Vehicle routing in an automated warehouse: Analysis and optimization

This study concerns the design of an operating system for vehicles in an automated warehouse. The layout of the warehouse and the number and properties of the vehicles are given. The objective is to maximize the throughput. Using a partial enumeration technique, we simulate several alternatives for the control and interplay of the vehicles within a reasonable time horizon. A subproblem is solved by network flow techniques. The algorithm is implemented as part of an automatic control system, and it has led to a satisfactory performance.     

Coverage Maximization with Autonomous Agents in Fast Flow Environments

This work examines the cooperative motion of a group of autonomous vehicles in a fast flow environment. The magnitude of the flow velocity is assumed to be greater than the available actuation to each agent. Collectively, the agents wish to maximize total coverage area defined as the set of points reachable by any agent within T time. The reachable set of an agent in a fast flow is characterized using optimal control techniques. Specifically, this work addresses the complementary cases where the static flow field is smooth, and where the flow field is piecewise constant. The latter case arises as a proposed approximation of a smooth flow that remains analytically tractable. Furthermore, the techniques used in the piecewise constant flow case enable treatment for obstacles in the environment. In both cases, a gradient ascent method is derived to maximize the total coverage area in a distributed fashion. Simulations show that such a network is able to maximize the coverage area in a fast flow.     

Integrating simulation modelling and GIS: spatial decision support systems for evacuation planning

A prototype spatial decision support system (SDSS) has been designed for contingency planning for emergency evacuations which combines simulation techniques with spatial data handling and display capabilities of a geographical information system (GIS). It links together the topographical support and analysis provided by the GIS–ARC/INFO, with a simulation model designed to simulate the dynamics of an evacuation process in detail. Our aim has been to design a SDSS so that it provides an interactive evacuation simulator with dynamic graphics that allows for experimentation with policies by providing rapid feedback from the simulation. The idea is that emergency planners will be able to use the SDSS to experiment with emergency evacuation plans in order to plan for different contingencies. This paper concentrates on the issues involved in designing an effective integration link interface between the GIS and the simulation model when building a SDSS of this type.     

Extended Fuzzy C-Means and Genetic Algorithms to Optimize Power Flow Management in Hybrid Electric Vehicles

The need for personal transportation must be harmonized by considering the impact of so huge number of vehicles on the environment. The adoption of hybrid electric vehicles can provide a sensible improvement from an environmental viewpoint, but at the same time makes more difficult the definition and implementation of the overall powertrain control mechanism. In fact, powertrain control problems are known to be very complex due to conflicting requirements, and this difficulty augments in case of hybrid electric vehicles. Most of the features of the future hybrid electric vehicles are enabled by a new energy flow management unit designed to split the instantaneous power demand between the internal combustion engine and the electric motor, ensuring both an efficient power supply and reduced emissions. Classic approaches that rely on static thresholds, optimized on a fixed drive cycle, cannot face the high dynamicity and unpredictability of real-life drive conditions. The need to actually control a real vehicle stimulates the research of innovative methodologies for the real-time identification of the operating points of each energy source. This paper is framed into this context: after a brief discussion about a non-conventional formalization of the energy flows problem based on a multiobjective function, a knowledge-based control system for splitting the vehicle's power demand between the engine and motor is presented. The proposed approach exploits a fuzzy clustering criterion that combined with a genetic algorithm, permits to achieve better results, both in terms of a reduced computational effort and an improved efficiency of the control system over various driving cycles. To validate the proposed approach, simulation tests and comparisons with other energy management strategies are discussed.     

Locating replenishment stations for electric vehicles: application to Danish traffic data

Environment-friendly electric vehicles have gained popularity and increased attention in recent years. The deployment of a network of recharging stations is essential given their limited travel range. This paper considers the problem of locating electronic replenishment stations for electric vehicles on a traffic network with flow-based demand. The objective is to optimize the network performance, for example to maximize the flow covered by a prefixed number of stations or to minimize the number of stations needed to cover traffic flows. Two integer linear programming formulations are proposed to model the problem. These models are tested on real-life traffic data collected in Denmark.     

Signal design for an isolated intersection during congestion

This paper presents an algorithm for the signal design of an isolated intersection that will be able to alleviate long queues during severe congestion conditions, which cause both lengthy delays and harsh environmental damage. During such periods, the total effective green light is a scarce resource; its best allocation is crucial for the smooth operation of the intersection and sometimes even for a large network. The aim of the procedure presented here is to maximize the average throughput of the intersection. By achieving this goal, the number of vehicles in the queue is reduced at the fastest possible rate, and the period of congestion is shortened. The maximum throughput is achieved when the marginal flow in each phase is equal to the average throughput. The algorithm developed takes into account the decreasing flow rates of existing queues during long periods of green.     

Admission and pricing optimization of on-street parking with delivery bays

We analyze a parking lot, modeled as a loss queue, with passenger and delivery vehicles. The arrival process of delivery vehicles is exogenous, while that of passenger vehicles is a function of the parking price rate and accessibility. The aim of the parking operator is to maximize the revenue generated from passenger vehicles while providing a sufficient service level for delivery vehicles, in terms of their probability to find an available parking spot. Two levels of control are exercised: pricing and admission. From a Markov decision process approach, we prove that the optimal policy is a state-dependent reservation threshold policy that randomizes in at most one state. When some parking spots should be reserved for delivery vehicles, the price rate is selected to saturate the service level constraint, whereas when it is optimal not to restrict the parking lot accessibility, the price can also be selected as the unique local maximum of the revenue or to incentivize all potential passenger vehicles to arrive. Pricing should be used as a primary tool to control the flow of passenger vehicles. In complement, admission control is exercised with a limited use of reservation only when the service level guarantee for delivery vehicles is high.     

Supporting disciplinary and interdisciplinary knowledge development and design thinking in an informal,pre‐engineering program: A Workplace Simulation Project

In this paper, we examined students’ engagement in an implementation of a Workplace Simulation Project (WSP). The WSP was designed to actively engage students in learning disciplinary content by inviting engineers from industry to have a physical presence within the school building to collaborate with teachers and students to complete projects which simulate the tasks authentic to their work. We focus on the first year implementation of the program that partnered a high school in the rural Midwest with an engineering unit of a government organization. Using a multiple methods study design, we analyzed disciplinary and interdisciplinary pre and posts test along with students’ interviews to determine learning gains as well as students’ interpretations of creative and critical thinking as experienced in the project and their knowledge of the engineering design process. Effect sizes showed that students in the WSP group had notable gains over the control group participants. Additionally, students’ knowledge of core elements of the design process were identified in inductive analyses of the interviews. Findings from this study will provide usable knowledge about effective ways to support systems and design thinking and ways to support expert‐novice collaboration to ensure success.     

Optimizing base station location and configuration in UMTS networks

Radio planning and coverage optimization are critical issues when deploying and expanding third generation cellular systems. We investigate mixed integer programming models for locating and configuring base stations in UMTS networks so as to maximize coverage and minimize installation costs. The overall model considers both uplink and downlink directions, that we studied separately in Amaldi et al. (2002, 2003b). The two-stage Tabu Search algorithm we propose exploits solutions of a simplified model for the uplink direction to drastically reduce the computational time required to find good approximate solutions of the overall uplink and downlink model.Computational results obtained for realistic instances %with voice as well as data traffic are reported and discussed. Research carried out within the national project “Optimization, simulation and complexity in the design and management of telecommunication networks”funded by the Italian Ministry of Education, University and Scientific Research (MIUR).     

Mathematical modeling of catalytic wet oxidation in trickle-bed reactors by a diffusion–convection–reaction approach embedded with an interstitial CFD framework

In this work, we propose a diffusion–convection–reaction methodology to gain further insights into the heterogeneous multiphase flow of trickle beds. Our case-study encompasses a multi-fluid model embedded within an interstitial framework on the numerical simulation of continuous catalytic wet oxidation of hazardous compounds. First, with the proviso that phase holdup, pressure drop, and liquid distribution are fundamental criteria for the efficient design of trickle beds, the multiphase flow constitutive equations have been developed and solved by the conservative unstructured finite volume method. Second, several numerical variables were parametrically optimized based on the application of different under-relaxation parameters, mesh densities, and time stepping strategies. The segregated solver has been found to reveal good properties in terms of convergence and stability criteria, which endorsed the further corroboration. Finally, this theoretical probing-sensing scheme enabled the characterization of liquid flow texture accomplished by three-dimensional flow patterns exposing their deviation from ideal plug flow. The diffusion–convection–reaction framework coupled within a CFD model can then be further exploited on the simulation of complex multiphase reactive flows with adjustable parameters.     

On the modelling of heat exchangers and heat exchanger network dynamics using bond graphs

ABSTRACT

Heat exchanger networks are important systems in most thermal engineering systems and are found in applications ranging from power plants and the process industry to domestic heating. Achieving cost-effective design of heat exchanger networks relies heavily on mathematical modelling and simulation-based design. Today, stationary design calculations are carried out for all new designs, but for some special applications, the transient response of complete networks has been researched. However, simulating large heat exchanger networks poses challenges due to computational speed and stiff initial value problems when flow equations are cast in differential algebraic form. In this article, a systems approach to heat exchanger and heat exchanger network modelling is suggested. The modelling approach aims at reducing the cost of system model development by producing modular and interchangeable models. The approach also aims at improving the capability for large and complex network simulation by suggesting an explicit formulation of the network flow problem.     

Multi-criteria robust design of a JIT-based cross-docking distribution center for an auto parts supply chain

We present a solution framework based on discrete-event simulation and enhanced robust design technique to address a multi-response optimization problem inherent in logistics management. The objective is to design a robust configuration for a cross-docking distribution center so that the system is insensitive to the disturbances of supply uncertainty, and provides steady parts supply to downstream assembly plants. In the proposed approach, we first construct a simulation model using factorial design and central composite design (CCD), and then identify the models that best describe the relationship between the simulation responses and system factors. We employ the response surface methodology (RSM) to identify factor levels that would maximize system potential.     

Performance and limitations of the unsteady RANS approach

Turbulent flows in complex geometries often exhibit an oscillating behavior of large coherent structures, even in the case of steady state boundary conditions. Recently, numerous efforts have been made to resolve these oscillations by means of numerical simulations. Unfortunately, large-eddy simulations are often very time- and memory-consuming in the case of complex flows. Therefore, the unsteady RANS (URANS) approach is an attractive alternative, especially when numerical simulations are used as a design and optimization tool. Here, two complex flow situations are presented, the tundish flow and a jet in a crossflow. For these flows, relationships between the Strouhal number and important flow parameters are known from experiments. In the paper, URANS models are applied to resolve those relationships also numerically. The evaluation of the numerical results demonstrates the abilities and the limitations of the URANS approach when resolving the dynamics of large coherent structures in complex flows. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Routing a mix of conventional,plug-in hybrid,and electric vehicles

We introduce an electric vehicle routing problem combining conventional, plug-in hybrid, and electric vehicles. Electric vehicles are constrained in their service range by their battery capacity, and may require time-consuming recharging operations at some specific locations. Plug-in hybrid vehicles have two engines, an internal combustion engine and an electric engine using a built-in rechargeable battery. These vehicles can avoid visits to recharging stations by switching to fossil fuel. However, this flexibility comes at the price of a generally higher consumption rate and utility cost.To solve this complex problem variant, we design a sophisticated metaheuristic which combines a genetic algorithm with local and large neighborhood search. All route evaluations, within the approach, are based on a layered optimization algorithm which combines labeling techniques and greedy evaluation policies to insert recharging stations visits in a fixed trip and select the fuel types. The metaheuristic is finally hybridized with an integer programming solver, over a set partitioning formulation, so as to recombine high-quality routes from the search history into better solutions. Extensive experimental analyses are conducted, highlighting the good performance of the algorithm and the contribution of each of its main components. Finally, we investigate the impact of fuel and energy cost on fleet composition decisions. Our experiments show that a careful use of a mixed fleet can significantly reduce operational costs in a large variety of price scenarios, in comparison with the use of a fleet composed of a single vehicle class.     

Analytical studies on the instabilities of heterogeneous intelligent traffic flow

It has been widely reported in literature that a small perturbation in traffic flow such as a sudden deceleration of a vehicle could lead to the formation of traffic jams without a clear bottleneck. These traffic jams are usually related to instabilities in traffic flow. The applications of intelligent traffic systems are a potential solution to reduce the amplitude or to eliminate the formation of such traffic instabilities. A lot of research has been conducted to theoretically study the effect of intelligent vehicles, for example adaptive cruise control vehicles, using either computer simulation or analytical method. However, most current analytical research has only applied to single class traffic flow. To this end, the main topic of this paper is to perform a linear stability analysis to find the stability threshold of heterogeneous traffic flow using microscopic models, particularly the effect of intelligent vehicles on heterogeneous (or multi-class) traffic flow instabilities. The analytical results will show how intelligent vehicle percentages affect the stability of multi-class traffic flow.     

Computational fluid dynamics modelling of an entrained flow biomass gasifier

A mathematical model, based on the Computational Fluid Dynamics package CFX4, has been developed to study the flow within an entrained flow biomass gasifier. The gasifier is designed to convert sawdust and chopped cotton gin trash into a low calorific value gas which can be burned in a modified engine to run a generator. Calculations of the flowfield are performed using the standard k–ϵ model and a Differential Reynolds Stress Model (DSM). In line with current thinking, it is shown that the k–ϵ model gives unphysical results for complex swirling flows, whereas the DSM model performs well. Particle tracking was performed to determine typical trajectories for the biomass and char and the results used to determine means of avoiding slagging in the gasifier base. The simulations have proved to be very useful to the designers who are now using the model to optimise the design.     

A Final-Exam-Scheduling Package

This paper presents the implementation of a multi-phase final-exam-scheduling package at a large university. The problem as it had evolved over time is discussed and a solution is presented. The implementation was undertaken over a three-year period and involved a multi-phase approach. The multi-phase final-exam-scheduling process consists of first grouping the final exams into sets called `blocks' in such a way as to minimize the number of students with simultaneous exams. Second, the blocks are assigned to exam days while minimizing the number of students with two or more exams per day. Third, the exam days and exam blocks within days are arranged so as to minimize the number of students with consecutive exams. Fourth, exams are assigned to classrooms so as to maximize the space utilization. New formulations and solution methods for the stated four phases are presented. The approach is well suited for a practical application and has been implemented at a large university.     

Instability of cooperative adaptive cruise control traffic flow: A macroscopic approach

This paper proposes a macroscopic model to describe the operations of cooperative adaptive cruise control (CACC) traffic flow, which is an extension of adaptive cruise control (ACC) traffic flow. In CACC traffic flow a vehicle can exchange information with many preceding vehicles through wireless communication. Due to such communication the CACC vehicle can follow its leader at a closer distance than the ACC vehicle. The stability diagrams are constructed from the developed model based on the linear and nonlinear stability method for a certain model parameter set. It is found analytically that CACC vehicles enhance the stabilization of traffic flow with respect to both small and large perturbations compared to ACC vehicles. Numerical simulation is carried out to support our analytical findings. Based on the nonlinear stability analysis, we will show analytically and numerically that the CACC system better improves the dynamic equilibrium capacity over the ACC system. We have argued that in parallel to microscopic models for CACC traffic flow, the newly developed macroscopic will provide a complete insight into the dynamics of intelligent traffic flow.     

A graphics processor for comparative studies using microscopic traffic simulation models

Computer traffic simulation models are valuable tools for the design and deployment of Intelligent Transportation Systems (ITS). Simulations of traffic flow can be used for the analysis and assessment of potential ITS technologies. Using simulations, alternative systems can be tested under identical conditions so the effects of oversaturated conditions, spillback, queuing, and overlapping bottlenecks can be measured. The Federal Highway Administration (FHWA) microscopic traffic simulation models, NETSIM, FRESIM, and CORSIM, are regarded as highly comprehensive but somewhat difficult to use. A graphics processor, TRAFVU, has recently been developed for analyzing the output of these microscopic models. TRAFVU was designed to support direct comparison of alternatives to facilitate design and evaluation. Applications of the CORSIM traffic simulation model and the TRAFVU graphics processor to interchange design and developing incident management strategies are presented.