This dissertation considers daily operations management for a fleet of trucks providing container pickup and delivery service to a port. Truck congestion at access points for ports may lead to serious inefficiencies in drayage operations, and the resultant cost impact to the intermodal supply chain can be significant. Recognizing that port congestion is likely to continue to be a major problem for drayage operations given the growing volume of international containerized trade, this research seeks to develop optimization approaches for maximizing the productivity of drayage firms operating at congested seaports. Specifically, this dissertation addresses two daily drayage routing and scheduling problems. In the first half of this dissertation, we study the problem of managing a fleet of trucks providing container pickup and delivery service to a port facility that experiences different access wait times depending on the time of day. For this research, we assume that the wait time can be estimated by a deterministic function. We develop a time-constrained routing and scheduling model for the problem that incorporates the time-dependent congestion delay function. The model objective is to find routes and schedules for drayage vehicles with minimum total travel time, including the waiting time at the entry to the port due to congestion. We consider both exact and heuristic solution approaches for this difficult optimization problem. Finally, we use the framework to develop an understanding of the potential impact of congestion delays on drayage operations, and the value of planning with accurate delay information. In the second half of this dissertation, we study methods for managing a drayage fleet serving a port with an appointment-based access control system. Responding to growing access congestion and its resultant impacts, many U.S. port terminals have implemented appointment systems, but little is known about the impact of such systems on drayage productivity. To address this knowledge gap, we develop a drayage operations optimization approach based on a column generation integer programming heuristic that explicitly models a time-slot port access control system. The approach determines pickup and delivery sequences with minimum transportation cost. We use the framework to develop an understanding of the potential efficiency impacts of access appointment systems on drayage operations. Findings indicate that the set of feasible drayage tasks and the fleet size required to complete them can be quite sensitive to small changes in time-slot access capacities at the port.

This research focuses on fleet management in freight transportation systems. Effective management requires effective planning and control decisions. Plans are often generated using estimates of how the system will evolve in the future; during execution, control decisions need to be made to account for differences between actual realizations and estimates. The benefits of minimum cost plans can be negated by performing costly adjustments during the operational phase. A planning approach that permits effective control during execution is proposed in this dissertation. This approach is inspired by recent work in robust optimization, and is applied to (i) dynamic asset management and (ii) vehicle routing problems. In practice, the fleet management planning is usually decomposed in two parts; the problem of repositioning empty, and the problem of allocating units to customer demands. An alternative integrated dynamic model for asset management problems is proposed. A computational study provides evidence that operating costs and fleet sizes may be significantly reduced with the integrated approach. However, results also illustrate that not considering inherent demand uncertainty generates fragile plans with potential costly control decisions. A planning approach for the empty repositioning problem is proposed that incorporates demand and supply uncertainty using interval around nominal forecasted parameters. The intervals define the uncertainty space for which buffers need to be built into the plan in order to make it a robust plan. Computational evidence suggests that this approach is tractable. The traditional approach to address the Vehicle Routing Problem with Stochastic Demands (VRPSD) is through cost expectation minimization. Although this approach is useful for building routes with low expected cost, it does not directly consider the maximum potential cost that a vehicle might incur when traversing the tour. Our approach aims at minimizing the maximum cost. Computational experiments show that our robust optimization approach generates solutions with expected costs that compare favorably to those obtained with the traditional approach, but also that perform better in worst-case scenarios. We also show how the techniques developed for this problem can be used to address the VRPSD with duration constraints.

The emphasis on minimizing the logistics costs to decrease overall system costs has led the researchers to consider the interdependencies between the decisions of locating facilities and planning the routes from those facilities. The location-routing problems considering this issue are the subject of this thesis study. A two-level hierarchical distribution system is considered in which goods are delivered from the sources (plants) to the facilities (depots) and then from the facilities to the customers. The facilities are uncapacitated and operate within the shift times defined. The goods are to be delivered to the customers within their time windows by the vehicles that are capacitated. Both a mathematical model and a genetic algorithm based heuristic solution approach are proposed for this problem. We discuss the problem specific issues integrated with the general framework of the genetic algorithm applications. The computational studies are realized on a number of test problems. The results indicate that the genetic algorithm based heuristic gives satisfactory results compared with a sequential solution methodology.

In this study, the capacitated vehicle routing problem with shift based time constraints is taken into consideration. The study stemmed from an application in a cross dock. The considered cross dock is assumed to feed directly the production lines of its customer. The customer has a just-in-time production system that requires producing only in necessary quantities at the necessary times. This necessitates the arrival of the parts/products collected from different suppliers at the customer at the beginning of each shift of production. The shift times constitute deadlines for the products to be collected from the suppliers and used in each shift. The collection problem then can be seen as the capacitated vehicle routing problem with shift based time constraints. The objective of the collection problem is to minimize the routing costs. For the accomplishment of this objective it is required to decide on products of which shift(s) should be taken from a supplier when a vehicle arrives at that supplier. For the solution of the problem a mathematical model is formulated. Since the dealt problem is NP-Hard, meta-heuristic solution approaches based on variable neighborhood search and simulated annealing are proposed. Computational experimentation is conducted on the test problems which are tailored from the capacitated vehicle routing instances from the literature.

Thesis (Ph.D.)--Mississippi State University. Department of Industrial and Systems Engineering. / Title from title screen. Includes bibliographical references.

Στην παρούσα μεταπτυχιακή διπλωματική εργασία μελετήθηκε το πρόβλημα Δρομολόγησης Στόλου Οχημάτων με Παράθυρα Χρόνου (VRPTW) κάτω από ένα φιλικό προς το περιβάλλον πρίσμα που απαιτεί την δημιουργία ισορροπημένων και συμπαγών συστάδων. Παρουσιάζεται μια νέα ευρετική προσέγγιση που αποτελείται από τρεις φάσεις: (i) συσταδοποίηση των πελατών με συμβατά παράθυρα χρόνου, (ii) συσταδοποίηση των πελατών που βρίσκονται γεωγραφικά κοντά χρησιμοποιώντας διάφορες μεθόδους (φυσικές αποκοπές, KaHIP, τετραδικά δένδρα), (iii) μια φάση εκλέπτυνσης που είτε χωρίζει μια συστάδα σε μικρότερες, είτε συγχωνεύει συστάδες δημιουργώντας μια συμπαγή μεγαλύτερη συστάδα. Η νέα προσέγγιση αποδίδει πολύ καλά όταν χρησιμοποιείται σε δυναμικά σενάρια στα οποία ζητούνται αλλαγές στην αρχικά υπολογισμένη διαδρομή (προσθήκη μιας νέας παραγγελίας ή ακύρωση κάποιας παραγγελίας). Η νέα μέθοδος αποτελεί ένα πολύ καλό σημείο εκκίνησης για επανεξέταση και περαιτέρω βελτιστοποίηση της λύσης του προβλήματος Δρομολόγησης Στόλου Οχημάτων με Παράθυρα Χρόνου. Πειράματα που έγιναν με πραγματικά σύνολα δεδομένων δείχνουν ότι η νέα προσέγγιση υπερέχει σε σχέση με τις συνήθεις προσεγγίσεις που ξεκινούν από μία βασική λύση. / We investigate the Vehicle Routing Problem with Time Windows (VRPTW) under a new approach, consisting of three major phases: (i) a first clustering of customers with compatible time windows; (ii) a second clustering of customers with close geographic proximity based on various methods (natural cuts, KaHIP, quad trees); (iii) a refinement phase that either splits a cluster into smaller ones, or merges clusters to form a bigger compact cluster. Our approach turns out to be beneficial when used in an on-line environment, where changes to the initial tour are requested (add a new customer to the tour or drop some customers). The new method serves as a warm starting point for re-evaluating and further optimizing the solution of VRPTW. Experiments with real data sets demonstrate that our approach compares favorably with standard approaches that start from a basic (cold) solution.

We present an optimization procedure for solving the vehicle routing problem with a fixed heterogeneous fleet of vehicle. We want to minimize the passage price. We look and probe these methods: minimal element, Vogel’s Approximation and heuristic. The modeling vehicle routing problem is based on mathematical formulation. This paper present very well known problems – TSP Traveling Salesperson Problem and M-TSP. Vehicle routing problem is liked M-TSP with some specification, vehicle with a fixed carrying capacity must deliver order of goods to n customers from a single depot. Knowing the distance between customers, the problem is to find tours for the vehicles in such a way that: the total distance traveled by the vehicles is minimized, only one vehicle handles the deliveries for a given customer, the total quantity of goods that a single vehicle delivers cannot be larger than cars capacity.

Esta tesis explora el problema de la integración de procesos, dentro de la logística y también con otras áreas de la empresa, desde el punto de vista de la planificación estratégica.El primer estudio, explora tres estrategias de distribución: el problema clásico de rutas de vehículos; la estrategia de "master routing", y una estrategia multi-objetivo: minimización de costes y mejoría del servicio al cliente. Una meta-heurística, Búsqueda Local Iterativa, se usa para resolver los modelos y comparar estrategias. El modelo multi-objetivo ofrece varias soluciones no-dominadas que pueden verse como un equilibrio bueno entre perfeccionar el servicio al cliente y reducir costes de transporte. En el segundo estudio, se desarrollan dos modelos para un sistema de inventario en el que el inventario es manejado por el distribuidor. La conclusión principal es que los modelos de decisión integrados producirán un proceso de decisión global bueno. El tercer estudio, explora la coordinación de funciones de la logística: Transporte e Inventario. La conclusión principal es de qué podrían obtenerse reducciones del coste cuando se consideran el inventario y el transporte de una manera integrada. / This thesis explores the key issue integrating logistic processes both inside the logistics field and with other areas of the firm, from a strategic planning point of view.The first study, explores three different distribution strategies: the classical vehicle routing problem; the master route strategy, and the third is a multi-objective strategy: minimizing cost and improving customer service. A meta-heuristic based on Iterated Local Search is used to solve the models and compare the strategies. The main conclusion is that the multi-objective model gives several non-dominated solutions that can be seen as a good balance between optimizing the transportation cost and the customer service.In the second study, two models for an inventory system in which the distributor manages the inventory at the retailers' location, were developed. The main conclusion of this chapter is that developing integrated decision models will result in a better global decision process.In the third study, explores the coordination of two logistics planning functions: Transportation and Inventory. The main conclusion was that cost reductions could be obtained when considering inventory and routing in an integrated manner.

The issues of global warming and climate change are a worldwide concern and the UK government has committed itself to major reductions in CO2 emissions, the most significant of the six greenhouse gases. Road transport currently accounts for about 22% of total UK emissions of CO2, and has been steadily rising. Therefore, initiatives are required to try and reduce the gas emissions in this sector. The aim of this research has been to develop a computer based vehicle routing model that calculates the overall amount of CO2 emitted from road journeys, as well as time and distance. The model has been used to examine a number of delivery strategies to assess how CO2 emissions vary. The aim has not been to produce new mathematical theories, but to produce an innovative basis for routing which will provide new information and knowledge about how CO2 emissions vary for different minimisation and congestion criteria. The approach used in this research brings together elements from transportation planning and environmental modelling combined with logistics based vehicle routing techniques. The model uses a digitised road network containing predicted traffic volumes, to which speed flow formulae are applied so that a good representation of speed can be generated on each of the roads. This means that the model is uniquely able to address the issue of congestion in the context of freight vehicle routing. It uses driving cycle data to apply variability to the generated speeds to reflect acceleration and deceleration so that fuel consumption, and therefore CO2, can be estimated. Integrated within the model are vehicle routing heuristics to enable routes to be produced which minimise the specified criterion of time, distance or CO2. The results produced by the model show that there is a potential to reduce CO2 emissions by about 5%. However, when other transport externalities are considered overall benefits are dependent on road traffic volumes.

abstract: Mobile healthy food retailers are a novel alleviation technique to address disparities in access to urban produce stores in food desert communities. Such retailers, which tend to exclusively stock produce items, have become significantly more popular in the past decade, but many are unable to achieve economic sustainability. Therefore, when local and federal grants and scholarships are no longer available for a mobile food retailer, they must stop operating which poses serious health risks to consumers who rely on their services. To address these issues, a framework was established in this dissertation to aid mobile food retailers with reaching economic sustainability by addressing two key operational decisions. The first decision was the stocked product mix of the mobile retailer. In this problem, it was assumed that mobile retailers want to balance the health, consumer cost, and retailer profitability of their product mix. The second investigated decision was the scheduling and routing plan of the mobile retailer. In this problem, it was assumed that mobile retailers operate similarly to traditional distribution vehicles with the exception that their customers are willing to travel between service locations so long as they are in close proximity. For each of these problems, multiple formulations were developed which address many of the nuances for most existing mobile food retailers. For each problem, a combination of exact and heuristic solution procedures were developed with many utilizing software independent methodologies as it was assumed that mobile retailers would not have access to advanced computational software. Extensive computational tests were performed on these algorithm with the findings demonstrating the advantages of the developed procedures over other algorithms and commercial software. The applicability of these techniques to mobile food retailers was demonstrated through a case study on a local Phoenix, AZ mobile retailer. Both the product mix and routing of the retailer were evaluated using the developed tools under a variety of conditions and assumptions. The results from this study clearly demonstrate that improved decision making can result in improved profits and longitudinal sustainability for the Phoenix mobile food retailer and similar entities. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2016