On the use of genetic programming to evolve priority rules for resource constrained project scheduling problems

Chand, Shelvin; Huynh, Quang Nhat; Singh, Hemant Kumar; Ray, Tapabrata; Wagner, Markus

doi:10.1016/j.ins.2017.12.013

Cited by 82 publications

(58 citation statements)

References 26 publications

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“…In addition, there is no guarantee that these algorithms may work in stochastic and dynamic production environments without major modifications. In recent years, genetic programming (GP) has been adopted to solve a wide range of production and supply chain management problems [8], [9]. Due to its flexibility and powerful search mechanisms, GP can be easily customised to deal with different planning and scheduling decisions in complex production and logistics systems, which cannot be easily handled by other ML and optimisation methods.…”

Section: Introductionmentioning

confidence: 99%

People-Centric Evolutionary System for Dynamic Production Scheduling

Nguyen

Zhang

Alahakoon

et al. 2021

IEEE Trans. Cybern.

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Evolving production scheduling heuristics is a challenging task because of the dynamic and complex production environments and the interdependency of multiple scheduling decisions. Different genetic programming methods have been developed for this task and achieved very encouraging results. However, these methods usually have trouble in discovering powerful and compact heuristics, especially for difficult problems. Moreover, there is no systematic approach for the decision makers to intervene and embed their knowledge and preferences in the evolutionary process. This paper develops a novel peoplecentric evolutionary system for dynamic production scheduling. The two key components of the system are a new mapping technique to incrementally monitor the evolutionary process and a new adaptive surrogate model to improve the efficiency of genetic programming. The experimental results with dynamic flexible job shop scheduling show that the proposed system outperforms the existing algorithms for evolving scheduling heuristics in terms of scheduling performance and heuristic sizes. The new system also allows the decision makers to interact on the fly and guide the evolution towards desired solutions.

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Section: Introductionmentioning

confidence: 99%

People-Centric Evolutionary System for Dynamic Production Scheduling

Nguyen

Zhang

Alahakoon

et al. 2021

IEEE Trans. Cybern.

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“…Project scheduling problem (PSP) determines run times for a specific set of fixed activities with regard to precedent relationships and via an allocation of different resources in order to achieve predetermined goals [1][2][3][4]. Role of the PSP and baseline scheduling in the project management is crucial [5][6][7][8] and for this reason, Vanhoucke [9] presented nine time and eight cost forecasting methods for both project duration and cost.…”

Section: Introductionmentioning

confidence: 99%

“…These scheduling problems have many applications, ranging from production planning to project management [12]. Therefore, RCPSP determines a proper sequence of activities so that we involve in two main constraints: (1) Resource constraints and 2) Precedence relationships are satisfied at fashion time, and measurement criteria, such as time, cost, and quality are optimized [1]. The classification of RCPSP is summarized as follows [13] Since the RCPSP is NP-hard, and for more adaptation of the RCPSP with reality, some researchers have considered various extensions and different methods for solving the problem and working on exact methods using mixed-integer programming, constraint programming, and satisfiability modulo theories [14][15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

“…Gonzalez-Pardo et al [26] modeled the RCPSP by constraint satisfaction problems-graph and solved by ant colony optimization. Chand et al [1] regarded a genetic programming based hyper-heuristic to produce efficient priority rules for the RCPSP. Kadri and Boctor [27] addressed the RCPSP with transfer times to be known and deterministic, and proposed a GA for the solving.…”

Section: Introductionmentioning

confidence: 99%

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Designing a resource-constrained project scheduling model considering multiple routes for flexible project activities: meta-heuristic algorithms

2019

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Resource constrained project scheduling problem with multiple routes for flexible project activities (RCPSP-MR) is a generalization of the RCPSP, in which for the implementation of each flexible activity in main structure of the project, several exclusive sub-networks are considered. Each sub-network is regarded as a route for the flexible activity. The routes are considered for each flexible activity that are varied in terms of: 1) Number of activities required to execute; 2) Precedence relationship between activates; 3) Allocation of different renewable and nonrenewable resources to each activity; and 4) Effectiveness on the duration and cost of project completion. In this paper, a new mathematical formulation of RCPSP-MR is firstly presented. Then, two solving approaches based on particle swarm optimization (PSO) and genetic algorithm (GA) are proposed to minimize costs of project completion. To evaluate the effectiveness of these proposed approaches, 50 problems (in very small, small, medium, and large-sized test problems) are designed and then are solved. Finally, comparisons are provided. Computational results show that the proposed GA generates high-quality solutions in a timely fashion. Keywords: Resource constrained project scheduling problem (RCPSP), flexible activities, multiple routes, particle swarm optimization (PSO), genetic algorithm (GA) 0,1, 2,..., 1 JN  1 J Set of total project fixed activities, 1 JJ  2 J Set of total flexible project activities, 21 J J J    Set of routes of each flexible activity 2 J   h  Set of total sub network for flexible activity

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“…Dixit et al [9] solved the block spatial scheduling problem of shipbuilding projects using a priority rules-based simulation.Öner-Közen et al [10] modeled the prioritization problem in a make to order production system and studied the impact of prioritization on the on-time probability and expected delay of orders. Chand et al [11] proposed a genetic programmingbased hyper-heuristic algorithm for a resource-constrained project scheduling problem and compared the improved priority rules with the existing priority rules, demonstrating that the performance of the new method was significantly better than that using the existing priority rules.…”

Section: Introductionmentioning

confidence: 99%

Resource allocation optimization of equipment development task based on MOPSO algorithm

Zhang¹,

Yue-jin²,

Yang³

2019

Jsee

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Resource allocation for an equipment development task is a complex process owing to the inherent characteristics, such as large amounts of input resources, numerous sub-tasks, complex network structures, and high degrees of uncertainty. This paper presents an investigation into the influence of resource allocation on the duration and cost of sub-tasks. Mathematical models are constructed for the relationships of the resource allocation quantity with the duration and cost of the sub-tasks. By considering the uncertainties, such as fluctuations in the sub-task duration and cost, rework iterations, and random overlaps, the tasks are simulated for various resource allocation schemes. The shortest duration and the minimum cost of the development task are first formulated as the objective function. Based on a multi-objective particle swarm optimization (MOPSO) algorithm, a multi-objective evolutionary algorithm is constructed to optimize the resource allocation scheme for the development task. Finally, an uninhabited aerial vehicle (UAV) is considered as an example of a development task to test the algorithm, and the optimization results of this method are compared with those based on non-dominated sorting genetic algorithm-II (NSGA-II), non-dominated sorting differential evolution (NSDE) and strength pareto evolutionary algorithm-II (SPEA-II). The proposed method is verified for its scientific approach and effectiveness. The case study shows that the optimization of the resource allocation can greatly aid in shortening the duration of the development task and reducing its cost effectively.

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On the use of genetic programming to evolve priority rules for resource constrained project scheduling problems

Cited by 82 publications

References 26 publications

People-Centric Evolutionary System for Dynamic Production Scheduling

People-Centric Evolutionary System for Dynamic Production Scheduling

Designing a resource-constrained project scheduling model considering multiple routes for flexible project activities: meta-heuristic algorithms

Resource allocation optimization of equipment development task based on MOPSO algorithm

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