Complexity of flow time minimization in a crossdock truck scheduling problem with asymmetric handover relations

We address a novel truck scheduling problem arising in crossdocking logistics, in which inbound trucks carry items (pallets) which must be sorted and loaded onto outbound trucks. We minimize the utilisation of the warehouse by focusing on the synchronisation between the different related trucks. The problem is to assign the trucks to the doors of the warehouse and sequence them, in order to minimize the total time spent in the system by the pallets. We discuss the complexity of the problem, showing that even with a single door the problem is NP-hard in general, and discuss some special cases.     

A survey of recent research on location-routing problems

The design of distribution systems raises hard combinatorial optimization problems. For instance, facility location problems must be solved at the strategic decision level to place factories and warehouses, while vehicle routes must be built at the tactical or operational levels to supply customers. In fact, location and routing decisions are interdependent and studies have shown that the overall system cost may be excessive if they are tackled separately. The location-routing problem (LRP) integrates the two kinds of decisions. Given a set of potential depots with opening costs, a fleet of identical vehicles and a set of customers with known demands, the classical LRP consists in opening a subset of depots, assigning customers to them and determining vehicle routes, to minimize a total cost including the cost of open depots, the fixed costs of vehicles used, and the total cost of the routes. Since the last comprehensive survey on the LRP, published by Nagy and Salhi (2007), the number of articles devoted to this problem has grown quickly, calling a review of new research works. This paper analyzes the recent literature (72 articles) on the standard LRP and new extensions such as several distribution echelons, multiple objectives or uncertain data. Results of state-of-the-art metaheuristics are also compared on standard sets of instances for the classical LRP, the two-echelon LRP and the truck and trailer problem.     

A matheuristic for the truck and trailer routing problem

In the truck and trailer routing problems (TTRPs) a fleet of trucks and trailers serves a set of customers. Some customers with accessibility constraints must be served just by truck, while others can be served either by truck or by a complete vehicle (a truck pulling a trailer). We propose a simple, yet effective, two-phase matheuristic that uses the routes of the local optima of a hybrid GRASP × ILS as columns in a set-partitioning formulation of the TTRP. Using this matheuristic we solved both the classical TTRP with fixed fleet and the new variant with unlimited fleet. This matheuristic outperforms state-of-the-art methods both in terms of solution quality and computing time. While the best variant of the matheuristic found new best-known solutions for several test instances from the literature, the fastest variant of the matheuristic achieved results of comparable quality to those of all previous method from the literature with an average speed-up of at least 2.5.     

Stress analysis of a truck chassis with riveted joints

In this study, stress analysis of a truck chassis with riveted joints was performed by using FEM. The commercial finite element package ANSYS version 5.3 was used for the solution of the problem. Determination of the stresses of a truck chassis before manufacturing is important due to the design improvement. In order to achieve a reduction in the magnitude of stress near the riveted joint of the chassis frame, side member thickness, connection plate thickness and connection plate length were varied. Numerical results showed that stresses on the side member can be reduced by increasing the side member thickness locally. If the thickness change is not possible, increasing the connection plate length may be a good alternative.     

Noise emission assessment of a hybrid electric mid-size truck

In the attempt to improve urban environmental conditions, city or national incentives encourage the use of cleaner vehicles, including hybrid electric vehicles. This paper explores the actual noise impact of this alternative drivetrain technology on the noise emission of a mid-size delivery truck powered by a parallel hybrid powertrain, compared with an equivalent internal combustion engine truck on the basis of pass-by noise measurements. It investigates jointly the overall emission, the main noise sources and the vertical directivity of the vehicle. The essential benefit results from the existence of a full-electric mode below 50 km/h, with a significant noise reduction which may exceed 8 dB(A) at low constant speed. Even if smaller, this noise advantage is still valuable when the vehicle is accelerating or braking. Due to weaker noise emitted upwards, the benefit should be even greater for residents living on upper building floors. The rolling noise associated with the drive wheel/road contact is the main noise source in all driving situations in electric mode, and beyond 50 km/h in the configurations with engine.     

A nonlinear truck model and its treatment as a multibody system

A planar vertical truck model with nonlinear suspension and its multibody system formulation are presented. The equations of motion of the model form a system of differential-algebraic equations (DAEs). All equations are given explicitly, including a complete set of parameter values, consistent initial values, and a sample road excitation. Thus the truck model allows various investigations of the specific DAE effects and represents a test problem for algorithms in control theory, mechanics of multibody systems, and numerical analysis. Several numerical tests show the properties of the model.     

A new MIP model for mine equipment scheduling by minimizing maintenance cost

Mining investment has been recognized as capital intensive due mainly to the cost of large equipment. Equipment capital costs for a given operation are usually within the order of hundreds of million dollars but may reach to billion dollars for large companies operating multiple mines. Such large investments require the optimum usage of equipment in a manner that the operating costs are minimized and the utilization of equipment is maximized through optimal scheduling. This optimum usage is required to ensure that the business remains sustainable and financially stable. Most mining operations utilize trucks to haul the mined material. Maintenance is one of the major operating cost items for these fleets as it can reach approximately one hundred million dollars yearly. There is no method or application in the literature that optimizes the utilization for truck fleet over the life of mine. A new approach based on mixed integer programming (MIP) techniques is used for annually scheduling a fixed fleet of mining trucks in a given operation, over a multi-year time horizon to minimize maintenance cost. The model uses the truck age (total hours of usage), maintenance cost and required operating hours to achieve annual production targets to produce an optimum truck schedule. While this paper focuses on scheduling trucks for mining operation, concept can be used in most businesses using equipment with significant maintenance costs. A case study for a large scale gold mine showed an annual discounted (10% rate) maintenance cost saving of over $2M and more than 16% ($21M) of overall maintenance cost reduction over 10 years of mine life, compared with the spreadsheet based approach used currently at the operation.     

A linear model for surface mining haul truck allocation incorporating shovel idle probabilities

We present models of trucks and shovels in oil sand surface mines. The models are formulated to minimize the number of trucks for a given set of shovels, subject to throughput and ore grade constraints. We quantify and validate the nonlinear relation between a shovel’s idle probability (which determines the shovel’s productivity) and the number of trucks assigned to the shovel via a simple approximation, based on the theory of finite source queues. We use linearization to incorporate this expression into linear integer programs. We assume in our integer programs that each shovel is assigned a single truck size but we outline how one could account for multiple truck sizes per shovel in an approximate fashion. The linearization of shovel idle probabilities allows us to formulate more accurate truck allocation models that are easily solvable for realistic-sized problems.     

Optimizing single-terminal dispatch of large volume trips to trucks

In this paper, we consider the problem of delivering large volumes of products from a single supplier to a set of commercial outlets with the use of a non-homogeneous fleet of trucks. The non-homogeneity implies different costs and, hence, traditional methods which measure miles, hours, and/or number of trucks are not appropriate. The problem may be modeled as an elastic generalized assignment problem. A special purpose branch and bound algorithm is developed and a set of real-world distribution problems and solved.     

Benefits of a truck appointment system on the service quality of inland transport modes at a multimodal container terminal

Container terminals pay more and more attention to the service quality of inland transport modes such as tucks, trains and barges. Truck appointment systems are a common approach to reduce truck turnaround times. This paper provides a tool to use the truck appointment system to increase not only the service quality of trucks, but also of trains, barges and vessels. We propose a mixed integer linear programming model to determine the number of appointments to offer with regard to the overall workload and the available handling capacity. The model is based on a network flow representation of the terminal and aims to minimize overall delays at the terminal. It simultaneously determines the number of truck appointments to offer and allocates straddle carriers to different transport modes. Numerical experiments, conducted on actual data, quantify the benefits of this combined solution approach. Discrete-event simulation validates the results obtained by the optimization model in a stochastic environment.