Comment réduire l'impact environnemental pour le transport de marchandises ?

Avec l'internationalisation des activités économiques, la demande de transport a considérablement augmenté suite à l’allongement des distances et à la multiplication des fréquences d’envois. Pour répondre aux exigences du e-commerce et des livraisons à domicile, de nouveaux schémas logistiques sont indispensables. Ces défis doivent être relevés tout en minimisant les nuisances environnementales. A l'occasion de ce lunch-conférence, Sabine Limbourg exposera le résultat de ses recherches, basées sur des méthodes quantitatives, explorant trois options permettant de réduire l'impact environnemental pour le transport des marchandises. Ces options sont le transport intermodal qui combine efficacement plusieurs modes de transport, l'intégration de véhicules électriques en milieu urbain et le partage de ressources de transport réutilisables qui circulent en boucle entre les acteurs d’une chaîne logistique

La logistique collaborative

L'objectif d’une logistique collaborative et coopérative est de générer conjointement un profit en mettant en commun les ressources, en partageant et en tirant parti des forces et des capacités spécifiques des entreprises participantes

Solving Pallet loading Problem with Real-World Constraints

Efficient cargo packing and transport unit stacking play a vital role in enhancing logistics efficiency and reducing costs in the field of logistics. This article focuses on the challenging problem of loading transport units onto pallets, which belongs to the class of NP-hard problems. We propose a novel method for solving the pallet loading problem using a branch and bound algorithm, where there is a loading order of transport units. The derived algorithm considers only a heuristically favourable subset of possible positions of the transport units, which has a positive effect on computability. Furthermore, it is ensured that the pallet configuration meets real-world constraints, such as the stability of the position of transport units under the influence of transport inertial forces and gravity.Comment: 8 pages, 1 figure, project report pape

Supporting Your Basic Needs - A Base Support Approach for Static Stability Assessments in Air Cargo

Static stability is one of the most important constraints in the design and efficient calculation of safe air cargo pallets. To calculate the static stability of a cargo layout, base-focused methods such as full or partial base support are often used. Compared to mechanical or simulation-based methods, they offer high performance and simplicity. However, these methods currently reach their limits when dealing with the practical complexity of air cargo, making them difficult to apply in practice. In this research, we extend and generalize these support point methods by modeling irregular and multilevel cargo shapes, which enables improved practical applications. We follow a design-oriented approach to capture air cargo requirements, design an artifact, and evaluate its performance. Our results show a generalized approach that covers a greater practical complexity while maintaining its efficiency

Penerapan Six Sigma Untuk Perbaikan Masalah Kepadatan Dan Kemacetan Workstation Gudang Ekspor Di Perusahaan Cargo Handling Bandara Soekarno Hatta

Pengangkutan kargo melalui pesawat udara merupakan salah satu rantai distribusi barang yang memerlukan biaya dan juga standar keselamatan yang tinggi. PT Jasa Angkasa Semesta (JAS) merupakan salah satu perusahaan ground handling di Bandara Internasional Soekarno Hatta yang salah satu unit usahanya adalah penanganan kargo di gudang ekspor. Salah satu proses di gudang ekspor adalah proses menaikkan kargo ke dalam Unit Load Device (ULD) atau biasa disebut proses build up yang dilakukan di workstation. Kepadatan dan kemacetan sering terjadi di workstation dan akibatnya proses build up, proses penimbangan, pelaporan dan penarikan kargo ke pesawat menjadi terhambat. Penelitian terhadap kepadatan dan kemacetan di workstation bertujuan untuk memecahkan masalah di workstation. Penelitian ini menggunakan metode six sigma DMAIC (Define, Measure, Analyze, Improve dan Control) untuk memecahkan masalah. Dalam tahap define dimulai dengan membuat project charter dan menentukan tim yang akan terlibat. Pada tahap measure menunjukkan bahwa level sigma pada workstation berada pada σ = 3.18. Analisis dilakukan dengan menggunakan metode fishbone dan menunjukkan bahwa penyebab utama dari masalah kepadatan dan kemacetan adalah kapasitas workstation dan penjadwalan build up. Untuk tahap improvement dilakukan dengan melakukan brain storming untuk melakukan perbaikan dan control dilakukan untuk memastikan bahwa improvement yang dibuat akan selalu sesuai harapan. Hasil penerapan penjadwalan, dengan perbandingan hari ke hari, kepadatan dapat berkurang sebesar 28% dan hasil perbaikan penurunan defect sebesar 8.7%. ======================================================================================================== Air cargo transportation is one of goods distribution with high cost and high safety standard. PT Jasa Angkasa Semesta (JAS) is one of ground handling company in Soekarno Hatta International Airport where one of its businesses is cargo handling in export warehouse. One of the processes in the export warehouse is loading cargo into Unit Load Device (ULD) or build up in the workstation. Bottleneck often occurs in the workstation and it affects the build up process, weighing process, reporting, and cargo towing to the aircraft is late. The research was aiming to solve the problem in the workstation. The research used six Sigma DMAIC (Define, Measure, Analyze, Improve, and Control) methodologies to solve the problem. The define phase roll out the project charter and the team involved. On the measure phase reveals that the sigma level of the workstation was on σ = 3.18. The analysis was conducted using fishbone method and the main causes of the bottleneck were capacity and build up scheduling. On improve phase, brain storming was carried out to solve the issue and capability analysis was made to make sure no recurrence on suggested improvement. The result of Build up scheduling implementation reduced congestion by 28% and yield improvement 8.7%

A Mixed Integer Programming formulation for the three dimensional bin packing problem deriving from an air cargo application

The present paper looks into the problem of optimising the loading of boxes into containers. The goal is to minimise the unused volume. This type of problem belongs to the family of Multiple Bin Size Bin Packing Problems. The approach includes an extensive set of constraints encountered in real-world applications in the three-dimensional case: the stability, the fragility of the items, the weight distribution and the possibility to rotate the boxes. It also includes the specific situation in which containers are truncated parallelepipeds. This is typical in the field of air transportation. While most papers on cutting and packing problems describe ad-hoc procedures, this paper proposes a mixed integer linear program. The validity of this model is tested on small instances.COMEX project: Combinatorial Optimization: Metaheuristics and Exact method

A data-driven optimization approach for mixed-case palletization and three-dimensional bin packing

Palletization is the most standard method of packaging and transportation in the retail industry. Their building involves the solution of a three-dimensional packing problem with side practical constraints such as item support and pallet stability, leading to what is known as the mixed-case palletization problem. Motivated by the fact that solving industry-size instances is still very challenging for current methods, we propose a new solution methodology that combines data analysis at the instance level and optimization to build pallets. Item heights are first analyzed to identify possible layers and to derive relationships between item positions. Items are stacked in pairs and trios to create super items, which are then arranged to create layers of even height. The resulting layers are finally stacked to create pallets. The layers are constructed using a reduced-size mixed integer program as well as a two-dimension placement heuristic. Computational tests on industry data show that the solution approach is extremely efficient in producing high-quality solutions in fast computational times

Models and Solution Methods for the Pallet Loading Problem

The three-dimensional bin packing problem (3DBPP) seeks to find the minimum number of bins to pack a finite number of rectangular boxes. It has a wide array of applications, ranging from airline cargo transportation to warehousing. Its practical extension, the distributor's pallet loading problem (DPLP), requires the pallets to be stable, packable, and adhering to several industry requirements such as packing sequences and weight limits. Despite being studied extensively in the optimization literature, the 3DBPP is still one of the most difficult problems to solve. Currently, medium to large size instances are only solved heuristically and remain out of reach of exact methods. This also applies to the DPLP, as the addition of practical constraints further complicates the proposed models. A recent survey identified the scarcity of exact solution methods that are capable of handling practical versions of the problem and the lack of a realistic benchmark data set as major research gaps. In this thesis, firstly, we propose a novel formulation and an exact solution approach based on column generation for the 3DBPP, where the pricing subproblem is a two-dimensional layer generation problem. Layers are highly desirable in practical packings as they are easily packable and can accommodate important practical constraints such as item support, family groupings, isle friendliness, and load bearing. Being key to the success of the column generation approach, the pricing subproblem is solved optimally as well as heuristically, and is enhanced using item grouping, item replacement, layer reorganization, and layer spacing. We also embed the column generation approach within a branch-and-price framework. We conduct extensive computational experiments and compare against existing approaches. The proposed approach outperforms the best performing algorithm in the literature \boldred{in most instances} and succeeds to solve practical size instances in very reasonable computational times. Secondly, we extend the column generation scheme to incorporate practical constraints set by the warehousing industry. We introduce a nonlinear layer spacing model to improve the stability of the planned pallets, which we then reformulate as an SOCP. In order to calculate the weight distribution within pallets, we introduce a new graph representation for placed items. Finally, we propose construction and improvement heuristics to tackle each practical constraint, such as vertical support, different item shapes, planogram sequencing, load bearing, and weight limits. We conduct extensive computational experiments to demonstrate the good performance of the proposed methodology, and provide results for future benchmarking. To the best of our knowledge, this is the first approach to fully solve the DPLP. Computational experiments show that the proposed approach succeeds in solving industry size instances in record computational times and achieves high quality solutions that account for all practical constraints. Finally, we propose realistic benchmark instances by designing and training an instance generator using industry data. We apply clustering and curve fitting techniques to 342 industry instances with 166,406 items to obtain the distributions for item volumes, dimensions, and frequencies. We separate the instances into several classes and categories using $k$-clustering and generate multiple instances with different sizes. We, then, extend the generator to incorporate practical features such as weight, load capacity, shape, planogram sequencing, and reduced edge support

Üç boyutlu palet yükleme probleminin karışık tam sayılı programlama (MILP) ve hibrit genetik algoritma ile çözümü

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Bu tez çalışmasında, konteyner yükleme problemlerinin (KYP) bir çeşidi olan üç boyutlu palet yükleme problemi (3B-PYP), problemin doğası gereği göz önüne alınması gereken kısıtların yanı sıra, kontrollü döndürme kısıtı, kırılganlık kısıtı, yüke dayanım kısıtı ve bağlantılı nesnelerin bir arada olması kısıtı gibi ek yükleme kısıtları altında ele alınmıştır. Ele alınan 3B-PYP'nin optimum çözümü için bir karışık tam sayılı doğrusal programlama (MILP) modeli geliştirilmiştir. Geliştirilen model, küçük ölçekli 3B-PYP'yi optimize etmek için kullanılabilmekte fakat müşteri sayısı, nesne sayısı ve palet yükleme oranı gibi problem parametrelerindeki artışlara bağlı olarak büyük ölçekli gerçek hayat problemlerinin optimizasyonu için kabul edilebilir bir sürede cevap verememektedir. Bu sebeple büyük ölçekli problemlerin çözümü için biri yığın oluşturma tabanlı, diğeri yatay katman oluşturma tabanlı olmak üzere iki farklı sezgisel yaklaşım ile hibritlenmiş bir hibrit genetik algoritma (HGA) geliştirilmiştir. Önerilen HGA'daki yığın oluşturma tabanlı sezgisel yaklaşım, yüklenecek olan nesnelerden en az iki boyutu birbirine eşit olanları genetik algoritma (GA) arama yapısını kullanarak belirler ve bu nesneleri birbiriyle birleştirerek, iki nesneyi de kapsayan yeni bir nesne olarak tanımlar. Bu şekilde yerleştirilecek nesne sayısının azaltılması sağlanmış olur. Birleştirme işlemleri yapılırken GA'daki kromozom uzunlukları bozulabilir. Bu sebepten dolayı literatürdeki mevcut çaprazlama operatörleri kullanılamamaktadır ve akıllı dinamik çaprazlama operatörü (A-DÇO) adı verilen bir çaprazlama operatörü geliştirilmiştir. Önerilen HGA'daki bir diğer sezgisel algoritma da literatürde var olan en dip alt sol doldurma (DASD) algoritmasıdır. Bu algoritmanın adımları sayesinde tüm nesnelerin paletlere nihai yüklemesi yapılır ve tüm nesnelerin paletler üzerindeki koordinatları belirlenir. Önerilen HGA'nın klasik DASD ile test problemleri üzerinde karşılaştırılması yapılmış ve daha iyi çözümler verdiği istatistiksel olarak gösterilmiştir. Ayrıca önerilen HGA, literatürde var olan bir parçacık sürü eniyileme algoritması (PSO) ve HGA-L adı verilen bir başka HGA ile de test problemleri üzerinde karşılaştırılmıştır. Önerilen HGA'nın bu algoritmalardan da daha iyi sonuçlar verdiği istatistiksel olarak gösterilmiştir. Sonuç olarak, ele alınan 3B-PYP için önerilen HGA, daha iyi sonuçlar vermekte ve özellikle gerçek hayatta robot kolları vasıtası yapılan otomatik paletleme operasyonları için kullanılması önerilmektedir.In this thesis, the three-dimensional pallet loading problem (3D-PLP), which is a kind of container loading problems (CLP), was studied under the constraints as rotation, fragility, load-bearing strength, relative positioning as well as the constraints that should be considered due to the nature of the problem. A mixed integer linear programming (MILP) model was developed for the optimal solution of the studied 3D-PLP. The developed model can be used to optimize small-scale 3D-PLP. However, due to increase in some problem parameters as the number of customers, the number of objects and the pallet loading rate, it cannot be solved in an acceptable time for large-scale real-life problems. For this reason, a new hybrid genetic algorithm (HGA) was developed for solving large-scale problems. It was hybridized with two different heuristic approaches, one of them is based on a stack-building approach and the other one is based on a layer building approach. The stack-building approach determines the objects which have at least two equal dimensions by searching structure of genetic algorithm (GA). This operation reduces the number of objects to be placed. The chromosome lengths in the GA may change because of the combining operation. For this reason, existing crossover operators in the literature cannot be employed. And a crossover operator called the intelligent dynamic crossover operator (I-DCO) was developed. Another heuristic approach in the proposed HGA is deepest bottom left fill (DBLF) approach which is available in the literature. Under favor of the steps of DBLF, all objects can be loaded to the pallets and the coordinates of all objects on the pallets are determined. The proposed HGA was compared with classical DBLF on test problems and it was shown that the proposed HGA produced better solutions statistically. In addition, the proposed HGA was compared with two existing meta-heuristic algorithms on test problems. It was shown that the proposed HGA achieved better results than these algorithms. As a result, the proposed HGA for the 3D-PLP yielded much better results