Mode-Based versus Activity-Based Search for a Nonredundant Resolution of the Multimode Resource-Constrained Project Scheduling Problem

This paper addresses an energy-based extension of the Multimode Resource-Constrained Project Scheduling Problem (MRCPSP) called MRCPSP-ENERGY. This extension considers the energy consumption as an additional resource that leads to different execution modes (and durations) of the activities. Consequently, different schedules can be obtained. The objective is to maximize the efficiency of the project, which takes into account the minimization of both makespan and energy consumption. This is a well-known NP-hard problem, such that the application of metaheuristic techniques is necessary to address real-size problems in a reasonable time. This paper shows that the Activity List representation, commonly used in metaheuristics, can lead to obtaining many redundant solutions, that is, solutions that have different representations but are in fact the same. This is a serious disadvantage for a search procedure. We propose a genetic algorithm (GA) for solving the MRCPSP-ENERGY, trying to avoid redundant solutions by focusing the search on the execution modes, by using the Mode List representation. The proposed GA is evaluated on different instances of the PSPLIB-ENERGY library and compared to the results obtained by both exact methods and approximate methods reported in the literature. This library is an extension of the well-known PSPLIB library, which contains MRCPSP-ENERGY test cases.

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An Efficient Simulation Algorithm for Resource-Constrained Project Scheduling Problem

The Open Mechanical Engineering Journal ◽

10.2174/1874155x01408010009 ◽

2014 ◽

Vol 8 (1) ◽

pp. 9-13

Author(s):

L. Peng ◽

P. Wuliang

Keyword(s):

Project Scheduling ◽

Large Scale ◽

Exact Algorithms ◽

Scheduling Problem ◽

Simulation Algorithm ◽

Resource Constrained ◽

Resource Conflict ◽

Resource Constrained Project Scheduling ◽

Np Hard Problem ◽

Project Scheduling Problem

Since Resource-Constrained Project Scheduling Problem (RCPSP) is a well-known NP-hard problem, it is difficult to solve large-scale practical cases by using traditional exact algorithms. Genetic algorithm (GA) is a kind of intelligent algorithm for approximate optimization, which can ascertain global optimization or suboptimal solution within a reasonable time. This article presented a new simulation algorithm by using GA for solving Resource-Constrained Project Scheduling Problem. In the algorithm, the activity adjacency matrix and priority-based preemptive resource conflict resolution are used to prevent chromosome from generating infeasible schedules. Finally, the method was tested with an actual machine and electricity project case, and the results show that the presented method is efficient and practical for practical project cases.

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A new model and metaheuristic approach for the energy-based resource-constrained scheduling problem

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1177/0954405417711734 ◽

2017 ◽

Vol 233 (1) ◽

pp. 293-305 ◽

Cited By ~ 2

Author(s):

Daniel Morillo Torres ◽

Federico Barber ◽

Miguel A Salido

Keyword(s):

Energy Consumption ◽

Project Scheduling ◽

Current Trend ◽

Limited Resource ◽

Problem Instance ◽

Scheduling Problem ◽

Scheduling Problems ◽

Resource Constrained ◽

Resource Constrained Project Scheduling ◽

Project Scheduling Problem

This article focuses on obtaining sustainable and energy-efficient solutions for limited resource programming problems. To this end, a model for integrating [Formula: see text] and energy consumption objectives in multi-mode resource-constrained project scheduling problems (MRCPSP-ENERGY) is proposed. In addition, a metaheuristic approach for the efficient resolution of these problems is developed. In order to assess the appropriateness of theses proposals, the well-known Project Scheduling Problem Library is extended (called PSPLIB-ENERGY) to include energy consumption to each Resource-Constrained Project Scheduling Problem instance through a realistic mathematical model. This extension provides an alternative to the current trend of numerous research works about optimization and the manufacturing field, which require the inclusion of components to reduce the environmental impact on the decision-making process. PSPLIB-ENERGY is available at http://gps.webs.upv.es/psplib-energy/ .

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The R-PSO algorithm solving multi-skill resource-constrained project scheduling problem

Journal of Military Science and Technology ◽

10.54939/1859-1043.j.mst.csce5.2021.71-82 ◽

2021 ◽

pp. 71-82

Author(s):

Dang Quoc Huu

Keyword(s):

Project Scheduling ◽

Optimal Solution ◽

Simulated Data ◽

Pso Algorithm ◽

Scheduling Problem ◽

Resource Constrained ◽

Feasible Schedule ◽

Approximate Methods ◽

Resource Constrained Project Scheduling ◽

Project Scheduling Problem

The Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP) is a combinational optimization problem with many applications in science and practical areas. This problem aims to find out the feasible schedule for the completion of projects and workflows that is minimal duration or cost (or both of them - multi objectives). The researches show that MS-RCPSP is classified into NP-Hard classification, which could not get the optimal solution in polynomial time. Therefore, we usually use approximate methods to carry out the feasible schedule. There are many publication results for that problem based on evolutionary methods such as GA, Greedy, Ant, etc. However, the evolutionary algorithms usually have a limitation that is fallen into local extremes after a number of generations. This paper will study a new method to solve the MS-RCPSP problem based on the Particle Swarm Optimization (PSO) algorithm that is called R-PSO. The new improvement of R-PSO is re-assigning the resource to execute solution tasks. To evaluate the new algorithm's effectiveness, the paper conducts experiments on iMOPSE datasets. Experimental results on simulated data show that the proposed algorithm finds a better schedule than related works.

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