The objective of this study is to investigate if it is possible to reduce the operational cost of an online Demand Responsive Transportation System (DRT) by using probabilistic trip demands while leaving the optimization algorithm intact. The idea is that we use probabilistic demands in order to predict actual ones. If the prediction is accurate enough then the DRT's vehicle fleet reassigned in a better state. The innovation lies in the assumption that, given enough historical data on trip demands, the system's online nature can be reduced, resulting in a better solution (problem objective). The basic steps of the proposed methodology are: (a) Based on a real historical data set, a demand distribution probability created to describe online DRT's demands behavior. (b) During operation, for each incoming demand, create a set of additional probabilistic demands based on the distribution in (a) and calculate an initial solution. (c) Remove the probabilistic demands and optimize the solution further. (d) Comparatively analyze these solutions against those that would be produced without the use of probabilistic demands. The study revealed that using probabilistic demands improved the solutions in terms of cost (objective). Test data were recorded during an actual 30-day online DRT operation at the same location, the former municipality of Philippi in northern Greece.

In this paper, an online regret based dial-a-ride (OR-DARP) algorithm is introduced and its performance evaluated on an actual demand responsive transit (DRT) system. The innovative part of the algorithm is the design of the optimization engine. A signal communication scheme between the trip dispatcher and the algorithm is used to improve utilization of the available idle time that can then be devoted to the optimization engine. The basic concept is as follows: a. Every trip request is treated as an emergency request demanding an immediate answer, b. The optimization engine runs continuously, thereby consuming every idle time fragment unless interrupted by a new trip request. The trip data are real, and they are sourced from a DRT system operating at a municipality in northern Greece where a static dial-a-ride algorithm was used as the optimization engine. Given the fact that these trips data provide all trip details plus the show-up time (the most important feature for our study), these data are the ideal basis for an “a posteriori” evaluation of the proposed online approach. Another contribution of this paper is the identification of the critical parameters in the trade-off between benefits gained from continuing to optimize an online system versus the losses of non-served demands. This important issue when applying online algorithms has not been studied extensively in the literature so far (to the best of our knowledge).

Vendor Managed Inventory (VMI) systems seem to be at the core of most global supply chains. This is increasingly the case for electronics and automotive parts manufactured in China and assembled in the European Union countries. The main algorithmic component of VMI systems is the Inventory Routing Problem (IRP). In this paper, the authors propose an exact algorithm for the stochastic IRP. While the IRP is a well known problem, solving its stochastic version requires the development of solution policies. Solution policies can be either reactive or proactive depending on the usage of forecasts or not. In the context of the reactive policy, also known as wait and see policy, the transhipment between a supplier and retailers as well as among retailers is proposed in order to perform the required recourse actions. The problem is mathematically formulated as a stochastic program with recourse .It is solved under the context of a reactive policy by a mixed integer linear programming model that solves the IRP exactly based on a branch and cut method and an integer programming model for the execution of the recourse actions of the transshipment actions when unsatisfied demand is revealed. Computational results demonstrate that the transshipment is a powerful recourse action which may significantly improve the overall performance of a vendor managed inventory supply chain system.

Transportation is a crucial cog within the cog-wheel of our economies and modern lifestyles. Unfortunately, both the rising cost of energy production and the increasing demand for transportation pose the challenge of minimizing the energy consumption of automobiles. This paper proposes an offline driver behavior adaptation approach (eco-driving) for trains. An optimal driving behavior policy is computed using Simulated Annealing optimization search over a collection of real driving behavior data (realistic policy). Empirical findings show that if drivers would follow the recommended optimal policy, then an energy saving of up to 50 % is a realistic upper bound potential.

The process of checking inspection points on combat aircraft after a mission, is critical for their operational readiness. Manufacturers include specific inspection procedures in their maintenance handbooks. These procedures consist of detailed instructions for each check, the minimum time required to complete each check as well as a suggested sequence. However, it has been observed, that technical crews can complete inspection in less time than suggested by the manual, without violation of the time prescribed for each inspection point. In this work we will try to apply routing algorithms, to improve the total inspection time, by finding the optimal inspection sequence. This will be achieved without violating any constraint set by the manufacturer, except for the small reduction of the service time on some points. The algorithms we will use is the algorithmic set usually applied for the well-known PDPTW (pickup and delivery problem with time windows). Every inspection area is considered as a network G(N, A) consisting of nodes N, and arcs A. Every node (Inspection Point) is characterized by the Time Window, defined as the time interval earliest-latest [e(i), l(i)], during which the point should be inspected. It is also characterized by the Service Time s(i), defined as the time required to complete the check on this point. Every arc between two nodes describes the time c(od), needed for the technician to move from the first node (Origin) to second node (Destination). These algorithms will be run to produce the optimal path (sequence) or the best attainable suboptimal path that minimizes the objective Sigma(v is an element of V) Sigma(i is an element of P) (all) Sigma(j is an element of P) (all) x(ij)(v)c(ij).The data set consists of real data from inspections logs.

Studies demonstrated that inadequate transport services may create barriers and limit individual and group participation in the normal range of activities. Availability of sustainable transport services can potentially play a very important role in influencing many factors that are enveloped by the concept of social exclusion; in most circumstances inclusion means participation in processes and activities and participation strongly depends on the physical access to facilities.The right to mobility must be guarantee to mobility-impaired people (children, the elderly and the disabled) even in low demand areas and in the presence of a fragmented public transport service. The Public Transport Authorities (PTAs) can and have to influence and encourage the diffusion of IT based flexible transport systems, being able to link and optimize demand and the offer of transport.

In order to achieve an economically viable DRT system, it is important to assess whether the proposed transportation system could be profitable. In this paper we propose a new methodology for defining and evaluating critical operational parameters in order to formulate a profitable transportation system, using the “Converge Algorithm”. This algorithm utilizes data and conclusions drawn from user surveys and their subsequent analysis in order to find a critical point where the system becomes profitable. The algorithm starts from an initial parameter set, drawn from the survey analysis, and works towards a final set where the system is profitable. User demands are projected using a model where demand is a monotonic cumulative increasing function as we move towards a specific direction through this projection. The process uses “Dial-a-ride with Time Windows” algorithms, which are the result of recent research achievements on this scientific field.

This paper is concerned with the static dial–a–ride problem; it introduces an exact approach for the 2-vehicle problem, which then is used for designing an efficient multivehicle large scale neighborhood search heuristic. The exact algorithm is based on Psaraftis’ (2) Dynamic Programming pioneering design but computationally improved for memory management. The very large neighborhood heuristic algorithm iteratively redistributes requests between any two vehicles at a time until no more improvements cane be achieved. The computational results demonstrate the efficiency of the approach and quality of the produced solutions.