Management of tire waste is an important aspect of sustainable development due to its environmental, economical and social impacts. Key aspects of Reverse Logistics (RL) and Green Logistics (GL), such as recycling, re-manufacturing and reusable packaging, can improve the management of tire waste and support sustainability. Although these processes have been performed with a high degree of efficiency in other countries such as Japan, Spain and Germany, the application in Mexico and Russia has faced setbacks due to the absence of guidelines regarding legislation, RL processes, and social responsibility. Within this context, the present work aims to develop an integrated RL model to improve on these processes by considering the RL models from Russia and Mexico. For this, a review focused on RL in Mexico, Russia, Japan and the European Union (EU) was performed. Hence, the integrated model considers regulations and policies performed in each country to assign responsibilities regarding RL processes for the management of tire waste. As discussed, the implementation of efficient RL processes for the management of tire waste depends of different social entities such as the user (customer), private and public companies, and manufacturing and state-of-the-art approaches to transform waste into different products (diversification) to consider the RL scheme as a total economic system.

Background: The Capacitated Vehicle Routing Problem (CVRP) is one of the most important transportation problems in logistics and supply chain management. The standard CVRP considers a fleet of vehicles with homogeneous capacity that depart from a warehouse, collect products from (or deliver products to) a set of customer locations (points) and return to the same warehouse. However, the operation of carrier companies and third-party transportation providers may follow a different network flow for collection and delivery. This may lead to non-optimal route planning through the use of the standard CVRP.Objective: To propose a model for carrier companies to obtain optimal route planning.Method: A Capacitated Vehicle Routing Problem for Carriers (CVRPfC) model is used to consider the distribution scenario where a fleet of vehicles depart from a vehicle storage depot, collect products from a set of customer points and deliver them to a specific warehouse before returning to the vehicle storage depot. Validation of the model’s functionality was performed with adapted CVRP test problems from the Vehicle Routing Problem LIBrary. Following this, an assessment of the model’s economic impact was performed and validated with data from a real carrier (real instance) with the previously described distribution scenario.Results: The route planning obtained through the CVRPfC model accurately described the network flow of the real instance and significantly reduced its distribution costs.Conclusion: The CVRPfC model can thus improve the competitiveness of the carriers by providing better fares to their customers, reducing their distribution costs in the process.

Joint optimisation of price and order quantity has been proposed in earlier papers as a way to increase a firm's profits. However, most firms are not organised in such a way to support this joint decision. In this paper, we examine the benefits of joint price and order quantity optimisation as compared with a sequential decision process in which the price is determined first, followed by the determination of the order quantity. Numerical studies are performed that provide insight into how often it is advantageous to optimise jointly and how much benefit can be obtained. In general it is found that in many cases joint optimisation is of negligible value although it can be very beneficial in specific cases. Also, the natural tendency of firms to reduce prices and increase market size tends to reduce the benefits of joint optimization. This implies that firms should thoroughly understand their costs and market before incurring the expense of a reorganisation to support joint price and inventory optimisation.

This work is focused on the integration of the standard EOQ (Economic Order Quantity) model within the facility location decision model. This is proposed to extend on the facility location task which is usually performed based on just the overall demand of the customer locations to be served. If the inventory costs are considered within the demand supply process, these may affect the overall transportation costs as these are not linearly dependent of the demand. As such, the extended model considers, besides the distances, performance and capacity of the vehicles, the order quantities and the period in which they should be fulfilled. This model was tested with a reference instance of 200 suppliers and one distribution centre. The distances were estimated by considering the geographical locations of all elements in the network and the spherical model of the Earth's surface to obtain the metric in kilometres. As analysed, by considering the inventory costs within the facility location model, it can lead to refine the location to obtain long-term savings in transportation.

The zones design occurs when small areas or basic geographic units (BGU) must be grouped into acceptable zones under the requirements imposed by the case study. These requirements can be the generation of intra-connected and/or compact zones or with the same amount of habitants, clients, communication means, public services, etc. In this second point to design a territory, the selection and adaptation of a clustering method capable of generating compact groups while keeping balance in the number of objects that form each group is required.The classic partitioning stands out (also known as classification by partition among the clustering or classification methods [1]). Its properties are very useful to create compact groups.An interesting property of the classification by partitions resides in its capability to group different kinds of data. When working with geographical data, such as the BGU, the partitioning around medoids algorithms have given satisfactory results when the instances are small and only the objective of distances minimization is optimized. In the presence of additional restrictions, the K-medoids algorithms, present weaknesses in regard to the optimality and feasibility of the solutions.In this work we expose 2 variants of partitioning around medoids for geographical data with balance restrictions over the number of objects within each group keeping the optimality and feasibility of the solution. The first algorithm considers the ideas of k-meoids and extends it with a recursive constructive function to find balanced solutions. The second algorithm searches for solutions taking into account a balance between compactness and the cardinality of the groups (multiobjective). Different tests are presented for different numbers of groups and they are compared with some results obtained with Lagrange Relaxation. This kind of grouping is needed to solve aggregation for Territorial Design problems