Abstract

Abstract
Problem definition: Online retailers often receive customer orders comprising several products of differing origins. To fulfill these orders, retailers must ship multiple parcels from different locations and-unless they are grouped somewhere along the supply chain-these may reach the customer's doorstep one by one. Academic/practical relevance: We conjecture here that receiving products sequentially instead of all together affects a consumer's reaction to her purchases, possibly influencing-for good or ill-her decision to return products, as well as her overall service satisfaction. We use two-year granular data from an online fashion marketplace to test this hypothesis and characterize consumer behavioral responses to delivery consolidation and examine how it impacts supply chain stakeholders. Methodology: To achieve causal inference, we exploit the fact that the couriers used by the focal marketplace gather together certain parcels for reasons related more to the timing of their arrival than their actual customers, thereby exogenously consolidating the delivery of some orders. We construct a balanced sample of matched twin multiproduct orders that are alike in all respects except their delivery: consolidated (all parcels delivered jointly) versus otherwise (split). Results: We find that delivery consolidation benefits the marketplace and all its suppliers. By eliminating the stress associated with split deliveries, delivery consolidation pleases consumers as it leads to fewer returns and higher overall satisfaction. Managerial implications: Delivering all products in an order together, even if later, reduces the probability of a return, which improves the financial performance of the marketplace and its suppliers and reduces reverse logistics. Our results suggest that in our context, delivery speed matters less than the convenience of receiving all ordered goods in a single delivery, and we provide directions for adapting logistics strategies accordingly. Our empirical findings also imply that the return decisions of multiple products purchased at once should not be considered to be independent. Finding tractable ways of modeling this feature will be necessary in further driving retail practice through theoretical research that accounts for the behavioral implications of delivery consolidation when optimizing fulfillment decisions.

Abstract

Abstract
As the demand for electric charging accelerates, so does the stress on the relatively insufficient public charging infrastructure. To appropriately manage and scale charging infrastructure, there is a need for support tools capable of predicting the utilization and sales of charging stations, as well as the traffic flow of users from their original location to the charging stations. Therefore, this article proposes a generic methodology for infrastructure placement, namely forecasting demand and predicting its flow to the supply points. The methodology is applied in a case study to the electric charging grid of Portugal with real data, in the context of the needs of a particular charging point operator (CPO). Demand is first forecasted at a high-granularity level with a demand disaggregation model, followed by its capture by the grid of chargers using a parameterized gravity model. Validation is performed by comparing actual with predicted sales per charging station. Adequate visualizations to support decision-making are presented.

Abstract
Data clustering combined with multiobjective optimization has become attractive when the structure and the number of clusters in a dataset are unknown. Data clustering is the main task of exploratory data mining and a standard statistical data analysis technique used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. This project analyzes data to extract possible failure patterns in Solar Photovoltaic (PV) Panels. When managing PV Panels, preventive maintenance procedures focus on identifying and monitoring potential equipment problems. Failure patterns such as soiling, shadowing, and equipment damage can disturb the PV system from operating efficiently. We propose a multiobjective evolutionary algorithm that uses different distance functions to explore the conflicts between different perspectives of the problem. By the end, we obtain a non-dominated set, where each solution carries out information about a possible clustering structure. After that, we pursue a-posteriori analysis to exploit the knowledge of non-dominated solutions and enhance the fault detection process of PV panels.

Abstract