What drives the Rebound Effect in transportation? An evaluation based on a Traveling Purchaser Problem

Limiting climate change is one of the most important challenges of the 21st century. Focusing on the transport sector, encouraging the use of more energy-efficient transport modes, and improving the performance of vehicles are the main targets in the fight for GHG reductions. However, due to Rebound Effect (RE), it is proven that improvements in engine fuel efficiency result in lower cost per kilometer driven and can induce individuals to use vehicles more often or to drive longer distances. As a result, the potential energy savings from improved energy efficiency could be partially or totally offset. Therefore, we decided to examine "What drives the Rebound Effect in transportation". To answer this research question, a Traveling Purchaser Problem was evaluated. This simple real-life business application models a situation in which a company owns one or several vehicles and has to buy specific products. The goal is to select and visit a subset of suppliers to satisfy a given demand for each product while minimizing both purchasing and travel costs. In total, 510 instances of this problem with various characteristics and parameters were generated and solved using the optimization software AIMMS. The impact of five main experimentations was deeply investigated. In addition, the trends obtained from these experiments were confirmed by fitting a logistic regression and a decision tree. The results of the various experiments showed that four variables can influence the occurrence of RE in a transportation network. On the one hand, RE tended to increase with the number of potential suppliers from which the firm can choose and the number of vehicles that the company owns to procure the products. On the other hand, the exclusivity of the products to source, as well as the introduction of a distance-traveled tax, reduced the occurrence of RE. To sum up, significant conclusions could be drawn from the experiments and the results can be easily transferred to real-life business applications. Recommendations for possible future studies were also discussed.nhhma

Intentional fragmentation for material storage

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2004.Includes bibliographical references (p. 165-167).A novel technique (location-relaxed storage) of mixing products within warehouse storage bins is presented and evaluated. Analyses of warehouse operations, storage space efficiency, error sensitivity, and placement policies are presented and compared to traditional warehousing techniques. The major factors that drive the performance differences between traditional, highly organized storage and location-relaxed storage are shown to include the number of unique stock keeping units (SKUs) served by the warehouse and the picking lot size characteristic of demand. The analyses demonstrate traditional storage techniques have greater difficulty dealing with a large SKU base. Furthermore, location-relaxed storage is shown to have a lower sensitivity to operation errors and a greater opportunity for cost savings through optimization opportunities. Finally, a new placement strategy especially suited for location-relaxed storage is presented. As the popularity of Radio Frequency Identification (RFID) increases and the technical issues of widespread RFID implementation are addressed, new applications of RFID technology will change the way the world operates. An ongoing, industry-wide effort to implement RF-tags throughout the material goods supply chain has the support of manufacturers, retailers, and technology companies. RFID in the supply chain represents an enabling technology that will allow warehouse operations to break away from traditional methodologies and adopt revolutionary techniques, such as location-relaxed storage.by Stephen Ho.Ph.D

An Approach to Pathfinding for Real-World Situations

People plan their routes through new environments every day, but what factors influence these wayfinding decisions? In a world increasingly dependent on electronic navigation assistance devices, finding a way of automatically selecting routes suitable for pedestrian travel is an important challenge. With a greater freedom of movement than vehicular transport, and different requirements, an alternative approach should be taken to find an answer for pedestrian journeys than those taken in cars. Although previous research has produced a number of pedestrian route recommendation systems, the majority of these are restricted to a single route type or user group. The aim of this research was to develop an approach to route suggestion which could recommend routes according to the type of journey (everyday, leisure or tourist) a person is making. To achieve this aim, four areas of research were undertaken. Firstly, six experiments containing 450 participants were used to investigate the preference of seven different environment and route attributes (length, turns, decision points, vegetation, land use, dwellings and points of interest) for two attribute categories (simplicity and attractiveness) and three journey types (everyday, leisure and tourist). These empirically determined preferences were then used to find the rank-orders of the attributes, by comparing more of them simultaneously than earlier studies, and found either new rankings (for attractiveness, leisure journeys and tourist journey) or extended those already known (everyday journeys). Using these ranks and previously accepted relationships, an environment model was defined and built based on an annotated graph. This model can be built automatically from OpenStreetMap data, and is therefore simple enough to be applicable to many geographical areas, but it is detailed enough to allow route selection. Algorithms based on an extended version of Dijkstra’s shortest path algorithm were constructed. These used weighted minimum cost functions linked with attribute ranks, to select routes for different journey types. By avoiding the computational complexity of previous approaches, these algorithms could potentially be widely used in a variety of different platforms, and extended for different groups of users. Finally, the routes suggested by the algorithms were compared to participant recommendations for ‘simple’ routes with five start/end points, and for each of the three journey types (everyday, leisure and tourist). These comparisons determined that only length is required to select simple and everyday routes, but that the multi-attribute cost functions developed for leisure and tourist journeys select routes that are similar to those chosen by the participants. This indicates that the algorithms’ routes are appropriate for people to use in leisure and tourist journeys