Solving the Pickup and Delivery Problem with 3D Loading Constraints and Reloading Ban

In this paper, we extend the classical Pickup and Delivery Problem (PDP) to an integrated routing and three-dimensional loading problem, called PDP with 3D loading constraints (3L-PDP). A set of routes of minimum total length has to be determined such that each request is transported from a loading site to the corresponding unloading site. In the 3L-PDP, each request is given as a set of 3D rectangular items (boxes) and the vehicle capacity is replaced by a 3D loading space. This paper is the second one in a series of articles on 3L-PDP. In both articles we investigate which constraints will ensure that no reloading effort will occur, i.e. that no box is moved after loading and before unloading. In this paper, the focus is laid on the so-called reloading ban, a packing constraint that ensures identical placements of same boxes in different packing plans. We propose a hybrid algorithm for solving the 3L-PDP with reloading ban consisting of a routing and a packing procedure. The routing procedure modifies a well-known large neighborhood search for the 1D-PDP. A tree search heuristic is responsible for packing boxes. Computational experiments were carried out using 54 3L-PDP benchmark instances

Hybrid Algorithms for the Vehicle Routing Problem with Pickup and Delivery and Two-dimensional Loading Constraints

We extend the classical Pickup and Delivery Problem (PDP) to an integrated routing and two-dimensional loading problem, called PDP with two-dimensional loading constraints (2L-PDP). A set of routes of minimum total length has to be determined such that each request is transported from a loading site to the corresponding unloading site. Each request consists of a given set of 2D rectangular items with a certain weight. The vehicles have a weight capacity and a rectangular two-dimensional loading area. All loading and unloading operations must be done exclusively by movements parallel to the longitudinal axis of the loading area of a vehicle and without moving items of other requests. Furthermore, each item must not be moved after loading and before unloading. The problem is of interest for the transport of rectangular-shaped items that cannot be stacked one on top of the other because of their weight, fragility or large dimensions. The 2L-PDP also generalizes the well-known Capacitated Vehicle Routing Problem with Two-dimensional Loading Constraints (2L-CVRP), in which the demand of each customer is to be transported from the depot to the customer’s unloading site.This paper proposes two hybrid algorithms for solving the 2L-PDP and each one consists of a routing and a packing procedure. Within both approaches, the routing procedure modifies a well-known large neighborhood search for the one-dimensional PDP and the packing procedure uses six different constructive heuristics for packing the items. Computational experiments were carried out using 60 newly proposed 2L-PDP benchmark instances with up to 150 requests

A Hybrid Algorithm for the Vehicle Routing Problem with Pickup and Delivery and 3D Loading Constraints

In this paper, we extend the classical Pickup and Delivery Problem (PDP) to an integrated routing and three-dimensional loading problem, called PDP with 3D loading constraints (3L-PDP). A set of routes of minimum total length has to be determined such that each request is transported from a loading site to the corresponding unloading site. In the 3L-PDP, each request is given as a set of 3D rectangular items (boxes) and the vehicle capacity is replaced by a 3D loading space. We investigate which constraints will ensure that no reloading effort will occur, i.e. that no box is moved after loading and before unloading. A spectrum of 3L-PDP variants is introduced with different characteristics in terms of reloading effort. We propose a hybrid algorithm for solving the 3L-PDP consisting of a routing and a packing procedure. The routing procedure modifies a well-known large neighborhood search for the 1D-PDP. A tree search heuristic is responsible for packing boxes. Computational experiments were carried out using 54 newly proposed 3L-PDP benchmark instances

Résolution de problèmes de tournées avec synchronisation : applications au cas multi-échelons et au cross-docking

Transportation systems are more and more interconnected, this has lead to a new kind of constraints, called synchronization constraints, in vehicle routing problems. In this thesis, we study two cases in which this type of constraints arises. First, we propose a heuristic method for a two-echelon problem arising in City Logistics. Second, we study the integration of a cross-dockin pickup and delivery vehicle routing problems. To that end we propose a matheuristic for the vehicule routing problem with cross-docking, and we propose an extension of this problem that integrates specific resource synchonization constraints arising at the cross-dock. A method for a 3D loading problem is also presented.L’interconnexion croissante dans les systèmes de transports a conduit à la modélisation de nouvelles contraintes, dites contraintes de synchronisation, dans les problèmes de tournées de véhicules. Dans cette thèse, nous nous intéressons à deux cas dans lesquels ce type de problématiques apparaît. Dans un premier temps, nous proposons une méthode heuristique pour un problème à deux échelons rencontré pour la distribution de marchandises en ville. Dans un second temps, nous étudions l’intégration d’un cross-dock dans des tournées de collectes et livraisons. Une première contribution à ce sujet concerne le problème de tournées de véhicules avec cross-docking, et une seconde contribution intègre, en plus, des contraintes de ressources au cross-dock dans le problème de routage. Une méthode pour un problème de chargement 3D, étudié lors d’un stage doctoral en entreprise, est également présentée

Optimizing rail-truck intermodal drayage operations

This thesis presents a case study of the trucking (or drayage) portion of rail-truck intermodal freight transportation. The approach used was to examine in detail the current costs and potential for improvement at one New Jersey intermodal terminal. The analysis is conducted using a mathematical programming model to find an optimal scheduling plan for the drayage operation. To solve the model more efficiently, a modification is made to explore the special structure of the original problem which has a sparse constraint matrix. The model is solved first with an objective function that minimizes the total cost of the operation, and then with an objective function that minimizes the total tractor fleet size required to move the containers. The model results indicate a 19.2% and 52.7% reduction in overall costs respectively for the objectives of minimizing total cost and minimizing fleet size. This reduction is achieved by repositioning and reloading containers, after they have been unloaded at consignees

Dynamic optimization for same-day delivery operations

Same-day delivery (SDD) is a service where consumers place orders online on the same day that these are processed and delivered to the customer. Providing a delivery service requires order management at the stocking location (depot), including request acceptance and processing; and order distribution from the depot to final customer locations, including order dispatch and delivery via vehicle routes. In SDD these processes are highly interrelated, are simultaneously executed, and have a high degree of information dynamism. In this thesis, we formulate and solve dynamic optimization problems to improve the operation of SDD systems. We study how our approaches perform over computationally simulated instances and provide managerial insights for SDD practitioners, including structural solution properties that any common SDD service should have, the trade-offs between common SDD objectives, and the logistics cost of operational constraints in SDD.Ph.D

Applying computational intelligence to a real-world container loading problem in a warehouse environment

One of the problems presented in the day-to-day running of a warehouse is that of optimally selecting and loading groups of heavy rectangular palletised goods into larger rectangular containers while satisfying a number of practical constraints. The research presented in this thesis was commissioned by the logistics department in NSK Europe Ltd, for the purpose of providing feasible solutions to this problem. The problem is a version of the Container Loading Problem in the literature, and it is an active research area with many practical applications in industry. Most of the advances made in this area focus more on the optimisation of container utility i.e. volume or weight capacity, with very few focusing on the practical feasibility of the loading layout or pattern produced. Much of the work done also addresses only a few practical constraints at a time, leaving out a number of constraints that are of importance in real-world container loading. As this problem is well known to be a combinatorial NPhard problem, the exact mathematical methods that exist for solving it are computationally feasible for only problem instances with small sizes. For these reasons, this thesis investigates the use of computational intelligence techniques for solving and providing near-optimum solutions to this problem while simultaneously satisfying a number of practical constraints that must be considered for the solutions provided to be feasible. In proposing a solution to this problem and dealing with all the constraints considered, an algorithmic framework that decomposes the CLPs into sub-problems is presented. Each subproblem is solved using an appropriate algorithm, and a combination of constraints particular to each problem is satisfied. The resulting hybrid algorithm solves the entire problem as a whole and satisfies all the considered constraints. In order to identify and select feasible container layouts that are practical and easy to load, a measure of disorder, based on the concept of entropy in physics and information theory, is derived. Finally, a novel method of directing a Monte-Carlo tree search process using the derived entropy measure is employed, to generate loading layouts that are comparable to those produced by expert human loaders. In summary, this thesis presents a new approach for dealing with real-world container loading in a warehouse environment, particularly in instances where layout complexity is of major importance; such as the loading of heavy palletised goods using forklift trucks. The approach can be used to deal with a number of relevant practical constraints that need to be satisfied simultaneously, including those encountered when the heavy goods are arranged and physically packed into a container using forklift trucks

Zaman pencereli ve değişken başlama zamanlı bir araç rotalama problemi için sütun türetme temelli matsezgiseller

In this study, a vehicle routing problem with time windows is investigated, where the costs depend on the total duration of vehicle routes and the starting time from the depot for each vehicle is determined by a decision maker. In order to solve the problem, two column generation based mat-heuristics are developed, where the first one makes use of the iterated local search and the second one uses the variable neighbourhood search. In order to assess the accuracy of the mat-heuristics, they are first compared with an exact algorithm on small instances taken from the literature. Since their performance are quite satisfactory, they are further tested on 87 large instances by running each algorithm 3 times for each instance. The computational results prove that the mat-heuristic using the variable neighbourhood search outperforms the other one. Hence, this enables to obtain a good feasible solution in a very short time when it is not possible to solve large instances with an exact solution method in a reasonable CPU time.Bu çalışmada, araçların kullanıldıkları süreye bağlı maliyetlerin oluştuğu ve araçların depodan başlama zamanının bir karar verici tarafından belirlendiği zaman pencereli bir araç rotalama problemi ele alınmaktadır. Problemi çözmek için biri yinelemeli yerel arama meta-sezgiselinden, diğeri değişken komşuluk arama meta-sezgiselinden yararlanan iki sütun türetme temelli mat-sezgisel geliştirilmiştir. Geliştirilen mat-sezgiseller ilk önce literatürden alınarak türetilen küçük bir veri kümesi üzerinde problemin eniyi sonucunu bulan kesin bir yöntem ile karşılaştırılarak kaliteli sonuçlar ürettiklerini kanıtlamışlardır. Yöntemlerin ürettikleri sonuçların doğruluk derecesinden emin olunduktan sonra, daha büyük 87 örnek üzerinde her mat-sezgisel her örnekte 3 kere çalıştırılarak test edilmiştir. Bilgisayımsal sonuçlar değişken komşuluk arama meta-sezgiseli kullanan mat-sezgiselin, daha kaliteli ve verimli sonuçlar vererek daha başarılı bir algoritma olduğunu göstermiştir. Bu sayede kesin bir yöntemle makul bir ana işlemci zamanında çözülemeyen büyük ölçülü problemler için çok kısa bir zaman içerisinde iyi bir olurlu çözüm elde etmek mümkün hale gelmiştir.TÜBİTAK BİDEBWOS:000486923100003Scopus - Affiliation ID: 60105072TR - DizinScience Citation Index ExpandedQ4ArticleUluslararası işbirliği ile yapılmayan - HAYIROcak2019YÖK - 2018-1