Abstract

Abstract
Interest in integrating lot-sizing and cutting stock problems has been increasing over the years. This integrated problem has been applied in many industries, such as paper, textile and furniture. Yet, there are only a few studies that acknowledge the importance of uncertainty to optimise these integrated decisions. This work aims to address this gap by incorporating demand uncertainty through stochastic programming and robust optimisation approaches. Both robust and stochastic models were specifically conceived to be solved by a column generation method. In addition, both models are embedded in a rolling-horizon procedure in order to incorporate dynamic reaction to demand realisation and adapt the models to a multistage stochastic setting. Computational experiments are proposed to test the efficiency of the column generation method and include a Monte Carlo simulation to assess both stochastic programming and robust optimisation for the integrated problem. Results suggest that acknowledging uncertainty can cut costs by up to 39.7%, while maintaining or reducing variability at the same time.

Abstract
Optimising operations in bulk cargo ports is of great relevance due to their major participation in international trade. In inbound operations, which are critical to meet due dates, the product typically arrives by train and must be transferred to the stockyard. This process requires several machines and is subject to frequent disruptions leading to uncertain processing times. This work focuses on the scheduling problem of unloading the wagons to the stockyard, approaching both the deterministic and the stochastic versions. For the deterministic problem, we compare three solution approaches: a Mixed Integer Programming model, a Constraint Programming model and a Greedy Randomised algorithm. The selection rule of the latter is evolved by Genetic Programming. The stochastic version is tackled by dispatching rules, also evolved via Genetic Programming. The proposed approaches are validated using real data from a leading company in the mining sector. Results show that the new heuristic presents similar results to the company's algorithm in a considerably shorter computational time. Moreover, we perform extensive computational experiments to validate the methods on a wide spectrum of randomly generated instances. Finally, as managing uncertainty is fundamental for the effectiveness of these operations, distinct strategies are compared, ranging from purely predictive to completely reactive scheduling. We conclude that re-scheduling with high frequency is the best approach to avoid performance deterioration under schedule disruptions, and using the evolved dispatching rules incur fewer deviations from the original schedule.

Abstract
In several online retail contexts, such as grocery retailing, customers have to be present at the moment of delivery, that is, an attended home delivery service is in place. This requirement adds new challenges to this channel, often leading to narrow profitability. From an operations perspective, this service is performed with the retailer offering multiple time slots for the customer to choose from. Retailers target a cost-efficient delivery process that also accounts for customers' preferences by properly managing the options to show to customers, that is, time slot management. This study analyzes a dynamic slotting problem, that is, choosing the best slots to show for each customer, which is close to many practical cases pursuing a customer service orientation. We study two new strategies to improve customer service while satisfying cost-efficiency goals: (i) enforcing a constraint on the minimum number or percentage of slots to show to customers and (ii) integrating multiple days when tackling this challenging problem. Our results show under which conditions these proposed strategies can lead to win-win situations for both customer service and profit.

Abstract
In the current competition environment, transportation costs continue to rise, causing a reduction in the profit margins of companies. There are several tools in the literature to support the planning of logistics activities, but individualised solutions are not yet effective. In this study, a linear programming model is proposed to jointly plan the demand fulfilment of two competing companies by encouraging the search for synergies that enhance collaboration in the use of existing resources. To demonstrate the validity of the proposed mode, a case study is carried out and the results obtained with the initiation of the collaboration are evaluated. In conclusion, the proposed model reduces the logistics costs by up to 13%, as well as decreases the carbon footprint by 37%. By focusing on optimising economic and environmental aspects, this approach serves as a guide for companies to promote collaborations and to facilitate decision making at a managerial level. © 2023, IFIP International Federation for Information Processing.

Abstract
To encourage customers to take a chance in finding the right product, retailers and marketplaces implement benevolent return policies that allow users to return items for free without a specific reason. These policies contribute to a high rate of returns, which result in high shipping costs for the retailer and a high environmental toll on the planet. This paper shows that these negative impacts can be significantly minimized if inventory is exchanged within the supplier network of marketplaces upon a return. We compare the performance of this proposal to the standard policy where items are always sent to the original supplier. Our results show that our proposal—returning to a closer supplier and using a predictive heuristic for fulfilment—can achieve a 16% cost reduction compared to the standard—returning to the original supplier and using a myopic rule for fulfilment. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.