimizing techniques. Finally, the challenges of the conventional meta-heuristics are explored, which may be helpful for future studies to apply new suitable techniques to solve the Resource-Constrained Project Scheduling Problem (RCPSP).