Abstract
The fast-moving consumer goods sector relies on economies of scale. However, its assortments have been overextended as a means of market share appropriation and top-line growth. This paper studies the selection of the optimal set of products for fast-moving consumer goods producers to offer, as there is no previous model for product line selection that satisfies the requirements of the sector. Our mixed-integer programming model combines a multi-category attraction model with a capacitated lot-sizing problem, shared setups and safety stock. The multi-category attraction model predicts how the demand for each product responds to changes within the assortment. The capacitated lot-sizing problem allows us to account for the indirect production costs associated with different assortments. As seasonality is prevalent in consumer goods sales, the production plan optimally weights the trade-off between stocking finished goods from a long run with performing shorter runs with additional setups. Finally, the safety stock extension addresses the effect of the demand uncertainty associated with each assortment. With the computational experiments, we assess the value of our approach using data based on a real case. Our findings suggest that the benefits of a tailored approach are at their highest in scenarios typical fast-moving consumer goods industry: when capacity is tight, demand exhibits seasonal patterns and high service levels are required. This also occurs when the firm has a strong competitive position and consumer price-sensitivity is low. By testing the approach in two real-world instances, we show that this decision should not be made based on the current myopic industry practices. Lastly, our approach obtains profits of up to 9.4% higher than the current state-of-the-art models for product line selection. © 2020 Elsevier Ltd

Abstract
Soon, a new generation of Collaborative Robots embodying Human-Robot Teams (HRTs) is expected to be more widely adopted in manufacturing. The adoption of this technology requires evaluating the overall performance achieved by an HRT for a given production workflow. We study this performance by solving the underlying scheduling problem under different production settings. We formulate the problem as a Multimode Multiprocessor Task Scheduling Problem, where tasks may be executed by two different types of resources (humans and robots), or by both simultaneously. Two algorithms are proposed to solve the problem - a Constraint Programming model and a Genetic Algorithm. We also devise a new lower bound for benchmarking the methods. Computational experiments are conducted on a large set of instances generated to represent a variety of HRT production settings. General instances for the problem are also considered. The proposed methods outperform algorithms found in the literature for similar problems. For the HRT instances, we find optimal solutions for a considerable number of instances, and tight gaps to lower bounds when optimal solutions are unknown. Moreover, we derive some insights on the improvement obtained if tasks can be executed simultaneously by the HRT. The experiments suggest that collaborative tasks reduce the total work time, especially in settings with numerous precedence constraints and low robot eligibility. These results indicate that the possibility of collaborative work can shorten cycle time, which may motivate future investment in this new technology.

Abstract
There are extensive studies in the literature about the reorder point/order quantity policies for inventory management, also known as (r,Q) policies. Over time different algorithms have been proposed to calculate the optimal parameters given the demand characteristics and a fixed cost structure, as well as several heuristics and meta-heuristics that calculate approximations with varying accuracy. This work proposes a new meta-heuristic that evolves closed-form expressions for both policy parameters simultaneously - Cooperative Coevolutionary Genetic Programming. The implementation used for the experimental work is verified with published results from the optimal algorithm, and a well-known hybrid heuristic. The evolved expressions are compared to those algorithms, and to the expressions of previous Genetic Programming approaches available in the literature. The results outperform the previous closed-form expressions and demonstrate competitiveness against numerical methods, reaching an optimality gap of less than (Formula presented.), while being two orders of magnitude faster. Moreover, the evolved expressions are compact, have good generalisation capabilities, and present an interesting structure resembling previous heuristics. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

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