Solving Resource Constrained Project Scheduling Research Articles

Deep reinforcement learning for solving resource constrained project scheduling problems with resource disruptions

Deep reinforcement learning for solving resource constrained project scheduling problems with resource disruptions

A two-stage multi-operator differential evolution algorithm for solving Resource Constrained Project Scheduling problems

The Resource Constrained Project Scheduling problem (RCPSP) is a complex and combinatorial optimization problem mostly relates with project management, construction industries, production planning and manufacturing domains. Although several solution methods have been proposed, no single method has been shown to be the best. Further, optimal solution of this type of problem requires different requirements of the exploration and exploitation at different stages of the optimization process. Considering these requirements, in this paper, a two-stage multi-operator differential evolution (DE) algorithm, called TS-MODE, has been developed to solve RCPSP. TS-MODE starts with the exploration stage, and based on the diversity of population and the quality of solutions, this approach dynamically place more importance on the most-suitable DE, and then repeats the same process during the exploitation phase. A complete evaluation of the components and parameters of the algorithms by a Design of Experiments technique is also presented. A number of single-mode RCPSP data sets from the project scheduling library (PSPLIB) have been considered to test the effectiveness and performance of the proposed TS-MODE against selected recent well-known state-of-the-art algorithms. Those results reveal the efficiency and competitiveness of the proposed TS-MODE approach.

New procedures with new activity assumptions for solving resource constrained project scheduling problems

New procedures with new activity assumptions for solving resource constrained project scheduling problems

Memetic algorithm for solving resource constrained project scheduling problems

The resource constrained project scheduling problem (RCPSP) has a wide variety of practical applications in construction, manufacturing, project planning, and other areas. Since the 1960s many optimization algorithms have been proposed to solve this NP-hard problem, and their performances are evaluated in well-known test problems with different complexities. Although it is desirable to find an algorithm which can provide promising solutions with reasonable computational efforts for any problem under consideration, no single algorithm can meet that condition. To deal with this challenge, we present a genetic algorithm based memetic algorithm (MA) for solving RCPSP. The algorithm is initiated by a critical path-based heuristic and a variant of the Nawaz, Enscore, and Ham (NEH) heuristic. The algorithm involves a similar block order crossover and a variable insertion based local search. An automatic restart scheme is also presented which assists the algorithm to escape from local optima. In addition, a design-of-experiment (DOE) method is used to determine the set of suitable parameters for the proposed MA. Numerical results, statistical analysis and comparisons with state-of-the-art algorithms demonstrate the effectiveness of the proposed approach.

A new mixed-integer linear programming formulation and particle swarm optimization based hybrid heuristic for the problem of resource investment and balancing of the assembly line with multi-manned workstations

Resource investment and balancing problem of an assembly line with parallel multi-manned workstations can be defined as the assignment of tasks to reduce the cost of the line, which includes the cost of opened workstations and required renewable resources. Although mentioned problem has been commonly occurred in industrial environment that produce large scale products in high volumes, there have been restricted number of studies in the literature about this field. This article proposes a new mixed-integer linear programming approach that can be used in solving small size instances of the problem. In addition, a new hybrid method has been developed to solve larger scale instances by combining particle swarm optimization algorithm with a special constructive heuristic. In the constructive heuristic, serial schedule generation scheme widely used in solving resource constrained project scheduling problems has been adapted to resource investment problem with some modifications. Proposed metaheuristic has been compared against a tabu search and cuckoo search algorithm taken from the assembly line balancing literature. Many precedence diagrams commonly used in solving various assembly line balancing problems in the literature, have been used to generate test instances for the considered problem type. After solving these test instances using each solution methods, it has been observed that the proposed hybrid metaheuristic yielded the solutions, which have acceptable deviations from the lower bounds.

Novel Approach to Solve Resource Constrained Project Scheduling Problem (RCPSP)

In this paper, resource constrained project scheduling problem is taken and solved using genetic algorithm. This algorithm solves the problem as a whole in software development, with the limited resource the project has to be scheduled to the team members. The main aim is to schedule and optimize the resource to complete the project within time. Due to resource constraint environment, the complexity of solving the algorithm increases exponentially. So the traditional methods are not suitable to solve the resource constraint problem. The Genetic Algorithm is taken to solve the multiple resource constraints project scheduling needs. This typical NP-hard problem is solved via mathematical model via genetic algorithm. Software development projects were considered to be resource constrained and project scheduling solution makes the algorithm time efficient.

A new approach for solving resource constrained project scheduling problems using differential evolution algorithm

One subcategory of project scheduling is the resource constrained project scheduling problem (RCPSP). The present study proposes a differential evolution algorithm for solving the RCPSP making a small change in the method to comply with the model. The RCPSP is intended to program a group of activities of minimal duration while considering precedence and resource constraints. The present study introduces a differential evolution algorithm and local search was added to improve the performance of the algorithm. The problems were then solved to evaluate the performance of the algorithm and the results are compared with genetic algorithm. Computational results confirm that the differential evolution algorithm performs better than genetic algorithm.

A study on the performance of differential search algorithm for single mode resource constrained project scheduling problem

Article history: Received March 29, 2015 Received in revised format: May 12, 2015 Accepted May 12, 2015 Available online May 18 2015 Differential Search (DS) algorithm is a new meta-heuristic for solving real-valued numerical optimization. This paper introduces a new method based on DS for solving Resource Constrained Project Scheduling Problem (RCPSP). The RCPSP is aimed to schedule a set of activities at minimal duration subject to precedence constraints and the limited availability of resources. The proposed method is applied to PSPLIB case studies and its performance is evaluated in comparison with some of state of art methods. Experimental results show that the proposed method is effective. Also, it is among the best algorithms for solving RCPSP. Growing Science Ltd. All rights reserved. 5 © 201

Using a fuzzy clustering chaotic-based differential evolution with serial method to solve resource-constrained project scheduling problems

Abstract The resource-constrained problem seeks to find the optimal sequence that minimizes project duration under current precedence constraints and resource limitations. This study integrates the fuzzy c-means clustering technique and the chaotic technique into the Differential Evolution (DE) algorithm to develop the Fuzzy Clustering Chaotic-based Differential Evolution (FCDE) algorithm, an innovative approach to solving complex optimization problems. Within the FCDE, the chaotic technique prevents the optimization algorithm from premature convergence and the fuzzy c-means clustering technique acts as several multi-parent crossover operators in order to utilize population information efficiently and enhance convergence efficiency. Further, this study applies a serial method to reflect individual-user priorities into the active schedule and the project duration calculations. The FCDE and serial method are then integrated into a novel optimization model called the Fuzzy Clustering Chaotic-based Differential Evolution for Solving Resource Constrained Project Scheduling Problem (FCDE-RCPSP). Experiments run indicate that the proposed FCDE-RCPSP obtains optimal results more reliably and efficiently than the benchmark algorithms considered. The FCDE-RCPSP is a promising alternative approach to handling resource-constrained project scheduling problems.

Hybrid Local Search Methods in Solving Resource Constrained Project Scheduling Problem

Now-a-days different meta-heuristic approaches, their variants and hybrids are being applied for solving Combinatorial Optimization Problems (COP). In this paper Resource Constrained Project Scheduling Problem (RCPSP) has been presented as a COP. This is a common problem for many construction projects. It is highly constrained and is categorized as a NP-hard problem. In our earlier work Simulated Annealing (SA_RCP) outperformed other meta-heuristics, like, Genetic Algorithm, Tabu Search, Particle Swarm Optimization and its variant in solving benchmark instances of this problem. Having been inspired by this result we have further developed new hybrids of Simulated Annealing and Tabu Search. In this work, we have proposed five more methods developed by combining Simulated Annealing and Tabu Search and applied them for solving a benchmark instance of this problem. The results show that Simulated Annealing incorporated with Tabu List, Greedy Selection Heuristic and aspiration criteria (GTSA_AC_RCP) outperforms other methods in getting optimal results with maximum hit and minimum fluctuations.

An ABC-Genetic method to solve resource constrained project scheduling problem

The aim of this work is to study the effect of hybridization on the performance of the Artificial Bee Colony (ABC) as arecently introduced metaheuristic for solving Resource Constrained Project Scheduling Problem (RCPSP). For thispurpose the ABC is combined with the Genetic Algorithm (GA). At the initial time, the algorithm generates a set ofschedules randomly. The initial solution is evaluated against constraints and the infeasible solutions are resolved tofeasible ones. Then, the initial schedules will be improved iteratively using hybrid method until termination condition ismet. The proposed method works by interleaving the ABC and GA search processes. The GA method updates schedulesby considering the best solution found by the ABC approach. Next the ABC approach picks the solutions found by GAsearch. A new approach is used by the algorithm to maintain the priorities of the activities in feasible ranges. Theperformance of the proposed algorithm is compared against a set of state-of-art algorithms. The simulation results showedthat the proposed algorithm provides an efficient way for solving RCPSP and produce competitive results compared toother algorithms investigated in this work.

Ensuring quality in resource constrained project scheduling

This study constitutes an initial effort for modeling and solving resource constrained project scheduling problems with the objective of maximizing quality. Two mixed integer formulations of the problem are introduced. The first formulation assumes integral activity durations and completion times. These assumptions are relaxed in the second formulation. In both formulations the quality of a project is measured by the amount of rework required and the corresponding additional cost incurred. These quality measures are expressed in a common unit of measure and incorporated into the objective function. The second mixed integer formulation is found to be more efficient and thus computational results are reported based on that model. A set of resource constrained scheduling problems are solved using the quality objective and traditional objective functions from the literature. In general it is found that the schedules obtained when maximizing quality outperform the schedules obtained with minimizing makespan or maximizing the net present value of project cash flows objectives in terms of makespan, cost and computational times.