The accelerated growth of cities, population increase and economic development have leveraged waste generation globally. This trend is expected to continue, with a significant increase projected in the coming years. Therefore, efficient waste management has become a crucial concern for local, national and international authorities. Transportation plays a key role in waste collection and disposal, being directly related to traffic congestion, fuel consumption and environmental pollution. Despite the existing studies on household waste collection, there is a gap in the literature regarding routing for residential waste collection in medium-sized cities, especially in emerging and frontier developing countries. Therefore, this study seeks through the science tree metaphor and PRISMA methodology, to find studies focused on the vehicle routing problem in waste collection operations, considering aspects such as Modeling approaches and solution techniques, applied Vehicle Routing Problems variants, objective functions, decision variables and constraints, applications in real environments, applied algorithms, and studies considering uncertainty and real conditions. A methodological outline of Vehicle Routing Problems in waste collection operations is presented, where central research topics are identified such as processes developed with Geographic Information System and their integration with exact methods, time windows, multi-objective capacitated vehicle routing problems, the application of stochastic models consider the uncertainty in waste collection, which has allowed including future prediction and optimization as prediction models, based on neural networks, to foresee uncertain conditions of the operations. This article analyzes the evolution in the optimization of municipal solid waste collection routes since 1964, highlighting the transition from iterative models to advanced technologies and multi-objective approaches. The importance of tools such as 3D Geographic Information System and heuristic/metaheuristic algorithms in improving planning and efficiency, despite limitations in the face of uncertainty, is emphasized. The systematic review shows a trend towards sustainable and efficient solutions, indicating future directions for research in urban waste management.

This research focused on intervening in the optimization algorithm used by the Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) tool to analyze small-angle scattering (SAS) profiles using the Rigid-Body model. CREASE uses the genetic algorithm (GA) with dynamic adaptation as its optimization algorithm. The aim is to evaluate the performance of CREASE by replacing the GA with a Harmony Search (HS)-based metaheuristic, specifically the Nobel Global Harmony Search (NGHS), in the analysis of SAS profiles of low-concentration solutions vesicles-assembled amphiphilic macromolecules. Results showed that NGHS achieved similar accuracy to GA but with higher efficiency, achieving similar quality solutions with only one-sixth, and in some cases one-tenth, the number of fitness function evaluations used by GA. Besides, CREASE-NGHS achieved SAS profile analysis convergence with less than half the number of fitness function evaluations, saving computational resources and facilitating a more complete analysis. In addition, NGHS addressed some shortcomings of the GA optimization process and facilitated its use and adaptation to distinct types of samples for users with little experience in optimization.

Evolving market trends, characterized by an increasing demand for personalized products with short life cycles and variable demands, pose a significant challenge to the industry. One of the industry's strategies is to adopt robotic assembly systems to improve productivity and increase system flexibility. The widespread adoption of robots in assembly processes is evident; however, success is not guaranteed with implementation alone. Equally critical is addressing assembly planning and scheduling problems in robotic systems. To facilitate understanding, this review offers, in Section 2, a classification of robotic assembly systems, with an emphasis on a new layout termed the robotic matrix-structure assembly system. Section 3 classifies the planning and scheduling problems applied to the robotic assembly systems. In Section 4, we discuss the approaches and techniques used to formulate and solve the planning and programming challenges. Finally, statistical data are presented to illustrate current research trends and identify gaps for future research.

In this work, the indirect reductive amination of terephthalaldehyde with aqueous ammonia was studied. The condensation of terephthalaldehyde with ammonia afforded a new compound containing two aldehyde groups (CHO), one imino group (C=N), and an aminal carbon (N–CH–N). Reaction of this compound with sodium borohydride resulted in the reduction of the aldehyde groups, the imino carbon, and the aminal carbon. Analysis of the condensed Fukui functions and the LUMO orbital allowed us to propose a mechanism that explains this reactivity. Furthermore, a general mechanism for the condensation of aromatic aldehydes with aqueous ammonia is presented.

This paper proposes a GRASP approach for solving the Bus Crew Scheduling Problem (BCSP) to find high-quality solutions within short computing times. The BCSP described the process related to the assignment of drivers and conductors to a bus company's regular daily operation of a mass transit system, seeking to minimize the cost of operation and, at the same time, the improvement of the working environment by considering the satisfaction of the drivers with the assigned shifts. The BCSP has drivers in charge of covering the demand for shifts, with an assignment that contains several constraints, such as minimum and maximum work blocks, minimum rest days, and shift sequences that must not be assigned. The former GRASP algorithm is proposed with a constructive procedure, a solution repair procedure, and two local search operators. Classical instances from the literature have been adapted for the shift assignment problem by adding a satisfaction variable. Besides, the proposed approach has been tested for a real company operating articulated and feeder vehicles. The results show that the satisfaction function adds value to the assignments, substantially improving the work environment and generating favorable results in terms of time and quality of the solution.

Efficiency in logistics is often affected by the fair distribution of the customers along the routes and the available depots for goods delivery. From this perspective, in this study, the Multi-depot Vehicle Routing Problem (MDVRP), by considering two objectives, is addressed. The two objectives in conflict for MDVRP are the distance traveled by vehicles and the standard deviation of the routes’ length. A significant standard deviation value provides a small distance traveled by vehicles, translated into unbalanced routes. We have used a weighted average objective function involving the two objectives. A Variable Neighborhood Search algorithm within a Chu-Beasley Genetic Algorithm has been proposed to solve the problem. For decision-making purposes, several values are chosen for the weight factors multiplying the terms at the objective function to build up a non-dominated front of solutions. The methodology is tested in large-size instances for the MDVRP, reporting noticeable results for managerial insights.

The Inventory Routing Problem (IRP) has been highlighted as a valuable strategy for tackling routing and inventory problems. This paper addresses the IRP but considers the forward delivery and the use of Returnable Transport Items (RTIs) in the distribution strategy. We develop an optimization model by considering inventory routing decisions with RTIs collection (backhaul customers) of a Closed-Loop Supply Chain (CLSC) within a short-term planning horizon. RTIs consider reusable packing materials such as trays, pallets, recyclable boxes, or crates. The RTIs represent an essential asset for many industries worldwide. The solution of the model allows concluding that if RTIs are considered for the distribution process, the relationship between the inventory handling costs of both the final goods and RTIs highly determines the overall performance of the logistics system under study. The obtained results show the efficiency of the proposed optimization scheme for solving the combined IRP with RTIs, which could be applied to different real industrial cases.

Operations with Electric Vehicles (EVs) on logistic companies and power utilities are increasingly related due to the charging stations representing the point of standard coupling between transportation and power networks. From this perspective, the Multi-depot Electric Vehicle Routing Problem (MDEVRP) is addressed in this research, considering a novel hybrid matheheuristic approach combining exact approaches and a Chu-Beasley Genetic Algorithm. An existing conflict is shown in three objectives handled through the experimentations: routing cost, cost of charging stations, and increased cost due to energy losses. EVs driving range is chosen as the parameter to perform the sensitivity analysis of the proposed MDEVRP. A 25-customer transportation network conforms to a newly designed test instance for methodology validation, spatially combined with a 33 nodes power distribution system.

This paper considers the closed-loop supply chain design problem by examining financial criteria and uncertainty in the parameters. A robust multiobjective optimization methodology is proposed by considering financial measures such as maximizing the net present value (NPV) and minimizing the financial risk (FR). The proposed methodology integrates various multiobjective optimization elements based on epsilon constraints and robustness measurements through the FePIA (named after the four steps of the procedure: Feature–Perturbation–Impact–Analysis) methodology. Similarly, an analysis of the parameter variability using scenarios was considered. The proposed method's efficiency was tested with real information from a multinational company operating in Colombia. The results show the effectiveness of the methodology in addressing real problems associated with supply chain design.

This document addresses the Nurse Scheduling Problem (NSP) and presents a dynamic multi-criteria optimization model for its solution considering a predefined time horizon. The purpose is to maximize the level of "work well-being" of nurses formulated as the minimization of "aversion" which translates into costs or penalties for certain undesirable work shifts. For this, a series of criteria are defined to estimate the preference structure of nurses according to the hospital center specifications by assigning costs for undesirable shift assignments. The proposed methodology involves developing a heuristic to decompose the global problem into daily subproblems for which a dynamic algorithm is implemented that considers a cost accumulation process for all criteria and all nurses. Daily models are dynamically solved by modifying the coefficients of the well-being function to achieve equity throughout the planning period by updating and accumulating different averages. This methodology has shown satisfactory results for scheduling work shifts for doctors, paramedics, security guards, and drivers in numerous hospital centers in Colombia.