Efficient patient admission scheduling is a key challenge in hospital management, as it directly impacts resource utilization and the quality of care. The Patient Admission Scheduling Problem (PASP) involves assigning patients to hospital beds over a planning horizon while considering medical constraints and hospital capacity. Due to its complexity, heuristic and metaheuristic approaches are often used to find high-quality solutions within a reasonable time. In this work, we propose a Variable Neighborhood Search (VNS) metaheuristic to solve the PASP. VNS systematically explores different neighborhoods to escape local optima and improve solution quality. To assess the impact of local search strategies, we implement four versions of VNS, each using a different method for modifying patient assignments. The proposed approach is evaluated on benchmark instances, where we conduct parameter tuning and analyze computational performance. Experimental results demonstrate the effectiveness of the method, showing that the appropriate choice of local search strategies significantly impact the quality of the results.

In this article, we present a comprehensive study regarding the problem of allocating a fleet of electric vehicles to charging stations according to charging time and battery constraints. Each charging station’s capacity as well as the necessary charging time is known in advance while each vehicle’s arrival time is provided by a GPS device. We provide an integer programming model solved with an exact method to effectively handle this combinatorial problem along with a set of metaheuristic algorithms. To evaluate the performance of this solution framework, computational experiments are conducted on large-scale randomly generated instances simulating a real-world scenario.

In recent years, there has been an increased demand for fish and seafood, highlighting the essential role of fish protein as a primary source of animal protein consumption in various regions worldwide. Seafood plays a crucial role in supporting future food security needs. However, the seafood industry generates a significant amount of waste, posing challenges in its proper management. The seafood supply chain is complex due to the involvement of multiple actors and the numerous flows between them. To address this issue, this paper proposes an efficient multi-echelon closed-loop seafood supply chain network (CLSC). This network is designed to globally oversee the seafood supply chain and its associated waste generated by its actors, showing that the waste generated by end customers is essential for creating added value to the seafood supply chain, which can be further utilized by other industries. Implementing this network helps reduce overall operational costs by introducing a novel mixed-integer linear programming (MILP) mathematical model aimed at minimizing total costs, including facility operating expenses and transportation costs. The application of this model is demonstrated through tested scenarios with various dimensions. The results of this analysis illustrate the promising outcomes of employing the proposed model.

Efficient inventory management (IM) presents an important key driver for supply chain (SC) sustainability. This latter becomes a crucial concern for decision-makers and managers in all domains, particularly in the matter of sensitive areas that affect human well-being, namely the pharmaceutical industry. Medicines IM for a sustainable Pharmaceutical Supply Chain (PSC) brought further particularities compared to the traditional SCs. Besides the economic preoccupation, social and environmental issues might be considered. In this work, we assess the impact of the Lateral Transshipment (LT) strategy on the sustainability of the IM process. We compare the total costs of two cases, IM with and without LT strategy. We propose an IM model that seeks the optimal replenishment order quantity of multiple types of products and the shipment time in a two-echelon PSC under a centralized setting. The considered PSC consists of a pharmaceutical company (PC), a Pharma-distributor (PD), and multiple hospitals. The mathematical model takes into account the transportation costs including LT costs -in the case when LT is included- as well as shortage, and products with high deterioration rate costs. We attempt to minimize unused medicines leftover by minimizing the deterioration rate of products at both distributor and hospital sites.

This paper focuses on the optimization of job-shop scheduling in industrial production systems in the just-in-time configuration. The scheduling function plays a crucial role in determining the performance and competitiveness of a company, especially with the increasing demand for timely product delivery. The main challenge in job-shop scheduling is to find the optimal scheduling of multiple jobs on several machines, taking into account their due dates and processing times. The earliness-tardiness model adds another layer of complexity to the problem by considering the impact of delivering jobs early or late, incurring inventory costs or tardiness penalties, respectively. Due to the multiple constraints and objectives involved, finding the optimal solution is a challenging task. To tackle this problem, we in this paper investigate the use of recent advances in deep reinforcement learning techniques to optimize the earliness-tardiness cost in the job-shop. The experimental results showed that the proposed method performed at least as good as the state of the art in 65% of the studied instances, while strictly improving 10% of the same benchmark.