Dynamic approach to solve the daily drayage problem with travel time uncertainty

The intermodal transport chain can become more e cient by means of a good organization of drayage movements. Drayage in intermodal container terminals involves the pick up and delivery of containers at customer locations, and the main objective is normally the assignment of transportation tasks to the di erent vehicles, often with the presence of time windows. This scheduling has traditionally been done once a day and, under these conditions, any unexpected event could cause timetable delays. We propose to use the real-time knowledge about vehicle position to solve this problem, which permanently allows the planner to reassign tasks in case the problem conditions change. This exact knowledge of the position of the vehicles is possible using a geographic positioning system by satellite (GPS, Galileo, Glonass), and the results show that this additional data can be used to dynamically improve the solution

A Real-Time Rolling Horizon Chance Constrained Optimization Model for Energy Hub Scheduling

With the increasing consumption of energy, it is of high significance to improve energy efficiency and realize optimal operation of the multi-energy system. Among the many energy system modeling methods, the concept of “energy hub (EH)” is an emerging one. However, the previous EH models only included one or a few of constituting components. The construction of an energy hub model that integrates energy storage systems, photovoltaic (PV) components, a combined cooling heating and power (CCHP) system and electric vehicles (EVs) is explained in this thesis. The inclusion of the CCHP system helps to meet the energy demand and improve the mismatch of heat-to-electric ratio between the energy hub and the load. Additionally, vehicle-to-grid (V2G) technology is applied in this EH; that is, EVs are regarded not only as load demands but also as power suppliers. The energy hub optimization scheduling problem is formulated as a multi-period stochastic problem with the minimum total energy cost as the objective. Compared to 24-hour day-ahead scheduling, rolling horizon optimization is used in the EH scheduling and shows its superiority. In real-time rolling horizon scheduling, the optimization principle ensured that the result is optimized each moment, so it avoids energy waste caused by overbuying energy. As part of electricity loads, EVs have certain influence on energy hub scheduling. However, due to the randomness of the driving patterns, it is still very difficult to perfectly predict the driving consumption and the charging availability of the EVs one day in advance. Chance constrained programming can hedge the risk of uncertainty for a big probability and drop the extreme case with a very low probability. By restricting the probability of chance constraints over a specific level, the influence of the uncertainty of electric vehicle charging behavior on energy hub scheduling can be reduced. Simulation results show that the energy hub optimization scheduling with chance constrained programming results in a less energy cost and it can make better use of time-varying PV energy as well as the peak-to-valley electricity price

A Real-time Rolling Horizon Chance Constrained Optimization Model for Energy Hub Scheduling

With the increasing consumption of energy, it is of high significance to improve energy efficiency and realize optimal operation of the multi-energy system. Among the many energy system modeling methods, the concept of “energy hub (EH)” is an emerging one. However, the previous EH models only included one or a few of constituting components. The construction of an energy hub model that integrates energy storage systems, photovoltaic (PV) components, a combined cooling heating and power (CCHP) system and electric vehicles (EVs) is explained in this thesis. The inclusion of the CCHP system helps to meet the energy demand and improve the mismatch of heat-to-electric ratio between the energy hub and the load. Additionally, vehicle-to-grid (V2G) technology is applied in this EH; that is, EVs are regarded not only as load demands but also as power suppliers. The energy hub optimization scheduling problem is formulated as a multi-period stochastic problem with the minimum total energy cost as the objective. Compared to 24-hour day-ahead scheduling, rolling horizon optimization is used in the EH scheduling and shows its superiority. In real-time rolling horizon scheduling, the optimization principle ensured that the result is optimized each moment, so it avoids energy waste caused by overbuying energy. As part of electricity loads, EVs have certain influence on energy hub scheduling. However, due to the randomness of the driving patterns, it is still very difficult to perfectly predict the driving consumption and the charging availability of the EVs one day in advance. Chance constrained programming can hedge the risk of uncertainty for a big probability and drop the extreme case with a very low probability. By restricting the probability of chance constraints over a specific level, the influence of the uncertainty of electric vehicle charging behavior on energy hub scheduling can be reduced. Simulation results show that the energy hub optimization scheduling with chance constrained programming results in a less energy cost and it can make better use of time-varying PV energy as well as the peak-to-valley electricity price

Production Scheduling in Integrated Steel Manufacturing

Steel manufacturing is both energy and capital intensive, and it includes multiple production stages, such as iron-making, steelmaking, and rolling. This dissertation investigates the order schedule coordination problem in a multi-stage manufacturing context. A mixed-integer linear programming model is proposed to generate operational (up to the minute) schedules for the steelmaking and rolling stages simultaneously. The proposed multi-stage scheduling model in integrated steel manufacturing can provide a broader view of the cost impact on the individual stages. It also extends the current order scheduling literature in steel manufacturing from a single-stage focus to the coordinated multi-stage focus. Experiments are introduced to study the impact of problem size (number of order batches), order due time and demand pattern on solution performance. Preliminary results from small data instances are reported. A novel heuristic algorithm, Wind Driven Algorithm (WDO), is explained in detail, and numerical parameter study is presented. Another well-known and effective heuristic approach based on Particle Swarm Optimization (PSO) is used as a benchmark for performance comparison. Both algorithms are implemented to solve the scheduling model. Results show that WDO outperforms PSO for the proposed model on solving large sample data instances. Novel contributions and future research areas are highlighted in the conclusion

Dynamic vehicle routing problems: Three decades and counting

Since the late 70s, much research activity has taken place on the class of dynamic vehicle routing problems (DVRP), with the time period after year 2000 witnessing a real explosion in related papers. Our paper sheds more light into work in this area over more than 3 decades by developing a taxonomy of DVRP papers according to 11 criteria. These are (1) type of problem, (2) logistical context, (3) transportation mode, (4) objective function, (5) fleet size, (6) time constraints, (7) vehicle capacity constraints, (8) the ability to reject customers, (9) the nature of the dynamic element, (10) the nature of the stochasticity (if any), and (11) the solution method. We comment on technological vis-à-vis methodological advances for this class of problems and suggest directions for further research. The latter include alternative objective functions, vehicle speed as decision variable, more explicit linkages of methodology to technological advances and analysis of worst case or average case performance of heuristics.© 2015 Wiley Periodicals, Inc

An Overview and Categorization of Approaches for Train Timetable Generation

A train timetable is a crucial component of railway transportation systems as it directly impacts the system’s performance and the customer satisfaction. Various approaches can be found in the literature that deal with timetable generation. However, the approaches proposed in the literature differ significantly in terms of the use case for which they are in tended. Differences in objective function, timetable periodicity, and solution methods have led to a confusing number of works on this topic. Therefore, this paper presents a com pact literature review of approaches to train timetable generation. The reviewed papers are briefly summarized and categorized by objective function and periodicity. Special emphasis is given to approaches that have been applied to real-world railway data

The one container drayage problem with soft time windows

Intermodal freight transport consists of using different modes of transport without changing the load unit. This results in a significant reduction in the time that goods spend at intermodal terminals, where transshipment takes place. Drayage refers to the transport of freight on trucks among intermodal terminals, depots, customers and suppliers. In spite of the fact that drayage only represents between 5 and 10 percent of total distance, it may amount up to more than 30 percent of the total costs. The aim of this work is to study drayage operations. First, an extensive literature review is undertaken. Since the intermodal transport chain can become more efficient by means of a proper organisation of the drayage movements, the optimization of the daily drayage problem has been identified as one of the main ways of reducing the drayage cost and improving intermodal operations. On this problem, the lack of a common benchmark has hindered reaching further conclusions from all the research carried out. Therefore, this paper proposes a common framework and presents a generalized formulation of the problem, which allows modeling most drayage policies, with the limitation of only considering one-container problems. Results show that flexible tasks in the repositioning of empty containers as well as soft time windows can reduce the operating costs and facilitate the management of drayage companies. This work may help consider adequate policies regarding drayage operations in intermodal terminals

Sea Container Terminals

Due to a rapid growth in world trade and a huge increase in containerized goods, sea container terminals play a vital role in globe-spanning supply chains. Container terminals should be able to handle large ships, with large call sizes within the shortest time possible, and at competitive rates. In response, terminal operators, shipping liners, and port authorities are investing in new technologies to improve container handling infrastructure and operational efficiency. Container terminals face challenging research problems which have received much attention from the academic community. The focus of this paper is to highlight the recent developments in the container terminals, which can be categorized into three areas: (1) innovative container terminal technologies, (2) new OR directions and models for existing research areas, and (3) emerging areas in container terminal research. By choosing this focus, we complement existing reviews on container terminal operations