Abstract: The assembly line balancing problem is a classical optimisation problem whose objective is to assign each production task to one of the stations on the assembly line so that the total efficiency of the line is maximized. This study proposes a novel hybrid method to solve the simple version of the problem in which the number of stations is fixed, a problem known as SALBP-2. The hybrid differs from previous approaches by encoding individuals of a genetic algorithm as instances of a modified problem that contains only a subset of the solutions to the original formulation. These individuals are decoded to feasible solutions of the original problem during fitness evaluation in which the resolution of the modified problem is conducted using a dynamic programming based approach that uses new bounds to reduce its state space. Computational experiments show the efficiency of the method as it is able to obtain several new best-known solutions for some of the benchmark instances used in the literature for comparison purposes.

Abstract: In order to minimize costs, manufacturing companies have been relying on assembly lines for the mass production of commodity goods. Among other issues, the successful operation of an assembly line requires balancing work among the stations of the line in order to maximize its efficiency, a problem known in the literature as the assembly line balancing problem, ALBP. In this work, we consider an ALBP in which task assignment and equipment decisions are jointly considered, a problem that has been denoted as the robotic ALBP. Moreover, we focus on the case in which equipment has different costs, leading to a cost-oriented formulation. In order to solve the problem, which we denote as the cost-oriented robotic assembly line balancing problem, cRALBP, a hybrid metaheuristic is proposed. The metaheuristic embeds results obtained for two special cases of the problem within a genetic algorithm in order to obtain a memetic algorithm, applicable to the general problem. An extensive computational experiment shows the advantages of the hybrid approach and how each of the components of the algorithm contributes to the overall ability of the method to obtain good solutions. (C) 2018 Elsevier Ltd. All rights reserved.

Abstract: Supply chain network design aims at the integration of the different actors of a supply chain within a single framework in order to optimise the total profit of the system. In this paper, we consider the integration of line balancing issues within the tactical decisions of the supply chain, and we offer a novel model and a solution approach for the problem. The new approach decomposes the problem into multiple line balancing problems and a mixed integer linear model, which is easier to solve than the previously available non-linear mixed integer formulation. The results show that the new method is able to solve previously studied models within a fraction of the reported running times, and also allows us to solve larger instances than those reported in earlier works. Finally, we also provide some analysis on the influence of the cost structure, the demand and the structure of the assembly process on the final configuration of the assemblies and the distribution network.

Abstract: The flowtime network restoration problem was introduced by Averbakh and Pereira (2012) who presented a Minimum Spanning Tree heuristic, two local search procedures, and an exact branch-and-bound algorithm. This note corrects the computational results in Averbakh and Pereira (2012).

Abstract: Last-mile logistics corresponds to the last leg of the supply chain, i.e., the delivery of goods to final cus-tomers, and they comprise the core activities of postal and courier companies. Because of their role in the supply chain, last-mile operations are critical for the perception of customers regarding the perfor-mance of the whole logistic process. In this sense, the sustained growth of e-commerce, which has been abruptly catalyzed by the irruption of the COVID-19 pandemic, has hanged the habits of customers and overtaxed the operational side of delivery companies, hindering their viability and forcing their adap-tation to the novel conditions. Many of these habits will remain after we overcome the sanitary crisis, which will permanently reshape the structure and emphasis of postal supply chains, demanding compa-nies to implement organizational and operational changes to adapt to these new challenges. In this work we address a last-mile logistic design problem faced by a courier and delivery company in Chile, although the same problem is likely to arise in the last-mile delivery operation of other postal companies, in particular in the operation of express delivery services. The operational structure of the company is based on the division of an urban area into smaller territories (districts) and the outsourcing of the delivery operation of each territory to a last-mile contractor. Because of the increasing volume of postal traffic and a decreasing performance of the service, in particular for the case of express deliveries, the company is forced to redesign its current territorial arrangement. Such redesign results in a novel optimization problem that resembles a classical districting problem with the additional quality of service requirements. This novel problem is first formulated as a mathematical programming model and then a specially tailored heuristic is designed for solving it. The proposed approach is tested on instances from the real-life case study, and the obtained results show significant improvements in terms of the percent-age of on-time deliveries achieved by the proposed solution when compared to the current districting design of the company. By performing a sensitivity analysis considering different levels of demand, we show that the proposed approach is effective in providing districting designs capable of enduring signifi-cant increases in the demand for express postal services.

Abstract: Student dropout, defined as the abandonment of a high education program before obtaining the degree without reincorporation, is a problem that affects every higher education institution in the world. This study uses machine learning models over two Chilean universities to predict first-year engineering student dropout over enrolled students, and to analyze the variables that affect the probability of dropout. The results show that instead of combining the datasets into a single dataset, it is better to apply a model per university. Moreover, among the eight machine learning models tested over the datasets, gradient-boosting decision trees reports the best model. Further analyses of the interpretative models show that a higher score in almost any entrance university test decreases the probability of dropout, the most important variable being the mathematical test. One exception is the language test, where a higher score increases the probability of dropout.

Abstract: The simple assembly line balancing problem (SALBP) considers work division among different workstations of a serially arranged assembly process to maximise its efficiency under workload (cumulative) and technological (precedence) constraints. In this work, we consider a variant of the SALBP which allows parallel workstations. To study the effect of parallel stations, we propose a new problem (the parallel station assembly line balancing problem or PSALBP) in which the objective is to minimise the number of parallel stations required to obtain the maximum theoretical efficiency of the assembly process. We study the complexity of the problem and identify a polynomially solvable case. This result is then used as a building block for the development of a heuristic solution procedure. Finally, we carry out a computational experiment to identify the characteristics of assembly lines that may benefit from station paralleling and to evaluate the performance of the proposed heuristic.

Abstract: Small-medium enterprises (SMEs) represent 90% of business globally. Digital Transformation (DT) affects SMEs differently from larger companies because although SMEs have more flexibility and agility for adapting to new circumstances, they also have more limited resources and specialization capabilities. Thus, it is fundamental to measure SMEs' performance considering different perspectives. Here, we describe and analyze the state-of-the-art of DT in SMEs, focusing on performance measurement. We center on whether the tools used by SMEs encompass the triple bottom line of sustainability (i.e., environmental, social, and economic aspects). To do so, in December 2021, we performed a comprehensive systematic literature review (SLR) on the Web of Science and Scopus. In addition, we also explored a novel approach for SLR: topic modeling with a machine learning technique (Latent Dirichlet Allocation). The differences and interchangeability of both methods are discussed. The findings show that sustainability is treated as a separate topic in the literature. The social and environmental aspects are the most neglected. This paper contributes to sustainable development goals (SDGs) 1, 5, 8, 9, 10, and 12. A conceptual framework and future research directions are proposed. Thus, this paper is also valuable for policymakers and SMEs switching their production paradigm toward sustainability and DT.

Abstract: We address the problem of finding an arborescence of minimum total edge weight rooted at a given vertex in a directed, edge-weighted graph. If the arborescence must span all vertices the problem is solvable in polynomial time, but the non-spanning version is NP-hard. We propose reduction rules which determine vertices that are required or can be excluded from optimal solutions, a modification of Edmonds algorithm to construct arborescences that span a given set of selected vertices, and embed this procedure into an iterated local search for good vertex selections. Moreover, we propose a cutset-based integer linear programming formulation, provide different linear relaxations to reduce the number of variables in the model and solve the reduced model using a branch-and-cut approach. We give extensive computational results showing that both the heuristic and the exact methods are effective and obtain better solutions on instances from the literature than existing approaches, often in much less time. (C) 2020 Elsevier B.V. All rights reserved.

Abstract: In this paper, we consider a parallel machine scheduling problem in which machines have a limited workload capacity and jobs have deadlines and release dates. The problem is motivated by the operation of energy storage management systems for microgrids under emergency conditions and generalizes some problems that have already been studied in the literature for their theoretical value. In this work, we propose heuristic and exact algorithms to solve the problem. The heuristics are adaptations of classical bin packing heuristics in which additional conditions on the feasibility of a solution are imposed, whereas the exact method is a branch-and-price approach. The results show that the branch-andprice approach is able to optimally solve random instances with up to 250 jobs within a time limit of one hour, while the heuristic procedures provide near optimal solution within reduced running times. Finally, we also provide additional complexity results for a special case of the problem. (C) 2019 Elsevier Inc. All rights reserved.