Ernest Benedito

Optimal manufacturing policy in a reverse logistic system with dependent stochastic returns and limited capacities

The stochastic models of systems with reverse logistics usually assume that the quantity of products returned is independent of sales. This hypothesis is obviously not true and can lead to suboptimal production policies. In this paper a new sales-dependent returns model is described. In this model, the returns depend on the useful life of the products sold and on the probability of an end-of-life product being returned. A Markov decision problem is formulated in order to obtain the optimal manufacturing policy. A numerical example is provided to illustrate the use of the defined model. An approximated Markov decision model is defined where the optimal policy is easily obtained. The optimal policies of the original and the approximated models are compared.

Optimal production and storage capacities in a system with reverse logistics and periodic demand

Reverse logistics has a considerable influence both on production planning and management and on the determination of optimal production and storage capacities. Few studies, however, have developed models that take account of reverse logistics when calculating optimal production capacities and policies. This paper describes such a method for calculating production and storage capacity in a reverse logistics system in which demand is deterministic.

A multivariate process capability index that complies with industry requirements

Process capability indices (PCIs) are widely used in production processes in order to evaluate the product quality. Taking into account the benefits of these quality indices, six requirements that a PCI must achieve within production processes are proposed by the authors. According to the current industry requirements, the existing PCIs have limitations. Hence, a new multivariate process capability index (MPCI) is proposed. The proposed MPCI (which complies with all proposed requirements) is compared to other MPCIs from the literature and is validated in a German luxury automobile manufacturing company.

Strictly convex loss functions for port-Hamiltonian based optimization algorithm for MTDC networks

In this work we propose a primal-dual method that can be cast in a port-Hamiltonian framework for minimizing the power losses in a multi-terminal DC network. The main contribution consists of proposing an alternative power loss function by means of a change of variables that translates the convex objective function into a strictly convex objective function. The results hold under some restrictive assumptions, but necessary to make steps towards a complete algorithm in future research. The obtained results are validated via numerical simulations.

Embodying decisions on work shifts into strategic manufacturing capacity planning

In spite of the obvious impact that decisions on workforce planning have on the capacity of a production system, they are rarely mentioned in strategic capacity planning literature. This paper analyses the implications of embodying decisions on work shifts in strategic capacity planning and proposes a mathematical programming model that allows treating any type of relationship between the intensity in the use of the production equipment and maintenance and variable production costs. A computational experiment shows that the model can be solved in acceptable computing times for moderate values of the number of types of equipment and of work shifts. Using the model, the optimal solutions for diverse scenarios are presented and compared with those obtained under the assumption that the work shifts must be kept invariable over the entire horizon.

Minimizing rework costs in multistage production processes by modifying quality specification limits

Multistage production processes are becoming more important in the industry to ensure levels of flexibility, efficiency and modularity. Thus, the way in which companies define optimal production parameters related to production costs and quality must be adapted to this reality. In this paper we introduce a multi-response optimization (MRO) model for a two stage production process. The model gives first stage quality specification limits which minimize the rework costs caused by the nonconforming parts of the whole process. The proposed model is applied to an example based on a production process of the automotive industry. The benefits of the model are evaluated by comparing the capability and the rework costs of the multistage production process before and after the optimization.

Single-site strategic capacity planning considering renewal, maintenance, inventory, taxes and cash flow management

This paper deals with strategic capacity planning of a single-site manufacturing system. We propose a MILP model that includes relevant business aspects and possibilities, some of which are only partially or not at all found in the literature. Specifically, we consider decisions on expansion, reduction and renewal of production capacity, and acquisition of storage capacity. In addition, we model aspects such as (a) maintenance costs and unit variable costs depending, respectively, on age and characteristics of facilities, (b) seasonality of the demand and (c) cash flow management, including taxes and, therefore, depreciation of assets. The model maximises the after-tax cash balance at the end of the planning horizon. We also present a computational experiment with 54 instances to show that the model can be solved for a wide range of sizes in a reasonable computing time using comercial software.