Applied Simulation and Optimization 2

In urban areas, intersections are the main constraints on road capacity while traffic flows do not necessarily directly conform to the speed-flow relationship. It is rather the signal timing and the interplay between the clearing rate of each intersection which determines the formation and duration of congestion. Junctions often differ in their design and throughput. General conclusions on the relationship between vehicle speed and traffic flows on a junction link are rarely possible. Well-adapted models are required for a comprehensive study of the behaviour of each intersection as well for the interactions between junctions. This chapter assesses the potential benefits of adaptive traffic plans for improved network management strategies, under varying traffic conditions. Queueing analysis in association with advanced simulation techniques reveal congestion mitigation actions when the pre-timed actuation plan is replaced by the max-pressure feedback control. The case of unpredicted local demand fluctuation is studied, where uncontrolled congestion is progressively propagated to the entire network under the open-loop policy. Travel-time variability is measured under both plans and within all traffic schemes while frequency of stop-and-go actions are also encountered. Reliability of predictable trip durations is a major factor to be considered when ensuring “on time” arrivals and the related costs when the time is converted into benefits.

The urban transport mobility is one of the most important problems for the cities, and involves many aspects that concern to citizens, governments and the economical growth of the countries. Mobility in Mexico City is also a huge problem since the city size makes it insoluble and citizens prefer to use private transportation instead of the public transport network because it offers a poor coverage and a lack of modal transfer centers. With the purpose of analyzing the mobility problems in Mexico City as well as detecting areas of opportunity, the objective of this chapter is to model and simulate the public transportation network from the complex network perspective to asses network structural vulnerability and resilience, considering mobility and accessibility aspects. Firstly, we analyze the urban transport infrastructure in Mexico City taking into account the planning process and sustainability criteria. Secondly, we model and simulate the Mexico City’s public transportation network as a complex network. Thirdly, we characterize the complex network topology of the Mexico City’s public transportation network, and finally we present the main results.

Manufacturers are facing an ever-increasing demand for customized products on the one hand and environmentally friendly products on the other. This situation affects both the product and the process life cycles. To guide decision-making across these life cycles, the performance of today’s manufacturing systems is monitored by collecting and analyzing large volumes of data, primarily from the shop floor. A new research field, Data Mining, can uncover insights hidden in that data. However, insights alone may not always result in actionable recommendations. Simulation models are frequently used to test and evaluate the performance impacts of various decisions under different operating conditions. As the number of possible operating conditions increases, so does the complexity and difficulty to understand and assess those impacts. This chapter describes a decision-making methodology that combines data mining and simulation. Data mining develops associations between system and performance to derive scenarios for simulation inputs. Thereafter, simulation is used in conjunction with optimization is to produce actionable recommendations. We demonstrate the methodology with an example of a machine shop where the concern is to optimize energy consumption and production time. Implementing this methodology requires interface standards. As such, this chapter also discusses candidate standards and gaps in those standards for information representation, model composition, and system integration.

Traditionally airport systems have been studied using an approach in which the different elements of the system are studied independently. Until recently scientific community has put attention in developing models and techniques that study the system using holistic approaches for understanding cause and effect relationships of the integral system. This chapter presents a case of an airport in which the authors have implemented an approach for improving the turnaround time of the operation. The novelty of the approach is that it uses a combination of simulation, parameter analysis and optimization for getting to the best amount of vehicles that minimize the turnaround time of the airport under study. In addition, the simulation model is such that it includes the most important elements within the aviation system, such as terminal manoeuvring area, runway, taxi networks, and ground handling operation. The results show clearly that the approach is suitable for a complex system in which the amount of variables makes it intractable for getting good solutions in reasonable time.

International airports are complex systems that require efficient operation and coordination of all their departments. Therefore, suitable personnel and equipment scheduling solutions are vital for efficient operation of an airport as a system. Many general solutions for fleet scheduling are available; however, there is a lack of scheduling solutions for airport ground crews, especially for work groups with overlapping skills. In the presented case, a scheduling solution for airport ground crew and equipment in a small international airport is described. As analytical methods are unsuitable for the system in question, the proposed scheduling solution is based on heuristics. A combined agent based and discrete event simulation model was developed to validate and improve the heuristic algorithms until they produced acceptable schedules and shifts. The algorithms first compute the requirements for workforce and equipment based on flight schedules and stored heuristic criteria. Workforce requirements are then optimized using time shifting of tasks and task reassignments, which smooth the peaks in workforce requirements, and finally the simulation model is used to verify the generated schedule. The scheduling procedure is considerably faster than manual scheduling and allows dynamic rescheduling in case of disruptions. The presented schedule generation and optimization solution is flexible and adaptable to other similar sized airports.

With the regular growth of air traffic, airports are becoming the most critical part of the aircraft path. Improving ground operations to absorb the delays generated is becoming a necessity. This chapter presents a new departure sequencing algorithm based on operation research methods in the context of the CDM implementation over the European airports. This algorithm is described and results and benefits are demonstrated using data from Paris Charles de Gaulle airport. The performance of the algorithm is also investigated using a fast-time simulation tool.

This chapter describes the application of optimization to power flow in hybrid electric vehicles, first using a strategy based on bang-bang optimal control and then comparing it with Pontryagin’s alternative. The first strategy, known as the planetary gears system (PGS), focuses on satisfying the kinematic and dynamic constraints of the gears system, starting from the allocation of the electric machine power. The second uses Pontryagins minimum principle (PMP) to solve the energy management problem and decide the amount of power that the electric machine and combustion engine should provide. The approach of the PMP strategy entails three basic elements, namely: first of all, getting the demanded power to be supplemented by the drive machines; secondly, maintaining the state of charge at a level in and around a reference so as to avoid discharging and overloading the batteries and thirdly, saving on fuel. By using the above considerations, a cost function is set out that considers the power from both machines to be inputs. The simulations were performed in Matlab’s Simulink using detailed models of the elements of a hybrid diesel-electric city bus in parallel configuration. The demands are represented by driving cycles while the combustion engine and electric machine are coupled using a planetary gears system.

This chapter presents an approach involving both discrete event simulation (DES) and optimization to address operational problems faced by a distribution system. In the system modeled, vendors may require minimum purchase requirements for each order. The model can be used to determine whether retail outlets should order product directly from the vendors, or through a centralized warehouse, as well as whether each retail outlet should violate its pre-specified inventory policy in order to meet vendor-minimum requirements. In addition, the model can be of use as an aid in negotiation with vendors with respect to minimum purchase requirements. The work is based on a project performed for an actual company with a centralized warehouse, located in Louisville, Kentucky, and 19 retail outlets, located throughout the United States.

In this chapter, the combined use of optimization and simulation in the design of a distribution network for hazardous materials in the northern region of Mexico City is assessed. A mathematical programming model was developed to allow for fuel dispatch truck allocation, minimizing the total distribution cost. Heuristics were used to solve the model and different simulation scenarios were applied to do what-if analysis to be able to decide on different managerial situations. Reviewing simulation and optimization results, an appropriate estimate of the fuel quantity to order (EOQ), the best type of truck to carry out the supply, as well as the ordering schedule that minimizes the associated costs of distribution and inventory, is provided. This real-life Mexican case study shows how a combined optimization-simulation approach, specifically taking advantage of heuristic methods to diminish computing time, can provide a practical, efficient and flexible tool for optimization assessment in operational research.