Robust job-sequencing with an uncertain flexible maintenance activity

In this study, the problem of scheduling a set of jobs and one uncertain maintenance activity on a single machine, with the objective of minimizing the makespan is addressed. The maintenance activity has a given duration and must be executed within a given time window. Furthermore, duration and time window of the maintenance are uncertain, and can take different values which can be described by different scenarios. The problem is to determine a job sequence which performs well, in terms of makespan, independently on the possible variation of the data concerning the maintenance. A robust scheduling approach is used for the problem, in which four different measures of robustness are considered, namely, maximum absolute regret, maximum relative regret, worst-case scenario, and ordered weighted averaging. Complexity and approximation results are presented. In particular, we show that, for all the four robustness criteria, the problem is strongly NP-hard. A number of special cases are explored, and an exact pseudopolynomial algorithm based on dynamic programming is devised when the number of scenarios is fixed. Two Mixed Integer Programming (MIP) models are also presented for the general problem. Several computational experiments have been conducted to evaluate the efficiency and effectiveness of the MIP models and of the dynamic programming approach

Preventive Maintenance Supply Chain Management Optimal Scheduling on VMACL Machines by Implementing Simulation Annealing Algorithms

PT. Braja Mukti Cakra uses various types of engines to produce parts for truck cars. Vertical Lathe Automatic Chucking Machine (VMACL) is a machine that has the highest frequency of damage when compared to other machines. To reduce damage costs, preventive maintenance is well scheduled. This scheduling problem solving is done using the Annealing Simulation Algorithm. The results of the analysis give direction that the scheduling that must be done are: The maintenance action schedule for the Lifter component is at month 1,6,7,22,24,34, for the Insert component at 4,15,18,27,33 months, and for the Door component at the 2nd month, 12,13,16,17,30,36. Replacement actions for the Lifter component were carried out in the 4,5th month, 1,17,20,29, for the Insert component in the 9,19,22,23,35 months, and for the Door component in the 1,20,27 months. . Scheduling for 36 months using the Simulated Annealing Algorithm will cost IDR. 84,119,244.60 and produce greater reliability than the previous reliability of 58.44%

Joint optimization of production and maintenance scheduling for unrelated parallel machine using hybrid discrete spider monkey optimization algorithm

This paper considers an unrelated parallel machine scheduling problem with variable maintenance based on machine reliability to minimize the maximum completion time. To obtain the optimal solution of small-scale problems, we firstly establish a mixed integer programming model. To solve the medium and large-scale problems efficiently and effectively, we develop a hybrid discrete spider monkey optimization algorithm (HDSMO), which combines discrete spider monkey optimization (DSMO) with genetic algorithm (GA). A few additional features are embedded in the HDSMO: a three-phase constructive heuristic is proposed to generate better initial solution, and an individual updating method considering the inertia weight is used to balance the exploration and exploitation capabilities. Moreover, a problem-oriented neighborhood search method is designed to improve the search efficiency. Experiments are conducted on a set of randomly generated instances. The performance of the proposed HDSMO algorithm is investigated and compared with that of other existing algorithms. The detailed results show that the proposed HDSMO algorithm can obtain significantly better solutions than the DSMO and GA algorithms

Single-machine scheduling with tool changes: a constraint-based approach

The paper addresses the scheduling of a single machine with tool changes in order to minimize total completion time. A constraint-based model is proposed that makes use of global constraints and also incorporates various dominance rules. With these techniques, our constraint-based approach outperforms previous exact solution methods

Formulações matemáticas para o problema de sequenciamento de tarefas com manutenções periódicas e tempos de setup

The single-machine scheduling problem with periodic maintenances and sequencedependent setup times aims at scheduling jobs on a single machine in which periodic maintenances and setups are required. The objective is the minimization of the makespan. We propose an exact algorithm based on the iterative solution of three alternative arc-time-indexed models. Extensive computational experiments are carried out on 420 benchmark instances with up to 50 jobs, and on 360 newly proposed instances involving up to 125 jobs. We compare the results found by all formulations with those obtained by the best available mathematical formulation. All instances from the existing dataset are solved to optimality for the first time.O problema de sequenciamento em uma m´aquina estudado neste trabalho tem como objetivo ordenar tarefas em apenas uma m´aquina com per´ıodos de indisponibilidade fixos, levando em considera¸c˜ao tempos de setup dependentes da sequˆencia. O objetivo ´e minimizar o makespan. Neste trabalho ´e proposto um algoritmo exato que resolve, iterativamente, uma de trˆes formula¸c˜ao matem´aticas de arcos indexados no tempo apresentadas. Experimentos computacionais extensivos s˜ao conduzidos em 420 instˆancias da literatura de at´e 50 tarefas, e em 360 instˆancias, envolvendo at´e 125 tarefas, propostas neste trabalho. Os resultados s˜ao comparados com aqueles obtidos pela melhor formula¸c˜ao matem´atica dispon´ıvel na literatura. Pela primeira vez, todas as instˆancias do conjunto existente foram resolvidas na otimalidade

Single-machine Scheduling with Splitable Jobs and Availability Constraints

This paper deals with a single machine scheduling problem with availability constraints. The jobs are splitable and lower bound on the size of each sub-job is imposed. The objective is to find a feasible schedule that minimizes the makespan. The proposed scheduling problem is proved to be NP-hard in the strong sense. Some effective heuristic algorithms are then proposed. Additionally, computational results show that the proposed heuristic performs well