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%

A note on "scheduling of nonresumable jobs and flexible maintenance activities on a single machine to minimize makespan"

In a recent paper, Chen [J.S. Chen, Scheduling of nonresumable jobs and flexible maintenance activities on a single machine to minimize makespan, European Journal of Operational Research 190 (2008) 90-102] proposes a heuristic algorithm to deal with the problem Scheduling of Nonresumable Jobs and Flexible Maintenance Activities on A Single Machine to Minimize Makespan. Chen also provides computational results to demonstrate its effectiveness. In this note, we first show that the worst-case performance bound of this heuristic algorithm is 2. Then we show that there is no polynomial time approximation algorithm with a worst-case performance bound less than 2 unless P=NP, which implies that Chen's heuristic algorithm is the best possible polynomial time approximation algorithm for the considered scheduling problem.Scheduling Single machine Maintenance Heuristic algorithm Worst-case analysis

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

A maintenance model for the supply-buffer-demand production system

Master'sMASTER OF ENGINEERIN

The Integration of Maintenance Decisions and Flow Shop Scheduling

In the conventional production and service scheduling problems, it is assumed that the machines can continuously process the jobs and the information is complete and certain. However, in practice the machines must stop for preventive or corrective maintenance, and the information available to the planners can be both incomplete and uncertain. In this dissertation, the integration of maintenance decisions and production scheduling is studied in a permutation flow shop setting. Several variations of the problem are modeled as (stochastic) mixed-integer programs. In these models, some technical nuances are considered that increase the practicality of the models: having various types of maintenance, combining maintenance activities, and the impact of maintenance on the processing times of the production jobs. The solution methodologies involve studying the solution space of the problems, genetic algorithms, stochastic optimization, multi-objective optimization, and extensive computational experiments. The application of the problems and managerial implications are demonstrated through a case study in the earthmoving operations in construction projects