Heuristics for the score-constrained strip-packing problem

This paper investigates the Score-Constrained Strip-Packing Problem (SCSPP), a combinatorial optimisation problem that generalises the one-dimensional bin-packing problem. In the construction of cardboard boxes, rectangular items are packed onto strips to be scored by knives prior to being folded. The order and orientation of the items on the strips determine whether the knives are able to score the items correctly. Initially, we detail an exact polynomial-time algorithm for finding a feasible alignment of items on a single strip. We then integrate this algorithm with a packing heuristic to address the multi-strip problem and compare with two other greedy heuristics, discussing the circumstances in which each method is superior

A genetic programming hyper-heuristic approach for evolving 2-D strip packing heuristics

We present a genetic programming (GP) system to evolve reusable heuristics for the 2-D strip packing problem. The evolved heuristics are constructive, and decide both which piece to pack next and where to place that piece, given the current partial solution. This paper contributes to a growing research area that represents a paradigm shift in search methodologies. Instead of using evolutionary computation to search a space of solutions, we employ it to search a space of heuristics for the problem. A key motivation is to investigate methods to automate the heuristic design process. It has been stated in the literature that humans are very good at identifying good building blocks for solution methods. However, the task of intelligently searching through all of the potential combinations of these components is better suited to a computer. With such tools at their disposal, heuristic designers are then free to commit more of their time to the creative process of determining good components, while the computer takes on some of the design process by intelligently combining these components. This paper shows that a GP hyper-heuristic can be employed to automatically generate human competitive heuristics in a very-well studied problem domain

3-D Packing in Container using Teaching Learning Based Optimization Algorithm

تهدف الورقة إلى اقتراح خوارزمية التحسين القائم على التعلم (TLBO) لحل مشكلة التعبئة ثلاثية الأبعاد في الحاويات. الهدف الذي يمكن تقديمه في نموذج رياضي هو تحسين استخدام المساحة في الحاوية. ، تراقب هذه الخوارزمية أيضًا، إلى جانب تأثير التفاعل بين الطلاب والمدرس، عملية التعلم بين الطلاب في الفصل والتي لا تحتاج إلى أي معلمات تحكم. وبالتالي ، يوفر TLBO لمرحلة المعلمين ومرحلة الطلاب كعملية تحديث رئيسية لإيجاد أفضل حل. بتعبير أدق ، للتحقق من فعالية الخوارزمية ، فقد تم تنفيذها في ثلاث حالات نموذجية. كانت هناك بيانات صغيرة تحتوي على 5 أنواع من الأحجام مع 12 وحدة ، وبيانات متوسطة تحتوي على 10 أنواع من الأحجام مع 106 وحدة ، وبيانات كبيرة تحتوي على 20 نوعًا من أنواع الأحجام مع 110 وحدة. وتمت مقارنتها، علاوة على ذلك ، بخوارزمية أخرى تسمى خوارزمية البحث عن الجاذبية (GSA). وفقًا للنتائج الحسابية في تلك الحالات النموذجية ، يمكن استنتاج أن العدد الأكبر من السكان والتكرارات يمكن أن يجلب فرصًا أكبر للحصول على حل أفضل حل. ويُظهر TLBO أداءً أفضل في حل مشكلة التعبئة ثلاثية الأبعاد مقارنةً بـ GSA.The paper aims to propose Teaching Learning based Optimization (TLBO) algorithm to solve 3-D packing problem in containers. The objective which can be presented in a mathematical model is optimizing the space usage in a container. Besides the interaction effect between students and teacher, this algorithm also observes the learning process between students in the classroom which does not need any control parameters. Thus, TLBO provides the teachers phase and students phase as its main updating process to find the best solution. More precisely, to validate the algorithm effectiveness, it was implemented in three sample cases. There was small data which had 5 size-types of items with 12 units, medium data which had 10 size-types of items with 106 units, and large data which had 20 size-types of items with 110 units. Moreover, it was also compared with another algorithm called Gravitational Search Algorithm (GSA). According to the computational results in those example cases, it can be concluded that higher number of population and iterations can bring higher chances to obtain a better solution. Finally, TLBO shows better performance in solving the 3-D packing problem compared with GSA.         

Automating the packing heuristic design process with genetic programming

The literature shows that one-, two-, and three-dimensional bin packing and knapsack packing are difficult problems in operational research. Many techniques, including exact, heuristic, and metaheuristic approaches, have been investigated to solve these problems and it is often not clear which method to use when presented with a new instance. This paper presents an approach which is motivated by the goal of building computer systems which can design heuristic methods. The overall aim is to explore the possibilities for automating the heuristic design process. We present a genetic programming system to automatically generate a good quality heuristic for each instance. It is not necessary to change the methodology depending on the problem type (one-, two-, or three-dimensional knapsack and bin packing problems), and it therefore has a level of generality unmatched by other systems in the literature. We carry out an extensive suite of experiments and compare with the best human designed heuristics in the literature. Note that our heuristic design methodology uses the same parameters for all the experiments. The contribution of this paper is to present a more general packing methodology than those currently available, and to show that, by using this methodology, it is possible for a computer system to design heuristics which are competitive with the human designed heuristics from the literature. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains

A genetic programming hyper-heuristic approach to automated packing

This thesis presents a programme of research which investigated a genetic programming hyper-heuristic methodology to automate the heuristic design process for one, two and three dimensional packing problems. Traditionally, heuristic search methodologies operate on a space of potential solutions to a problem. In contrast, a hyper-heuristic is a heuristic which searches a space of heuristics, rather than a solution space directly. The majority of hyper-heuristic research papers, so far, have involved selecting a heuristic, or sequence of heuristics, from a set pre-defined by the practitioner. Less well studied are hyper-heuristics which can create new heuristics, from a set of potential components. This thesis presents a genetic programming hyper-heuristic which makes it possible to automatically generate heuristics for a wide variety of packing problems. The genetic programming algorithm creates heuristics by intelligently combining components. The evolved heuristics are shown to be highly competitive with human created heuristics. The methodology is first applied to one dimensional bin packing, where the evolved heuristics are analysed to determine their quality, specialisation, robustness, and scalability. Importantly, it is shown that these heuristics are able to be reused on unseen problems. The methodology is then applied to the two dimensional packing problem to determine if automatic heuristic generation is possible for this domain. The three dimensional bin packing and knapsack problems are then addressed. It is shown that the genetic programming hyper-heuristic methodology can evolve human competitive heuristics, for the one, two, and three dimensional cases of both of these problems. No change of parameters or code is required between runs. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains

Choice function based hyper-heuristics for multi-objective optimization

A selection hyper-heuristic is a high level search methodology which operates over a fixed set of low level heuristics. During the iterative search process, a heuristic is selected and applied to a candidate solution in hand, producing a new solution which is then accepted or rejected at each step. Selection hyper-heuristics have been increasingly, and successfully, applied to single-objective optimization problems, while work on multi-objective selection hyper-heuristics is limited. This work presents one of the initial studies on selection hyper-heuristics combining a choice function heuristic selection methodology with great deluge and late acceptance as non-deterministic move acceptance methods for multi-objective optimization. A well-known hypervolume metric is integrated into the move acceptance methods to enable the approaches to deal with multi-objective problems. The performance of the proposed hyper-heuristics is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, they are applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the non-deterministic move acceptance, particularly great deluge when used as a component of a choice function based selection hyper-heuristic

Heuristics approaches for three-dimensional strip packing and multiple carrier transportation plans

In transport logistic operations, an efficient delivery plan and better utilisation of vehicles will result in fuel cost savings, reduced working hours and even reduction of carbon dioxide emissions. This thesis proposes various algorithmic approaches to generate improved performance in automated vehicle load packing and route planning. First, modifications to best-fit heuristic methodologies are proposed and then incorporated into a simple but effective “look-ahead” heuristic procedure. The results obtained are very competitive and in some cases best-known results are found for different sets of constraints on three-dimensional strip packing problems. Secondly, a review and comparison of different clustering techniques in transport route planning is presented. This study shows that the algorithmic approach performs according to the specific type of real-world transport route planning scenario under consideration. This study helps to achieve a better understanding of how to conduct the automated generation of vehicle routes that meet the specific conditions required in the operations of a transport logistics company. Finally, a new approach to measuring the quality of transportation route plans is presented showing how this procedure has a positive effect on the quality of the generated route plans. In summary, this thesis proposes new tailored and effective heuristic methodologies that have been tested and incorporated into the real-world operations of a transport logistics company. The research work presented here is a modest yet significant advance to better understanding and solving the difficult problems of vehicle loading and routing in real-world scenarios