ABSTRACT Defining delivery routes is an effective way to reduce transportation costs. In particular, a cross-dock distribution center in a retail network requires coordinated decisions on delivery routes and internal operations, leading to the cross-dock scheduling with routing decisions problem. To the best of our knowledge, no empirical study has shown the advantage of integrating these decisions. Previous works mainly focus on fresh food distribution centers with specific operational features. Based on a retail network, we address this problem by developing two mixed-integer models: one basic and another with additional routing variables that improve the problem representation. Furthermore, we assess the importance of integrating routing and cross-dock scheduling by comparing integrated and hierarchical strategies. Overall, our results indicate that integration can improve the distribution system’s scheduling efficiency under different demand patterns and dependency levels between customers and suppliers.
ABSTRACT We address the Positive Influence Dominating Set problem (PIDS), which involves identifying the smallest group of vertices in a given network that maximizes influence throughout the network. Formally, given a graph G = (V, E), representing the network, we look for a subset of vertices P ⊆ V such that each vertex v i ∈ V \ P has at least ρ deg G (v i ) neighbors in P, where deg G (v i ) is the degree of v i , and 0 < ρ < 1. The problem is known to be NP-hard in general graphs and remains NP-hard even when restricted to certain particular classes, such as chordal graphs. In this paper, we develop an exact algorithm based on the Branch and Bound approach for solving PIDS on chordal graphs. The effectiveness of the proposed algorithm is demonstrated through numerical experiments conducted on various randomly generated chordal graphs.
ABSTRACT In the construction industry, lattice joists (final items) are precast structures that combine a steel truss and a concrete base. Their production is based on a client’s order that specifies the quantity, length, and deadline for delivery. A process manager uses this information to create a weekly production plan that determines the number and type of final items to produce in each period. The primary concerns of the factory include minimizing the waste of steel bars (objects) and optimizing setups and inventory costs. In this paper, decisions regarding purchasing steel bars and managing stocks in different sizes are considered extending the literature by adding an extra level of decision. We propose two alternative mathematical formulations for this integrated lot-sizing and cutting stock problem. Further, heuristic solution strategies are proposed to address the resulting mixed-integer programming formulations. Computational tests are conducted to evaluate the effectiveness of the proposed solution approaches.
ABSTRACT Traditional methods for evaluating airports users’ Level of Satisfaction (LoS) often aggregate customers ratings compensatorily, masking critical weaknesses and ignoring that passengers evaluate services based on different criteria sets. This paper aims to face this issue by proposing a non-compensatory sorting framework that respects passengers’ partial evaluations and identifies improvement issues for airport managers. We inputted the evaluations provided by 19,643 passengers across 15 Brazilian airports into the pessimistic procedure of ELECTRE TRI ME, considering five categories of classification. In a pioneer way, we use the values of the credibility degree to organize airports into groups, resulting in grouping them according to their similarities profile in the credibility degree. This framework provides airport managers with insights to support their decision-making: a transparent LoS classification avoiding compensatory distortions, and cluster-based insights for actions. From a methodological viewpoint, this paper presents an innovative approach to utilizing the complete values calculated for the credibility degree. It also is the first application of ELETRE TRI ME to a huge dataset.
ABSTRACT Last-mile delivery-the final and most critical stage of fulfilling customer orders-is typically the most costly and time-consuming component of the distribution process. Optimizing this stage is therefore essential for improving the overall efficiency and sustainability of logistics operations. This study begins with a comprehensive review of the principal contributions to last-mile delivery optimization. Building on insights from existing models and metaheuristics, we propose a new two-level framework in which customers are served through distribution centers, ensuring that each customer location lies within a designated coverage area. The framework consists of two interconnected levels: depot to distribution center routing and distribution center to customer delivery. To support this structure, we develop a new optimization model and a Simulated Annealing-based metaheuristic inspired by the vehicle routing problem with time windows and tailored specifically to last-mile customer service operations. Computational results demonstrate that the proposed approach provides both economic benefits-through reduced travel distance-and environmental advantages through lower CO2 emissions. Moreover, the routing plan generated at the second level allows for the quantification of customer satisfaction-averaging 72.5% for the optimization model and 100% for the metaheuristic-thereby providing a more comprehensive and robust evaluation of delivery.
