Using an enhanced scatter search algorithm for a resource-constrained project scheduling problem

Abstract

The resource-constrained project scheduling problem is one of the classical problems in the field of operations research. There are many criteria to efficiently determine the desired schedule of a project. In this paper, a well-known criterion namely project's makespan is considered. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional methods. Therefore, an enhanced scatter search, based on a new path relinking and two prominent permutation-based and crossover operators, is devised to solve the problem. In order to validate the performance of the proposed algorithm, in terms of solution quality, the algorithm is applied to various test problems available on the literature and the reliability of it, is compared with wellreported benchmark algorithms. The computational results reveal that the proposed algorithm has appropriate results in comparison with the existing benchmark algorithms.

Scheduling Optimization of Prefabricated Construction Projects by Genetic Algorithm

“…It can be seen from Figure 7 that the calculation time of the proposed algorithm of the research increases approximately linearly with the increase in project activities. The algorithm in this study is more efficient in terms of time compared with the other algorithms that have exponentially increasing computing time [80,87,88]. Therefore, the algorithm has more advantages in solving large-scale project scheduling problems.…”

A Building‐Block‐Based Genetic Algorithm for Solving the Robots Allocation Problem in a Robotic Mobile Fulfilment System

“…Equation (7) shows that the transfer time of resource "robots" between task i and task j is the travel time of task j in step 1, and 1 is variable depending on task i. Constraints (8)- (11) are the constraints on the starting time of the task so that the finishing time of the task can be calculated according to (2)-(4) after its starting time has been determined. Constraint (8) is the precedence constraint of tasks in the same picking station, and it means that task j can arrive at the picking station only after its direct predecessor task j-1 arrives.…”

“…The priority rule-based construction heuristic is widely used in RCPSP to generate feasible initial schedules. Then, the obtained schedules are improved by various neighbourhood search methods and meta-heuristic algorithm, including tabu search [9], simulated annealing [10], scatter search [11], ant colony optimization [12], particle swarm optimization [13], and genetic algorithm [14][15][16].…”

Comparative study of pheromone control heuristics in ACO algorithms for solving RCPSP problems

“…Finish Time (LFT) [46,47] priority rule, among other. In this new set of experiments we have used the ACO model using the Dynamic Oblivion Rate and the proposed CRO-based Oblivion Rate.…”