Simultaneous dock assignment and sequencing of inbound trucks under a fixed outbound truck schedule in multi-door cross docking operations

• Published in: International journal of production economics Vol. 141; no. 1; pp. 212 - 229
• Main Authors: Liao, T.W., Egbelu, P.J., Chang, P.C.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.01.2013; Elsevier; Elsevier Sequoia S.A
• Subjects: Algorithms; Ant colony optimization; Assignment problem; Combinatorial optimization; Cross docking; Differential evolution; Dock assignment; Docking; Docks; Effectiveness studies; Evolution; Heuristic; Hybrid metaheuristics; Optimization; Optimization algorithms; Projection; Sequencing; Simulated annealing; Tabu search; Trucks; Cross docking; Ant colony optimization; Tabu search; Simulated annealing; Differential evolution; Hybrid metaheuristics; Dock assignment; Combinatorial optimization; Sequencing
• ISSN: 0925-5273; 1873-7579
• DOI: 10.1016/j.ijpe.2012.03.037

This paper studies the simultaneous dock assignment and sequencing of inbound trucks for a multi-door cross docking operation with the objective to minimize total weighted tardiness, under a fixed outbound truck departure schedule. The problem is newly formulated and solved by six different metaheuristic algorithms, which include simulated annealing, tabu search, ant colony optimization, differential evolution, and two hybrid differential-evolution algorithms. To evaluate the total weighted tardiness associated with any given inbound-truck sequence and dock assignment, an operational policy is developed. This policy is employed by every metaheuristic algorithm in searching for the optimal dock assignment and sequence. Each metaheuristic algorithm is tested with 40 problems. The major conclusions are: (1) metaheuristic is generally an effective optimization method for the subject problem; (2) population based metaheuristic algorithms are generally more effective than projection based metaheuristic algorithms; (3) proper selection of algorithmic parameters is important and more critical for projection based metaheuristic algorithms than population based algorithms; (4) the two best algorithms are ant colony optimization and hybrid differential evolution 2; among them, ACO takes less time than hybrid 2 and thus can be declared the best among all the six metaheuristic algorithms tested.