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The vehicle routing problem with private fleet and common carrier (VRPPC) is a generalization of the classical vehicle routing problem in which the owner of a private fleet can either visit a customer with one of the owner’s vehicles or assign the customer to a common carrier. The latter case occurs if the demand exceeds the total capacity of the private fleet or if it is more economically convenient to do so. The owner’s objective is to minimize the variable and fixed costs for operating the owner’s fleet plus the total cost charged by the common carrier. This family of problems has many practical applications, particularly in the design of last-mile distribution services and has received some attention in the literature, in which some heuristics were proposed. We extend here the VRPPC by considering more realistic cost structures that account for quantity discounts on outsourcing costs and by considering time windows resulting in a rich VRPPC (RVRPPC). We present an exact approach based on a branch-and-cut-and-price algorithm for the RVRPPC and test the algorithm on instances from the literature.

The tariff calculations of transport requests by carriers often depend on distance, load, and/or time. In case external carriers are used for shipping, these different calculations can be used to minimize the shipper's costs. A selection of multiple carriers during an optimization process can gain cost savings compared to the planning of only single carriers. Therefore, this paper proposes an exact formulation of carrier selection between different carriers for the vehicle routing problem with time windows using additional valid inequalities, which are added in a branch-and-cut algorithm. We show that the respective tariff calculation has an impact on the solution structure and that these differences can be used to generate synergies and achieve better results than the separate consideration of single carriers.

The problem reported in this paper is a variant of the classical vehicle routing problem, where customer requests for a transportation company can be served either by its private fleet of vehicles or assigned to an external common carrier. The latter case occurs if the demand exceeds the total capacity of the private fleet or if it is more economical to do so. Accordingly, the objective is to minimize the variable and fixed costs of the private fleet plus the costs charged by the common carrier. A tabu search heuristic with a neighbourhood structure based on ejection chains is proposed to solve this problem. It is empirically demonstrated that this algorithm outperforms the best approaches reported in the literature on a set of benchmark instances with both homogeneous and heterogeneous fleets.

The purpose of this article is to propose a perturbation metaheuristic for the vehicle routing problem with private fleet and common carrier (VRPPC). This problem consists of serving all customers in such a way that (1) each customer is served exactly once either by a private fleet vehicle or by a common carrier vehicle, (2) all routes associated with the private fleet start and end at the depot, (3) each private fleet vehicle performs only one route, (4) the total demand of any route does not exceed the capacity of the vehicle assigned to it, and (5) the total cost is minimized. This article describes a new metaheuristic for the VRPPC, which uses a perturbation procedure in the construction and improvement phases and also performs exchanges between the sets of customers served by the private fleet and the common carrier. Extensive computational results show the superiority of the proposed metaheuristic over previous methods.

In this paper, we investigate a routing problem arising in the last-mile delivery of small packages. The problem, called Multi-Depot Vehicle Routing Problem with Private fleet and Common carriers (MDVRPPC), is an extension of the Multi-Depot Vehicle Routing Problem (MDVRP) where customers can either be served by the private fleet based at self-owned depots or by common carriers, i.e., subcontractors. We develop an effective Variable Neighborhood Search algorithm based on the use of cyclic-exchange neighborhoods that incorporates an adaptive mechanism to bias the random shaking step. The approach is successfully used to solve MDVRPPC as well as closely related problems, such as the MDVRP and the single-depot VRP with Private fleet and Common carriers (VRPPC), obtaining high quality solutions within short computing time. Our extensive testing on these problems shows the positive impact of the adaptive mechanism with respect to a standard VNS algorithm.

Several FIB-based methods that have been developed to fabricate needle-shaped atom probe specimens from a variety of specimen geometries, and site-specific regions are reviewed. These methods have enabled electronic device structures to be characterized. The atom probe may be used to quantify the level and range of gallium implantation and has demonstrated that the use of low accelerating voltages during the final stages of milling can dramatically reduce the extent of gallium implantation.

Designing a heuristic approach to solve time-definite common carrier operation planning problem is the purpose of this research. The time-definite common carrier operation planning is implemented using the arcs and nodes of a hierarchical hub-and-spoke network. In light of the complexity involved in determining the types of the vehicles, as well as their associated routing and scheduling simultaneously, we design an algorithm, which employs the concept of tabu search to solve the network structure problem. The testing data are obtained from the second largest time-definite common carrier in Taiwan. When applied to the data of this test case company, the designed tabu search algorithm can obtain final solutions in a relatively short time regardless of the level of service required. Carriers can plan their operations according to the solutions on the operation networks. Although it is possible that the designed heuristic algorithm may converge to a local optimal solution, it can be a meaningful approach for the industrial application that emphasizes the value of time and can be satisfied with a near-optimal solution.

Driver fatigue is internationally recognized as a significant factor in approximately 15%-20% of commercial road transport crashes. In their efforts to increase road safety and improve working conditions of truck drivers, governments worldwide are enforcing stricter limits on the amount of working and driving time without rest. This paper describes an effective optimization algorithm for minimizing transportation costs for a fleet of vehicles considering business hours of customers and hours of service regulations. The algorithm combines the exploration capacities of population-based metaheuristics, the quick improvement abilities of local search, with forward labeling procedures for checking compliance with complex hours of service regulations. Several speed-up techniques are proposed to achieve an overall efficient approach. The proposed approach is used to assess the impact of different hours of service regulations from a carrier-centric point of view. Extensive computational experiments for various sets of regulations in the United States, Canada, the European Union, and Australia are conducted to provide an international assessment of the impact of different rules on transportation costs and accident risks. Our experiments demonstrate that European Union rules lead to the highest safety, whereas Canadian regulations are the most competitive in terms of economic efficiency. Australian regulations appear to have unnecessarily high risk rates with respect to operating costs. The recent rule change in the United States reduces accident risk rates with a moderate increase in operating costs.

Accidents involving large commercial trucks kill over 3,000 motorists every year in the United States. A substantial number of these accidents stem from truck drivers operating their trucks while excessively fatigued. This concern has resulted in regulatory agencies establishing hours-of-service (HOS) rules that carriers must ensure their drivers abide by. In this study we examine the relationship between carriers' capability at monitoring their truck drivers using electronic technologies and carrier-level compliance with HOS rules. Drawing on principles from deterrence theory, we explain why this relationship should be sigmoidal (S-shaped) in nature such that motor carriers receive the greatest gains from investing in electronic monitoring capability when they have a moderate level of this capability. We subject our theorized prediction to empirical testing using a longitudinal research design that combines primary data on motor carriers' electronic monitoring capability and secondary data from regulators regarding carrier-level compliance with HOS rules. Results from our econometric analysis corroborate the hypothesized sigmoidal relationship, which stands up to stringent robustness testing. These results hold important implications for theory and practice.

Our study involves the decision-making problems that railway infrastructure managers face in a rail network with dedicated tracks and shared-use corridors. We will analyze the consolidation strategy for shared-use corridors, where the track serves passenger and freight trains. In the stochastic demand case, we will provide an analytical model for the railway infrastructure manager to compute the expected long-term profit using a consolidation system. We will pinpoint the different characteristics of passenger and freight trains, and analytically derive the optimum track allocation and consolidation time, together with the optimum price, in all such cases, using two different model structures, i.e., the additive and the multiplicative forms. We will extend our model further to consider the due-date requirements and volume incentives for railway operators. Our experiments will use realistic parameter values, based on the Dutch railway system.