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.

Abstract

Abstract
Algorithmic systems based on artificial intelligence (AI) increasingly play a role in decision-making processes, both in government and industry. These systems are used in areas such as retail, finances, and manufacturing. In the latter domain, the main priority is that the solutions are interpretable, as this characteristic correlates to the adoption rate of users (e.g., schedulers). However, more recently, these systems have been applied in areas of public interest, such as education, health, public administration, and criminal justice. The adoption of these systems in this domain, in particular the data-driven decision models, has raised questions about the risks associated with this technology, from which ethical problems may emerge. We analyse two important characteristics, interpretability and trustability, of AI-based systems in the industrial and public domains, respectively.

Abstract
Procurement plays an essential role in the supply of materials for the production of goods or products. The success of procurement management to fulfill demand with high service levels and on-time delivery relies on the suppliers' performance. Suppliers should be appropriately selected to source materials with the right quality, in the right quantity, at the right time, and for the right price. The scope of this problem, as well as other aspects such as the sourcing strategy, will depend on the type of items. Critical items, which represent high-profit impacts and high supply risks, should be approached comprehensively by considering all the main activities of the procurement process. This study focuses on a supplier selection problem integrated with inventory management under a multi-sourcing strategy, by taking into account stochastic demand and supply disruptions. This problem is approached by a simulation-optimization method, composed of discrete-event simulation and a genetic algorithm (GA). Finally, a numerical example is provided to illustrate the solution procedure.

Abstract
This work addresses the well-known capacitated lot sizing problem (CLSP) which is proven to be an NP-hard optimization problem. Simple period-by-period heuristics are popular solution approaches due to the extremely low computational effort and their suitability for rolling planning horizons. The aim of this work is to apply genetic programming (GP) to automatically generate specialized heuristics specific to the instance class. Experiments show that we are able to obtain better solutions when using GP evolved lot sizing rules compared to state-of-the-art constructive heuristics. © 2018 Elsevier Ltd