|
|
|
|
|
 |
Search published articles |
 |
|
Showing 2 results for Truck
Mr. Mirmohammad Musavi, Dr. Reza Tavakkoli-Moghaddam, Ms. Farnaz Rayat,
Volume 8, Issue 1 (4-2017)
Abstract
We present a bi-objective model for a green truck scheduling and routing problem at a cross-docking system. This model determines three key decisions at the cross dock: (1) defining a sequence and schedule of inbound trucks at the receiving door, (2) specifying a sequence and a schedule of outbound trucks at the shipping door, and (3) determining the routes of the outbound truck while serving customers. The first objective function is related to responsiveness of the network that minimizes time window violations and the second objective function minimizes total fuel consumption of trucks in order to consider the environmental factor of the network. Also, a learning effect is considered in loading and unloading process times. To solve the bi-objective model, an archived multi-objective simulated annealing (AMOSA) is used and modified. Finally, a number of test problems are solved and the efficiency of the proposed AMOSA is compared with the e-constraint method.
Maedeh Alinasab, Mohammad Fallah, Iraj Mahdavi, Ghasem Tohidi,
Volume 17, Issue 2 (9-2026)
Abstract
This study develops a multi-objective mathematical model for planning and optimizing patrol operations in surveillance networks using an integrated truck–quadrotor system. The proposed model simultaneously considers five conflicting objectives: maximizing network coverage, minimizing operational costs, operational risks, mission duration and environmental emission. To balance these objectives, the LP-metric distance-to-ideal-point approach is employed to transform the multi-objective formulation into an equivalent single-objective model. Benders decomposition is subsequently applied to efficiently solve larger-scale instances and reduce computational complexity. The performance of the proposed approach is evaluated through numerical experiments and sensitivity analyses. Results show that network coverage reaches approximately 89% in the baseline scenario and increases to 96% under a coverage-oriented strategy. Meanwhile, total operational cost decreases from approximately 47800 to 39900 cost units under a cost-focused strategy, while operation time varies between 590 and 710 minutes across different scenarios. Sensitivity analysis indicates that a 20% increase in truck deployment cost reduces network coverage by 22% and increases operational cost by 10%, whereas a 30% increase in quadrotor energy consumption increases operation time by 18% and cost by 7%. The findings demonstrate that the proposed integrated framework provides an effective decision-support tool for patrol planning and resource allocation.
|
|
|
|
|
|
|
|
|
