Abstract
This paper proposes a new problem by integrating the job shop scheduling, the part feeding, and the automated storage and retrieval problems. These three problems are intertwined and the performance of each of these problems influences and is influenced by the performance of the other problems. We consider a manufacturing environment composed of a set of machines (production system) connected by a transport system and a storage/retrieval system. Jobs are retrieved from storage and delivered to a load/unload area (LU) by the automated storage retrieval system. Then they are transported to and between the machines where their operations are processed on by the transport system. Once all operations of a job are processed, the job is taken back to the LU and then returned to the storage cell. We propose a mixed-integer linear programming (MILP) model that can be solved to optimality for small-sized instances. We also propose a hybrid simulated annealing (HSA) algorithm to find good quality solutions for larger instances. The HSA incorporates a late acceptance hill-climbing algorithm and a multistart strategy to promote both intensification and exploration while decreasing computational requirements. To compute the optimality gap of the HSA solutions, we derive a very fast lower bounding procedure. Computational experiments are conducted on two sets of instances that we also propose. The computational results show the effectiveness of the MILP on small-sized instances as well as the effectiveness, efficiency, and robustness of the HSA on medium and large-sized instances. Furthermore, the computational experiments clearly shown that importance of optimizing the three problems simultaneous. Finally, the importance and relevance of including the storage/retrieval activities are empirically demonstrated as ignoring them leads to wrong and misleading results.

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Abstract
Geolocation data is fundamental to businesses relying on vehicles such as logistics and transportation. With the advance of the technology, collecting geolocation data become increasingly accessible and affordable, which raised new opportunities for business intelligence. This paper addresses the application of geolocation data for monitoring logistics processes, namely for detecting vehicle-based operations in real time. A stream of geolocation entries is used for inferring stationary events. Data from an international logistics company is used as a case study, in which operations of loading/unloading of goods are not only identified but also quantified. The results of the case study demonstrate the effectiveness of the solution, showing that logistics operations can be inferred from geolocation data. Further meaningful information may be extracted from these inferred operations using process mining techniques.

Abstract
Geolocation data identifies the geographic location of people or objects, which may unveil the performance of some activity or operation. A good example is, if a vehicle is in a gas station then one may assume that the vehicle is being refuelled. This work aims to obtain vehicle-based operations from geolocation data by analysing the stationary states of vehicles, which may identify some motionless event (e.g. bus line stops and traffic incidents). Ultimately, these operations may be analysed with Process Mining techniques in order to discover the most significant ones and extract process related information. In this work, we studied the application of diverse approaches for detecting vehicle-based operations and identified different operations related to the bus services. The operations were also characterized according the distribution of their events, allowing to identify specific operations characteristics. The public transport network of Rio de Janeiro is used as a case study, which is supported by a real-time data stream of buses geolocations.

Abstract
The retail industry is becoming increasingly competitive; as a result, companies are seeking to reduce inefficiencies in their supply chains. One way of increasing the efficiency of operations inside a warehouse is by better allocating products in the available spaces. In this paper, we propose a new heuristic approach to solving the storage location assignment problem (SLAP) considering precedence constraints, in multi-aisle, multi-product picking warehouses. A two-phase heuristic procedure is developed: the products are clustered and assigned to the available spaces. We tested the procedure in the non-perishables warehouse of a real-world Portuguese retail chain, which supplies 191 stores per day. The results show that the new assignment of products allows for an improvement of up to 15% on the distance travelled by the pickers, which implies savings of approximately 477 km per month. This problem is a special case of SLAP since we are dealing with large percentages of non-uniform products. This procedure incorporates four relevant criteria for the allocation decision: the products' similarity, demand and weight, and the distance travelled by the picker. By using a two-phase heuristic method, this study offers companies and academics an alternative and more effective solution for SLAP than the usual methods based on the creation of density zones.