Early Tardy Minimization for Joint Scheduling of Jobs and Maintenance Operations on a Single Machine (original) (raw)
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Journal of Intelligent Manufacturing
In this paper, we consider the problem of scheduling a set of M preventive maintenance tasks to be performed on M machines. The machines are assigned to execute production tasks. We aim to minimize the total preventive maintenance cost such that the maintenance tasks have to continuously be run during the schedule horizon. Such a constraint holds when the maintenance resources are not sufficient. We solve the problem by two exact methods and meta-heuristic algorithms. As exact procedures we used linear programming and branch and bound methods. As meta-heuristics, we propose a local search approach as well as a genetic algorithm. Computational experiments are performed on randomly generated instances to show that the proposed methods produce appropriate solutions for the problem. The computational results show that the deviation of the meta-heuristics solutions to the optimal one is very small, which confirms the effectiveness of meta-heuristics as new approaches for solving hard scheduling problems.
Heuristic for production scheduling on job-shop plants considering preventive maintenance tasks
The simultaneous analysis of production scheduling and preventive maintenance task attracts special attention of researchers due to its complexity and therefore the necessity to seek efficient methods for solving this kind of combinatorial problems. This paper presents a heuristic approach to solve this issue on job shop plants. The solution method includes a linear programming model, based on the Traveling Salesman Problem, where the setup time is considered as distance measure. The method´s aim is to obtain a sequence of production orders and preventive maintenance tasks that reduce the idle time and the backlogs simultaneously, accomplishing the maintenance program. After finding an optimal solution for each machine a Correction Factor (CF) is determined as new distance measure. The CF considers the structure of the initial solution, the machine utilization and the product priorities. Then, the final solution is reached running the linear programming model using the distance updated values. Finally, the proposed heuristic is applied to real case study of the Cuban industry. The experimental results indicated a significant idle time reduction for the company under examination.
A comparative study of heuristic algorithms to solve maintenance scheduling problem
Journal of Quality in Maintenance Engineering, 2007
Purpose -The purpose of this paper is to compare the effectiveness of two meta-heuristics in solving the problem of scheduling maintenance operations and jobs processing on a single machine. Design/methodology/approach -The two meta-heuristic algorithms, tabu search and simulated annealing are hybridized using the properties of an optimal schedule identified in the existing literature to the problem. A lower bound is also suggested utilizing these properties. Finding -In a numerical experimentation with large size problems, the best-known heuristic algorithm to the problem is compared with the tabu search and simulated annealing algorithms. The study shows that the meta-heuristic algorithms outperform the heuristic algorithm. In addition, the developed meta-heuristics tend to be more robust against the problem-related parameters than the existing algorithm. Research limitations/implications -A future work may consider the possibility of machine failure along with the preventive maintenance. This relaxes the assumption that the machine cannot fail but it is rather maintained preventively. The multi-criteria scheduling can also be considered as an avenue of future work. The problem can also be considered with stochastic parameters such that the processing times of the jobs and the maintenance related parameters are random and follow a known probability distribution function. Practical implications -The usefulness of meta-heuristic algorithms is demonstrated for solving a large scale NP-hard combinatorial optimization problem. The paper also shows that the utilization of the directed search methods such as hybridization could substantially improve the performance of a meta-heuristic. Originality/value -This research highlights the impact of utilizing the directed search methods to cause hybridization in meta-heuristic and the resulting improvement in their performance for large-scale optimization.
Journal of Heuristics, 2007
This study addresses a class of single-machine scheduling problems involving a common due date where the objective is to minimize the total job earliness and tardiness penalties. A genetic algorithm (GA) approach and a simulated annealing (SA) approach utilizing a greedy local search and three well-known properties in the area of common due date scheduling are developed. The developed algorithms enable the starting time of the first job not at zero and were tested using a set of benchmark problems. From the viewpoints of solution quality and computational expenses, the proposed approaches are efficient and effective for problems involving different numbers of jobs, as well as different processing time, and earliness and tardiness penalties.
Computers & Industrial Engineering, 2008
This paper considers a single-machine scheduling problem with several maintenances periods. Specifically, two situations are investigated. In the first one, maintenance periods are periodically fixed: maintenance is required after a periodic time interval. In the second one, the maintenance is not fixed but the maximum continuous working time of the machine which is allowed is determined. The objective is to minimize the maximum tardiness. These problems are known to be strongly NP-hard. We propose some dominance properties and an efficient heuristic. Branch-and-bound algorithms, in which the heuristics, the lower bounds and the dominance properties are incorporated, are proposed and tested computationally.
Improved heuristics for the early/tardy scheduling problem with no idle time
2005
In this paper we consider the single machine earliness/tardiness scheduling problem with no idle time. We present two new heuristics, a dispatch rule and a greedy procedure, and also consider the best of the existing dispatch rules. Both dispatch rules use a lookahead parameter that had previously been set at a fixed value. We develop functions that map some instance statistics into appropriate values for that parameter. We also consider the use of dominance rules to improve the solutions obtained by the heuristics. The computational results show that the function-based versions of the heuristics outperform their fixed value counterparts and that the use of the dominance rules can indeed improve solution quality with little additional computational effort.
Applied Computer Systems
A problem of minimizing the total weighted tardiness in the preemptive single machine scheduling for discrete manufacturing is considered. A hyper-heuristic is presented, which is composed of 24 various heuristics, to find an approximately optimal schedule whenever finding the exact solution is practically intractable. The three heuristics are based on the well-known rules, whereas the 21 heuristics are introduced first. Therefore, the hyper-heuristic selects the best heuristic schedule among 24 schedule versions, whose total weighted tardiness is minimal. Each of the 24 heuristics can solely produce a schedule which is the best one for a given scheduling problem. Despite the percentage of zero gap instances decreases as the greater number of jobs is scheduled, the average and maximal gaps decrease as well. In particular, the percentage is not less than 80 % when up to 10 jobs are scheduled. The average gap calculated over nonzero gaps does not exceed 4 % in the case of scheduling 7...
Heuristic Algorithms for a Job-Shop Problem with Minimizing Total Job Tardiness
In practice, it is often required to process a set of jobs without operation preemptions satisfying temporal and resource constraints. Temporal constraints say that some jobs have to be finished before some others can be started. Resource constraints say that operations processed on the same machine cannot be processed simultaneously. The objective is to construct a schedule specifying when each operation starts such that both temporal and resource constraints are satisfied and the given objective function has a minimum value. One can model such a scheduling process via the following job-shop problem.
A tabu search algorithm for the single machine total weighted tardiness problem
European Journal of Operational Research, 2007
In this study, a tabu search (TS) approach to the single machine total weighted tardiness problem (SMTWT) is presented. The problem consists of a set of independent jobs with distinct processing times, weights and due dates to be scheduled on a single machine to minimize total weighted tardiness. The theoretical foundation of single machine scheduling with due date related objectives reveal that the problem is NP-hard, rendering it a challenging area for meta-heuristic approaches. This paper presents a totally deterministic TS algorithm with a hybrid neighborhood and dynamic tenure structure, and investigates the strength of several candidate list strategies based on problem specific characteristics in increasing the efficiency of the search. The proposed TS approach yields very high quality results for a set of benchmark problems obtained from the literature.