A layer-building algorithm for the three-dimensional multiple bin packing problem: a case study in an automotive company

Saraiva, Rommel Dias; Nepomuceno, Napoleão; Pinheiro, Plácido Rogério

doi:10.1016/j.ifacol.2015.06.129

Cited by 18 publications

(14 citation statements)

References 8 publications

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“…giving the maximum total weight the layer can support. We will call this requirement the compression constraint (see [14,18,19,21] for other studies that consider this constraint in a similar way). Note that giving a zero compression factor to layers not satisfying (1) implies that these layers can be only packed as the top layer of a pallet.…”

Section: Methodsmentioning

confidence: 99%

“…For discussions of problems with constraints deriving from real applications, similar to the one in this paper, we refer to [14][15][16][17][18][19][20][21]. In particular, Ancora et al [14] works with a hybrid genetic strategy; in [15][16][17][18], the authors use, respectively, heuristics, greedy approach, the genetic and differential evolution algorithm, and the branch and bound way to solve the DPLP; Gzara et al [19] exploits a layer-based column generation; and Ancora et al [14] works with a hybrid genetic strategy.…”

Section: Introductionmentioning

confidence: 99%

“…For a variant of this problem, the so-called container loading problem, which differs in the fact that, in general, constraints limiting the possibilities of layers overlapping are not explicit, we refer to [20][21][22][23][24][25][26][27][28][29]. (ILP strategy [23], GRASP method [20,24,25], heuristic way [27], heuristic-genetic algorithm [28], heuristics and MILP method [26], layer-based greedy strategy [21]).…”

Section: Introductionmentioning

confidence: 99%

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Solving a Real-Life Distributor’s Pallet Loading Problem

Dell’Amico

Magnani

2021

MCA

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We consider the distributor’s pallet loading problem where a set of different boxes are packed on the smallest number of pallets by satisfying a given set of constraints. In particular, we refer to a real-life environment where each pallet is loaded with a set of layers made of boxes, and both a stability constraint and a compression constraint must be respected. The stability requirement imposes the following: (a) to load at level k+1 a layer with total area (i.e., the sum of the bottom faces’ area of the boxes present in the layer) not exceeding α times the area of the layer of level k (where α≥1), and (b) to limit with a given threshold the difference between the highest and the lowest box of a layer. The compression constraint defines the maximum weight that each layer k can sustain; hence, the total weight of the layers loaded over k must not exceed that value. Some stability and compression constraints are considered in other works, but to our knowledge, none are defined as faced in a real-life problem. We present a matheuristic approach which works in two phases. In the first, a number of layers are defined using classical 2D bin packing algorithms, applied to a smart selection of boxes. In the second phase, the layers are packed on the minimum number of pallets by means of a specialized MILP model solved with Gurobi. Computational experiments on real-life instances are used to assess the effectiveness of the algorithm.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Solving a Real-Life Distributor’s Pallet Loading Problem

Dell’Amico

Magnani

2021

MCA

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“…This problem is known as the bin packing problem, and it is a very popular challenge amongst the researchers across various domains. There are many variants of this problem, such as three-dimensional packing [18], packing by weight [19], packing by cost [20], etc. They have many applications, such as filling up containers [21], loading trucks with weight capacity constraints [22], and job scheduling [23].…”

Section: Figure 5: Bin Packing Problemmentioning

confidence: 99%

Multi-constrained Network Occupancy Optimization

Halilovic

Zaimovic

Feyzmahdavian

et al. 2021

7th Conference on the Engineering of Computer Based Systems

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Increasingly different types of information are required in systems, resulting in the diverse and widespread use of networks. The increase in the number of devices on networks significantly increases network traffic and network load, limiting network propagation speed, and increasing the possibility of collisions and lost information. One way to identify the size of the network load is by measuring the network occupancy. Although speeds are constantly increasing, some parts of the network can still become the bottleneck. Therefore, it is always desirable to minimize network occupancy. In this paper, an approach for the multi-constrained network occupancy optimization is presented. The objective is to minimize network occupancy by optimizing the packing process. The problem is identified as a bin packing problem, which is strongly NP-hard. The modification of the Best-Fit Decreasing algorithm is implemented to find the best solution while satisfying multiple constraints. The approach considers grouping signals that are sent to different controllers in the same packages. The analysis is done on the medium-sized plant model, and different topologies were tested. The results show that the proposed solution gives less network occupancy compared to the reference case. Also, by changing the relative placement of network nodes, a significant reduction in network occupancy is achieved.

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“…However, while the physical product parameters (dimensions, weight, SKU type) and product stacking properties influence the physically possible picking sequence in order to build stable ULs, researchers rarely take into account these aspects during SLA and routing optimisation. Furthermore, many researchers have attained valuable results in the fields of Pallet Loading and Bin Packing Problem (e.g., [18,17,3,21,11]), but the solutions are rarely harmonised with SLA and routing algorithms. Shiau et al solved the multi-container loading problem and defined the order picking sequence but they avoided the SLA [22].…”

Section: Introductionmentioning

confidence: 99%

Necessity and complexity of order picking routing optimisation based on pallet loading features

Bódis

Botzheim

Földesi

2017

Acta Universitatis Sapientiae, Informatica

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Abstract. Order picking is the most labour-intensive and costly activity of warehouses. The main challenges of its improvement are the synchronisation of warehouse layout, storage assignment policy, routing, zoning, and batching. Furthermore, the competitiveness of the warehouse depends on how it adapts to the unique customer demands and product parameters and the changes. The operators usually have to manage the picking sequence based on best practices taking into consideration the product stacking factors and minimising the lead time. It is usually necessary to support the operators by making effective decisions. Researchers of the pallet loading problem, bin packing problem, and order picking optimisation provide a wide horizon of solutions but their results are rarely synchronised. Necessity and complexity of OPRO based on PLF 163The research defines the order picking routing problem based on Pallet Loading Feature (PLF). It describes measurement and product stacking rule evaluation methods to highlight when the PLF based optimisation is necessary. The paper shows that in order picking problems based on PLF, the number of combinations a brute-force search algorithm has to examine grows exponentially, which highlights the importance of meta-heuristic optimisation. The study describes a Simulated Annealing algorithm for order picking based on PLF.

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A layer-building algorithm for the three-dimensional multiple bin packing problem: a case study in an automotive company

Cited by 18 publications

References 8 publications

Solving a Real-Life Distributor’s Pallet Loading Problem

Solving a Real-Life Distributor’s Pallet Loading Problem

Multi-constrained Network Occupancy Optimization

Necessity and complexity of order picking routing optimisation based on pallet loading features

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