Although air transport contributes to globalization, airline emissions have attracted focus in green logistics. In this work, we investigate the airline-fleet assignment problem from a risk-averse perspective in which uncertain demand and fuel price are considered simultaneously. The objective is to maximise the total profit in a risk-averse fashion, i.e., the weighted sum of the expected profit and the conditional value at risk of profit. An appropriate assignment can reduce fuel use and carbon dioxide emissions. For the problem, a two-stage stochastic programming model is constructed. The first stage consists of assigning aircraft families to flight legs, while the second stage determines specific aircraft deployment with the realized information. To solve the problem, a sample average approximation (SAA) approach is firstly applied. An efficient string-based heuristic is, further, developed. Numerical experiments are conducted and sensitivity analysis is performed. The results show the efficiency of the proposed heuristic and managerial insights are drawn.

Cargo (laden container) routing and empty container repositioning are crucial components in liner shipping, which are closely relevant. However, most existing works focus on cargo routing and empty container repositioning separately, which cannot optimize the global shipping cost. In addition, (1) appropriate repacking operations for laden containers can curtail the total handling costs, and (2) the perfect information for uncertain laden and empty container demands may be difficult to obtain due to insufficient historical data. This work investigates an integrated cargo routing and empty container repositioning problem with repacking operations in which only partial information about uncertain laden and empty container demands is known. The objective is to minimize the total costs including repacking operation cost and laden and empty container transportation costs. For the problem, a new chance-constrained programming model based on moment-based ambiguous sets is formulated. Then, four distribution-free solution approaches are adopted to solve the investigated problem. Numerical experiments are conducted to compare the proposed methods.

Under support of industry 4.0, researchers have shown an increased interest in MapReduce scheduling problems to process big data. However, very few studies investigate MapReduce scheduling problems under parallel batch machine environment, which is also common in practice. Motivated by this, we study a parallel batch machine scheduling problem in which all the jobs are belonging to MapReduce type. The objective of the considered problem is of minimizing the total weighted tardiness. For solving this problem, we first establish a mixed integer linear programming model, and then a rule-based genetic algorithm is developed to solve it. Numerical experiments are also conducted to demonstrate the effectiveness of the proposed method.

Urban logistics in metropolitan areas faces mounting pressure to deliver faster while controlling operational costs under strict fleet size constraints. Traditional vehicle routing models assume unlimited vehicle availability, overlooking realistic fleet utilization and spatial-temporal demand imbalances. This paper introduces the fleet-constrained metropolitan logistics problem (FCMLP), a novel framework integrating trunk linehaul scheduling, two-echelon routing, multi-trip operations, and anticipatory fleet positioning. We model the FCMLP as a Markov Decision Process capturing the stochastic and dynamic nature of metropolitan delivery flows. Our solution framework combines interpretable Bayesian Network-based demand forecasting for transparent proactive vehicle relocation decisions, parameterized cost-function approximation for dynamic order-to-linehaul assignment, and Adaptive Large Neighborhood Search for multi-trip vehicle routing. Computational experiments on synthetic instances and real-world data from a major e-commerce platform in Jakarta demonstrate 20–26% total cost reduction. Multi-trip operations alone reduce fleet size by 23%, while interpretable predictive relocation further improves performance by 7% through a 20% reduction in emergency deployments. The framework’s interpretability enhances operator trust and facilitates practical adoption, offering logistics platforms a path to improve vehicle utilization through operational efficiency and transparent predictive intelligence without expanding fleet size.