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Purpose This paper aims to develop a multi-objective problem for scheduling the operations of trucks entering and exiting cross-docks where the number of unloaded or loaded products by trucks is fuzzy logistic. The first objective function minimizes the maximum time to receive the products. The second objective function minimizes the emission cost of trucks. Finally, the third objective function minimizes the number of trucks assigned to the entrance and exit doors. Design/methodology/approach Two steps are implemented to validate and modify the proposed model. In the first step, two random numerical examples in small dimensions were solved by GAMS software with min-max objective function as well as genetic algorithms (GA) and particle swarm optimization. In the second step, due to the increasing dimensions of the problem and computational complexity, the problem in question is part of the NP-Hard problem, and therefore multi-objective meta-heuristic algorithms are used along with validation and parameter adjustment. Findings Therefore, non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are used to solve 30 random problems in high dimensions. Then, the algorithms were ranked using the TOPSIS method for each problem according to the results obtained from the evaluation criteria. The analysis of the results confirms the applicability of the proposed model and solution methods. Originality/value This paper proposes mathematical model of truck scheduling for a real problem, including cross-docks that play an essential role in supply chains, as they could reduce order delivery time, inventory holding costs and shipping costs. To solve the proposed multi-objective mathematical model, as the problem is NP-hard, multi-objective meta-heuristic algorithms are used along with validation and parameter adjustment. Therefore, NSGA-II and NRGA are used to solve 30 random problems in high dimensions.

Background: Scheduling the arrival of external trucks in container terminals is a critical operational decision that faces both terminal managers and trucking companies. This issue is crucial for both stakeholders since the random arrival of trucks causes congestion in the terminals and extended delays for the trucks. The objective of scheduling external truck appointments is not only to control the workload inside the terminal and the costs resulting from the excessive waiting times of trucks but also, to reduce the truck turnaround time. Methods: A binary programming model was proposed to minimize the waiting time cost, demurrage cost, and container delivery cost. Moreover, a sensitivity analysis was performed to compare various scenarios in terms of cost and to study to what extent the workload level is affected. The mathematical model was solved using Gurobi© 8.1.0 software. Results: 30 instances found in the literature were solved and evaluated in terms of the objective function value (i.e., cost) and truck turnaround time before and after controlling the workload inside the container terminal using the new proposed constraint. Conclusions: The obtained results showed a better distribution of the terminal workload, as well as a lower truck turnaround time that reduces the total cost.

With the global trend of e-commerce, companies pursue a higher quality of parcel delivery service since customers expect faster transportation in this fast-paced society. Several of them are committed to improving the efficiency of logistics and reducing operating costs and increasing their market competitiveness in the industry. The parcel distribution center thus plays a critical role in parcel delivery industries to sort and consolidate parcel flows to full truckloads. The benefit of the strategy is significant such as reducing transfer time and related costs. On the other hand, an automated sorting system (ASS) is also highly used in many supply chains with impressive characteristics like fast operation speed, large capacity, high reliability. The goal of this research is to apply the scheduling method to reduce the sorting time in an automated sorting system to improve distribution efficiency. This study focuses on the truck scheduling problem with fixed outbound schedules in a closed-loop conveyor system with shortcuts. The objective is to minimize the costs of extra trucks used to deliver delayed parcels and holding cost of parcels at each shipping dock door. If a parcel fails to be loaded onto the pre-determined outbound trucks, an extra outbound truck will be used to deliver the parcel. The problem is modeled with a mixed integer nonlinear programming model. This problem is proven to be NP-hard in the strong sense. As a result, an adaptive genetic algorithm with local search (LSAGA) is developed to solve the problem under twelve scenarios and compared with other algorithms, and a full factorial design of experiment was conducted. The computational experiments show that four factors, layout, inbound truck, outbound truck, and algorithm are significant to the objective value, and the proposed algorithm can obtain high-quality solutions with more stability. A sensitivity analysis is also conducted and bring some managerial insights.

Considered the cooperation of the container truck and quayside container crane in the container terminal, this paper constructs the model of the quay cranes operation and trucks scheduling problem in the container terminal. And the hybrid intelligence swarm algorithm combined the particle swarm optimization algorithm(PSO) with artificial fish swarm algorithm (AFSA) was proposed. The hybrid algorithm (PSO-AFSA) adopt the particle swarm optimization algorithm to produce diverse original paths, optimization of the choice nodes set of the problem, use AFSA's preying and chasing behavior improved the ability of PSO to avoid being premature. The proposed algorithm has more effectiveness, quick convergence and feasibility in solving the problem. The results of stimulation show that the scheduling operation efficiency of container terminal is improved and optimized.

In most developed countries working hours of truck drivers are constrained by hours of service regulations. When optimizing vehicle routes, trucking companies must consider these constraints to assure that drivers can comply with the regulations. This paper studies the combined vehicle routing and truck driver scheduling problem (VRTDSP), which generalizes the well-known vehicle routing problem with time windows by considering working hour constraints. A branch-and-price algorithm for solving the VRTDSP is presented. This is the first algorithm that solves the VRTDSP to proven optimality.

The container terminal is a key node in global trade and logistics, where trucks connect quay cranes, storage yards, and vessels. Optimizing truck scheduling is crucial for enhancing port efficiency by addressing issues such as low truck utilization, excessive quay crane waiting times, and extended equipment completion times. This paper develops a container terminal simulation model based on graph theory, with the objective of minimizing the maximum completion time of terminal equipment. A collaborative scheduling algorithm for truck fleets, based on Deep Double Q-Networks (DDQN), is proposed. The algorithm designs five heuristic rules as the action space and refines state features and reward functions to optimize scheduling effectively. Experimental results indicate that this algorithm consistently identifies optimal scheduling strategies, outperforming both the five heuristic rules and the Deep Q-Network (DQN) algorithm. It significantly reduces quay crane waiting times and equipment completion times, improves truck utilization, and enhances overall container terminal efficiency.

A critical component in the public health response to pandemics is the ability to determine the spread of diseases via diagnostic testing kits. Currently, diagnostic testing kits, treatments, and vaccines for the COVID-19 pandemic have been developed and are being distributed to communities worldwide, but the spread of the disease persists. In conjunction, a strong level of social distancing has been established as one of the most basic and reliable ways to mitigate disease spread. If home testing kits are safely and quickly delivered to a patient, this has the potential to significantly reduce human contact and reduce disease spread before, during, and after diagnosis. This paper proposes a diagnostic testing kit delivery scheduling approach using the Mothership and Drone Routing Problem (MDRP) with one truck and multiple drones. Due to the complexity of solving the MDRP, the problem is decomposed into 1) truck scheduling to carry the drones and 2) drone scheduling for actual delivery. The truck schedule (TS) is optimized first to minimize the total travel distance to cover patients. Then, the drone flight schedule is optimized to minimize the total delivery time. These two steps are repeated until it reaches a solution minimizing the total delivery time for all patients. Heuristic algorithms are developed to further improve the computational time of the proposed model. Experiments are made to show the benefits of the proposed approach compared to the commonly performed face-to-face diagnosis via the drive-through testing sites. The proposed solution method significantly reduced the computation time for solving the optimization model (less than 50 minutes) compared to the exact solution method that took more than 10 hours to reach a 20% optimality gap. A modified basic reproduction rate (i.e., m R 0 ) is used to compare the performance of the drone-based testing kit delivery method to the face-to-face diagnostic method in reducing disease spread. The results show that our proposed method ( m R 0 = 0.002) outperformed the face-to-face diagnostic method ( m R 0 = 0.0153) by reducing m R 0 by 7.5 times.

In this work, a stochastic dynamic version of the container drayage problem is studied. The presented model incorporates uncertainty in the form of stochastic loading and unloading times at both terminals and customers, as well as stochastic travel times, conditionally dependent upon the departure time, allowing robust planning with respect to varying processing times. Moreover, the presented model is dynamic, allowing flexible orders and having the capability of re-solving the optimization problem in case of last-minute orders. Finally, the model also incorporates a truck appointment system operating at each terminal. First, a description of the general model is given, which amounts to a mixed integer non-linear program. In order to efficiently solve the optimization problem, and linearize both the objective and the conditional chance constraints, it is reformulated based on time window partitioning, yielding a purely integer linear program. As a test case, a large road carrier operating in the port of Antwerp is considered. We demonstrate that the model is efficiently solvable, even for instances of up to 300 orders. Moreover, the impact of incorporating stochastic information is clearly illustrated.

As an innovative approach to city logistics, the truck and drone delivery systems piqued the interest of academia and various companies in recent years, which takes advantage of both trucks’ large capacity and drones’ high speed. In congested city areas, dense traffic significantly impacts delivery time and all three sustainable dimensions (economic, environmental, and social) of delivery systems. For this reason, we focused on the sustainable time-dependent truck and drone routing problem with rendezvous locations. This work processes with a hybrid variable neighborhood search algorithm. We carried out numerous computational experiments to evaluate the performance of the proposed algorithm, and the results show its efficiency. Finally, by performing a detailed sensitivity analysis, the result highlights that the proposed model can reduce the completion time, operational costs, truck emissions, and social penalties in comparison to the flying sidekick traveling salesman model.

We propose an algorithmic framework that successfully addresses three vehicle routing problems: the multidepot VRP, the periodic VRP, and the multidepot periodic VRP with capacitated vehicles and constrained route duration. The metaheuristic combines the exploration breadth of population-based evolutionary search, the aggressive-improvement capabilities of neighborhood-based metaheuristics, and advanced population-diversity management schemes. Extensive computational experiments show that the method performs impressively in terms of computational efficiency and solution quality, identifying either the best known solutions, including the optimal ones, or new best solutions for all currently available benchmark instances for the three problem classes. The proposed method also proves extremely competitive for the capacitated VRP.