This paper introduces the problem of finding the least expected travel time path between two nodes in a network with travel times that are both random and time-dependent (e.g., a truck, rail, air or bus network). It first shows that standard shortest path algorithms (such as the Dijkstra algorithm) do not find the minimum expected travel time path on such a network, then proposes a method which does find the minimum path. Next, this paper shows that the optimal “route choice” is not a simple path but an adaptive decision rule. The best route from any given node to the final destination depends on the arrival time at that node. Because the arrival time is not known before departing the origin, a better route can be selected by deferring the final choice until later nodes are reached. A method for finding the optimal adaptive decision rule is proposed.

This paper develops an analytic method for minimizing the cost of distributing freight by truck from a supplier to many customers. It derives formulas for transportation and inventory costs, and determines the optimal trade-off between these costs. The paper analyzes and compares two distribution strategies: direct shipping (i.e., shipping separate loads to each customer) and peddling (i.e., dispatching trucks that deliver items to more than one customer per load). The cost trade-off in each strategy depends on shipment size. Our results indicate that, for direct shipping, the optimal shipment size is given by the economic order quantity (EOQ) model, while for peddling, the optimal shipment size is a full truck. The peddling cost trade-off also depends on the number of customers included on a peddling route. This trade-off is evaluated analytically and graphically. The focus of this paper is on an analytic approach to solving distribution problems. Explicit formulas are obtained in terms of a few easily measurable parameters. These formulas require the spatial density of customers, rather than the precise locations of every customer. This approach simplifies distribution problems substantially while providing sufficient accuracy for practical applications. It allows cost trade-offs to be evaluated quickly using a hand calculator, avoiding the need for computer algorithms and mathematical programming techniques. It also facilitates sensitivity analyses that indicate how parameter value changes affect costs and operating strategies.

No abstract

This paper investigates the construction of routes for local delivery of packages. The primary objective of this research is to provide realistic models to optimize vehicle dispatching when customer locations and demands vary from day to day while maintaining driver familiarity with their service territories, hence dispatch consistency. The objective of increasing driver familiarity tends to make routes or service territories fixed. On the other hand, to serve varying demand it is advantageous to reassign vehicles/drivers and service territories each day. To balance the trade-offs between these two objectives, we developed the concepts of “cell,” “core area,” and “flex zone,” and created a two-stage vehicle routing model—strategic core area design and operational cell routing—and explicitly evaluated the effect of driver familiarity through the use of learning and forgetting curves.

No abstract