Models for Reducing Deadheading through Carrier and Shipper Collaboration

The competitive nature in the trucking industry has forced trucking firms to develop innovative solutions to improve their operational efficiency and decrease marginal costs. There is also a great need to reduce deadheading miles of heavy trucks to help reduce the amount of air pollutants they emit. One way carriers and shippers are attempting to accomplish these goals is through various collaborative operational strategies. This work focuses on developing multiple collaboration frameworks and formulating optimization models for each framework that demonstrates the operations and reveals the potential cost savings of each framework.;The first collaboration framework focuses on how a medium level shipper or carrier can introduce collaboration in their operations by fulfilling a collaborative carrier\u27s or shipper\u27s delivery requests on its backhaul route. Two optimization models are developed to route the carrier of interest\u27s backhaul routes and select collaborative shipments to fulfill; one is formulated as an integer program and the other is formulated as a mixed integer program. Two solution methodologies, a greedy heuristic and tabu search, are used to solve the two problems, and numerical analysis is performed with a real world freight network. Numerical analysis on a real world freight network reveals that the percentage of cost savings for backhaul routes can be as high as 27%.;The second collaboration framework focuses on a group of shippers that collaborate their operations and form cycles between their long-haul shipping lanes. If the shippers provide the bundled lanes, as loops, to a common carrier they can realize cost savings from the carrier. The problem is formulated as a mixed integer program and forms least cost loops between the shipping lanes. A tabu search heuristic is used to solve the second collaboration framework and results using a real freight network reveal collaborative network costs savings between 7% to 12%. Three cost allocation mechanisms are proposed for the problem to distribute the costs to the shippers involved in the collaboration and computational results are provided for each of the allocation mechanisms

On the use of biased-randomized algorithms for solving non-smooth optimization problems

Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory management, it is possible to consider stock-outs generated by unexpected demands; and in manufacturing processes and project management, it is frequent that some deadlines cannot be met due to delays in critical steps of the supply chain. However, capacity-, size-, and time-related limitations are included in many optimization problems as hard constraints, while it would be usually more realistic to consider them as soft ones, i.e., they can be violated to some extent by incurring a penalty cost. Most of the times, this penalty cost will be nonlinear and even noncontinuous, which might transform the objective function into a non-smooth one. Despite its many practical applications, non-smooth optimization problems are quite challenging, especially when the underlying optimization problem is NP-hard in nature. In this paper, we propose the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and non-smooth optimization problems in many practical applications. Biased-randomized algorithms extend constructive heuristics by introducing a nonuniform randomization pattern into them. Hence, they can be used to explore promising areas of the solution space without the limitations of gradient-based approaches, which assume the existence of smooth objective functions. Moreover, biased-randomized algorithms can be easily parallelized, thus employing short computing times while exploring a large number of promising regions. This paper discusses these concepts in detail, reviews existing work in different application areas, and highlights current trends and open research lines

Metaheuristic Approaches For Estimating In-Kind Food Donations Availability And Scheduling Food Bank Vehicles

Food banks provide services that allow households facing food insecurity to receive nutritious food items. Food banks, however, experience operational challenges as a result of constrained and uncertain supply and complex routing challenges. The goal of this research is to explore opportunities to enhance food bank operations through metaheuristic forecasting and scheduling practices. Knowledge discovery methods and supervised machine learning are used to forecast food availability at supermarkets. In particular, a quasi-greedy algorithm which selects multi-layer perceptron models to represent food availability is introduced. In addition, a new classification of the vehicle routing problem is proposed to manage the distribution and collection of food items. In particular, variants of the periodic vehicle routing problem backhauls are introduced. In addition to discussing model formulations for the routing problems, a hybrid genetic algorithm is introduced which finds good solutions for larger problem instances in a reasonable computation time

Applications of biased-randomized algorithms and simheuristics in integrated logistics

Transportation and logistics (T&L) activities play a vital role in the development of many businesses from different industries. With the increasing number of people living in urban areas, the expansion of on-demand economy and e-commerce activities, the number of services from transportation and delivery has considerably increased. Consequently, several urban problems have been potentialized, such as traffic congestion and pollution. Several related problems can be formulated as a combinatorial optimization problem (COP). Since most of them are NP-Hard, the finding of optimal solutions through exact solution methods is often impractical in a reasonable amount of time. In realistic settings, the increasing need for 'instant' decision-making further refutes their use in real life. Under these circumstances, this thesis aims at: (i) identifying realistic COPs from different industries; (ii) developing different classes of approximate solution approaches to solve the identified T&L problems; (iii) conducting a series of computational experiments to validate and measure the performance of the developed approaches. The novel concept of 'agile optimization' is introduced, which refers to the combination of biased-randomized heuristics with parallel computing to deal with real-time decision-making.Las actividades de transporte y logística (T&L) juegan un papel vital en el desarrollo de muchas empresas de diferentes industrias. Con el creciente número de personas que viven en áreas urbanas, la expansión de la economía a lacarta y las actividades de comercio electrónico, el número de servicios de transporte y entrega ha aumentado considerablemente. En consecuencia, se han potencializado varios problemas urbanos, como la congestión del tráfico y la contaminación. Varios problemas relacionados pueden formularse como un problema de optimización combinatoria (COP). Dado que la mayoría de ellos son NP-Hard, la búsqueda de soluciones óptimas a través de métodos de solución exactos a menudo no es práctico en un período de tiempo razonable. En entornos realistas, la creciente necesidad de una toma de decisiones "instantánea" refuta aún más su uso en la vida real. En estas circunstancias, esta tesis tiene como objetivo: (i) identificar COP realistas de diferentes industrias; (ii) desarrollar diferentes clases de enfoques de solución aproximada para resolver los problemas de T&L identificados; (iii) realizar una serie de experimentos computacionales para validar y medir el desempeño de los enfoques desarrollados. Se introduce el nuevo concepto de optimización ágil, que se refiere a la combinación de heurísticas aleatorias sesgadas con computación paralela para hacer frente a la toma de decisiones en tiempo real.Les activitats de transport i logística (T&L) tenen un paper vital en el desenvolupament de moltes empreses de diferents indústries. Amb l'augment del nombre de persones que viuen a les zones urbanes, l'expansió de l'economia a la carta i les activitats de comerç electrònic, el nombre de serveis del transport i el lliurament ha augmentat considerablement. En conseqüència, s'han potencialitzat diversos problemes urbans, com ara la congestió del trànsit i la contaminació. Es poden formular diversos problemes relacionats com a problema d'optimització combinatòria (COP). Com que la majoria són NP-Hard, la recerca de solucions òptimes mitjançant mètodes de solució exactes sovint no és pràctica en un temps raonable. En entorns realistes, la creixent necessitat de prendre decisions "instantànies" refuta encara més el seu ús a la vida real. En aquestes circumstàncies, aquesta tesi té com a objectiu: (i) identificar COP realistes de diferents indústries; (ii) desenvolupar diferents classes d'aproximacions aproximades a la solució per resoldre els problemes identificats de T&L; (iii) la realització d'una sèrie d'experiments computacionals per validar i mesurar el rendiment dels enfocaments desenvolupats. S'introdueix el nou concepte d'optimització àgil, que fa referència a la combinació d'heurístiques esbiaixades i aleatòries amb informàtica paral·lela per fer front a la presa de decisions en temps real.Tecnologies de la informació i de xarxe

Optimal Fleet Size And Mix For A Rental Car Company

In this paper, a linear programming model for optimizing the fleet size and mix for a rental car company is developed and solved. Rental car companies depend on their fleet of vehicles for generating the entirety of their income. Additionally, the investments required are typically very significant due to the high cost of vehicles. Consequently, the composition of the fleet could significantly affect the company’s profitability and sustainability in a volatile demand environment. Determining the optimal fleet size and mix has been the focus of research in particular in revenue and yield management and VRP streams. However, most models focused on cost minimization without taking into account the resale value of vehicles once retired from the fleet. This paper addresses the problem from a return maximization perspective while taking into account resale values of vehicles. Sensitivity analysis is carried out to gain further insight into the problem and enable the model to support the company’s management in refining the strategic plan

A case study of two-echelon multi-depot vehicle routing problem

The Vehicle Routing Problem (VRP) is a classic combinatorial optimization problem and a topic still studied for practical applications. Current research focuses on single echelon distribution systems such as distribution centers serving customers. However, in typical distribution, goods flows among regional distribution centers, local warehouses and customers, defined as a two-echelon network. The two-echelon multiple depot VRP problem is documented and applied to two stages illustrated by a small scale computational example. In the first stage, the simulated annealing algorithm is employed to determine the routes between local warehouses and final customers. For the second stage, trial-and-error is applied to obtain the number and location of regional distribution centers and the routes between regional distribution centers and local warehouses. Matlab is utilized to simulate annealing iterations and cost functions are analyzed. The convergence tendency of simulated annealing is depicted in figures by Matlab coding. Contributions include demonstration between the SA algorithm and a specific combinatorial optimization problem, and an application of the algorithm

Two Efficient Meta-Heuristic Algorithms for the Robust Inventory Routing Problem with Backhaul

The inventory routing problem (IRP) involves the integration and coordination of two components of the logistics value chain: inventory management and vehicle routing. Therefore, consideration of this issue can be effective in decision making of the organization and will lead to lower costs or other goals. Our objective in this article is to examine a new inventory-routing model and solve it with meta-heuristic methods. For more flexibility of the model, and approaching the real world, the model of this article is considered multi-period and multi-product. Also, two objective functions, including minimizing system costs and transportation risk, are included in this model. Given that the main parameter of the model, that is, demand, is uncertain, we have used a robust optimization approach to solve it, and since this model is in the classification of NP-Hard problems, we have used two meta-heuristic algorithms consisting of non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective imperialist competitive algorithm (MOICA). By examining the model in two deterministic and robust conditions, according to two criteria, the mean values of the objective function and its standard deviation, it has been determined that in almost all cases, the robust optimization model produces better solutions. Also, between the two meta-heuristics method, the NSGA-II algorithm has shown better quality according to the mentioned criteria. Obviously, taking into account the different features of a model increases its efficiency. But this, obviously, makes the model even more complex. However, this complexity of models can work like a real system. Our attention in this article has been to this subject. To analyze such models, exact methods do not have the required performance and paying attention to heuristic and meta-heuristic methods is very effective. In this paper, a robust optimization and meta-heurictic approaches focus on these goals

PENENTUAN RUTE DISTRIBUSI AIR MINUM GALON MENGGUNAKAN VEHICLE ROUTING PROBLEM WITH PICK UP AND DELIVERY DENGAN METODA INSERTION HEURISTIC (STUDI KASUS : PT TAMANSARI DIAN PERSADA KABUPATEN BOGOR)

Proses distribusi merupakan bagian dari sebuah jaringan distribusi, yang didalamnya ada rute distribusi, lalu yang lainnya seperti fasilitas yang tersedia untuk persediaan atau distribution center yaitu seperti gudang. Hal tersebut akan berpengaruh terhadap biaya transportasi, persediaan, dan penanganan terkait. Maka apabila berbicara mengenai rute distribusi berarti berkaitan dengan sistem logistik yang ada pada sebuah perusahaan. PT. Tamansari Persada merupakan perusahaan yang bergerak di bidang air minum dalam kemasan yang melakukan proses pendistribusian secara langsung kepada konsumen. Kapasitas angkut yang terbatas mengakibatkan perusahaan harus melakukan pengiriman dengan beberapa kali ritase pengiriman. Akibatnya jarak tempuh kendaraan dan biaya distribusi yang dikeluarkan menjadi lebih besar. Maka dari itu diperlukan kendaraan usulan yang dapat meminimasi jarak tempuh dan biaya distribusi yang dikeluarkan. Dengan model yang digunakan adalah Vehicle Routing Problem with Pick Up and Delivery, dikarenakan proses distribusi yang dilakukan adalah perusahaan mengirim sekaligus mengangkut kembali produknya. Metode yang digunakan untuk menyelesaikan rute distribusi yang harus dilakukan dengan kendaraan usulan ini adalah Insertion Heuristic. Didapatkan total jarak tempuh hasil dari rute distribusi dengan menggunakan Insertion Heuristic adalah sebesar 1912,7 km selama 12 hari, sedangkan total jarak tempuh sebelumnya adalah 2665,55 km selama 12 hari. Lalu untuk total biaya distribusi yang dihasilkan oleh rute distribusi usulan adalah sebesar Rp. 162.676.135 per tahun, sedangkan total biaya distribusi pada kondisi eksisting adalah sebesar Rp. 198.112.623 per tahunnya. Maka terlihat penurunan total jarak tempuh sebesar 28% dan penurunan untuk total biaya distribusi sebesar 18% dari kondisi eksisting. Kata Kunci : Vehicle Routing Problem with Pick Up and Delivery, Insertion Heuristic, Jarak Tempuh, Biaya distribus