Population-Based Approaches to the Resource-Constrained and Discrete-Continuous Scheduling

Ratajczak-Ropel, Ewa; Skakovski, Aleksander

doi:10.1007/978-3-319-62893-6

Cited by 9 publications

(4 citation statements)

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“…JABAT was successfully used for solving the RCPSP and MRCPSP. [19][20][21] In this paper, the implementation for solving the DRCMPSP, named ATMAS, is proposed. ATMAS is implemented in JABAT and dedicated for DRCMPSP.…”

Section: Jabat For Solving the Drcmpsp à à à Atmasmentioning

confidence: 99%

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A-Team Solving Distributed Resource-Constrained Multi-Project Scheduling Problem

Jędrzejowicz

Ratajczak-Ropel

2019

Vietnam J. Comp. Sci.

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In this paper, a multi-agent system (MAS) based on the A-Team concept is proposed to solve the Distributed Resource-Constrained Multi-Project Scheduling Problem (DRCMPSP). In the DRCMPSP, multiple distributed projects are considered. Hence, the local task schedule for each project and a coordination of the shared decisions are considered. The DRCMPSP belongs to the class of the strongly NP-hard optimization problems. Multi-agent system seems the natural way of solving such problems. The A-Team MAS, proposed in this paper, has been built using the JABAT environment where two types of the optimization agents are used: local and global. Local optimization agents are used to¯nd solutions for the local projects, and global optimization agents are responsible for the coordination of the local projects and for¯nding the global solutions. The approach has been tested experimentally using 140 benchmark problem instances from MPSPLIB library with minimizing the Average Project Delay (APD) as global optimization criterion.

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Section: Jabat For Solving the Drcmpsp à à à Atmasmentioning

confidence: 99%

“…. each local SolutionManager uses population of 30 solutions and DCI strategy using reinforcement learning rules 24,21 ; . computations are stopped where the average diversity in each local population is less than¯xed threshold (in this approach, 0.01%).…”

Section: P J¼ Edrzejowicz and E Ratajczak-ropelmentioning

confidence: 99%

A-Team Solving Distributed Resource-Constrained Multi-Project Scheduling Problem

Jędrzejowicz

Ratajczak-Ropel

2019

Vietnam J. Comp. Sci.

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“…The utilization of efficient heuristics and metaheuristics has been employed in the pursuit of locating optimal or near-optimal solutions for the MRCPSP, given its classification as an NP-hard issue [31]. The problem of finding an optimal schedule cannot be solved with full enumeration or exact algorithms [32].…”

Section: Introductionmentioning

confidence: 99%

“…Jiang et al [38] highlights the GWO's high precision, rapid convergence, and straightforward implementation. Variants of the GWO algorithm have successfully addressed various optimization problems, including the maximum power point tracking [39], feature selection [40], power scheduling [41], UAV path planning [42], and job-machine scheduling [32]. Notably, there is a gap in the literature concerning the application of the GWO algorithm to project scheduling problems involving carbon-resource constraints.…”

Section: Introductionmentioning

confidence: 99%

A Mathematical Model for Optimizing NPV and Greenhouse Gases for Construction Projects Under Carbon Emissions Constraints

Hussain,

Salman Khalid,

Alkahtani

et al. 2024

IEEE Access

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Traditionally, projects have been scheduled with a primary focus on optimizing economic benefits, often at the expense of environmental considerations. However, in today's context, there is an increasing societal demand for organizations to incorporate sustainability practices into their project management policies. This research aims to address this pressing issue by developing a comprehensive mathematical model that takes into account two crucial objectives: project net present value (NPV) and greenhouse gas (GHG) emissions. This model introduces carbon emission constraints on a per-period basis, alongside the usual precedence and resource constraints. the problem represented by this model is proven to be NP-hard, which adds complexity to its solution. To tackle this challenge, two multi-objective metaheuristic algorithms, specifically the Modified Grey Wolf Optimizer (MGWO) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2), have been employed to solve the model. Extensive comparative analyses of these me-ta heuristics across various multi-objective performance measures indicate that MGWO consistently outperforms SPEA2 in most scenarios. To illustrate the practical applicability of the pro-posed model, a real-world case study has been utilized. The results of this case study offer compelling insights, showing that project NPV can be significantly improved with only a marginal increase in GHG emissions. This finding highlights the model's potential to provide pragmatic solutions and valuable trade-off options for project planners and practitioners striving to balance economic objectives with environmental sustainability.

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A-Team Solving Distributed Resource-Constrained Multi-project Scheduling Problem

Jędrzejowicz

Ratajczak-Ropel

2018

Computational Collective Intelligence

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Population-Based Approaches to the Resource-Constrained and Discrete-Continuous Scheduling

Cited by 9 publications

References 0 publications

A-Team Solving Distributed Resource-Constrained Multi-Project Scheduling Problem

A-Team Solving Distributed Resource-Constrained Multi-Project Scheduling Problem

A Mathematical Model for Optimizing NPV and Greenhouse Gases for Construction Projects Under Carbon Emissions Constraints

A-Team Solving Distributed Resource-Constrained Multi-project Scheduling Problem

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