A decomposition algorithm for robust lot sizing problem with remanufacturing option

In this paper, we propose a decomposition procedure for constructing robust optimal production plans for reverse inventory systems. Our method is motivated by the need of overcoming the excessive computational time requirements, as well as the inaccuracies caused by imprecise representations of problem parameters. The method is based on a min-max formulation that avoids the excessive conservatism of the dualization technique employed by Wei et al. (2011). We perform a computational study using our decomposition framework on several classes of computer generated test instances and we report our experience. Bienstock and Özbay (2008) computed optimal base stock levels for the traditional lot sizing problem when the production cost is linear and we extend this work here by considering return inventories and setup costs for production. We use the approach of Bertsimas and Sim (2004) to model the uncertainties in the input

Green Lot-Sizing

The lot-sizing problem concerns a manufacturer that needs to solve a production planning problem. The producer must decide at which points in time to set up a production process, and when he/she does, how much to produce. There is a trade-off between inventory costs and costs associated with setting up the production process at some point in time. Traditionally, the lot-sizing model focuses solely on cost minimisation. However, production decisions also affect the environment in many ways. In this dissertation, the classic lot-sizing model is extended into several different directions, in order to take various environmental considerations into account. First, items that are returned from customers are included in the lot-sizing problem, within the context of reverse logistics. These items can be remanufactured to fulfil customer demand. In another extension, a minimum is imposed on the size of a production batch, in order to reduce the pollution associated with producing many small batches. Furthermore, a lot size model is considered in which there is a maximum on the amount of pollutants, such as carbon dioxide. This model can also be seen as a bi-objective lot-sizing problem. The mathematical models that arise from these extensions are fundamentally harder to solve than the classic lot-sizing problem. Several approaches to solving these problems are developed, based on mathematical optimisation techniques such as mixed integer programming, dynamic programming and fully polynomial time approximation schemes

Stochastic improvement of cyclic railway timetables

Real-time railway operations are subject to stochastic disturbances. Thus a timetable should be designed in such a way that it can cope with these disturbances as well as possible. For that purpose, a timetable usually contains time supplements in several process times and buffer times between pairs of consecutive trains. This paper describes a Stochastic Optimization Model that can be used to allocate the time supplements and the buffer times in a given timetable in such a way that the timetable becomes maximally robust against stochastic disturbances. The Stochastic Optimization Model was tested on several instances of NS Reizigers, the main operator of passenger trains in the Netherlands. Moreover, a timetable that was computed by the model was operated in practice in a timetable experiment on the so-called “Zaanlijn”. The results show that the average delays of trains can often be reduced significantly by applying relatively small modifications to a given timetable