Assignment and scheduling trucks in cross-docking system with energy consumption consideration and trucks queuing

fard, Shayan Shahram; Vahdani, Behnam

doi:10.1016/j.jclepro.2018.12.106

Cited by 37 publications

(7 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among the proposed metaheuristic algorithms, the EA-based algorithms and the SA-based algorithms were found to be the most common. Several studies adopted multi-objective EA-based algorithms to tackle CDT truck scheduling problems [45][46][47][48]50]. dissatisfaction of the transportation providers, total travel distance by the internal transportation equipment, fuel consumption, and energy consumption).…”

Section: Solution Methods Adoptedmentioning

confidence: 99%

See 1 more Smart Citation

Truck Scheduling at Cross-Docking Terminals: A Follow-Up State-Of-The-Art Review

et al. 2019

View full text Add to dashboard Cite

Recent trends in the management of supply chains have witnessed an increasing implementation of the cross-docking strategy. The cross-docking strategy, being the one that can potentially improve supply chain operations, has received a lot of attention from researchers in recent years, especially over the last decade. Cross-docking involves the reception of inbound products, deconsolidation, sorting, consolidation, and shipping of the consolidated products to the end customers. The number of research efforts, aiming to study and improve the cross-docking operations, increases every year. While some studies discuss cross-docking as an integral part of a supply chain, other studies focus on the ways of making cross-docking terminals more efficient and propose different operations research techniques for various decision problems at cross-docking terminals. In order to identify the recent cross-docking trends, this study performs a state-of-the-art review with a particular focus on the truck scheduling problem at cross-docking terminals. A comprehensive evaluation of the reviewed studies is conducted, focusing on the major attributes of the cross-docking operations. These attributes include terminal shape considered, doors considered, door service mode considered, preemption, internal transportation mode used, temporary storage capacity, resource capacity, objectives considered, and solution methods adopted. Based on findings from the review of studies, some common issues are outlined and future research directions are proposed.

show abstract

Section: Solution Methods Adoptedmentioning

confidence: 99%

“…Fard and Vahdani [50] addressed the problem of truck scheduling at a multiple door CDT. The study specifically focused on the efficient assignment of trucks and forklifts to the available CDT doors.…”

Section: Multi-objective Cdt Truck Schedulingmentioning

confidence: 99%

Truck Scheduling at Cross-Docking Terminals: A Follow-Up State-Of-The-Art Review

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The results in Table 6 are the computation results of the experiment. After testing, the best of the proposed heuristics was DE, which used Equation (14). Then, we sought to discover how Equation (14) worked with this problem by plotting the best-known solution during the simulation within 100,000 iterations, a graph of which is shown in Figure 3.…”

Section: Name Of Methods Ac Equationmentioning

confidence: 99%

“…From Figure 3, we can see DE-AC2 starts with one of the poorer solutions obtained from all proposed methods, but it is able to quickly find a better solution, and the solution always improved over time, which means that Equation (14) was very effective in terms of the required behaviors of the metaheuristics, diversification and intensification, while other equations provided diversification without intensification of the search, as was the case with DE and DE-AC1, or provided intensification but finally become stuck on a local optimum, as was the case for DE-AC3.…”

Section: Name Of Methods Ac Equationmentioning

confidence: 99%

“…The extension of this assignment problem is often called the transportation problem, which not only assigns the task to the machine, but also forms the transportation sequence when there is more than one task assigned to one machine: this is called the vehicle routing problem (VRP). There are plenty of examples of the transportation problem in the literature, such as transportation mode selection (road, waterway, railway) [10,11], connecting the transport mode by assigning the inbound and outbound gates of the product [12][13][14][15], transportation planning with time windows [14,[15][16][17], and optimal location assignment [18][19][20][21]. However, the AP aims to increase efficiency and sustainability by reducing time, cost, fuel consumption, and carbon dioxide emissions, increasing customer satisfaction levels and profits, and making other improvements.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Modified Differential Evolution Algorithm for a Transportation Software Application

Supattananon

Akararungruangkul

2019

Journal of Open Innovation: Technology, Market, and Complexity

View full text Add to dashboard Cite

This research developed a solution approach that is a combination of a web application and the modified differential evolution (MDE) algorithm, aimed at solving a real-time transportation problem. A case study involving an inbound transportation problem in a company that has to plan the direct shipping of a finished product to be collected at the depot where the vehicles are located is presented. In the newly designed transportation plan, a vehicle will go to pick up the raw material required by a certain production plant from the supplier to deliver to the production plant in a manner that aims to reduce the transportation costs for the whole system. The reoptimized routing is executed when new information is found. The information that is updated is obtained from the web application and the reoptimization process is executed using the MDE algorithm developed to provide the solution to the problem. Generally, the original DE comprises of four steps: (1) randomly building the initial set of the solution, (2) executing the mutation process, (3) executing the recombination process, and (4) executing the selection process. Originally, for the selection process in DE, the algorithm accepted only the better solution, but in this paper, four new selection formulas are presented that can accept a solution that is worse than the current best solution. The formula is used to increase the possibility of escaping from the local optimal solution. The computational results show that the MDE outperformed the original DE in all tested instances. The benefit of using real-time decision-making is that it can increase the company’s profit by 5.90% to 6.42%.

show abstract

Multi-objective Grey Wolf Optimizer

Mirjalili

Dong

2019

Multi-Objective Optimization Using Artificial Intelligence Techniques

View full text Add to dashboard Cite

Assignment and scheduling trucks in cross-docking system with energy consumption consideration and trucks queuing

Cited by 37 publications

References 43 publications

Truck Scheduling at Cross-Docking Terminals: A Follow-Up State-Of-The-Art Review

Truck Scheduling at Cross-Docking Terminals: A Follow-Up State-Of-The-Art Review

Modified Differential Evolution Algorithm for a Transportation Software Application

Multi-objective Grey Wolf Optimizer

Contact Info

Product

Resources

About