A hyper-heuristic based ensemble genetic programming approach for stochastic resource constrained project scheduling problem

Chen, HaoJie; Ding, Guofu; Qin, Shengfeng; Zhang, Jian

doi:10.1016/j.eswa.2020.114174

Cited by 35 publications

(21 citation statements)

References 53 publications

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“…As sole "wins" and non-whole-group "wins" are rare also, it is impossible to select the best sorting approach. Instead, a hyper-heuristic comprising the sorting approaches (i. e., the whole group, where each sorting is applied) may be constructed (although the process of obtaining a solution will be slowed down, unless a parallelization is used) [23,24].…”

Section: Discussionmentioning

confidence: 99%

Sorting Approaches in the Heuristic Based on Remaining Available and Processing Periods to Minimize Total Weighted Tardiness in Progressive Idling-Free 1-Machine Preemptive Scheduling

Romanuke

2021

KPI SN

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Background. In preemptive job scheduling, total weighted tardiness minimization is commonly reduced to solving a combinatorial problem, which becomes practically intractable as the number of jobs and the numbers of their processing periods increase. To cope with this challenge, heuristics are used. A heuristic, in which the decisive ratio is the weighted reciprocal of the maximum of a pair of the remaining processing period and remaining available period, is closely the best one. However, the heuristic may produce schedules of a few jobs whose total weighted tardiness is enormously huge compared to the real minimum. Therefore, this heuristic needs further improvements, one of which already exists for jobs without priority weights with a sorting approach where remaining processing periods are minimized. Three other sorting approaches still can outperform it, but such exceptions are quite rare. Objective. The goal is to determine the influence of the four sorting approaches and try to select the best one in the case where jobs have their priority weights. The heuristic will be applied to tight-tardy progressive idling-free 1-machine preemptive scheduling, where the release dates are given in ascending order starting from 1 to the number of jobs, and the due dates are tightly set after the release dates. Methods. To achieve the said goal, a computational study is carried out with applying each of the four heuristic approaches to minimize total weighted tardiness. For this, two series of 4151500 scheduling problems are generated. In the solution of a scheduling problem, a sorting approach can “win” solely or “win” in a group of approaches, producing the heuristically minimal total weighted tardiness. In each series, the distributions of sole-and-group “wins” are ascertained. Results. The sole “wins” and non-whole-group “wins” are rare: the four sorting approaches produce schedules with the same total weighted tardiness in over 98.39 % of scheduling problems. Although the influence of these approaches is different, it is therefore not really significant. Each of the sorting approaches has heavy disadvantages leading sometimes to gigantic inaccuracies, although they occur rarely. When the inaccuracy occurs to be more than 30 %, this implies that 3 to 9 jobs are scheduled. Conclusions. Unlike the case when jobs do not have their priority weights, it is impossible to select the best sorting approach for the case with job priority weights. Instead, a hyper-heuristic comprising the sorting approaches (i. e., the whole group, where each sorting is applied) may be constructed. If a parallelization can be used to process two or even four sorting routines simultaneously, the computation time will not be significantly affected.

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Section: Discussionmentioning

confidence: 99%

Sorting Approaches in the Heuristic Based on Remaining Available and Processing Periods to Minimize Total Weighted Tardiness in Progressive Idling-Free 1-Machine Preemptive Scheduling

Romanuke

2021

KPI SN

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“…Almeida et al (2020) proposed a MOEA/D-based hyper heuristic algorithm and demonstrated that the algorithm can be well applied in a permutation flow shop scheduling problem. Chen et al (2021) proposed a hyper heuristic algorithm-based ensemble genetic programming method for solving stochastic resource constrained project scheduling problem by evolving an ensemble of priority rules. As we can see, the high-level strategy in hyper heuristic algorithm plays an important role in solution searching process.…”

Section: Optimization Methodsmentioning

confidence: 99%

A shuffled frog leaping-based hyper heuristic algorithm for material feeding scheduling optimization in an automatic monorail shuttle system

Zhou

Zhao

2023

Preprint

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In recent years, mixed-model assembly lines (MMALs) have been widely adopted by the automotive enterprises to face the requirements of mass customization. Meanwhile, the diversified components of various end products are placing the material feeding processes at great challenges. Under this circumstance, to raise the material feeding efficiency, this paper proposes an automatic monorail shuttle system (AMSS) based on line-integrated supermarkets applied to the MMALs by introducing the load-exchangeable shuttles and crossovers. Considering the importance of cost-control in manufacturing enterprises, we establish a mathematical model with the objective to minimize the total costs of the material feeding system, which includes the installation costs of crossovers and the input and operation costs of load-exchangeable shuttles. Then, due to the NP-hard nature of the proposed problem, a shuffled frog leaping-based hyper heuristic (SBHH) algorithm is developed to determine the allocation and scheduling of the shuttles and crossovers, which adopts the shuffled frog leaping algorithm (SFLA) as the high-level heuristic to select the low-level heuristics. To improve the performance of the algorithm, the concept of ‘dynamic decision unit’ is presented to raise the solution accuracy and the convergence speed. Finally, the simulation results verified the superiority of the proposed SBHH algorithm both in solution quality and convergence speed by comparing with other optimization algorithm in the existing literature.

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“…A multi-agent tool is proposed in [31] to determine the effect of employees and task allocation approaches in a different dynamic environment. Recently, A hyper-heuristic based ensemble genetic programming (HH-EGP) method is proposed for solving stochastic resourceconstrained project scheduling problem (SRCPSP) by evolving an ensemble of priority rules [44]. In addition, a sequence voting mechanism is designed to deal with collaborative decision-making in the scheduling process.…”

Section: A Personality In Software Engineeringmentioning

confidence: 99%

Multi-Skills Resource Constrained and Personality Traits Based Project Scheduling

et al. 2022

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One of the primary jobs of a software project manager is to assign available resources to software development tasks in such a way that results in a high-quality product at a low cost. Software Project Scheduling (SPS) allocates the most appropriate human resource to project activities at the right time to reduce software project failure risks and minimise project makespan. In literature, the SPS problem is referred to as the Multiple Resource-Constrained Project Scheduling Problem (MRCPSP). The MRCPSP assigns human resources with multiple skills and proficiency levels to various project activities. Human abilities can be distinguished into technical/hard and non-technical/soft skills. The former describes the skills related to technology, tools, etc. While the latter deals with the skills related to the personality, such as being introvert, extrovert, sensing, etc. Recent studies have shown that some tasks may require specific soft skills. Moreover, the efficiency and productivity of the assigned resource significantly reduce if the soft skill requirements are ignored during task allocation. Ultimately, the development process might end up in lower-quality software products with higher development costs; worst case, the project may even fail. Several MRCPSP-based SPS approaches have been designed to reduce the development costs of software projects. These mechanisms consider the hard skills of a human resource with different proficiency levels. However, they overlook the soft skills required leading to the inefficiency of the allocated human resources. This will increase the project makespan and may cause higher development costs or even project failures. Therefore, to fill this gap, we propose Multi-Skill Resource Constrained and Personality Traits based Project Scheduling (MSRCPPS) considering the soft skills as well as the technical skills of a human resource during SPS. The main objective is to minimize software project makespan. Finally, the effectiveness of our proposed approach is evaluated against existing state-of-the-art using extensive simulations.

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A hyper-heuristic based ensemble genetic programming approach for stochastic resource constrained project scheduling problem

Cited by 35 publications

References 53 publications

Sorting Approaches in the Heuristic Based on Remaining Available and Processing Periods to Minimize Total Weighted Tardiness in Progressive Idling-Free 1-Machine Preemptive Scheduling

Sorting Approaches in the Heuristic Based on Remaining Available and Processing Periods to Minimize Total Weighted Tardiness in Progressive Idling-Free 1-Machine Preemptive Scheduling

A shuffled frog leaping-based hyper heuristic algorithm for material feeding scheduling optimization in an automatic monorail shuttle system

Multi-Skills Resource Constrained and Personality Traits Based Project Scheduling

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