Heuristic Algorithm to Minimize Total Weighted Tardiness on the Unrelated Parallel Machine with Sequence Dependent Setup and Future Ready Time

This study presents a heuristic algorithm to minimize total weighted tardiness on unrelated parallel machines with sequence-dependent setup time and future ready time. We propose a new rule based on Apparent Tardiness Cost (ATC). The performance of the rule is evaluated on unrelated parallel machines. In order to solve a problem, we use a look-ahead method and a job-swap method. When a machine becomes idle, the heuristic compares the jobs on the machine and selects the one with the smallest total tardiness value to carry out a process. The propose heuristic is divided into three stages: The first stage employs the newly introduced dispatching rule, ATC with continuous setup and ready time for unrelated parallel machines (ATCSR_UP), along with a look-ahead heuristic to select the initial job for each machine. The second stage, consisting of several iterations, schedules the rest of the job on the machine. Each iteration starts by finding the job with the smallest tardiness. The ATCSR_Rm rule proposed by Lin and Hsieh (2013) concerns the unrelated-parallel-machine scheduling which this study examines, so we compare our ATC-based rule with their proposed rule. Although they study a separable setup time in their research, no other paper than Lin and Hsieh (2003) focus on unrelated parallel machine with future ready times. In their WSPT term, they consider the processing time for each job; our own rule considers processing time, setup time, job ready time, and machine time. We consider the setup time, job ready time, and machine time because — according to the continuous sequence-dependent setup rule — setup time should be included in processing time (Yue and Jang 2013). In addition, job ready time and machine time should also be included in the processing time. Adding setup time 〖(s〗_(i,j)), job ready time (r_j), and machine time (t_m) to the formula thus makes the formula more accurate. Lin and Hsieh (2013) use max(r_j,t_i+s_(i,j) ) for the slack term, and they compare the ready time with the sum of the machine available time 〖(t〗_i) and the setup time 〖(s〗_(i,j)). However, in our formula, we consider ready time, machine time, and current time. Current time (t) is used when a job might come at a future time when the machine in question is idle or has finished the job. The last term of the propose heuristic is the ready term, which uses both ready time (r_j) and machine time (t_m), because it needs to specify whether ready time (r_j) or machine time (t_m) goes first. If a job is ready to be processed but the machine is not ready, the job has to wait. We use ready time (r_j) and machine time (t_m) because this makes the formula more suitable for practical, real-world us

Dynamic Scheduling for Maintenance Tasks Allocation supported by Genetic Algorithms

Since the first factories were created, man has always tried to maximize its production and, consequently, his profits. However, the market demands have changed and nowadays is not so easy to get the maximum yield of it. The production lines are becoming more flexible and dynamic and the amount of information going through the factory is growing more and more. This leads to a scenario where errors in the production scheduling may occur often. Several approaches have been used over the time to plan and schedule the shop-floor’s production. However, some of them do not consider some factors present in real environments, such as the fact that the machines are not available all the time and need maintenance sometimes. This increases the complexity of the system and makes it harder to allocate the tasks competently. So, more dynamic approaches should be used to explore the large search spaces more efficiently. In this work is proposed an architecture and respective implementation to get a schedule including both production and maintenance tasks, which are often ignored on the related works. It considers the maintenance shifts available. The proposed architecture was implemented using genetic algorithms, which already proved to be good solving combinatorial problems such as the Job-Shop Scheduling problem. The architecture considers the precedence order between the tasks of a same product and the maintenance shifts available on the factory. The architecture was tested on a simulated environment to check the algorithm behavior. However, it was used a real data set of production tasks and working stations