1,190 research outputs found
Container Loading Problems: A State-of-the-Art Review
Container loading is a pivotal function for operating supply chains efficiently. Underperformance results in unnecessary costs (e.g. cost of additional containers to be shipped) and in an unsatisfactory customer service (e.g. violation of deadlines agreed to or set by clients). Thus, it is not surprising that container loading problems have been dealt with frequently in the operations research literature. It has been claimed though that the proposed approaches are of limited practical value since they do not pay enough attention to constraints encountered in practice.In this paper, a review of the state-of-the-art in the field of container loading will be given. We will identify factors which - from a practical point of view - need to be considered when dealing with container loading problems and we will analyze whether and how these factors are represented in methods for the solution of such problems. Modeling approaches, as well as exact and heuristic algorithms will be reviewed. This will allow for assessing the practical relevance of the research which has been carried out in the field. We will also mention several issues which have not been dealt with satisfactorily so far and give an outlook on future research opportunities
Robotic Learning the Sequence of Packing Irregular Objects from Human Demonstrations
We address the unsolved task of robotic bin packing with irregular objects,
such as groceries, where the underlying constraints on object placement and
manipulation, and the diverse objects' physical properties make preprogrammed
strategies unfeasible. Our approach is to learn directly from expert
demonstrations in order to extract implicit task knowledge and strategies to
achieve an efficient space usage, safe object positioning and to generate
human-like behaviors that enhance human-robot trust. We collect and make
available a novel and diverse dataset, BoxED, of box packing demonstrations by
humans in virtual reality. In total, 263 boxes were packed with
supermarket-like objects by 43 participants, yielding 4644 object
manipulations. We use the BoxED dataset to learn a Markov chain to predict the
object packing sequence for a given set of objects and compare it with human
performance. Our experimental results show that the model surpasses human
performance by generating sequence predictions that humans classify as
human-like more frequently than human-generated sequences.Comment: 8 pages, 7 figure
Algorithms for the Bin Packing Problem with Scenarios
This paper presents theoretical and practical results for the bin packing
problem with scenarios, a generalization of the classical bin packing problem
which considers the presence of uncertain scenarios, of which only one is
realized. For this problem, we propose an absolute approximation algorithm
whose ratio is bounded by the square root of the number of scenarios times the
approximation ratio for an algorithm for the vector bin packing problem. We
also show how an asymptotic polynomial-time approximation scheme is derived
when the number of scenarios is constant. As a practical study of the problem,
we present a branch-and-price algorithm to solve an exponential model and a
variable neighborhood search heuristic. To speed up the convergence of the
exact algorithm, we also consider lower bounds based on dual feasible
functions. Results of these algorithms show the competence of the
branch-and-price in obtaining optimal solutions for about 59% of the instances
considered, while the combined heuristic and branch-and-price optimally solved
62% of the instances considered
Constraint programming methods in three-dimensional container packing
Cutting and packing problems are present in many, at first glance
unconnected, areas, therefore it's beneficial to have a good understanding of
their underlying structure, to select proper techniques for finding solutions.
Cutting and packing problems are a class of combinatorial problems in which
there are specified two classes of objects: big and small items and the task is
to place the small items within big items. Even in the 1-dimensional case,
bin-packing is strongly NP-hard (Garey 1978), which suggests, that exact
solutions may not be found in a reasonable time for bigger instances. In the
literature, there are presented many various approaches to packing problems,
e.g. mixed-integer programming, approximation algorithms, heuristic solutions,
and local search algorithms, including metaheuristic approaches like Tabu
Search or Simulated Annealing.
The main goal of this work is to review existing solutions, survey the
variants arising from the industry applications, present a solution based on
constraint programming and compare its performance with the results in the
literature. Optimization with constraint programming is a method searching for
the global optima, hence it may require a higher workload compared to the
heuristic and local search approaches, which may finish in a local optimum. The
performance of the presented model will be measured on test data used in the
literature, which were used in many articles presenting a variety of approaches
to three-dimensional container packing, which will allow us to compare the
efficiency of the constraint programming model with other methods used in the
operational research
Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review
Industry 4.0 has become a crucial part in the majority of processes, components, and related modelling, as well as predictive tools that allow a more efficient, automated and sustainable approach to industry. The availability of large quantities of data, and the advances in IoT, AI, and data-driven frameworks, have led to an enhanced data gathering, assessment, and extraction of actionable information, resulting in a better decision-making process. Product picking and its subsequent packing is an important area, and has drawn increasing attention for the research community. However, depending of the context, some of the related approaches tend to be either highly mathematical, or applied to a specific context. This article aims to provide a survey on the main methods, techniques, and frameworks relevant to product packing and to highlight the main properties and features that should be further investigated to ensure a more efficient and optimised approach
The three-dimensional single-bin-size bin packing problem: combining metaheuristic and machine learning approaches
The Three-Dimensional Single-Bin-Size Bin Packing Problem is one of the most studied problem in the Cutting & Packing category. From a strictly mathematical point of view, it consists of packing a finite set of strongly heterogeneous “small” boxes, called items, into a finite set of identical “large” rectangles, called bins, minimizing the unused volume and requiring that the items are packed without overlapping. The great interest is mainly due to the number of real-world applications in which it arises, such as pallet and container loading, cutting objects out of a piece of material and packaging design. Depending on these real-world applications, more objective functions and more practical constraints could be needed. After a brief discussion about the real-world applications of the problem and a exhaustive literature review, the design of a two-stage algorithm to solve the aforementioned problem is presented. The algorithm must be able to provide the
spatial coordinates of the placed boxes vertices and also the optimal boxes input sequence, while guaranteeing geometric, stability, fragility constraints and a reduced computational time. Due to NP-hard complexity of this type
of combinatorial problems, a fusion of metaheuristic and machine learning techniques is adopted. In particular, a hybrid genetic algorithm coupled with a feedforward neural network is used. In the first stage, a rich dataset is created starting from a set of real input instances provided by an industrial company and the feedforward neural network is trained on it. After its training, given a new input instance, the hybrid genetic algorithm is able to run using the neural network output as input parameter vector, providing as output the optimal solution. The effectiveness of the proposed works is confirmed via several experimental tests
Learning Physically Realizable Skills for Online Packing of General 3D Shapes
We study the problem of learning online packing skills for irregular 3D
shapes, which is arguably the most challenging setting of bin packing problems.
The goal is to consecutively move a sequence of 3D objects with arbitrary
shapes into a designated container with only partial observations of the object
sequence. Meanwhile, we take physical realizability into account, involving
physics dynamics and constraints of a placement. The packing policy should
understand the 3D geometry of the object to be packed and make effective
decisions to accommodate it in the container in a physically realizable way. We
propose a Reinforcement Learning (RL) pipeline to learn the policy. The complex
irregular geometry and imperfect object placement together lead to huge
solution space. Direct training in such space is prohibitively data intensive.
We instead propose a theoretically-provable method for candidate action
generation to reduce the action space of RL and the learning burden. A
parameterized policy is then learned to select the best placement from the
candidates. Equipped with an efficient method of asynchronous RL acceleration
and a data preparation process of simulation-ready training sequences, a mature
packing policy can be trained in a physics-based environment within 48 hours.
Through extensive evaluation on a variety of real-life shape datasets and
comparisons with state-of-the-art baselines, we demonstrate that our method
outperforms the best-performing baseline on all datasets by at least 12.8% in
terms of packing utility.Comment: ACM Transactions on Graphics (TOG
グリーンロジスティクスのためのコンテナ積載と配車配送経路の最適化に関する研究
京都大学0048新制・課程博士博士(工学)甲第16840号工博第3561号新制||工||1538(附属図書館)29515京都大学大学院工学研究科機械理工学専攻(主査)教授 椹木 哲夫, 教授 西脇 眞二, 教授 松原 厚学位規則第4条第1項該当Doctor of Philosophy (Engineering)Kyoto UniversityDFA
Analysing trade-offs in container loading: Combining load plan construction heuristics with agent-based simulation
This is the accepted version of the following article: Analysing Trade-offs in Container Loading: Combining Load Plan Construction Heuristics with Agent-based Simulation. International Transactions in Operational Research, 20(4): 471-491which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/itor.12017/abstractIn this paper we describe two operations research techniques, cutting and packing optimisation (CPO) and simulation, and present a multi-methodology approach for analysing the trade-offs between loading efficiency and various important practical considerations in relation to the cargo, such as its stability, fragility or possible cross-contamination between different types of items over time. The feasibility of this approach is demonstrated by considering a situation where the items to be loaded have differing degrees of perishability and where badly deteriorated items can affect those in their immediate vicinity (e.g. through the spread of mould). Our approach uses the output of the CPO algorithms to create agents that simulate the spread of mould through proximity-based interactions between the agents. The results show the trade-offs involved in container utilisation and the propagation of mould, without evidence of any correlation between them. The contribution of this research is the methodology and the feasibility study
Advances in flexible manipulation through the application of AI-based techniques
282 p.Objektuak hartu eta uztea oinarrizko bi eragiketa dira ia edozein aplikazio robotikotan. Gaur egun, "pick and place" aplikazioetarako erabiltzen diren robot industrialek zeregin sinpleak eta errepikakorrak egiteko duten eraginkortasuna dute ezaugarri. Hala ere, sistema horiek oso zurrunak dira, erabat kontrolatutako inguruneetan lan egiten dute, eta oso kostu handia dakarte beste zeregin batzuk egiteko birprogramatzeak. Gaur egun, industria-ingurune desberdinetako zereginak daude (adibidez, logistika-ingurune batean eskaerak prestatzea), zeinak objektuak malgutasunez manipulatzea eskatzen duten, eta oraindik ezin izan dira automatizatu beren izaera dela-eta. Automatizazioa zailtzen duten botila-lepo nagusiak manipulatu beharreko objektuen aniztasuna, roboten trebetasun falta eta kontrolatu gabeko ingurune dinamikoen ziurgabetasuna dira.Adimen artifizialak (AA) gero eta paper garrantzitsuagoa betetzen du robotikaren barruan, robotei zeregin konplexuak betetzeko beharrezko adimena ematen baitie. Gainera, AAk benetako esperientzia erabiliz portaera konplexuak ikasteko aukera ematen du, programazioaren kostua nabarmen murriztuz. Objektuak manipulatzeko egungo sistema robotikoen mugak ikusita, lan honen helburu nagusia manipulazio-sistemen malgutasuna handitzea da AAn oinarritutako algoritmoak erabiliz, birprogramatu beharrik gabe ingurune dinamikoetara egokitzeko beharrezko gaitasunak emanez
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