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Subject: search.filters.subject.Production scheduling

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    Minimize total tardiness and machine unavailability on single machine scheduling problem: bi-objective branch and bound algorithm
    (Springer, 2018) Khoudi, Asmaa; Berrichi, Ali
    The joint production scheduling and preventive maintenance problems have recently attracted researchers’ attention given their contribution, both the production and the maintenance functions and their integration, to the firms’ efficiency. In this paper, we deal with production scheduling and preventive maintenance (PM) planning on single machine problem. The aim is to find an appropriate sequencing of production jobs and a PM planning to minimize two objectives simultaneously: total tardiness of jobs and machine unavailability. We propose a bi-objective exact algorithm, that we called BOBB, based on bi-objective branch and bound method to find the efficient set. We introduced several properties and bound sets to enhance the performance of the proposed BOBB algorithm. Furthermore, we propose a hybrid method, that we called GA-BBB, based on genetic algorithm and binary branch and bound algorithm to compute an approximate efficient set to be used as an initial upper bound set in the BOBB algorithm. An experimental study was conducted to show the efficiency of the GA-BBB and the BOBB algorithms
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    Fuzzy rules for joint integration of production schedule and maintenance planning
    (Old City Publishing, 2016) Berrichi, Ali; Yalaoui, Farouk; Yalaoui, Alice
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    Bi-objective optimization algorithms for joint production and maintenance scheduling : application to the parallel machine problem
    (Springer, 2009) Berrichi, A.; Amodeo, L.; Yalaoui, F.; Châtelet, E.; Mezghiche, Mohamed
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    Efficient bi-objective ant colony approach to minimize total tardiness and system unavailability for a parallel machine scheduling problem
    (Springer, 2013) Berrichi, Ali; Yalaoui, Farouk
    In recent years, decision makers give more im- portance to the maintenance function, viewing its substantial contribution to business productivity. However, most litera- ture on scheduling studies does not take into account main- tenance planning when implementing production schedules. The achievement of production plan without taking into account maintenance activities increases the probability of machine breakdowns, and inversely, considering mainte- nance actions in production planning elongates the achieve- ment dates of orders and affects deadlines. In this paper, we propose a bi-objective model to deal with production sched- uling and maintenance planning problems simultaneously. The performance criteria considered for production and maintenance are, respectively, the total tardiness and the unavailability of the production system. The start times of preventive maintenance actions and their number are not fixed in advance but considered, with the execution dates of production tasks, as decisions variables of the problem. The solution of the integrated model is based on multi-objective ant colony optimization approach. The proposed algorithm (Pareto ant colony optimization) is compared, on the basis of several metrics, with well-known multi-objective genetic algorithms, namely NSGA-II and SPEA 2, and a hybrid particle swarm optimization algorithm. Interesting results are obtained via empirical study
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    Bi-objective ant colony optimization approach to optimize production and maintenance scheduling
    (Elsevier, 2010) Mezghiche, Mohamed; Amodeo, L.; Yalaoui, F.; Berrichi, A.
    This paper presents an algorithm based on Ant Colony Optimization paradigm to solve the joint production and maintenance scheduling problem .This approach is developed to deal with the model previously proposed in [3] for the parallel machine case. This model is formulated according to a bi- objective approach to find trade-off solutions between both objectives of production and maintenance. Reliability models are used to take in to account the maintenance aspect. To improve the quality of solutions found in our previous study, an algorithm based on Multi-Objective Ant Colony Optimization (MOACO) approach is developed. The goal is to simultaneously determine the best assignment of production tasks to machines as well as preventive maintenance (PM) periods of the production system, satisfying at best both objectives of production and maintenance. The experimental results show that the proposed method out performs two well-known Multi-Objective Genetic Algorithms (MOGAs): SPEA 2and NSGAII