Unmanned aerial vehicles (UAVs), or aerial drones, are an emerging technology with significant market potential. UAVs may lead to substantial cost savings in, for instance, monitoring of difficult-to-access infrastructure, spraying fields and performing surveillance in precision agriculture, as well as in deliveries of packages. In some applications, like disaster management, transport of medical supplies, or environmental monitoring, aerial drones may even help save lives. In this article, we provide a literature survey on optimization approaches to civil applications of UAVs. Our goal is to provide a fast point of entry into the topic for interested researchers and operations planning specialists. We describe the most promising aerial drone applications and outline characteristics of aerial drones relevant to operations planning. In this review of more than 200 articles, we provide insights into widespread and emerging modeling approaches. We conclude by suggesting promising directions for future research.

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

Home health care routing and scheduling

Decision support systems for agriculture 4.0: Survey and challenges

Ein Decision Support System umfast die Komponenten Daten, Dialog und Modell, weshalb in diesem Kontext auch von DDM-Paradigma gesprochen wird.1 Die Komponente Daten sollte derart gestaltet sein, das die benotigten Informationen adaquat bereitgehalten werden. Durch die Komponente Dialog ist sicherzustellen, das die Interaktion des Anwenders mit dem Decision Support System und damit auch der Zugriff auf die Daten einfach und leicht verstandlich gestaltet ist. Die Komponente Modell sollte der Aufgabenstellung angemessene Modellierungs- und Analysemoglichkeiten zur Verfugung stellen. Ein Decision Support System, welches dem DDM-Paradigma Rechnung tragt, fugt die beschriebenen Komponenten geeignet zusammen.

Decision Support System

Nature has been considered as an inspiration of several recent meta-heuristic algorithms. This paper firstly studies and mimics the behavior of Scottish red deer in order to develop a new nature-inspired algorithm. The main inspiration of this meta-heuristic algorithm is to originate from an unusual mating behavior of Scottish red deer in a breading season. Similar to other population-based meta-heuristics, the red deer algorithm (RDA) starts with an initial population called red deers (RDs). They are divided into two types: hinds and male RDs. Besides, a harem is a group of female RDs. The general steps of this evolutionary algorithm are considered by the competition of male RDs to get the harem with more hinds via roaring and fighting behaviors. By solving 12 benchmark functions and important engineering as well as multi-objective optimization problems, the superiority of the proposed RDA shows in comparison with other well-known and recent meta-heuristics.

https://link.springer.com/content/pdf/10.1007/s00500-020-04812-z.pdf

Red deer algorithm (RDA): a new nature-inspired meta-heuristic

A matheuristic for routing real-world home service transport systems facilitating walking

This paper analyzes cascade effects of natural disasters and investigates their impact on relief operations with respect to critical infrastructure, in particular, the transport infrastructure, electricity and human health. Causal-Loop-Diagrams (CLDs) are generated in order to provide useful information for decision makers. CLDs clearly visualize cascade effects which enable one to identify non-linear critical feedback processes and to analyze the behavior of the considered system. In order to investigate if the identified behavior occurred in real-world cases, we apply the CLDs to concrete natural disaster events, the European flood of 2002 and the European heat wave of 2003. Independent of the type of disaster, it can be concluded that cascade effects negatively affect critical infrastructure. The impacts on disaster relief operations can be proven for the case of the flood of 2002. In case of the heat wave of 2003 some of the assumed interdependencies did not appear in that analysis. The analysis shows that cascade effects and interdependencies between the different sectors of critical infrastructure can be visualized by the presented CLDs and are of high importance in order to understand their impact on disaster relief operations.

Understanding the impact of cascade effects of natural disasters on disaster relief operations

This paper introduces a DSS to support coordination in disaster relief distribution.An MIP and an agent-based simulation with optimization procedures are presented.Road closures and off-road as well as unmanned aerial vehicles are considered.Computational experiments are based on flood scenarios of two Austrian regions.Results highlight potentials of coordinated disaster relief distribution. This paper presents a simulation and optimization based decision-support system (DSS) to facilitate disaster relief coordination between private and relief organizations. The DSS simulates disasters and plans shipments of relief goods via transfer points to demand points in the affected area. This enables decision-makers to analyze the last-mile distribution of goods by scheduling and routing trucks, off-road as well as unmanned aerial vehicles. Roads which are closed or opened during a disaster are considered allowing a dynamic adaptation to real world conditions and a comprehensive analysis over a rolling time-horizon. A mixed-integer problem formulation, an agent-based simulation, a heuristic-based scheduling and routing procedure as well as a Tabu Search metaheuristic are applied to analyze the given decision problem. Coordination between private and relief organization shows to be especially beneficial if time-losses resulting from closed roads are high and substantial unexpected demand occurs. Furthermore, results highlight the importance of selecting suitable transfer points and the potential of simulation and optimization based DSSs to improve disaster relief distribution.

A decision support system for coordinated disaster relief distribution

Waste collection is one of the most critical logistics activities in modern cities with considerable impact on the quality of life, urban environment, city attractiveness, traffic flows and municipal budgets. Despite the problem’s relevance, most existing work addresses simplified versions where container loads are considered to be known in advance and served by a single vehicle depot. Waste levels, however, cannot be estimated with complete certainty as they are only revealed at collection. Furthermore, in large cities and clustered urban areas, multiple depots from which collection routes originate are common, although cooperation among vehicles from different depots is rarely considered. This paper analyses a rich version of the waste collection problem with multiple depots and stochastic demands by proposing a hybrid algorithm combining metaheuristics with simulation. Our ‘simheuristic’ approach allows for studying the effects of cooperation among different depots, thus quantifying the potential savings this cooperation could provide to city governments and waste collection companies.

Christian Fikar

Papers

Home health care routing and scheduling

A matheuristic for routing real-world home service transport systems facilitating walking

Understanding the impact of cascade effects of natural disasters on disaster relief operations

A decision support system for coordinated disaster relief distribution

Supporting multi-depot and stochastic waste collection management in clustered urban areas via simulation–optimization