Claver Diallo

State of the art review of quality, reliability and maintenance issues in closed-loop supply chains with remanufacturing

The design of reverse logistics and remanufacturing processes and the recovery of end-of-life products have been well-studied in the literature. Quality, reliability, maintenance and warranty for recovered products and the remanufacturing activities that extend their life are integral issues in reverse logistics. This paper reviews recent and relevant literature on these issues in closed-loop supply chains, with a focus on remanufactured or second-hand products. The published literature is first classified into domain areas of research and practice. The wide array of mathematical tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarised and the main research gaps are highlighted.

(s,Q) Spare Parts Provisioning Strategy for Periodically Replaced Systems

A joint preventive maintenance and spare parts provisioning strategy is suggested for a failure prone system. Replacements are carried-out at failure, if spare parts are available, and at spare parts replenishment delivery instants. Spare parts are provisioned according to an (s, Q) control policy. The ordering parameters, and preventive maintenance interval are derived from a mathematical model which aims at maximizing the systems availability under a budget constraint. The model takes into account the system lifetime distribution, the preventive and corrective maintenance costs and durations, as well as the total spare parts inventory management cost. Unlike classical inventory management models, the s-expected total cost is derived using the system lifetime distribution. Because each demand for spare parts is triggered by failure, the probabilities of shortage, and surplus will be evaluated based on the system failure distribution rather than the demand distribution during the lead-time. Numerical results have been obtained for an illustrative example.

Makespan minimization for parallel machines scheduling with multiple availability constraints

The problem of makespan minimization for parallel machines scheduling with multiple planned nonavailability periods in the case of resumable jobs is considered. In the current state of the literature, there is a limited number of models and algorithms dealing with this problem and only for very small problem size, and nonavailability limited to some machines. The problem is first formulated as a mixed integer linear programming model and optimally solved using CPLEX for small to moderately large size problems with multiple availability constraints on all machines. An implicit enumeration algorithm using the lexicographic order is then designed to solve large-scale problems. Numerical results are obtained for several experiments and they show the validity and performance improvements procured by both the MILP model and the new enumeration algorithm.

Selective maintenance optimisation for series-parallel systems alternating missions and scheduled breaks with stochastic durations

This paper deals with the selective maintenance problem for a multi-component system performing consecutive missions separated by scheduled breaks. To increase the probability of successfully completing its next mission, the system components are maintained during the break. A list of potential imperfect maintenance actions on each component, ranging from minimal repair to replacement is available. The general hybrid hazard rate approach is used to model the reliability improvement of the system components. Durations of the maintenance actions, the mission and the breaks are stochastic with known probability distributions. The resulting optimisation problem is modelled as a non-linear stochastic programme. Its objective is to determine a cost-optimal subset of maintenance actions to be performed on the components given the limited stochastic duration of the break and the minimum system reliability level required to complete the next mission. The fundamental concepts and relevant parameters of this decision-making problem are developed and discussed. Numerical experiments are provided to demonstrate the added value of solving this selective maintenance problem as a stochastic optimisation programme.

Optimal selective maintenance decisions for large serial k-out-of-n: G systems under imperfect maintenance

Abstract The selective maintenance problem (SMP) arises in many large multicomponent systems which are operated for consecutive missions interspersed with finite breaks during which only a selected set of component repairs or replacements can be carried out due to limited time, budget, or resources. The problem is to decide which components and degree of repairs should be performed in order to guarantee a pre-specified performance level during the subsequent mission. Current SMP formulations in the literature are nonlinear, deal mainly with basic or series-parallel systems and mostly use heuristic methods to obtain solutions. This paper introduces the first SMP model for serial k-out-of-n systems. Two nonlinear formulations are developed, which can be used to solve the problem for small to moderate size k-out-of-n systems. For large k-out-of-n systems or complex reliability structures, we develop a new two-phase approach which transforms the problem into a multidimensional multiple-choice knapsack problem (MMKP). The new approach is shown to be efficient through multiple sets of numerical experiments.

Kernel estimator of maintenance optimization model for a stochastically degrading system under different operating environments

This paper investigates the preventive age replacement policy (ARP) for a system subject to random failures. Unlike most maintenance models in the literature, our model considers a system that is exploited under different operating environments each characterized by its own degree of severity. The system lifetimes follow a different distribution depending on the environment it is operating under. Furthermore, the system lifetimes distribution is assumed unknown and therefore estimated from field reliability data. The reliability of the system is calculated using two kernel estimators. This method offers the advantage of non-parametric estimation methods and completely determined by two parameters, namely the smoothing parameter and the kernel function. First, a probability maintenance cost model is derived and conditions under which an optimal preventive maintenance age exists are provided. Then, a statistical maintenance cost model is developed using two kernel estimators. The impact of the variability of the kernel smoothing parameter on the cost model is also investigated. Numerical experiments are provided to illustrate the proposed approach. Results obtained demonstrate the accuracy of the proposed statistical maintenance cost model.

Optimal opportunistic indirect grouping of preventive replacements in multicomponent systems

Complex systems requiring periodic replacement of major components are studied.Opportunistic replacements are needed for these economically dependent components.We present a new tree formulation of this opportunistic indirect grouping problem.An efficient depth-first shortest path algorithm in Python is proposed.Numerical experiments show that substantial savings are achieved. For complex systems operating in critical environments, original equipment manufacturers, operators and/or regulators often specify replacement intervals for major components before failure can occur. The fixed costs to teardown the overall system can be an important constituent of the total costs. Thus, when a preventive maintenance is scheduled to replace a given component, it may well be desirable to replace one or more other components that are within their replacement window (interval), so as to avoid repeating the teardown costs in a short while. This paper presents a novel network tree formulation of this opportunistic indirect grouping of periodic events problem. We show that, given a fixed time horizon and a moderately large number of major components, the replacement optimization problem can be represented as a tree of possible replacement combinations. Although these trees can become enormous, we have developed a Python implementation of a depth-first shortest path algorithm that can be very effective because many of the nodes of this tree do not need to be examined. Even when several million nodes need to be examined, only a few of them, typically a few hundreds, need to be maintained in memory at any one time. For larger number of components and longer time horizons, the trees can still become so large that it is impossible to examine it completely. In this case, the depth first search still rapidly finds a sequence of improving solutions and can be a very good heuristic for the problem.

Spare parts identification and provisioning models

This paper addresses the problem of spare parts identification and provisioning for multi-component systems. A decision tree considering technical, economical and strategical information available is presented. Mathematical models are proposed to predict, for each spare part, the required quantity over a given planning horizon. The objective may be to maximize either the reliability or the availability of the system. Analytic models are proposed to determine the inventory management parameters such as the order quantity, the order point, the safety stock and so forth. For different management strategies, short comments regarding some improvement issues are provided.

Integrated Spare Parts Management

Maintenance strategies are designed and implemented in order to reduce the frequency and duration of service interruptions, while satisfying constraints on budget, productivity, space, etc. A maintenance strategy is defined as the set of actions pertaining to maintaining or restoring a system in a specified state or in a state of readiness to accomplish a certain task. The main scientific contributions dealing with maintenance policies generally address the three following issues in a separate or combined way: the choice and the sequence of actions defining each strategy, the costs and durations of these actions, and the equipment lifetime and repair distributions. For many companies, the expenses incurred for keeping spare parts until they are used increase significantly the cost of their finished goods. Huge costs related to the inventory management of those parts have triggered studies on the provisioning and management decisions made in the process of acquiring and holding spare parts stocks.

Optimal Combination RebateWarranty Policy with Second-hand Products.

With the increased awareness for sustainability, many engineered products are being recovered and reconditioned for secondary useful lives. These second-hand products can serve as replacement products to honour warranty pledges. This paper presents two mathematical models to determine the optimal combination rebate warranty policy when refurbished products are used for replacements from both the manufacturer and consumer point of views. Several numerical experiments are conducted to derive useful managerial knowledge.