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The purpose of this paper is to present the design and evaluation of an integrated system that supports planners and dispatchers to deliver enhanced courier operations. In addition to regular deliveries and pickups, these operations include: first, mass deliveries to be served over a horizon of multiple days; and second, real-time dynamic requests (DRs) to be served within the same service period.

To address the aforementioned challenges, the authors developed an architecture that enhances a typical fleet management system by integrating purpose designed methods. Specifically, the authors plan mass deliveries taking into account typical routes of everyday operations. For planning DRs in real time, the authors propose an efficient insertion heuristic.

The results from testing the proposed optimization algorithms for planning mass deliveries and real-time DRs are encouraging, since the proposed algorithms outperform current practices. Testing in a practical courier environment, indicated that the enhanced planning system may improve significantly operational performance.

The proposed optimization algorithm for the dynamic aspect of this problem comprises a heuristic approach that reaches suboptimal solutions of high quality. The development of fast optimal algorithms for solving these very interesting and practical problems is a promising area for further research.

The proposed integrated system addresses significant problems of hybrid courier operations in an integrated, balanced manner. The tests showed that the allocation of flexible orders within a three-day time horizon improved the cost per flexible order by 7.4 percent, while computerized routing improved the cost of initial (static) routing by 14 percent. Furthermore, the proposed method for managing DRs reduced the excess cost per served request by over 40 percent. Overall, the proposed integrated system improved the total routing costs by 16.5 percent on average compared to current practices.

Both the planning problems and the related solution heuristics address original aspects of practical courier operations. Furthermore, the system integration and the proposed systematic planning contribute to the originality of the work.

Vehicle routing (VR) is critical in successful logistics execution. The emergence of technologies and information systems allowing for seamless mobile and wireless connectivity between delivery vehicles and distribution facilities is paving the way for innovative approaches to real‐time VR and distribution management. This paper investigates avenues for building upon recent trends in VR‐related research towards an integrated approach to real‐time distribution management. A review of the advances to‐date in both fields, i.e. the relevant research in the VR problem and the advances in mobile technologies, forms the basis of this investigation. Further to setting requirements, we propose a system architecture for urban distribution and real‐time event‐driven vehicle management.

The coping of demand oscillation is an important challenge in dynamic transport planning. A reliable request fulfillment must be provided even if the number of incoming requests temporarily climbs over the expected demand and resource scarceness appears. The aim of this paper is to propose an innovative planning approach that enables a transportation fleet to maintain a sufficiently high percentage of timely‐fulfilled customer requests even in demand peak situations.

The effectiveness of the new approach is verified in computational simulation experiments. Quantifications for the system's responsiveness are proposed. Then, the quantified knowledge about the intermediate responsiveness is exploited to adjust the decision model representing the next schedule update task in a rolling horizon re‐planning.

The observed simulation results suggest the suitability of the proposed approach. An adjustment of the plan update model supports the maintenance of a high percentage of timely completed requests during and after the demand peak.

The generic approach presented and evaluated here motivates an adaptation to other more practical problem settings, in order to show its general applicability.

The proposed methodology contributes to the current demand for computational support for increasing the responsiveness of logistic systems.

The original contribution of this paper is the autonomous feedback‐controlled adjustment of decision preferences which enables a rolling horizon re‐planning framework to maintain a stable output performance even if the input oscillates significantly over time.

The optimisation of forest fuels supply chain involves several entities actors, and particularities. To successfully manage these supply chains, efficient tools must be devised with the ability to deal with stakeholders dynamic interactions and to optimize the supply chain performance as a whole while being stable and robust, even in the presence of uncertainties. This work proposes a framework to coordinate different planning levels and event-based models to manage the forest-based supply chain. In particular, with the new methodology, the resilience and flexibility of the biomass supply chain is increased through a closed-loop system based on the system forecasts provided by a discrete-event model. The developed event-based predictive model will be described in detail, explaining its link with the remaining elements. The implemented models and their links within the proposed framework are presented in a case study in Finland and results are shown to illustrate the advantage of the proposed architecture.

The purpose of this paper is to formulate and solve a new emergency evacuation planning problem. This problem addresses the needs of both able and disabled persons who are evacuated from multiple pick-up locations and transported using a heterogeneous fleet of vehicles.

The problem is formulated using a mixed integer linear programming model and solved using a heuristic algorithm. The authors analyze the selected heuristic with respect to key parameters and use it to address theoretical and practical case studies.

Evacuating people with disabilities has a significant impact on total evacuation time, due to increased loading/unloading times. Additionally, increasing the number of large capacity vehicles adapted to transport individuals with disabilities benefits total evacuation time.

The mathematical model is of high complexity and it is not possible to obtain exact solutions in reasonable computational times. The efficiency of the heuristic has not been analyzed with respect to optimality.

Solving the problem by a heuristic provides a fast solution, a requirement in emergency evacuation cases, especially when the state of the theater of the emergency changes dynamically. The parametric analysis of the heuristic provides valuable insights in improving an emergency evacuation system.

Efficient population evacuation studied in this work may save lives. This is especially critical for disabled evacuees, the evacuation of whom requires longer operational times.

The authors consider a population that comprises able and disabled individuals, the latter with varying degrees of disability. The authors also consider a heterogeneous fleet of vehicles, which perform multiple trips during the evacuation process.

The purpose of this paper is to develop efficient heuristics for determining the route design and inventory management of inbound parts which are delivered for manufacturing, assembly, or distribution operations and for which there is limited storage space. The shipment frequencies and quantities are coordinated with the available storage space and the vehicle capacities.

Two heuristics that generate near optimal solutions are proposed. The first heuristic has an iterative routing phase that maximizes the savings realized by grouping suppliers together into routes without considering the storage constraint and then calculates the pickup frequencies in the second phase to accommodate the storage constraint. The second heuristic iteratively executes a routing and a pickup frequency phase that both account for the storage constraint. A lower bound is also developed as a benchmark for the heuristic solutions.

Near optimal solutions can be obtained in a reasonable amount of time by utilizing information about the amount of storage space in the route design process.

The traditional emphasis on high vehicle utilization in transportation management can lead to inefficient logistics operations by carrying excess inventory or by using longer, less efficient routes. Route formation and pickup quantities at the suppliers are simultaneously considered, as both are important from a logistics standpoint and are interrelated decisions.

The two proposed heuristics dynamically define seed sets such that the solutions to the capacitated concentrator location problem (CCLP) are accurately estimated. This increased accuracy helps in generating near‐optimal solutions in a practical amount of computing time.

There is usually a considerable shortage of resources and a lack of accurate data about the demand amount in a post-disaster situation. This paper aims to model the distribution and redistribution of relief items. When the new data on demand and resources become available the redistribution of previously delivered items may be necessary due to severe shortages in some locations and surplus inventory in other areas.

The presented model includes a vehicle routing problem in the first period and some network flow structures for succeeding periods of each run. Thereby, it can produce itineraries and loading plans for each vehicle in all periods when it is run in a rolling horizon manner. The fairness in distribution is sought by minimizing the maximum shortage of commodities among the affected areas while considering operational costs. Besides, equity of welfare in different periods is taken into account.

The proposed model is evaluated by a realistic case study. The results show that redistribution and multi-period planning can improve efficiency and fairness in supply after the occurrence of a disaster.

This paper proposes an operational model for distribution and redistribution of relief items considering the differences of items characteristics. The model integrates two well-known structures, vehicle routing problem with pickup and delivery and network flow problem to take their advantages. To get more practical results, the model relaxes some simplifying assumptions commonly used in disaster relief studies. Furthermore, the model is used in a realistic case study.

The paper examines the circumstances of success in telematic use and strategic effects resulting from the implementation and use of such technologies from SMEs in the Greek Market.

The analysis is based on a three‐phased triangulated research methodology; that is literature review, interviews from 15 logistics directors and a questionnaire survey of 73 logistics SMEs in Greece.

The basic finding that has been derived from both phases 2 and 3 (survey and interviews) is that although the penetration of telematics is still low in Greece, logistics operators understand the importance of mobile services and they already know which they plan to their customers.

An inherent limitation of this survey is the fact that it address, like all surveys, the requirements of its respondents only, without taking into account.

This paper explores customer perceptions and requirements for the implementation of mobile real‐time support services for city logistics. The requirements, elicited by the results of the survey, are used to propose a systemic representation of a real‐time vehicle management mechanism for urban transportation.

The purpose of this paper is to propose an efficient algorithm for trajectory planning of unmanned aerial vehicles (UAVs) in 2D spaces. This paper has been motivated by the challenge to develop a fast trajectory planning algorithm for autonomous UAVs through mid‐course waypoints (WPs). It is assumed that there is no prior knowledge of these WPs, and their configuration is computed as in‐flight procedure.

Since the off‐line techniques cannot be applied, it is required to apply an online trajectory planning algorithm. For this reason, based on the optimal control and the geometry, each segment of trajectory is designed with respect to a local frame. The algorithm is implemented as a real‐time manner in terms of the down‐range variable.

The proposed algorithm tries to find not only a feasible trajectory (the constraint includes the maximum heading angle rate) but also an optimal trajectory (the objective locally is to minimize the length of the path). This online trajectory planning algorithm gradually produces a smooth 2D trajectory aiming at reaching the mid‐course WPs and the final target so that they are smoothly connected with each other. The mid‐course WPs are described through the given down‐range, cross‐range, and heading angle.

Based on geometrical principles, this algorithm is capable of re‐planning the trajectory as in‐flight manner, and the computational burden approaches the online capabilities for UAVs with high velocity.

The purpose of this paper is to provide a more capable and holistic adjustable autonomy system, involving situation reasoning among all involved information sources, to make an adjustable autonomy system which knows what the situation is currently, what needs to be done in the present situation, and how risky the task is in the present situation. This will enhance efficiency for calculating the level of autonomy.

Situation reasoning methodologies are present in many autonomous systems which are called situation awareness. Situation awareness in autonomous systems is divided into three levels, situation perception, situation comprehension and situation projection. Situation awareness in these systems aims to make the tactical plans cognitive, but situation reasoning in adjustable autonomous systems aim to communicate mission assessments to unmanned vehicle or humans. Thus, in solving this problem, it is important to design a new situation reasoning module for the adjustable autonomous system.

The contribution of this paper is presenting the Situation Reasoning Module (SRM) for an adjustable autonomous system, which encapsulates event detection, cognitive situations, cognitive tasks, performance capacity assessment and integrated situation reason. The paper concludes by demonstrating the benefits of the SRM in a real‐world scenario, a situation reasoning simulation in unmanned surface vehicles (USV) while performing a navigation mission.

The method presented in this paper represents a new SRM to reason the situation for adjustable autonomous system. While the results presented in the paper are based on fuzzy logic and Bayesian network methodology. The results of this paper can be applicable to land, sea and air robotics in an adjustable autonomous system.