Modelling Route Choice Decisions of Car Travellers Using Combined GPS and Diary Data

The aim of this research is to identify the relationship between activity patterns and route choice decisions. The focus is twofold: on the one hand, the relationship between the purpose of a trip and the road categories used for the relocation is investigated; on the other hand, the relationship between the purpose of a trip and the deviation from the shortest path is studied. The data for this study were collected in 2006 and 2007 in Flanders, the Dutch speaking and northern part of Belgium. To estimate the relationship between the primary road category travelled on and the corresponding activity-travel behaviour a multinomial logit model is developed. To estimate the relationship between the deviation from the shortest path and the corresponding activity-travel behaviour a Tobit model is developed. The results of the first model point out that route choice is a function of multiple factors, not just travel time or distance. Crucial for modelling route choices or in general for traffic assignment procedures is the conclusion that activity patterns have a clear influence on the road category primarily driven on. Particularly, it was shown that the likelihood of taking primarily through roads is highest for work trips and lowest for leisure trips. The second model shows a significant relationship between the deviation from the shortest path and the purpose of the trip. Furthermore, next to trip-related attributes (trip distance), also socio-demographic variables and geographical differences play an important role. These results certainly suggest that traffic assignment procedures should be developed that explicitly take into account an activity-based segmentation. In addition, it was shown that route choices were similar during peak and off-peak periods. This is an indication that car drivers are not necessarily utility maximizers, or that classical utility functions in the context of route choices are omitting important explanatory variables

INTEGRATING CONSOLIDATION OPTIONS IN A NEW CONCEPTUAL FREIGHT TRANSPORTATION FRAMEWORK

ABSTRACT In a growing globalised context and consumption economy freight transport is of crucial importance. Being able to understand the drivers of freight flows makes it possible to forecast freight flows in the future and to calculate the impact of different policies on freight traffic. This will put policymakers in the position to get a better insight in the way the transport of goods comes about. Still, freight demand modelling is lacking behind on the efforts made in passenger transport models. For this the development of a comprehensive and reliable freight transport model is essential. In this paper a conceptual freight transportation framework is proposed. Special attention is paid to the different consolidation options of a forwarder

Modelling Shortest Path Decisions Using an Activity-Based Segmentation

peer reviewedThe aim of this research is to identify the relationship between activity patterns and route choice decisions. The focus is turned to the relationship between the purpose of a trip and whether or not the shortest path is chosen for the relocation. The data for this study were collected in 2006 and 2007 in Flanders, the Dutch speaking and northern part of Belgium. To estimate the relationship between the choice for the shortest path or not and the corresponding activity-travel behaviour a logistic regression model is developed. The results point out that, when analyzing the relationship between the activities of the people and whether or not the shortest path is chosen, there is no significant influence by the activity-based segmentation. However, when the deviation from the shortest path is related to the activities people perform, a significant relationship is found. Furthermore, next to trip-related attributes (trip distance), also socio-demographic variables and geographical differences play an important role

The use of time series forecasting in zone order picking systems to predict order pickers’ workload

In order to differentiate from competitors in terms of customer service, warehouses accept late orders while providing delivery in a quick and timely way. This trend leads to a reduced time to pick an order. This paper introduces workload forecasting in a warehouse context, in particular a zone picking warehouse. Improved workforce planning can contribute to an effective and efficient order picking process. Most order picking publications treat demand as known in advance. As warehouses accept late orders, the assumption of a constant given demand is questioned in this paper. The objective of this study is to present time series forecasting models that perform well in a zone picking warehouse. A real-life case study demonstrates the value of applying time series forecasting models to forecast the daily number of order lines. The forecast of order lines, along with order pickers’ productivity, can be used by warehouse supervisors to determine the daily required number of order pickers, as well as the allocation of order pickers across warehouse zones. Time series are applied on an aggregated level, as well as on a disaggregated zone level. Both bottom-up and top-down approaches are evaluated in order to find the best-performing forecasting method

Integrated scheduling of order picking operations under dynamic order arrivals

Study of solving the order batching, picker routing and batch scheduling problems in an integrated way, while new orders arrive during the planning period. Since this problem is NP-hard, a metaheuristic (large neighbourhood search) is used to solve this problem

On the choice of a demand distribution for inventory management models

An inventory system containing uncertainty, e.g., in demand, in costs, in lead time, or in supplied quantity or quality, needs a probability distribution of demand for reorder point models. In the literature on inventory control, many times reference is made to the Normal or Gamma distribution for describing the demand in the lead time. The Poisson distribution has been found to provide a reasonable fit when the demand is very low. However, information about the functional form of the probability distribution is often incomplete in practice. For example, it might be that only the first moments of the probability distribution are known. This incomplete information is a problem as the shape of the distribution is important in terms of the performance of inventory control. A procedure is described to determine shape characteristics when only the first two moments of the distribution of demand during the lead time are known, using a compound Poisson distribution and the Pearson chart. [Received 18 August 2006; Revised 15 November 2007; Accepted 11 February 2008]inventory management; demand uncertainty; shape characteristics; demand distribution; inventory control.

Analysis of collaborative savings and cost allocation techniques for the cooperative carrier facility location problem

Transport companies may cooperate to increase their efficiency levels by e.g. the exchange of orders or vehicle capacity. In this paper a new approach to horizontal carrier collaboration is presented: the sharing of distribution centres (DCs) with partnering organisations. This problem can be classified as a cooperative facility location problem and formulated as an innovative mixed integer linear program. To ensure cooperation sustainability, collaborative costs need to be allocated fairly to the different participants. To analyse the benefits of cooperative facility location and the effects of different cost allocation techniques, numerical experiments based on experimental design are carried out on a U.K. case study. Sharing DCs may lead to significant cost savings up to 21.6%. In contrast to the case of sharing orders or vehicles, there are diseconomies of scale in terms of the number of partners and more collaborative benefit can be expected when partners are unequal in size. Moreover, results indicate that horizontal collaboration at the level of DCs works well with a limited number of partners and can be based on intuitively appealing cost sharing techniques, which may reduce alliance complexity and enforce the strength of mutual partner relationships

Service-oriented performance of inventory models with partial information on unimodal demand lead-time distributions

Facing uncertainty in demand, companies try to avoid stock-outs by holding safety inventories, depending on a pre-set customer service level. The knowledge of the demand distribution during lead-time serves to determine the safety inventory level. Many times the distribution is not fully known, except maybe for its range, mean or variance. However literature shows that the performance of holding safety stock strongly depends on the characteristics of the distribution. One option is to protect against the worst case distribution given some information like range or moments. But this worst case is a two-point distribution, bringing unbelief to managers that such an occurrence would ever appear. Mostly they share the opinion that the demand distribution is unimodal. This research develops a technique to derive the safety stock for unimodal demand distributions of which the mode either is known or can be estimated. In this way, the managers obtain solutions to the decision problem including a higher belief that the related type of distribution might appear in practice

Analytical Solution of Safety Stock Determination in Case of Uncertain Unimodal Lead-Time Demand

As companies state that a delivery service is important to their customers, an out-of-stock is considered harmful and therefore they keep safety stock in case of uncertain demand. For decision making on the level of safety stock a complete formulation of the distributional form of the demand during lead time is required. In practice, this information may not be available. In such a case, only partial information on the distribution might be available, such as the range, the mode, the mean or the variance. Given a value for a service performance measure, the decision maker, in this case, is not confronted with a single value for the safety stock but rather with an interval. The present research shows how upper and lower bounds of the safety stock are obtained in an analytical way, given a pre-specified service level using a service performance measure, called ‘expected number of units short’. The technique is also illustrated and compared within the framework of the research

Integrating production scheduling and vehicle routing decisions at the operational decision level: A review and discussion

Production scheduling and vehicle routing are two well-studied problems in literature. Although these supply chain functions are interrelated, they are often solved sequentially. This uncoordinated approach can lead to suboptimal solutions. In the current competitive business environment, companies are searching for methods to save costs and improve their service level. Integrating production and distribution scheduling operations can be an approach to improve the overall performance. This paper focuses on integrated production-distribution operational level scheduling problems, which explicitly take into account vehicle routing decisions of the delivery process. Existing literature on integrated production scheduling and vehicle routing problems is reviewed and classified. Both the problem characteristics of mathematical models and the accompanying solution approaches are discussed to identify directions for further research.COMEX (Combinatorial Optimization: Metaheuristics & Exact Methods