A heuristic for the container loading problem: A tertiary-tree-based dynamic space decomposition approach

Abstract

(2008). A heuristic for the container loading problem: A tertiary-tree-based dynamic space decomposition approach. European Journal of Operational Research, 191 (1) AbstractIncreasing fuel costs, post-911 security concerns, and economic globalization provide a strong incentive for container carriers to use available container space more efficiently, thereby minimizing the number of container trips and reducing socio-economic vulnerability. A heuristic algorithm based on a tertiary tree model is proposed to handle the container loading problem (CLP) with weakly heterogeneous boxes. A dynamic space decomposition method based on the tertiary tree structure is developed to partition the remaining container space after a block of homogeneous rectangular boxes is loaded into a container. This decomposition approach, together with an optimal-fitting sequencing and an inner-right-corner-occupying placement rule, permits a holistic loading strategy to pack a container. Comparative studies with existing algorithms and an illustrative example demonstrate the efficiency of this algorithm.

A fast heuristic for a three-dimensional non-convex domain loading problem

“…• CLP in its basic form in George and Robinson (1980), George (1992), Gehring (1998), Pisinger (2002), Moura and Oliveira (2005), Wang et al (2008), Parreño et al (2008a,b), Egeblad and Pisinger (2009), Huang and He (2009); • box grouping constraints in Bischoff and Ratcliff (1995); • weight distribution constraints in Gehring and Bortfeldt (1997), Davies and Bischoff (1999), Eley (2002); • stability and orientation constraints in Terno et al (2000), Bortfeldt and Gehring (2001), Gehring and Bortfeldt (2002), Bortfeldt et al (2003); • bearing strength constraints in Bischoff (2006). Wang et al (2008) well summarize the main available heuristics for the heterogeneous three-dimensional packing problem and provide a view of the advances in the use of intelligent heuristics (simulated annealing, tabu search and genetic algorithms).…”

“…Wang et al (2008) well summarize the main available heuristics for the heterogeneous three-dimensional packing problem and provide a view of the advances in the use of intelligent heuristics (simulated annealing, tabu search and genetic algorithms). Dychoff (1990) and Dyckhoff et al (1997) proposed a taxonomy for cutting and packing problems based on four criterion (dimensionality, kind of assignment, assortment of large objects and small objects).…”

A hybrid ‘bee(s) algorithm’ for solving container loading problems

“…Rising fuel cost now provides a strong incentive for container carriers to maximize available container space, thereby minimizing the number of required trips across the global container transportation system [49]. The efficient loading of containers, that is, the minimization of empty spaces inside of them, is not only an economic requirement but also an ecological issue due to the adverse consequences of increased traffic on environmental resources [39].…”

Development of a Decision Support System for Solving Container Loading Problems

“…90% of all cargo moves in containers and approximately 250 million are shipped annually (van de Voort et al 2003). At the moment, rising fuel costs provide strong incentive for container carriers to maximize available container space, thereby minimizing the number of required trips across the global container transportation system (Wang et al 2008). us, it can be concluded that the loading phase is one of the critical parts of the container shipment process.…”