Search results

The purpose of this paper is to propose a stochastic optimisation model for integrating service and maintenance policies in order to solve the queuing problem and the cost of maintenance activities for public transport services, with a particular focus on urban ropeway system.

The authors adopt the following approaches: a discrete-event model that uses a set of interrelated queues for the formulation of the service problem using a cost-based expression; and a maintenance model consisting of preventive and corrective maintenance actions, which considers two different maintenance policies (periodic block-type and age-based).

The work shows that neither periodic block-type maintenance nor an age-based maintenance is necessarily the best maintenance strategy over a long system lifecycle; the optimal strategy must consider both policies.

The maintenance policies are then evaluated for their impact on the service and operation of the transport system. The authors conclude by applying the proposed optimisation model using an example concerning ropeway systems.

This is the first study to simultaneously consider maintenance policy and operational policy in an urban aerial ropeway system, taking up the problem of queuing with particular attention to the unique requirements public transport services.

The purpose of this paper is to deal with the problem of integration of production and maintenance policies. In this context, the authors consider production systems made of parallel machines producing a single product over a finite horizon made of equal periods for which a forecasted demand is known. The authors investigate the impact of switching production in case of failure of any given machine.

A mathematical model is first developed to find an optimal production plan which minimizes the average total storage, shortage and production costs. Then, using this optimal production plan and taking into account the influence of the production rate on the degradation of each machine, optimal preventive maintenance (PM) policies are proposed for the situations with and without switching.

Optimal production rates are determined for each production period and for each machine. Optimal PM periods are also computed for each machine.

Usually, in manufacturing systems, the production rate of a machine influences its failure rate. In case a machine fails, it takes a random time to repair it during which production is lost. The paper attempts to propose a switching policy (SP) according to which the lost production is compensated by all the other machines. The effects of the SP coupled with the PM strategy are shown through a numerical example.

Contrarily to previous works, the authors consider more realistic settings with a non-negligible random time for repairing failed machines. In order to compensate the lost production during the repair of a failed machine, a SP is proposed to transfer the load uniformly to all the other machines. As a result, those machines will produce at a higher production rate and will consequently have their failure rate increased. It will therefore be essential to determine an optimal PM schedule knowing that durations of these activities are not negligible. It is shown that the simultaneous implementation of periodic PM and load transfer in case of failure is the most economical integrated strategy.

The main contribution of this manuscript is to suggest new approaches in order to deal with dynamic lot-sizing and maintenance problem under aspect energetic and risk analysis. The authors introduce a new maintenance strategy based on the centroid approach to determine a common preventive maintenance plan for all machines to minimize the total maintenance cost. Thereafter, the authors suggest a risk analysis study further to unforeseen disruption of availability machines with the aim of helping the production stakeholders to achieve the obtained forecasting lot-size plan.

The authors tackle the dynamic lot-sizing problem using an efficient hybrid approach based on random exploration and branch and bound method to generate possible solutions. Indeed, the feasible solutions of random exploration method are used as input for branch and bound to determine the near-optimal solution of lot-size plan. In addition, our contribution to the maintenance part is to determine the optimal common maintenance plan for M machines based on a new algorithm called preventive maintenance (PM) periods means.

First, the authors have funded the optimal lot-size plan that should satisfy the random demand under service level requirement and energy constraint while minimizing the costs of production and inventory. Indeed, establishing a best lot-size plan is to determine the appropriate number of available machines and manufactured units per period. Second, for risk analysis study, the solution of subcontracting is proposed by specifying a maximum cost of subcontractor in the context of a calling of tenders.

For maintenance problem, the originality consists in regrouping the maintenance plans of M machines into only one plan. This approach lets us to minimize the total maintenance cost and reduces the frequent breaks of production. As a second part, this paper contributed to the development of a new risk analysis study further to unforeseen disruption of availability machines. This risk analysis developed a decision-making system, for production stakeholders, in order to achieve the forecasting lot-size plan and keeps its profitability, by specifying the unit cost threshold of subcontractor in the context of a calling of tender.

The purpose of this study is to gain some insights into the number of shortages resulting from two alternative demand allocation schemes between a contractor (machine M₁) and subcontractor (machine M₂), on the one hand, and from inventory accumulation, on the other hand. The shortages stem from random machine breakdowns, and each machine undergoes preventive maintenance. The motivation behind inventory accumulation is to allow demand to be fulfilled even when both machines are down.

The number of shortages stemming from all scenarios under consideration was established via computer simulation with the Arena^© language.

For demand allocation that remains unchanged for the duration of the planning horizon and constant reliability of M₁, it was found that, the less reliable M₂ is, the more biased in favour of M₁ will be the optimal demand allocation and the greater will be the number of shortages. Moreover, both dynamic demand reallocation over the planning horizon and inventory accumulation result in a substantial reduction in shortages.

The results are representative of the specific data, which were assumed in the simulation models. Nevertheless, this methodology is recommended for this type of analysis, as it is highly flexible and can take into account many practical considerations, which an analytical approach cannot.

Within the context of unreliable production machines, the most important practical implication of this study is that the dynamic reallocation of demand between a contractor and subcontractor, along with inventory accumulation, both have the potential to yield important reductions in the number of shortages.

The subject‐matter of this paper was not previously reported in the literature. Furthermore, the insights gained as a result of this study can yield substantial benefits to companies in terms of improving their service levels as measured by reduced shortages.

The purpose of this study is to propose and model an integrated production‐maintenance strategy for unreliable production systems producing conforming and non‐conforming items.

The proposed integrated policy is defined and modeled mathematically.

The paper focuses on finding simultaneously the optimal values of the lot size Q and the age T at which preventive maintenance must be performed. These values minimize the total average cost per time unit over an infinite horizon.

The paper attempts to integrate in a single model the three main aspects of any manufacturing system: production, maintenance, and quality. It deals with the lot‐sizing problem for a production system which may randomly shift to an out‐of‐control state and produce non‐conforming units. The system is submitted to an age‐based preventive maintenance policy. The effect of performing preventive maintenance on quality‐ and inventory‐related costs is shown through a numerical example.

The paper proposes an integrated model that links EMQ, quality and an age‐based preventive maintenance policy. It is shown that performing preventive maintenance yields reduction in inventory‐ and quality‐related costs.

This paper aims to investigate the optimization of the replacement with minimal repair policy for a system which experiences a time horizon of random length. Under such policy system replacement occurs at multiples of some period while minimal repair is performed at system failure between two successive replacements.

The objective function is the expected total cost composed of minimal repairs and replacements costs. A simple and compact expression is derived for the expected total costs and conditions under which an optimal replacement period exits are given. For sake of illustration, a numerical example is provided.

The paper finds that by the recent great technological development, the life cycle of present products is seen to be reduced more and more. This has motivated the development of maintenance optimization models for systems which experience an exact finite time horizon.

To ensure the benefits from the improved technologies, the information concerning the technological change must be taken into account. Such information is based on technological forecasting and difficult to obtain and merely rely on uncertainties.

The purpose of this paper is to investigate the optimal production policy and maintenance strategy for leased equipment under a lease contract with warranty periods. In order to have steady revenue, the lessor (owner) of the equipment may provide guaranty periods to encourage the lessee to sign a lease contract with a longer lease period.

Under this production/maintenance scheme, the mathematical model of the expected total cost is developed and the optimal production planning and the corresponding optimal maintenance policy are derived by choosing the optimal warranty periods for the lessee in order to minimize the total cost.

The influence of the production rates variation in the equipment degradation is considered by an increased failure rate according to both time and production rates. The impact of warranty periods on optimal maintenance planning will be studied thereafter. Finally, numerical examples are given to illustrate the analytical study and the effects of the warranty periods variation during the lease periods on the maintenance policy and consequently on the total cost.

The paper proposes a new idea of production and maintenance coupling in the leasing aspect. This study shows that it has a novelty and originality relative to this type of problem which considers and proposes a new maintenance strategy for leasing contract. This originality characterized by the influence of two factors on the equipment maintenance strategy. First factor is the influence of the production variation production rates on the machine degradation degree that is new in the literature charactering by analytical equation that shows the evolution of the machine failure rate according to its use (which is in our case the production rate for each period) respecting in the same time the continuity of the equipment reliability for a period to another.

This paper aims to propose an approach for the optimization of imperfect preventive maintenance and corrective actions performed on a single machine. After maintenance, the machine returns to an age between “as good as new” and “as bad as old”.

The approach is based on fuzzy logic and simulation‐based optimization. Fuzzy logic is preferred over crisp logic because it is relatively easy to implement in this situation considering that the human factor is hardly interpreted by analytical methods because of its unpredictable nature. Simulation‐based optimization is used to have a more reactive and accurate tool for practitioners.

Taking into account the impact of the imperfections due to human factors, the period for preventive maintenance, which minimizes the expected cost rate per unit of time or maximizes the availability of the system, is evaluated by the simulation‐based optimization.

Different and more realistic maintenance levels must be considered and the traceability of a specific system could be used to determine the most appropriate failure law. For this study, cost reduction was considered as the priority, but the model can be adjusted according to the user's preferences.

This paper considers a single repairable machine as a system that undergoes periodic preventive and corrective maintenance actions. Considering maintenance imperfections, rule‐based fuzzy logic can be integrated into the maintenance program to determine the times for the periodic preventive maintenance actions.

Considering human factors in maintenance programs is indispensable to assure more accurate and realistic results. However, due to the difficulty engendered by their modeling, most theoretical maintenance models do not consider these factors. Therefore, the proposed fuzzy model in the paper can be an important tool to include them.

The purpose of this paper is to determine a nearly optimal inspection sequence for a series system consisting of two components subject to gradual deterioration and whose failures are not self-announcing and can be detected only through inspection.

The problem is tackled in the context of condition-based maintenance (CBM) with a maintenance model in the class of the control-limit policies for which the maintenance decision is made following inspection by comparison of the deterioration level to critical thresholds. A mathematical model is developed to express the total expected cost per time unit as a function of the inspection instants.

For any given series system composed of two components with known critical deterioration threshold levels, and for any given set of costs related to inspection, inactivity due to failure, and preventive and corrective replacements of each component, a nearly optimal inspection sequence of the system is derived such as the total expected cost is reduced.

Due to the complexity of the cost model with the inspection instants (×1, ×2, ×3, …) being the decision variables, it has not been possible to derive the optimal solution. A quasi-optimal sequence of inspection times is derived along with the corresponding total average cost per time unit.

In many practical situations in which CBM is implemented, a tradeoff between inspection costs and inactivity and replacement costs has to be balanced when determining the intervals between successive inspections at which the degradation level of the components should be assessed and compared to predetermined critical threshold levels. Inspecting too often would increase inspection costs but in the same time it would also increase the probability to avoid a failure and end up with a preventive replacement, whereas not inspecting often enough would increase the probability to end up with a failure increasing replacement and inactivity costs.

While the inspection problem has been largely treated for single component systems, inspection policies become much more complex when considering multi-component systems. A two-component series system is considered in this paper.

Maintenance is a critical business function with a great impact on economic, environmental and social aspects. However, maintenance decisions' planning has been driven by merely economic and technical measures with inadequate consideration of environmental and social dimensions. This paper presents a review of the literature pertaining to sustainable maintenance decision-making models supported by a bibliometric analysis that seeks to establish the evolution of this research over time and identify the main research clusters.

A systematic literature review, supported with a bibliometric and network analysis, of the extant studies is conducted. The relevant literature is categorized based on which sustainability pillar, or possibly multiple ones, is being considered with further classification outlining the application area, modeling approach and the specific peculiarities characterizing each area.

The review revealed that maintenance and sustainability modeling is an emerging area of research that has intensified in the last few years. This fertile area can be developed further in several directions. In particular, there is room for devising models that are implementable, based on reliable and timely data with proven tangible practical results. While the environmental aspect has been considered, there is a clear scarcity of works addressing the social dimension. One of the identified barriers to developing applicable models is the lack of the required, accurate and timely data.

This work contributes to the maintenance and sustainability modeling research area, provides insights not previously addressed and highlights several avenues for future research. To the best of the authors' knowledge, this is the first review that looks at the integration of sustainability issues in maintenance modeling and optimization.