Abstract
Entities that participate in the same supply chain or that can complement each other can benefit from reductions in their operational costs and increased operating efficiency when collaborating by sharing information and resources or engaging in joint planning. However, setting up successful collaborations is still an active research topic, particularly in the forest sector, due to the lack of a clearly defined road map for bringing collaboration into practice. This paper builds on a comprehensive literature review to propose a framework for developing inter-firm collaboration, particularly for agents of forest-based supply chains. The framework encompasses identification of collaboration opportunities with potential entities, design of the collaborative strategy, and tools for its implementation. The framework is used to discuss cases of collaboration found in the literature. The framework is also applied to developing a new collaborative process in the wood furniture industry.

Abstract
Sectorization problems have significant challenges arising from the many objectives that must be optimised simultaneously. Several methods exist to deal with these many-objective optimisation problems, but each has its limitations. This paper analyses an application of Preference Inspired Co-Evolutionary Algorithms, with goal vectors (PICEA-g) to sectorization problems. The method is tested on instances of different size difficulty levels and various configurations for mutation rate and population number. The main purpose is to find the best configuration for PICEA-g to solve sectorization problems. Performance metrics are used to evaluate these configurations regarding the solutions’ spread, convergence, and diversity in the solution space. Several test trials showed that big and medium-sized instances perform better with low mutation rates and large population sizes. The opposite is valid for the small size instances. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The CrossLog project aims to investigate, study, develop and implement an automated and collaborative cross-docking system (aligned with Industry 4.0) capable of moving and managing the flow of products within the warehouse in the fastest and safest way. In CrossLog, the ability to generate intelligent three-dimensional packing patterns is essential to ensure the flexibility and productivity of the cross-docking system while ensuring the stability of the palletised load. In this work, a heuristic solution approach is proposed to generate efficient pallet packing patterns that simultaneously minimise the total number of pallets required and address the balance of weight and volume between pallets. Computational experiments with data from a real company demonstrate the quality of the proposed solution approach.

Abstract
Additive Layer Manufacturing, particularly Fused Deposition Modelling, faces significant batch loss risks during production. The traditional Concurrent Printing Mode produces all parts simultaneously (layer-by-layer, bottom-to-top), efficiently using printing space but risking complete batch failure if problems occur. In contrast, Sequential Printing Mode produces one part at a time, reducing the risk of total batch loss but utilising printing space less efficiently. In this work, we propose an algorithm that, given a set of parts, performs the nesting of the parts for Concurrent Printing Mode, and for the first time, for the Sequential Printing Mode. A no-fit polygon based approach is used to handle geometry between pairs of parts by using multiple horizontal 2D layer projections of 3D parts, to ensure non-overlapping constraints and prevent machine-part collisions. A Greedy Randomized Adaptive Search Procedure is proposed, tested and benchmarked against a commercial software, using a new set of real-world instances. The approach shows the ability to find high-quality solutions. The approach significantly reduces the number of batches, minimises waste, reduces manufacturing time, and promotes parts quality.

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