A hierarchical production planning structure enables manufacturing systems to handle customer disturbances with different measures on different planning levels. Two different kinds of customer order behavior can be observed and are as well discussed in literature.
Projects in mechanized tunneling frequently do not reach their targeted production performance. Reasons are often related to an undersized or disturbed supply-chain management of the surface jobsite. Due to the sensitive interaction of production and logistic processes, planning and analyzing the supply-chain is a challenging task.
The offshore wind industry in Europe is looking to move further from shore and increase the size of wind parks and wind turbines. As of now marine logistics during the operation and maintenance life cycle phase is, besides wind turbine reliability, the most important limitation for wind turbine service, repair and replacement, and pose a large risk for wind park operators and owners.
In this paper, we present an iterative scheme integrating simulation with an optimization model, for solving complex problems, viz., job shop scheduling. The classical job shop scheduling problem which is NP-Hard, has often been modelled as Mixed-Integer Programming (MIP) model and solved using exact algorithms (for example, branch-and-bound and branch-and-cut) or using meta-heuristics (for example, Genetic Algorithm, Particle Swarm Optimization and Simulated Annealing).
This paper presents preliminary analysis of the Panama Canal Expansion from the viewpoint of salinity in the Gatun Lake and the utilization of neural networks. This analysis utilized simulation modeling and artificial intelligence.
Various actors are involved in hinterland transportation of incoming rail containers along the maritime transport chain. To coordinate each actor’s logistics processes, and therefore to improve utilization of existing transport capacity, the early provision of information, e.g. in form of estimated time of arrival (ETA), is inevitable.
Physical Internet (PI) is a novel concept aiming to render more economically, environmentally and socially efficient and sustainable the way physical objects are transported, handled, stored, realized, supplied and used throughout the world. It enables, among other webs, the Mobility Web which deals with moving physical objects within an interconnected set of unimodal and multimodal hubs, transits, ports, roads and ways.
Recargo has been developing an agent-based model with the AnyLogic tool to help us simulate the charging patterns of electric vehicle drivers in California. Our goal is to better understand the potential value from delivering electricity grid services with these vehicles. Development has only been underway for a few weeks, but in that time we’ve been able to use AnyLogic’s accessible interface and Java coding tools to quickly build and test a proof-of-concept model with which we can explore the potential for a more sophisticated and complex effort.
Catastrophic events such as hurricanes, earthquakes or floods require emergency responders to rapidly distribute emergency relief supplies to protect the health and lives of victims. In this paper we develop a simulation and optimization framework for managing the logistics of distributing relief supplies in a multi-tier supply network. The simulation model captures optimized stocking of relief supplies, distribution operations at federal or state-operated staging facilities, demand uncertainty, and the dynamic progression of disaster response operations. We apply robust optimization techniques to develop optimized stocking policies and dispatch of relief supplies between staging facilities and points of distribution. The simulation framework accommodates a wide range of disaster scenarios and stressors, and helps assess the efficacy of response plans and policies for better disaster response.
A dual toll pricing is a conceptual policy in which policy maker imposes toll on both hazardous materials (hazmat) vehicles as well as regular vehicles for using populated road segments to mitigate a risk of hazmat transportation. It intends to separate the hazmat traffic flow from the regular traffic flow via controlling the dual toll. In order to design the dual toll pricing policy on a highly realistic road network environment and detailed human behaviors, an extended Belief-Desire-Intention (BDI) framework is employed to mimic human decision behaviors in great detail. The proposed approach is implemented in AnyLogic agent based simulation software with using a traffic data of Albany, NY. Also, search algorithms in OptQuest are used to determine the optimum dual toll pricing policy which results in the minimum risk and travel cost based on the simulation results. The result reveals the effectiveness of the proposed approach in devising a reli-able policy under the realistic road network conditions.