Multi-Skill Resource-Constrained Project Scheduling Research Articles

Weighted Multi-Skill Resource Constrained Project Scheduling: A Greedy and Parallel Scheduling Approach

Weighted Multi-Skill Resource Constrained Project Scheduling: A Greedy and Parallel Scheduling Approach

Rules Mining-Based Gene Expression Programming for the Multi-Skill Resource Constrained Project Scheduling Problem

The multi-skill resource-constrained project scheduling problem (MS-RCPSP) is a significant management science problem that extends from the resource-constrained project scheduling problem (RCPSP) and is integrated with a real project and production environment. To solve MS-RCPSP, it is an efficient method to use dispatching rules combined with a parallel scheduling mechanism to generate a scheduling scheme. This paper proposes an improved gene expression programming (IGEP) approach to explore newly dispatching rules that can broadly solve MS-RCPSP. A new backward traversal decoding mechanism, and several neighborhood operators are applied in IGEP. The backward traversal decoding mechanism dramatically reduces the space complexity in the decoding process, and improves the algorithm’s performance. Several neighborhood operators improve the exploration of the potential search space. The experiment takes the intelligent multi-objective project scheduling environment (iMOPSE) benchmark dataset as the training set and testing set of IGEP. Ten newly dispatching rules are discovered and extracted by IGEP, and eight out of ten are superior to other typical dispatching rules.

MEMINV: A hybrid efficient approximation method solving the multi skill-resource constrained project scheduling problem.

The Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP) is an NP-Hard problem that involves scheduling activities while accounting for resource and technical constraints. This paper aims to present a novel hybrid algorithm called MEMINV, which combines the Memetic algorithm with the Inverse method to tackle the MS-RCPSP problem. The proposed algorithm utilizes the inverse method to identify local extremes and then relocates the population to explore new solution spaces for further evolution. The MEMINV algorithm is evaluated on the iMOPSE benchmark dataset, and the results demonstrate that it outperforms. The solution of the MS-RCPSP problem using the MEMINV algorithm is a schedule that can be used for intelligent production planning in various industrial production fields instead of manual planning.

Investigation of benchmark dataset for many-objective Multi-Skill Resource Constrained Project Scheduling Problem

The paper presents a redefinition of Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP) as a many-objective optimization problem. In effect, it brings the problem closer to real-world applications. Five objectives have been defined: cost, duration, average cash flow, average usage of resources and skill overuse. To summarize MS-RCPSP usage, the paper presents a short survey (years 2015–2021) of multi- and many-objective MS–RCPSP solving methods. Moreover, results of investigation of many-objective MS–RCPSP for classic (greedy algorithm, Unified Non-dominated Sorting Genetic Algorithm III (U-NSGA-III)), single objective (decomposition-based) Differential Evolution And Greedy (DEGR) and state-of-the-art Non-dominated Tournament Genetic Algorithm (NTGA2) have been presented. Additionally, results are analysed using multi-objective quality measures (such as Pareto Front Size (PFS), Purity or Inverted Generational Distance (IGD)) to verify the efficiency of the used methods. The paper answers 4 research questions that investigate 5-objective MS-RCPSP domains, approximations of Pareto Fronts, relations between objectives and efficiency of NTGA2 compared to other methods. Finally, the paper is concluded with a summary and propositions for future work.

A Constraint Programming Approach to Solve Multi-skill Resource-constrained Project Scheduling Problem with Calendars

The multi-skill resource constrained project scheduling is an important and challenging problem in project management. Two key issues that turn this topic into a challenging problem are the assumptions that are considered to approximate the model to a real-world problem and exact solution approach for the model. In this paper, we deal with this two issues. To consider real-word situations, we take into account calendars specifying time intervals during which the resources are available. We proposed a constraint programming approach to solve the problem exactly. The problem with and without resource calendars are modeled with mathematical programming (MP) and constraint programming (CP). In addition, the performance of CP approach is evaluated by comparing Time-Indexed Model (TIM) and Branch and Price (B&P) approaches. Computational results show that the proposed approach can efficiently solve real-size instances.

Multi-skill resource constrained project scheduling using a multi-objective discrete Jaya algorithm

Multi-skill resource constrained project scheduling using a multi-objective discrete Jaya algorithm

Survey of quality measures for multi-objective optimization: Construction of complementary set of multi-objective quality measures

Abstract In recent years interest in multiobjective optimization has flourished. Many Quality Measures (QM) have been developed to allow comparison of results gained by many methods. Unfortunately significant amount of various QMs along with the lack of imposed taxonomy have caused vagueness in the naming conventions. Hence a cohesive taxonomy is proposed that allows for classification of both existing and future QMs. This paper additionally provides thorough description of recently used QMs while attempting to unify the nomenclature. Advantages and disadvantages are shown along with the various features of the measures in given problem – as an example Multi–Skill Resource Constrained Project Scheduling Problem is given. Finally, a complementary set of QMs is proposed that can create a meaningful comparison of obtained multiobjective solutions to a multiobjective problem. Supplementary measures are proposed for specialized applications and open issues in the field are identified.

Priority-based heuristics for the multi-skill resource constrained project scheduling problem

In this paper we investigate one of the most recent extensions of the Resource Constrained Project Scheduling Problem (RCPSP): the Multi-Skill Resource Constrained Project Scheduling Problem (MSRCPSP). For this complex problem we propose the use of a parallel scheduling scheme. Such scheme has been successfully applied to the RCPSP. Nevertheless, in order to apply it to the MSRCPSP two new concepts are developed: resource weight and activity grouping. We discuss such concepts and use them for the new heuristic framework proposed. A series of computational tests performed using a large number of instances and reported in this paper shows that the new heuristic is very effective in finding high quality solutions within very small CPU times.