The introduction of extended producer responsibility makes original equipment manufacturers formally responsible for take-back, recovery and reuse of discarded products. One of their key problems is to determine to what extent return products must be disassembled and which recovery and disposal options should be applied. On a tactical management level, this involves anticipating problems like meeting (legislative) recovery targets, limited secondary end markets and investments in recycling infrastructure. In this paper, a comprehensive model is presented, which determines an optimal product recovery and disposal strategy for one product type. The objective function takes into account technical, commercial and ecological criteria as well as uncertainty on these criteria due to lack of information, in particular regarding the quality level. Optimization is done on overall net profit and occurs using a two-phased DPalgorithm. The applicability of the model is shown in a case study.

The two primary functions of a warehouse include (1) temporary storage and protection of goods and (2) providing value added services such as fulfilment of individual customer orders, packaging of goods, after sales services, repairs, testing, inspection and assembly. To perform the above functions, the warehouse is divided into several functional areas such as reserve storage area, forward (order collation) area and cross-docking. The paper presents a mathematical model and a heuristic algorithm that jointly determine product allocation to the functional areas in the warehouse as well as the size of each area using data readily available to a warehouse manager.

We consider a single depot and a set of customers with known demands, each of which must be picked up and delivered at specified locations and within given time windows. We seek a route and a schedule for a single vehicle with known capacity, which minimizes the route duration, i.e., the difference between the arrival time and the departure time at the depot. We develop a local search method for this problem based on a variable-depth search, similar to the Lin-Kernighan algorithm for the traveling salesman problem. The method consists of two phases. In the first phase, a feasible route is constructed; in the second phase, it is iteratively improved. In both phases, we use a variable-depth search built up out of seven basic types of arc-exchange procedures. When tested on real-life problems, the method is shown to produce near-optimal solutions in a reasonable amount of computation time. In spite of this, there is the theoretical possibility that the method may end up with a poor or even infeasible solution. As a safeguard against such an emergency, we have developed an alternative algorithm based on simulated annealing. As a rule, it finds high quality solutions in a relatively large computation time.