In recent years, the Vehicle Routing Problem (VRP) has become an important issue for distribution companies. Also, the rapid development of communication means and the appearance of reverse logistics have given rise to new variants of the VRP. This article deals with an important variant of the VRP which is Dynamic Vehicle Routing Problem with Simultaneous Delivery and Pickup (DVRPSDP), in which new customers appear during the working day and each customer requires simultaneous delivery and pickup. A Memetic Algorithm (MA) that combines Genetic Algorithm (GA) and local search procedure have been proposed to solve the problem. The performance of the algorithm is evaluated with the tests carried out on a set of benchmarks found in the literature. The proposed memetic algorithm is very efficient and gives many good solutions.

Internal supply chains exist in many global enterprises, where manufacturing tasks and sales jobs operate separately, but the management needs to integrate their financial performance reports. In addition, the fabrication planning must meet specific operational goals, such as meeting external clients’ requirements on quality and short order due dates, avoiding internal fabricating interruptions due to inevitable equipment breakdowns, and minimizing overall manufacturing and stock holding costs. Motivated by helping multinational corporations deal with the issues mentioned earlier, this study aims to optimize a finite production rate (FPR)-based supplier-retailer cooperative problem with multi-shipment, rework, subcontracting, probabilistic failure, and expedited rate. Wherein using an outsourcer and expedited-rate help shorten the needed batch producing time significantly; the rework of defects and corrective action on unanticipated breakdown assist in up-keeping the quality and avoiding fabricating delay. We develop an FPR-based model to cautiously represent the considered manufacturing features and activities involved in transporting end products and retailers’ stock holding. Model’s formulating and investigating assists us in gaining the function of operating costs. In addition, optimization procedures with a proposed algorithm help us verify its convexity and decide the model’s best fabricating runtime solution. Finally, we validate how this study works and what important information our model can disclose using a numerical example to facilitate management’s decision-making to end our work.

Artificial neural networks (ANNs) are one of the artificial intelligence techniques used in real-world problems and applications encountered in almost all industries such as education, health, chemistry, food, informatics, logistics, transportation. ANN is widely used in many techniques such as optimization, modelling, classification and forecasting, and many empirical studies have been carried out in areas such as planning, inventory management, maintenance, quality control, econometrics, supply chain management and logistics related to ANN. The most important and just as hard stage of ANNs is the learning process. This process is about finding optimal values in the search space for different datasets. In this process, the values generated by training algorithms are used as network parameters and are directly effective in the success of the neural network (NN). In classical training techniques, problems such as local optimum and slow convergence are encountered. Meta-heuristic algorithms for the training of ANNs in the face of this negative situation have been used in many studies as an alternative. In this study, a new hybrid algorithm namely MVOSANN is suggested for the training of ANNs, using Simulated annealing (SA) and Multi-verse optimizer (MVO) algorithms. The suggested MVOSANN algorithm has been experimented on 12 prevalently classification datasets. The productivity of MVOSANN has been compared with 12 well-recognized and current meta-heuristic algorithms. Experimental results show that MVOSANN produces very successful and competitive results.

Batch adsorption and desorption kinetic experiments of fenitrothion on clay soil and sandy clay loam soil indicated that the equilibration time was approximately 30 hours. The kinetics of adsorption and desorption exhibited two distinct stages: a rapid process in the initial stages followed by a slow process. The pseudo-first-order model followed by the Elovich kinetic model fit the experimental adsorption and desorption data quite well, with high values of R2 and low values of ∆qe% and SSE. Accordingly, the pseudo-first-order model is most suitable for describing the adsorption and desorption kinetics of fenitrothion on clay soil and sandy clay soil. Pseudo-second-order model type-1 and type-2 models fit the experimental adsorption data; however, these models cannot be used to describe desorption kinetics. Moreover, the modified Freundlich model has limited applicability, and the intraparticle diffusion kinetic model cannot describe the kinetics of the adsorption and desorption of fenitrothion on clay and sandy clay loam soils.

This paper considers simultaneously two areas of facility location and assortment planning in a competitive environment. In fact, a chain store that has competitors in the market locates a new facility. As there are different products in the market that can substitute with each other, it is intended to determine the best product assortment as well. An integer nonlinear programming problem is proposed to model the mentioned subject. For solving the model, the problem is reformulated as a mixed integer linear programming one. Therefore, a MIP solver software can be used for solving the small- and medium-size problems. For large-scale problems, a firefly algorithm is designed for obtaining a satisfactory solution. By using the proposed model, it is numerically shown that, in addition to the optimal location, it is also necessary to determine simultaneously the best product assortment for the new store. Actually, comparison results reveal that the location significantly affects the assortment scenarios for the new store. In other words, the selection of new store locations may lead to loss of large profit if the assortment planning is neglected.

Trivalent analytes such as Fe³⁺, Cr³⁺ and A^l3+ play pivotal roles in various fields including environmental monitoring, biological sensing, and industrial processes. The development of efficient and selective detection methods for trivalent species is therefore of paramount importance. In this review, we present an overview of recent advances in the recognition of trivalent analytes using simple organic chromogenic and fluorogenic probes. In this review we discuss the strategies employed for the design and synthesis of chromogenic and fluorogenic probes tailored for trivalent analytes, highlighting key structural motifs and functional groups that contribute to their recognition capabilities.

Guided by a real case, this paper efficiently proposes a new metaheuristic algorithm based on Simulated Annealing to solve the Heterogeneous Vehicle Routing Problem with Time Windows to deliver fresh meat in urban environments. Our proposal generates an initial feasible solution using a hybrid heuristic based on the well-known Travelling Salesman Problem (TSP) solution and, subsequently, refining it through a Simulated Annealing (SA). We have tested the efficiency of the proposed approach in a company case study related to the planning of the transportation of a regional distribution center meat company to customers within the urban and rural perimeter of Bogotá, Colombia. The main goal is to reach a service level of 97% while reducing operational costs and several routes (used vehicles). The results show that the proposed approach finds better routes than the current ones regarding costs and service level within short computing times. The proposed scheme promises to solve the refrigerated vehicle routing problem.

The synthesis of 4-aryl-NH-1,2,3-triazoles in good to excellent isolated yields (75-95%) has been achieved via a [3+2] cycloaddition of aromatic nitroolefins and sodium azide catalyzed by recyclable heterogeneous sulfated tin oxide (STO, 10 mol%) in toluene at 60 0C. Aliphatic nitrooefines proved to be unsuccessful partners in the present methodology.

Due to their high synthetic efficiency, multi-component reactions (MCRs) have shown to be extraordinarily effective at producing compounds in a single synthetic operation. The chromeno[2,3-d] pyrimidine moieties represent important building blocks in synthetic bioactive compounds. The reaction of barbituric acid, aldehydes, and cyclohexane-1,3-diones in the presence of zinc acetate (5 mol%) under neat conditions at 70 0C, yielded a facile and one-pot synthesis of corresponding chromeno[2,3-d]pyrimidineones. Thirteen compounds containing neutral, withdrawing and electron donating groups (H, NO₂, -CN, -Cl; -OH, -CH₃ and -OCH₃) were synthesized in good, isolated yields ranging from 78-91%. The successful involvement of o-, m-, and p- substituted nitro benzaldehydes suggested that steric hindrance had no effect. All of the synthesized compounds are well-known, and this process is unique in that it makes use of zinc acetate, an accessible, low-cost Lewis acid catalyst.

A new series of Schiff bases derived from pyrazole-thiocarbohydrazide, namely (4a-d), were well-synthesized. The synthesis is carried out using monothiocarbohydrazone derivative (3), which was prepared via coupling of 5-chloro-pyrazole-4-carbaldehyde (1) with thiocarbohydrazide (2) in absolute ethanol that contains a catalytic quantity of acetic acid. The structures of newly synthesized compounds were fully clarified by various spectroscopic analyses (FT-IR, ¹H-NMR, ¹³C-NMR, and mass spectra) and elemental analysis. Also, the molecular docking was performed to investigate the binding interactions of the synthesized compounds (1, 3 and 4a-d) with COX-2 active site. The results revealed that most of them have robust hydrogen bonding networks and favorable binding energies compared to compound (4b). Understanding the anti-inflammatory behavior through understanding the specific interactions of these compounds with COX-2 will aid in the design and development of more effective inhibitors for therapeutic applications.