We consider heuristic algorithms for the resource-constrained project scheduling problem. Starting with a literature survey, we summarize the basic components of heuristic approaches. We briefly describe so-called X -pass methods which are based on priority rules as well as metaheuristic algorithms. Subsequently, we present the results of our in-depth computational study. Here, we evaluate the performance of several state-of-the-art heuristics from the literature on the basis of a standard set of test instances and point out to the most promising procedures. Moreover, we analyze the behavior of the heuristics with respect to their components such as priority rules and metaheuristic strategy. Finally, we examine the impact of problem characteristics such as project size and resource scarceness on the performance.

Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem

In this paper, an improved ant colony optimization (ICMPACO) algorithm based on the multi-population strategy, co-evolution mechanism, pheromone updating strategy, and pheromone diffusion mechanism is proposed to balance the convergence speed and solution diversity, and improve the optimization performance in solving the large-scale optimization problem. In the proposed ICMPACO algorithm, the optimization problem is divided into several sub-problems and the ants in the population are divided into elite ants and common ants in order to improve the convergence rate, and avoid to fall into the local optimum value. The pheromone updating strategy is used to improve optimization ability. The pheromone diffusion mechanism is used to make the pheromone released by ants at a certain point, which gradually affects a certain range of adjacent regions. The co-evolution mechanism is used to interchange information among different sub-populations in order to implement information sharing. In order to verify the optimization performance of the ICMPACO algorithm, the traveling salesmen problem (TSP) and the actual gate assignment problem are selected here. The experiment results show that the proposed ICMPACO algorithm can effectively obtain the best optimization value in solving TSP and effectively solve the gate assignment problem, obtain better assignment result, and it takes on better optimization ability and stability.

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An Improved Ant Colony Optimization Algorithm Based on Hybrid Strategies for Scheduling Problem

Swarm intelligence algorithms are a subset of the artificial intelligence (AI) field, which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications. In the past decades, numerous swarm intelligence algorithms have been developed, including ant colony optimization (ACO), particle swarm optimization (PSO), artificial fish swarm (AFS), bacterial foraging optimization (BFO), and artificial bee colony (ABC). This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures. It provides an overview of the various swarm intelligence algorithms and their advanced developments, and briefly provides the description of their successful applications in optimization problems of engineering fields. Finally, opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.

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A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends

In this paper, a knowledge-guided multi-objective fruit fly optimization algorithm (MOFOA) is proposed for the multi-skill resource-constrained project scheduling problem (MSRCPSP) with the criteria of minimizing the makespan and the total cost simultaneously. First, a solution is represented by two lists, i.e. resource list and task list. Second, the minimum total cost rule is designed for the initialization according to the property of the problem. Third, the smell-based search is implemented via the neighborhood based search operators that are specially designed for the MSRCPSP, while the vision-based search adopts the technique for the order preference by similarity to an ideal solution (TOPSIS) and the non-dominated sorting collaboratively to complete the multi-objective evaluation. In addition, a knowledge-guided search procedure is introduced to enhance the exploration of the FOA. Finally, the design-of-experiment (DOE) method is used to investigate the effect of parameter setting, and numerical tests based on benchmark instances are carried out. The results compared to other algorithms demonstrate the effectiveness of the MOFOA with knowledge-guided search in solving the multi-objective MSRCPSP.

A knowledge-guided multi-objective fruit fly optimization algorithm for the multi-skill resource constrained project scheduling problem

Multi-skill resource-constrained project scheduling problem (MS-RCPSP) is one of the most investigated problems in operations research and management science. In this paper, a genetic programming hyper-heuristic (GP-HH) algorithm is proposed to address the MS-RCPSP. Firstly, a single task sequence vector is used to encode the solution, and a repair-based decoding scheme is proposed to generate feasible schedules. Secondly, ten simple heuristic rules are designed to construct a set of low-level heuristics. Thirdly, genetic programming is utilized as a high-level strategy which can manage the low-level heuristics on the heuristic domain flexibly. In addition, the design-of-experiment (DOE) method is employed to investigate the effect of parameters setting. Finally, the performance of the GP-HH is evaluated on the intelligent multi-objective project scheduling environment (iMOPSE) benchmark dataset consisting of 36 instances. Computational comparisons between GP-HH and the state-of-the-art algorithms indicate the superiority of the proposed GP-HH in computing feasible solutions to the problem.

A genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem

In this paper, hybrid ant colony optimization (HAntCO) approach in solving multi-skill resource-constrained project scheduling problem (MS-RCPSP) has been presented. We have proposed hybrid approach that links classical heuristic priority rules for project scheduling with ant colony optimization (ACO). Furthermore, a novel approach for updating pheromone value has been proposed based on both the best and worst solutions stored by ants. The objective of this paper is to research the usability and robustness of ACO and its hybrids with priority rules in solving MS-RCPSP. Experiments have been performed using artificially created dataset instances based on real-world ones. We published those instances that can be used as a benchmark. Presented results show that ACO-based hybrid method is an efficient approach. More directed search process by hybrids makes this approach more stable and provides mostly better results than classical ACO.

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Hybrid ant colony optimization in solving multi-skill resource-constrained project scheduling problem

In this paper Hybrid Ant Colony Optimization (HAntCO) approach in solving Multi--Skill Resource Constrained Project Scheduling Problem (MS--RCPSP) has been presented. We have proposed hybrid approach that links classical heuristic priority rules for project scheduling with Ant Colony Optimization (ACO). Furthermore, a novel approach for updating pheromone value has been proposed, based on both the best and worst solutions stored by ants. The objective of this paper is to research the usability and robustness of ACO and its hybrids with priority rules in solving MS--RCPSP. Experiments have been performed using artificially created dataset instances, based on real--world ones. We published those instances that can be used as a benchmark. Presented results show that ACO--based hybrid method is an efficient approach. More directed search process by hybrids makes this approach more stable and provides mostly better results than classical ACO.

Krzysztof Oślizło

Papers

Hybrid ant colony optimization in solving multi-skill resource-constrained project scheduling problem

Hybrid Ant Colony Optimization in solving Multi-Skill Resource-Constrained Project Scheduling Problem