Manufacturing companies are continuously facing the challenge of operating their manufacturing processes and systems in order to deliver the required production rates of high quality products, while minimizing the use of resources. Production quality is proposed in this paper as a new paradigm aiming at going beyond traditional six-sigma approaches. This new paradigm is extremely relevant in technology intensive and emerging strategic manufacturing sectors, such as aeronautics, automotive, energy, medical technology, micro-manufacturing, electronics and mechatronics. Traditional six-sigma techniques show strong limitations in highly changeable production contexts, characterized by small batch productions, customized, or even one-of-a-kind products, and in-line product inspections. Innovative and integrated quality, production logistics and maintenance design, management and control methods as well as advanced technological enablers have a key role to achieve the overall production quality goal. This paper revises problems, methods and tools to support this paradigm and highlights the main challenges and opportunities for manufacturing industries in this context.

Design and management of manufacturing systems for production quality

The integration in production planning, scheduling, maintenance and quality has gained much attention from researchers in recent years. These areas are usually treated independently, which yielded independent models for each function. It is widely believed that these separate models will provide suboptimal solutions, due to the fact that they are interrelated. Proper understanding of this dependency will open a new avenue for more integrated models, and result in significant savings in operational cost and improved efficiency for any production system. This paper reviews the literature on models addressing different aspects of integration in these areas. This integration is treated in two different ways in the literature. The first way is to present models where a model is considered for one function taking into account the other. These models are referred to as interrelated models. The second way is to simultaneously model two or more elements of the production system. These models are referred to as integrated models. The objective of this paper is to review research in both interrelated and integrated modelling of production planning, scheduling, maintenance and quality to identify possible future directions.

Integrated models in production planning and scheduling, maintenance and quality: a review

https://isiarticles.com/bundles/Article/pre/pdf/25696.pdf

Preventive maintenance and replacement scheduling for repairable and maintainable systems using dynamic programming

We address the problem of simultaneously scheduling trains and planning preventive maintenance time slots (PMTSs) on a general railway network Based on network cumulative flow variables, a novel integrated mixed-integer linear programming (MILP) model is proposed to simultaneously optimize train routes, orders and passing times at each station, as well as work-time of preventive maintenance tasks (PMTSs) In order to provide an easy decomposition mechanism, the limited capacity of complex tracks is modelled as side constraints and a PMTS is modelled as a virtual train A Lagrangian relaxation solution framework is proposed, in which the difficult track capacity constraints are relaxed, to decompose the original complex integrated train scheduling and PMTSs planning problem into a sequence of single train-based sub-problems For each sub-problem, a standard label correcting algorithm is employed for finding the time-dependent least cost path on a time-space network The resulting dual solutions can be transformed to feasible solutions through priority rules Numerical experiments are conducted on a small artificial network and a real-world network adapted from a Chinese railway network, to evaluate the effectiveness and computational efficiency of the integrated optimization model and the proposed Lagrangian relaxation solution framework The benefits of simultaneously scheduling trains and planning PMTSs are demonstrated, compared with a commonly-used sequential scheduling method

Integrated optimization on train scheduling and preventive maintenance time slots planning

Stochastic control problems that arise in reliability and maintenance optimization typically assume that information used for decision-making is obtained according to a predetermined sampling schedule. In many real applications, however, there is a high sampling cost associated with collecting such data. It is therefore of equal importance to determine when information should be collected and to decide how this information should be utilized for maintenance decision-making. This type of joint optimization has been a long-standing problem in the operations research and maintenance optimization literature, and very few results regarding the structure of the optimal sampling and maintenance policy have been published. In this paper, we formulate and analyze the joint optimization of sampling and maintenance decision-making in the partially observable Markov decision process framework. We prove the optimality of a policy that is characterized by three critical thresholds, which have practical interpretation a...

Joint Optimization of Sampling and Control of Partially Observable Failing Systems

Optimal adaptive control policy for joint machine maintenance and product quality control

This paper finds the optimal integrated production schedule and preventive maintenance plan for a single machine exposed under a cumulative damage process, and investigates how the optimal preventive maintenance plan interacts with the opti- mal production schedule. The goal is to minimize the total tardiness. The optimal policy possesses the following properties: Under arbitrary maintenance plan when jobs have common processing time, and different due dates, the optimal production schedule is to order the jobs by earliest due date first rule; and when jobs have common due date and different processing times, the optimal production schedule is shortest processing time first. The optimal maintenance plan is of control limit type under any arbitrary production schedule when machine is exposed under a cumulative damage failure process. Numerical studies on the optimal main- tenance control limit of the maintenance plan indicate that as the number of jobs to be scheduled increases, the effect of jobs due dates on the optimal maintenance control limit diminishes. © 2007 Wiley Periodicals, Inc. Naval Research Logistics 54: 602-614, 2007

https://onlinelibrary.wiley.com/doi/pdf/10.1002/nav.20232

Integrated production scheduling and preventive maintenance planning for a single machine under a cumulative damage failure process

Efficient mixed integer programming models for family scheduling problems

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Optimal adaptive control policy for joint machine maintenance and product quality control

Integrated production scheduling and preventive maintenance planning for a single machine under a cumulative damage failure process

Efficient mixed integer programming models for family scheduling problems