On the operational logistic aspects of reuse.

For still more companies, it is or will become important to pay attention to the possibilities for reusing the products they produce and the items, like pallets and package materials, that they use for distributing their products or which are used by others for supplying their products to them. One important reason for the above is the growing concern for the natural environment, among others resulting in environmental laws which not only force companies to take back their products from their customers and the items used for the distribution of these products when these products or distribution items (DIs) are no longer desired by these customers, but also to take care of the environmentally friendly disposal of these products and Dis. However, due to the same reason, this disposal is becoming still more difficult and expensive (see e.g., Cairncross, 1990). Apart from being forced by law, companies feel forced to do the above because of competition and public opinion. But there are more reasons why it may be worthwhile for companies to consider reuse: there are products, components, materials and DIs that can be obtained cheaper or more quickly via reuse than via purchasing or producing anew

Substitutie van resources voor het oplossen van tijdelijke problemen

Substitutie van resources, waaronder mensen, machines, gereedschappen, materialen, toeleveranciers en transporteurs, voor het oplossen van tijdelijke problemen met standaard gebruikte resources is de inzet van alternatieve resources om alsnog tijdig aan een vraag te kunnen voldoen

Herstellen van productie-uitval

Het herstellen van productie-uitval kan worden omschreven als het transformeren van tijdens of direct na productie afgekeurde exemplaren of batches van producten in goede exemplaren of batches voordat deze naar afnemers worden gedistribueer

Lot-sizing for a single-stage single-product production system with rework of perishable production defectives

We consider a single-stage single-product production system. Produced units may be non-defective, reworkable defective, or non-reworkable defective. The system switches between production and rework. After producing a fixed number (N) of units, all reworkable defective units are reworked. Reworkable defectives are perishable or can become technologically obsolete. We assume that the rework time and the rework cost increase linearly with the time that a unit is held in stock. Therefore, N should not be too large. On the other hand, N should not be too small either, since there are set-up times and costs associated with switching between production and rework. For a given N, we derive an explicit expression for the average profit (sales revenue minus costs). Using this expression, the optimal value for N can be determined numerically. Moreover, it is easy to perform a sensitivity analysis, as we illustrate

Optimal core acquisition and remanufacturing policies under uncertain core quality fractions

Cores acquired by a remanufacturer are typically highly variable in quality. Even if the expected fractions of the various quality levels are known, then the exact fractions when acquiring cores are still uncertain. Our model incorporates this uncertainty in determining optimal acquisition decisions by considering multiple quality classes and a multinomial quality distribution for an acquired lot. We derive optimal acquisition and remanufacturing policies for both deterministic and uncertain demand. For deterministic demand, we derive a simple closed-form expression for the total expected cost. In a numerical experiment, we highlight the effect of uncertainty in quality fractions on the optimal number of acquired cores and show that the cost error of ignoring uncertainty can be significant. For uncertain demand, we derive optimal newsboy-type solutions for the optimal remanufacture-up-to levels and an approximate expression for the total expected cost given the number of acquired cores. In a further numerical experiment, we explore the effects of demand uncertainty on the optimal acquisition and remanufacturing decisions, and on the total expected cost

Aggregate overhaul and supply chain planning for rotables

We consider the problem of planning preventive maintenance and overhaul for modules that are used in a fleet of assets such as trains or airplanes. Each type of module, or rotable, has its own maintenance program in which a maximum amount of time/usage between overhauls of a module is stipulated. Overhauls are performed in an overhaul workshop with limited capacity. The problem we study is to determine aggregate workforce levels, turn-around stock levels of modules, and overhaul and replacement quantities per period so as to minimize the sum of labor costs, material costs of overhaul, and turn-around stock investments over the entire life-cycle of the maintained asset. We prove that this planning problem is strongly NP -hard, but we also provide computational evidence that the mixed integer programming formulation can be solved within reasonable time for real-life instances. Furthermore, we show that the linear programming relaxation can be used to aid decision making. We apply the model in a case study and provide computational results for randomly generated instances

Condition-based maintenance for complex systems based on current component status and Bayesian updating of component reliability

\u3cp\u3eWe propose a new condition-based maintenance policy for complex systems, based on the status (working, defective) of all components within a system, as well as the reliability block diagram of the system. By means of the survival signature, a generalization of the system signature allowing for multiple component types, we obtain a predictive distribution for the system survival time, also known as residual life distribution, based on which of the system's components currently function or not, and the current age of the functioning components.The time to failure of the components of the system is modeled by a Weibull distribution with a fixed shape parameter. The scale parameter is iteratively updated in a Bayesian fashion using the current (censored and non-censored) component lifetimes. Each component type has a separate Weibull model that may also include test data.The cost-optimal moment of replacement for the system is obtained by minimizing the expected cost rate per unit of time. The unit cost rate is recalculated when components fail or at the end of every (very short) fixed inter-evaluation interval, leading to a dynamic maintenance policy, since the ageing of components and possible failures will change the cost-optimal moment of replacement in the course of time. Via numerical experiments, some insight into the performance of the policy is given.\u3c/p\u3