Ant Colony Optimisation for Backward Production Scheduling

Abstract

The main objective of a production scheduling system is to assign tasks (orders or jobs) to resources and sequence them as efficiently and economically (optimised) as possible. Achieving this goal is a difficult task in complex environment where capacity is usually limited. In these scenarios, finding an optimal solution—if possible—demands a large amount of computer time. For this reason, in many cases, a good solution that is quickly found is preferred. In such situations, the use of metaheuristics is an appropriate strategy. In these last two decades, some out-of-the-shelf systems have been developed using such techniques. This paper presents and analyses the development of a shop-floor scheduling system that uses ant colony optimisation (ACO) in a backward scheduling problem in a manufacturing scenario with single-stage processing, parallel resources, and flexible routings. This scenario was found in a large food industry where the corresponding author worked as consultant for more than a year. This work demonstrates the applicability of this artificial intelligence technique. In fact, ACO proved to be as efficient as branch-and-bound, however, executing much faster.

The Unrelated Parallel Machine Scheduling with a Dependent Time Setup using Ant Colony Optimization Algorithm

“…Since the probability of selecting a node also depends on the number of pheromones in the arc, better solutions tend to influence other ants to choose the same path in the future. When all ants complete the required number of trips, the scheduling process ends, and the best responses are presented [26]. Dorigo et al, [27] revealed the ant algorithm steps, which were then adjusted to the parallel machine production scheduling application.…”

Developing the method of rational trucking routing based on the modified ant algorithm

“…The scope of solving the applied problems based on the ant algorithm is quite wide. Application of the ant algorithm for planning multiprofile operations in production systems is given in [19,20]. A multi-purpose programming model for optimizing an import crude oil transportation network based on the use of genetic and ant algorithms is given in [21].…”

Using ant colony optimization in software development project scheduling

“…In [16], a hybrid algorithm for resource constrained project scheduling problem, named ANGEL have been proposed that combined ACO with GA, while in [17], a hybrid algorithm named ACOSS that combined ACO, local search strategy and a scatter search have been proposed for RCPSP in real time. Many scheduling problems like backward production scheduling [18] were solved by ACO. Another paper [19] had implemented ACO on the project resource scheduling problem.…”