Network Topology and Time Criticality Effects in the Modularised Fleet Mix Problem

In this paper, we explore the interplay between network topology and time criticality in a military logistics system. A general goal of this work (and previous work) is to evaluate land transportation requirements or, more specifically, how to design appropriate fleets of military general service vehicles that are tasked with the supply and re-supply of military units dispersed in an area of operation. The particular focus of this paper is to gain a better understanding of how the logistics environment changes when current Army vehicles with fixed transport characteristics are replaced by a new generation of modularised vehicles that can be configured task-specifically. The experimental work is conducted within a well developed strategic planning simulation environment which includes a scenario generation engine for automatically sampling supply and re-supply missions and a multi-objective meta-heuristic search algorithm (i.e. Evolutionary Algorithm) for solving the particular scheduling and routing problems. The results presented in this paper allow for a better understanding of how (and under what conditions) a modularised vehicle fleet can provide advantages over the currently implemented system

CRIKEY! ― It's co-ordination in temporal planning

Temporal planning contains aspects of both planning and scheduling. Many temporal planners assume a loose coupling between these two sub-problems in the form of "blackbox" durative actions, where the state of the world is not known during the action's execution. This reduces the size of the search space and so simplifies the temporal planning problem, restricting what can be modelled. In particular, the simplification makes it impossible to model co-ordination, where actions must be executed concurrently to achieve a desired effect. Coordination results from logical and temporal constraints that must both be met, and for this reason, the planner and scheduler must communicate in order to find a valid temporal plan. This communication effectively increases the size of the search space, so must be done intelligently and as little as possible to limit this increase. This thesis contributes a comprehensive analysis of where temporal constraints appear in temporal planning problems. It introduces the notions of minimum and maximum temporal constraints, and with these isolates where the planning and scheduling are coupled together tightly, in the form of co-ordination, it characterises this with the new concepts of envelopes and contents. A new temporal planner written, called СRIKЕҮ, uses this theory to solve temporal problems involving co-ordination that other planners are unable to solve. However, it does this intelligently, using this theory to minimise the communication between the sub-solvers, and so does not expand the search space unnecessarily. The novel search space that CRIKEY uses docs not specify the timings of future events and this allows for the handling of duration inequalities, which again, few other temporal planners are able to solve. Results presented show СRIKЕҮ to be a competitive planner, whilst not making the same simplifying assumptions that other temporal planners make as to the nature of temporal planning problems

A satellite navigation system to improve the management of intermodal drayage

The intermodal transport chain can become more efficient by means of a good organization of the drayage movements. Drayage in intermodal container terminals involves the pick up or delivery of containers at customer locations, and the main objective is normally the assignment of transportation tasks to the different vehicles, often with the presence of time windows. The literature shows some works on centralised drayage management, but most of them consider the problem only from a static and deterministic perspective, whereas the work we present here incorporates the knowledge of the real-time position of the vehicles, which permanently enables the planner to reassign tasks in case the problem conditions change. This exact knowledge of position of the vehicles is possible thanks to a geographic positioning system by satellite (GPS, Galileo, Glonass), and the results show that this additional data can be used to dynamically improve the solution

A Quantitative Model for Truck Parking Utilization with Hours of Service Regulations

Continual growth in traffic volume on U.S. Highways and insufficient parking for commercial trucking vehicles has led to significant safety concerns for truck drivers. Hours of Service (HOS) regulations dictate driving and rest periods of truck drivers. When a truck driver must stop as designated by the HOS regulations and the nearest parking location is at capacity, the trucker must either continue driving past the HOS limit or park in an undesignated and possibly illegal or unsafe spot such as an off-ramp. The combination of these two variables play an important role in the safety of truck drivers on a daily basis. Previous research on truck parking shortages has followed a survey-based approach while research on HOS regulations in conjunction with truck routing and driver scheduling has not included the full suite of HOS regulations as well as restrictions on parking availability. Current research techniques do not take into account parking capacity on a driver’s route while following HOS regulations. Because there are limitations governing where along a route a driver can rest, including some customer locations and parking locations at capacity, these models do not prove to be an accurate measure of trip planning for truck drivers. This research aims to develop a mathematical model to link truck parking with hours of service regulations in order to determine feasible routes for truck drivers and optimal truck parking locations on the highway network

A metaheuristic for crew scheduling in a pickup-and-delivery problem with time windows

A vehicle routing and crew scheduling problem (VRCSP) consists of simultaneously planning the routes of a fleet of vehicles and scheduling the crews, where the vehicle-crew correspondence is not fixed through time. This allows a greater planning flexibility and a more efficient use of the fleet, but in counterpart, a high synchronisation is demanded. In this work, we present a VRCSP where pickup-and-delivery requests with time windows have to be fulfilled over a given planning horizon by using trucks and drivers. Crews can be composed of 1 or 2 drivers and any of them can be relieved in a given set of locations. Moreover, they are allowed to travel among locations with non-company shuttles, at an additional cost that is minimised. As our problem considers distinct routes for trucks and drivers, we have an additional flexibility not contemplated in other previous VRCSP given in the literature where a crew is handled as an indivisible unit. We tackle this problem with a two-stage sequential approach: a set of truck routes is computed in the first stage and a set of driver routes consistent with the truck routes is obtained in the second one. We design and evaluate the performance of a metaheuristic based algorithm for the latter stage. Our algorithm is mainly a GRASP with a perturbation procedure that allows reusing solutions already found in case the search for new solutions becomes difficult. This procedure together with other to repair infeasible solutions allow us to find high-quality solutions on instances of 100 requests spread across 15 cities with a fleet of 12-32 trucks (depending on the planning horizon) in less than an hour. We also conclude that the possibility of carrying an additional driver leads to a decrease of the cost of external shuttles by about 60% on average with respect to individual crews and, in some cases, to remove this cost completely

Vehicle Routing with Traffic Congestion and Drivers' Driving and Working Rules

For the intensively studied vehicle routing problem (VRP), two real-life restrictions have received only minor attention in the VRP-literature: traffic congestion and driving hours regulations. Traffic congestion causes late arrivals at customers and long travel times resulting in large transport costs. To account for traffic congestion, time-dependent travel times should be considered when constructing vehicle routes. Next, driving hours regulations, which restrict the available driving and working times for truck drivers, must be respected. Since violations are severely fined, also driving hours regulations should be considered when constructing vehicle routes, even more in combination with congestion problems. The objective of this paper is to develop a solution method for the VRP with time windows (VRPTW), time-dependent travel times, and driving hours regulations. The major difficulty of this VRPTW extension is to optimize each vehicle’s departure times to minimize the duty time of each driver. Having compact duty times leads to cost savings. However, obtaining compact duty times is much harder when time-dependent travel times and driving hours regulations are considered. We propose a restricted dynamic programming (DP) heuristic for constructing the vehicles routes, and an efficient heuristic for optimizing the vehicle’s departure times for each (partial) vehicle route, such that the complete solution algorithm runs in polynomial time. Computational experiments emonstrate the trade-off between travel distance minimization and duty time minimization, and illustrate the cost savings of extending the depot opening hours such that traveling before the morning peak and after the evening peak becomes possible

Workforce Scheduling with Order-Picking Assignments in Distribution Facilities

Scheduling the availability of order pickers is crucial for effective operations in a distribution facility with manual order pickers. When order-picking activities can only be performed in specific time windows, it is essential to jointly solve the order picker shift scheduling problem and the order picker planning problem of assigning and sequencing individual orders to order pickers. This requires decisions regarding the number of order pickers to schedule, shift start and end times, break times, as well as the assignment and timing of order-picking activities. We call this the order picker scheduling problem and present two formulations. A branch-and-price algorithm and a metaheuristic are developed to solve the problem. Numerical experiments illustrate that the metaheuristic finds near-optimal solutions at 80% shorter computation times. A case study at the largest supermarket chain in The Netherlands shows the applicability of the solution approach in a real-life business application. In particular, different shift structures are analyzed, and it is concluded that the retailer can increase the minimum compensated duration for employed workers from six hours to seven or eight hours while reducing the average labor cost with up to 5% savings when a 15-minute flexibility is implemented in the scheduling of break times