scholarly journals A Genetic Algorithm for Scheduling of Trucks with Inbound and Outbound Process in Multi-Door Cross Docking Terminals

2011 ◽  
Vol 37 (3) ◽  
pp. 248-257 ◽  
Author(s):  
Cheol-Min Joo ◽  
Byung-Soo Kim
Keyword(s):  
Genetic Algorithm ◽  
Cross Docking

Computational Intelligence in Cross Docking

2014 ◽  
Vol 2 (1) ◽  
pp. 1-8
Author(s):  
Bo Xing
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Simulated Annealing ◽  
Tabu Search ◽  
Computational Intelligence ◽  
Simple Concept ◽  
Simulated Annealing Genetic Algorithm ◽  
Cross Docking ◽  
Open Questions ◽  
Key Issues

Cross docking is a practice in logistics with the main operations of goods flow directly from receiving to the shipping docks without stopping or being put away into storage. It is a simple concept to talk about, but a challenging one to implement. So far, many different approaches have been followed in order to improve the efficiency of a cross docking system. However, as the complexity increases, the use of computational intelligence (CI) in those problems is becoming a unique tool of imperative value. In this paper, different CI methods, such as Tabu search, simulated annealing, genetic algorithm, and fuzzy logic. The key issues in implementing the proposed approaches are discussed, and finally the open questions are highlighted.

scholarly journals A Hybrid Genetic Algorithm for Integrated Truck Scheduling and Product Routing on the Cross-Docking System with Multiple Receiving and Shipping Docks

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Wooyeon Yu ◽  
Chunghun Ha ◽  
SeJoon Park
Keyword(s):  
Genetic Algorithm ◽  
Completion Time ◽  
Hybrid Genetic Algorithm ◽  
New Product ◽  
Benchmark Problems ◽  
Solution Quality ◽  
Cross Docking ◽  
Truck Scheduling ◽  
Chromosome Representation ◽  
Nested Structure

In this research, a truck scheduling problem for a cross-docking system with multiple receiving and shipping docks is studied. Until recently, single-dock cross-docking problems are studied mostly. This research is focused on the multiple-dock problems. The objective of the problem is to determine the best docking sequences of inbound and outbound trucks to the receiving and shipping docks, respectively, which minimize the maximal completion time. We propose a new hybrid genetic algorithm to solve this problem. This genetic algorithm improves the solution quality through the population scheme of the nested structure and the new product routing heuristic. To avoid unnecessary infeasible solutions, a linked-chromosome representation is used to link the inbound and outbound truck sequences, and locus-pairing crossovers and mutations for this representation are proposed. As a result of the evaluation of the benchmark problems, it shows that the proposed hybrid GA provides a superior solution compared to the existing heuristics.

Scheduling of Inbound Trucks at a Cross-Docking Facility

2012 ◽  
Vol 5 (1) ◽  
pp. 20-37 ◽  
Author(s):  
Mihalis M. Golias ◽  
Georgios K. D. Saharidis ◽  
Maria Boile ◽  
Sotirios Theofanis
Keyword(s):  
Genetic Algorithm ◽  
Service Time ◽  
Mixed Integer ◽  
Cross Docking ◽  
Conflicting Objectives ◽  
Integer Problem

This paper examines the problem of scheduling of inbound trucks to the inbound doors at a cross-docking facility. The authors optimize for two conflicting objectives: minimize the total service time for all the inbound trucks and minimize the delayed completion of service for a subset of the inbound trucks, which are considered as preferential customers. The problem is formulated as a bi-objective and as a bi-level mixed integer problem. Due to the nature of the former and the complexity of the latter formulation, a genetic algorithm and a k-th best based algorithm are proposed as the solution approaches. Computational examples are used to discuss the advantages and drawbacks of each formulation.

Simulation-Based Genetic Algorithm for Cross-Docking Center Operation Optimization under Supply Disruptions

2011 ◽  
Vol 201-203 ◽  
pp. 1103-1106
Author(s):  
Yi Fan Wu
Keyword(s):  
Genetic Algorithm ◽  
Customer Service ◽  
Performance Optimization ◽  
Transportation Cost ◽  
Search Space ◽  
Inventory Level ◽  
Cost Rate ◽  
Supply Disruptions ◽  
Cross Docking ◽  
Customer Service Level

Practical performance optimization of a cross-docking center has been rare in the literature so far. The measures representing operation efficiency are average inventory level and transportation cost rate, while average backorder level represents the customer service level. In this paper, a simulation optimization problem is considered and a solution framework has been developed by integrating simulation, genetic algorithm (GA) and smart computing budget allocation (SCBA) to find an optimized solution. Moreover, supply disruptions are considered in the simulation model. This problem has huge search space even for medium-sized problem scenarios. To address this difficulty, the framework employs simulation to estimate the performance measures, GA to search for better design and SCBA to efficiently allocate the simulation budget.

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