Abstract
This paper addresses the impact of consumer purchasing behaviour on the production planning of perishable food products for companies operating in the fast moving consumer goods using direct store delivery. The research presented here builds on previous marketing studies related to the effects of expiry dates in order to derive mathematical formulae, which express the age dependent demand for different categories of perishable products. These demand expressions take into account both customer willingness to pay and product quality risk. The paper presents deterministic and stochastic production planning models, which incorporate the customer's eagerness to pick up the fresher products available. Results indicate that model approximations neglecting the fact that customers pick up the fresher products or considering that all products have the same product quality risk have a reduced impact on profit losses. On the other hand, not considering the decreasing customer willingness to pay has an important impact both on the profit losses and on the amount of spoiled products.

Abstract
This paper presents a successful application of operations research techniques in guiding the decision making process to achieve a superior operational efficiency in core activities. We focus on a rich vehicle routing problem faced by a Portuguese food distribution company on a daily basis. This problem can be described as a heterogeneous fleet site dependent vehicle routing problem with multiple time windows. We use the adaptative large neighbourhood search framework, which has proven to be effective to solve a variety of different vehicle routing problems. Our plans are compared against those of the company and the impact that the proposed decision support tool may have in terms of cost savings is shown. The algorithm converges quickly giving the planner considerably more time to focus on value-added tasks, rather than manually correct the routing schedule. Moreover, contrarily to the necessary adaptation time of the planner, the tool is quite flexible in following market changes, such as the introduction of new customers or new products.

Abstract
In the current global market, managing supply is not a straightforward process and it becomes even more complex as uncertainty and disruptions occur. In order to mitigate their impact, the selection of suppliers of strategic items should have a more holistic view of the operations in the supply chain. We propose an integrated model for supplier selection, considering inventory management and inbound transportation. We approach this problem, incorporating stochastic demand and suppliers' imperfect quality. Imperfect quality triggers additional costs, including external failure and holding costs. Supply disruptions also affect the suppliers' lead time, resulting in delivery delays. We develop a methodology to address this challenge with simulation-optimisation. A genetic algorithm determines supplier selection decisions, while inventory decisions are computed analytically. Discrete-event simulation is used to evaluate the overall performance, as well as to update the lead time dynamically, according to the disruptions. Finally, sensitivity analysis providing managerial insights reveals that criteria in supplier selection should be given a different priority depending on the characteristics of the items, and the effectiveness of disruption mitigation strategies depends on the disruption characteristics.

Abstract
Online retail is transforming the way distribution networks are managed. One prominent change is that retailers can now use their full network to fulfil orders. This process involves allocating orders to fulfilment nodes and, depending on the setting, can include other operational decisions, such as order consolidation, shipping mode selection and product substitution. This order allocation problem (OAOR) has garnered considerable attention in recent years. However, there is no comprehensive view of what has been done in the literature, nor a consistent terminology across papers, which makes it hard to position existing work and identify research gaps. To address these concerns, we conduct a systematic literature review, where we find over 60 articles contributing to the OAOR literature. From this review, we formulate the baseline problem, consider multiple extensions, and identify key problem characteristics. Additionally, we analyse and categorize the solution methods found based on the optimization mechanism, policy class, and incorporation of future information and learning. Our review points to several avenues for future research, both in problems and in solution methods. © 2025 The Authors

Abstract
This paper presents an optimisation approach based on mixed-integer programming for tactical planning decisions within fresh fruit processing industries. It applies to fruits such as oranges, tomatoes, guavas and others, where diluted fruit juice needs to be concentrated in evaporators to produce semi-finished or finished products. It considers agricultural and industrial activities, integrating them to address complex and interconnected decisions. Agricultural tasks include planting, harvesting, and transporting fruits from fields to processing plants, while industrial activities involve the production, inventory, and transportation of semi-finished and final products. This approach accommodates multiple agricultural regions, fruit varieties, processing plants, and products, operating on a weekly basis within a one-year planning horizon. It offers a detailed solution for harvesting, the fruit juice concentration process, inventory management for the products produced, and transportation of raw materials and products among processing plants. Production of semi-finished products is modelled using the Proportional Lot-Sizing and Scheduling Problem and the production of finished products is modelled adopting a blending lot-sizing problem. The results were validated through computational experiments using a dataset from a company that processes tomatoes and guavas. Scenario analyses were conducted to evaluate the solution's consistency and real-world applicability. The findings indicate that the approach can support decision making in practice, highlighting its potential as a valuable managerial, analytical, and optimisation tool for some agri-food industries.

Abstract