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Every year, at least 100 million tons of solid waste globally comes from packaging waste, in which partly created by inefficient packaging. Multiple box arrangement or bin packing solution directly addresses this problem which also affects storing space in production, manufacturing and logistics sector. Smart packing algorithm is designed for solvi...
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... may input boxes dimensions (length, width, height) and the simulator will show the process of arranging the boxes throughout GA generations, and finally determine the optimum box arrangement. The workflow of the simulator is in figure 1. The packing strategy of this system is shown in figure 2 where initially the boxes are scattered in the volume space with random point of origin with random orientation and after every generation, the boxes will move closely to each other and finally arranged in a manner that the outer container has a minimum volume possible. ...
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... The 3D bin packing problem can be formulated as follows 27,28 : given a set of n three-dimensional items, each with width w i , height h i , and depth d i , and a set of identical three-dimensional bins, each with a fixed width W, height H, and depth D, the objective is to find a packing assignment that minimizes the number of bins used subject to the following constraints: (1) each item can only be packed once; (2) the total volume of the packed items in each bin cannot exceed the volume of the bin; (3) the orientation of each item is fixed, and it cannot be rotated or reflected. In other words, the problem can be formulated as an integer programming problem: which subject to: ...
The 3D bin packing problem is a challenging combinatorial optimization problem with numerous real-world applications. In this paper, we present a novel approach for solving this problem by integrating a generative adversarial network (GAN) with a genetic algorithm (GA). Our proposed GAN-based GA utilizes the GAN to generate high-quality solutions and improve the exploration and exploitation capabilities of the GA. We evaluate the performance of the proposed algorithm on a set of benchmark instances and compare it with two existing algorithms. The simulation studies demonstrate that our proposed algorithm outperforms both existing algorithms in terms of the number of used bins while achieving comparable computation times. Our proposed algorithm also performs well in terms of solution quality and runtime on instances of different sizes and shapes. We conduct sensitivity analysis and parameter tuning simulations to determine the optimal values for the key parameters of the proposed algorithm. Our results indicate that the proposed algorithm is robust and effective in solving the 3D bin packing problem. The proposed GAN-based GA algorithm and its modifications can be applied to other optimization problems. Our research contributes to the development of efficient and effective algorithms for solving complex optimization problems, particularly in the context of logistics and manufacturing. In summary, the proposed algorithm represents a promising solution to the challenging 3D bin packing problem and has the potential to advance the state-of-the-art in combinatorial optimization.
... Heuristic algorithms were used to calculate profitability and stability aspects and genetic algorithms were employed to run the profit optimization. A smart packing simulator for optimized box arrangements combined with the minimization of the outer container box size, which is based on GAs, is shown in [52]. Researchers developed an adaptable chromosome length GA, where the number of boxes controls the length. ...
Mixed palletizing constitutes one of the problems in the logistics domain aroused from the need for fast product movement to satisfy the continuously increasing number of customers with the demand for highly personalized goods. In a demanding environment, such as warehouses, where break bulk and the consolidation of loads take up most of the working time, the automation of mixed palletizing can lead to increased efficiency and speed while keeping errors to a minimum. Space utilization of pallets enables savings in storage and transportation costs, boosting the overall sustainable role of the Supply Chain sector. This paper presents the proposed approaches to mixed palletizing stemming from different fields, with a focus on recent developments in the application of Industry 4.0 technologies. Our research highlights quite a few areas that require attention from researchers.
... The heuristic and genetic algorithms were adopted for solving the profit-based multi pallet loading problem, which is mathematically formulated as a nonlinear integer programming problem. Recently, Khairuddin et al. (2020) developed a smart packing algorithm based on GA which is designed to solve three-dimensional bin/container packing problem (3DBPP). This algorithm was implemented to the problem of packing solid waste. ...
A two-step evolutionary algorithm for solving a real-life Distributor’s Pallet Loading Problem (DPLP) is presented. Previous literature considers multiple sizes of boxes to be loaded on the same size pallet for the DPLP. However, in this research, multiple-size pallets are allowed. Allowing multi-size pallets is new to the problem and makes it even harder to solve, because the DPLP with the same size pallet is NP-Hard already. Because of the complexity and size of the problem, a two-step algorithm, based on an Evolutionary Algorithm and a Differential Evolution Algorithm, is developed to solve the problem. The first step is to determine the number of pallets of each type, and to assign boxes onto each pallet. The second step is used for improving the solutions obtained from the first step. The effectiveness of the algorithm is evaluated by using a benchmark problem set based on 3D bin-packing problem, and real-life cases from a factory. For the benchmark problem set, the total number of pallets used, as suggested by the newly developed algorithm, is 0.42% higher than the worst results of the exact algorithm on average. However, the results, regarding computation time show that the proposed algorithm is able to find sufficiently good solutions with 78.54% less average calculation time of all problems which the algorithm performed better. As for the real-life cases, the solutions obtained by the algorithm are better both in terms of calculation time and the number of pallets required, which are 26.76% and 28.02% less of the average total occupied cross-section area, and 7 and 29 min of the average total computation time with α = 0.7 and α = 0.8, respectively.
... Sridhar et al. focus on the limitations like the direction of the box in the cargo industry, container stability, and weight, and they minimize the empty volume within a container by using a hybrid GA approach (Sridhar et al., 2017). Khairuddin et al. present an improved GA with an adaptable chromosome length for the solution of 3D-BPP (Khairuddin et al., 2020). Saraiva et al. propose a layer-building-based solution for 3D-BPP (Saraiva et al., 2015). ...
Harris Hawks Optimization (HHO) is a population-based meta-heuristic optimization algorithm that has been used for the solution of test functions and real-world problems by many researchers. However, HHO has a premature convergence problem. The main motive of the novel approach in this paper is that the performance of an MHA could be improved by simplification and by modifying the way random parameters are determined. The proposed algorithm aims to solve both continuous and discrete optimization problems. HHO is improved in three stages. First, the method to determine the random parameters is modified. Second, the strategy of HHO to produce a new solution is updated. Third, the six-step decision mechanism of HHO is shortened to four. The proposed algorithm is compared to five recently published competitor algorithms by applying to the CEC2019 test functions and a three-dimensional bin packing problem (3D-BPP) dataset with 320 samples. All the algorithms are run on the same computer and the results of 30 independent studies are saved. Minimum, average, and standard deviation values and solution times of CEC2019 functions are used as comparison parameters. For the 3D-BPP, the number of bins and the solution time are used as comparison parameters for in the Wilcoxon test. The proposed algorithm performs better than the selected competitors in terms of its %5 significance level. Moreover, the algorithm proposed in the 3D-BPP data set is the most successful algorithm with its 9745 bins. Besides, the proposed algorithm is also compared to the four most popular algorithms in the literature. The results obtained confirm the validity of the proposed algorithm.
... The optimization of operations inside a warehouse is currently characterized by several research areas. In particular, the implementation of a robotic system able to assist human workers in their tasks requires the capability to effectively plan and optimize the sequence of actions [2][3][4], to correctly localize persons and objects [5] and to interact with the environment and other systems in the network [6]. Since the advent of Industry 4.0, the requirements of optimizing the transport and distribution of products has encouraged the use of autonomous guided vehicles within modern automated warehouses [7]. ...
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... For instance, in manufacturing, the product to be manufactured can be considered to be a task to be accomplished within a time frame in a production cycle; the plant used for production is the bin to be allocated to the task (product), and finally, the goal of minimizing production cost is achieved by minimizing the number of bins [54]. Smart packing algorithm was designed by Khairuddin et al. [55] using the GA to solve the 3D bin or container packing problem. The authors in ref. [55] then applied their proposed method to reduce packaging waste due to ineffective packaging in the manufacturing process. ...
... Smart packing algorithm was designed by Khairuddin et al. [55] using the GA to solve the 3D bin or container packing problem. The authors in ref. [55] then applied their proposed method to reduce packaging waste due to ineffective packaging in the manufacturing process. Two-dimensional packing algorithm was similarly used in ref. [56] to solve the problem of composite material production in autoclave moulding. ...
... Sajini Anand and Guericke [66] proposed a variable size BPP to achieve this objective. A smart packaging scheme was proposed by Khairuddin et al. [55]. A scenario where a decision must be made from a list of permissible actions upon the arrival of a stochastic request was exploited by Banerjee and Freund [67]. ...
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... Walking time (WL w ) up and down the train during the supply time (S) is proportional (Fig. 2) to the number of coaches (N). Using the 'preloading method', defined in Section 3.1, N can be calculated solving the Bin Packing Problem (BPP) [18,19] or by Eq. (5) if standard containers fit perfectly in the coach. ...
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