The order picking process involves a series of activities in response to customer needs, such as the selection or programming of orders (batches), and the selection of different items from their storage location to shipment. These activities are accomplished by a routing policy that determines the picker sequence for retrieving the items from the storage location. Therefore, the order picking problem has been plenty investigated; however, in previous research, the proposed models were based on demand fulfilling, putting aside factors such as the product weight—which is an important criterion—at the time of establishing routes. In this article, a mathematical model is proposed; it takes into account the product’s weight derived from a case study. This model is relevant, as no similar work was found in the literature that improves the order picking by making simultaneous decisions on the storage location assignment and the picker-routing problem, considering precedence constraints based on the product weight and the characteristics of the case study, as the only location for each product in a warehouse with a general layout.

One of the critical warehousing processes is the order-picking process. This activity consists of retrieving items from their storage locations to fulfill the demand specified in the pick lists. Therefore, the storage location assignment affects the picking time and, consequently, reduces the operating costs of the warehouse. This work presents two alternative mixed-integer linear models and an adaptive multi-start heuristic (AMH) for solving the integrated storage location and picker-routing problem. The problem considers a warehouse with a general layout and precedence constraints for picking according to the products weight. Experimental work confirms the efficiency of the proposed reformulations since we found out a total of 334 tested instances and optimal solutions for 51 new cases and 62 new feasible solutions. The proposed AMH improved more than 29% of the best-known solutions and required an average execution time of 117 s. Consequently, our proposed algorithm is an attractive decision-making tool to achieve efficiency when solving practical situations in a warehouse.

Optimizing chitosan recovery from shrimp shells is one of the most effective measures in shrimp waste management. Incorporating machine learning-based models will significantly impact the optimization process. This research aimed to evaluate the optimization of chitosan extraction from Litopenaeus vannamei shrimp shells using deproteinization and exploratory machine learning-based similarity model approaches. Chitosan extraction from shrimp shells was optimized using a deproteinization method, where various NaOH concentrations (1, 2, 3, 4, 5, and 10%) were applied at room temperature (RT) and 50 ± 2 °C, while maintaining controlled conditions for demineralization and deacetylation. The chitosan products were characterized by ash content, moisture, yield percentage, deproteinization efficiency, FTIR, deacetylation degree (DD), XRD, crystallinity index (CI%), and scanning electron microscopy (SEM). A machine learning random forest regressor model was developed to evaluate the similarities between the laboratory-synthesized and commercial chitosan (CC) samples. The results confirmed the formation of chitosan with semi-complete deacetylation (DD% from 98.84 ± 0.1% to 99.27 ± 0.004%). Deproteinization efficacy was in the range of 93.39 ± 0.083% to 97.0 ± 0.31%. XRD and SEM analyses demonstrated that commercial chitosan (CC) possessed a predominately amorphous structure, whereas the isolated chitosan samples exhibited low crystallinity, with increased amorphism at higher NaOH concentrations and temperatures. The machine learning-based similarity model indicated that Ch3 and Ch4 samples exhibited the highest resemblance degrees to commercial chitosan, while the S1 sample showed the lowest similarity. However, most of the recovered chitosan samples showed low similarity to commercial chitosan; they retained their higher degree of deacetylation (DD%), structural integrity, and quality parameters, indicating the success of the deproteinization route in enhancing chitosan production.