This paper considers the design and analysis of algorithms for vehicle routing and scheduling problems with time window constraints. Given the intrinsic difficulty of this problem class, approximation methods seem to offer the most promise for practical size problems. After describing a variety of heuristics, we conduct an extensive computational study of their performance. The problem set includes routing and scheduling environments that differ in terms of the type of data used to generate the problems, the percentage of customers with time windows, their tightness and positioning, and the scheduling horizon. We found that several heuristics performed well in different problem environments; in particular an insertion-type heuristic consistently gave very good results.

https://www.jstor.org/stable/pdfplus/170697.pdf

Algorithms for the vehicle routing and scheduling problems with time window constraints

Estimation and inference in two-stage, semi-parametric models of production processes

Data envelopment analysis (DEA) - Thirty years on

Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in taiwan

https://www.grips.ac.jp/r-center/wp-content/uploads/07-08.pdf

Network DEA: A slacks-based measure approach

This chapter points out serious shortcomings in DEA's treatment of price efficiency, illustrates the dangers of misspecification errors in DEA, and suggests extentions of the basic DEA formulation that address these shortcomings.

Data Envelopment Analysis: Critique and Extensions

Network DEA: efficiency analysis of organizations with complex internal structure

We show how to use DEA to model DMUs that produce in two stages, with output from the first stage becoming input to the second stage. Our model allows for any orientation or scale assumption. We apply the model to Major League Baseball, demonstrating its advantages over a standard DEA model. Our model detects inefficiencies that standard DEA models miss, and it can allow for resource consumption that the standard DEA model counts towards inefficiency. Additionally, our model distinguishes inefficiency in the first stage from that in the second stage, allowing managers to target inefficient stages of the production process.

Two-Stage DEA: An Application to Major League Baseball

A set of n customers is given. Each customer has a desired point of pickup, a desired point of delivery and a desired time of delivery. The problem is to determine the order of pickup and delivery and the times of pickup and delivery of these n customers by a single vehicle in order to minimize total customer inconvenience. Here, a mathematical programming formulating of this problem is subjected to Benders' decomposition procedure. The result is a heuristic routing and scheduling algorithm which is shown to produce high quality solutions in reasonable computation time by testing on moderately sized real data bases from both Gaithers-burg, Maryland, and Baltimore, Maryland. This study is divided into two parts, the first detailing the scheduling analysis and the second focusing on the routing component.

Optimizing Single Vehicle Many-to-Many Operations with Desired Delivery Times: I. Scheduling

Data envelopment analysis is a linear programming–based method that has clear advantages over competing approaches. However, its own limitations should not be overlooked.

Thomas R. Sexton

Papers

Data Envelopment Analysis: Critique and Extensions

Network DEA: efficiency analysis of organizations with complex internal structure

Two-Stage DEA: An Application to Major League Baseball

Optimizing Single Vehicle Many-to-Many Operations with Desired Delivery Times: I. Scheduling

The methodology of data envelopment analysis