The civil construction industry faces increasing complexity in subcontractor selection and activity scheduling, driven by multiple conflicting criteria, resource constraints, and tight project deadlines. These decisions are inherently interdependent but are often addressed separately in the literature. To address this gap, this article proposes an integrated decision-support methodology that combines preference-based subcontractor selection with activity scheduling optimization. The methodology comprises three stages: problem definition; subcontractor ranking using the FITradeoff method, which considers trade-offs among cost, duration, quality, cooperation, and know-how while reducing cognitive burden; and application of a Genetic Algorithm to simultaneously select subcontractors and generate a feasible project schedule under budgetary and contractual constraints. The main contributions of this study are threefold: (i) a unified framework that jointly addresses subcontractor selection and scheduling; (ii) the incorporation of decision-maker preferences prior to optimization, guiding the search toward preference-consistent solutions rather than ex post Pareto analysis; and (iii) a problem-specific chromosome representation that enables the application of the proposed framework to real-scale construction projects. The applicability of the methodology is demonstrated through a real construction project, showing its ability to produce feasible schedules and transparent decision support for construction managers.

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This paper addresses the problem of maintenance optimization in multi-component systems, which must undergo maintenance actions within a specified timeframe to prepare for the next mission. Due to constraints in time, budget, and resources, it is not possible to perform top-level maintenance on all components. As a result, it is necessary to select a subset of components and corresponding maintenance actions for optimization. In the field of maintenance modeling, a significant portion of the literature predominantly focuses on single-component systems and often relies on single-objective optimization frameworks to define decision variables. While these approaches have advanced the field, they may result in decision-making with a narrower perspective, particularly in the context of complex systems. To address these gaps, we propose the Bi-Objective Selective Maintenance and Repairperson Assignment Problem for k-out-of-n complex systems, introducing an innovative framework that simultaneously maximizes system reliability and minimizes maintenance costs. To solve this problem, we propose a binary nonlinear programming model along with an approximate algorithm based on the Adaptive Large Neighborhood Search metaheuristic. We tested our approach on two instances-one artificial and one drawn from the literature-and found that the metaheuristic provided near-optimal solutions with significantly reduced computational time compared to benchmark algorithms. Finally, we evaluated the effectiveness of our proposed approach through a simulation of the system operation, demonstrating its value in guiding decision-makers.

This paper addresses a stochastic single-machine scheduling problem with energy consumption. In this problem, job processing times are random variables, and total energy consumption depends on job scheduling, as each job has its own energy use and each period follows a Time-Of-Use tariff policy. To solve the problem, we propose a simheuristic algorithm that combines the metaheuristics Simulated Annealing and Greedy Randomized Adaptive Search Procedure to explore the solution space, along with Monte Carlo Simulation to better evaluate the solutions during the search. The solutions obtained are compared with those derived from a deterministic approach, and the results show that the simheuristic outperforms the deterministic method in terms of Average, Value at Risk, and Conditional Value at Risk, emphasizing the importance of incorporating uncertainty into the solution methods.

This article addresses a new variant of the School Bus Routing Problem named the Multi-Period School Bus Routing Problem. In this problem, routes are created to pick up students at the selected bus stops considering a set of periods, ensuring that students are consistently allocated to the same stop along the subset of periods in which they require transportation. The objective is to minimize the total distance traveled by the fleet in all the considered periods, considering vehicle capacity and maximum walking constraints. To solve the problem, a mathematical model, based on Mixed Integer Linear Programming, and a matheuristic algorithm, based on Iterated Local Search and Variable Neighborhood Descent, are proposed. Moreover, two new strategies to address the student allocation sub-problem are presented. Instances from previous literature are extended to consider the student period-dependent demands, resulting in 448 new instances, which are used to evaluate the algorithms by means of computational experiments. The results obtained show that the proposed algorithm is capable of solving large instances with a low computational effort, obtaining optimal solutions or small percentage gaps. Furthermore, it also highlights the positive impact of the multi-period approach on the total distance traveled compared to the single-period approach.

The discrete parallel machine makespan scheduling location (ScheLoc) problem is an integrated combinatorial optimization problem that combines facility location and job scheduling. The problem consists in choosing the locations of multiple machines among a finite set of candidates and scheduling a set of jobs on these machines, aiming to minimize the makespan. Depending on the machine location, the jobs may have different release dates, and thus the location decisions have a direct impact on the scheduling decisions. To solve the problem, it is proposed a new arc-flow formulation, a column generation and three heuristic procedures that are evaluated through extensive computational experiments. By embedding the proposed procedures into a framework algorithm, we are able to find proven optimal solutions for all benchmark instances from the related literature and to obtain small percentage gaps for a new set of challenging instances.

The discrete parallel machine makespan scheduling location (ScheLoc) problem is an integrated combinatorial optimization problem that combines facility location and job scheduling. The problem consists in choosing the locations of $p$ machines among a finite set of candidates and scheduling a set of jobs on these machines, aiming to minimize the makespan. Depending on the machine location, the jobs may have different release dates, and thus the location decisions have a direct impact on the scheduling decisions. To solve the problem, it is proposed a new arc-flow formulation, a column generation and three heuristic procedures that are evaluated through extensive computational experiments. By embedding the proposed procedures into a framework algorithm, we are able to find proven optimal solutions for all benchmark instances from the related literature and to obtain small percentage gaps for a new set of challenging instances.

benchmark, e mostrou ser capaz de obter soluc ¸ões de alta qualidade, 10 das quais são melhores do que as melhores encontradas na literatura.

The capacitated p-center problem requires to select p facilities from a set of candidates to service a number of customers, subject to facility capacity constraints, with the aim of minimizing the maximum distance between a customer and its associated facility. The problem is well known in the field of facility location, because of the many applications that it can model. In this paper, we solve it by means of search algorithms that iteratively seek the optimal distance by solving tailored subproblems. We present different mathematical formulations for the subproblems and improve them by means of several valid inequalities, including an effective one based on a 0-1 disjunction and the solution of subset sum problems. We also develop an alternative search strategy that finds a balance between the traditional sequential search and binary search. This strategy limits the number of feasible subproblems to be solved and, at the same time, avoids large overestimates of the solution value, which are detrimental for the search. We evaluate the proposed techniques by means of extensive computational experiments on benchmark instances from the literature and new larger test sets. All instances from the literature with up to 402 vertices and integer distances are solved to proven optimality, including 13 open cases, and feasible solutions are found in 10 minutes for instances with up to 3038 vertices.

Resumo: O Brasil passou por um processo de expansão econômica durante as primeiras décadas do século XXI, elevando seu PIB em 378% e saltando da 11° para 5° posição no ranking das economias globais. Tal crescimento econômico foi acompanhado pela expansão da movimentos de bens pelos portos brasileiros. No país, 95% de toda carga movimentada nos portos nacionais passam por 19 complexos portuários, menos da metade dos 40 distribuídos ao longo da costa e do interior do Brasil. Estes portos competem entre si pelo tráfego de cargas do interior do território nacional e para o interior do território nos casos exportação e importação respectivamente. Com o objetivo de se estudar a distribuição de cargas dos portos para o interior do país este trabalho se propõe a analisar a estrutura espacial da hinterland dos portos brasileiros. Para tanto foram selecionados quatro grandes grupos de carga: Granel sólido agrícola, granel sólido não agrícola, granel líquido e carga geral e por meio de modelos de interação espacial foi estudado sua distribuição para as 137 mesorregiões do país. As cargas de granel agrícola e não agrícola aproximadamente 62% dos portos entregam mais que 60% da carga para o estado aonde está localizado o porto e aqueles que fazem fronteira com os mesmos. Os itens carga geral e granel líquido a apenas 15% dos portos entregam mais que 60% da carga para o estado aonde está localizado o porto e aqueles que fazem fronteira com os mesmos.

•We introduce a rich vehicle routing problem arising in the field of pharmaceutical distribution.•An simple and effective ILS algorithm is proposed.•The impact of using auxiliary depots and anticipated deliveries on the routing costs is evaluated.•Realistic and artificial instances containing up to 300 customers are solved.

This article deals with the bi‐objective pollution‐routing problem (bPRP), a vehicle routing variant that arises in the context of green logistics. The two conflicting objectives considered are the minimization of the CO2 emissions and the costs related to driver's wages. A multi‐objective approach based on the two‐phase Pareto local search heuristic is employed to generate a good approximation of the Pareto front. During the first phase of the method, a first set of potentially efficient solutions is obtained by solving a series of weighted sum problems with an efficient heuristic originally developed to solve the single‐objective PRP. A dichotomous scheme is used to generate the different weight sets in an automatic way. In the second phase, the set is improved with an efficient Pareto local search (PLS) procedure. The use of PLS allows to limit the number of computational demanding weighted sum problems solved in the first phase, while keeping high‐quality results. Extensive computational experiments over existing benchmark instances show that the proposed approach leads to better results in less CPU time when compared to those obtained by state‐of‐the‐art methods.

The vertex p-center problem consists in selecting p centers among a finite set of candidates and assigning a set of clients to them, with the aim of minimizing the maximum dissimilarity between a client and its associated center. State-of-the-art algorithms for the problem are based on the solution of a series of covering subproblems, and are particularly efficient when additional reduction rules are used to limit the size of the subproblems. In fact, these algorithms do not scale well when the cardinality of the dissimilarity matrix grows above a few million entries, as the time and space required to compute and store the matrix start dominating those required for solving the subproblems. We introduce a scalable relaxation-based iterative algorithm that does not rely on the computation of the entire matrix, but rather relies on the computation of a typically much smaller sub-matrix that is only enlarged if deemed necessary. This method can solve to proven optimality p-center problems derived from the TSP library and containing up to one million clients for small but realistic values of p.