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

Abstract

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.

Multiple Types of Plug-In Charging Facilities’ Location-Routing Problem with Time Windows for Mobile Charging Vehicles

“…VRPTW, where customer nodes have to be reached within a specified time interval [37], is an important area handling the realistic complications and generalizations of the basic routing model [38,39]. Time windows were applied in bank deliveries [7,40], postal delivers [41,42], 4 48 school bus routing [43,44], and dial-a-ride services [45,46]. Concerning the wide use of BEVs, Schneider et al [47] 43 developed the electric vehicle routing problem with time windows (E-VRPTW) as an extension of VRPTW.…”

A heuristic two‐phase solution approach for the multi‐objective dial‐a‐ride problem

“…In case, the objective of minimum user inconvenience is also treated, this is either done by applying it instead of the minimum cost objective or by means of a weighted sum objective function. Sexton and Bodin [52,53], e.g., entirely focus on user inconvenience. They design a routing and scheduling heuristic minimizing the difference between actual and desired delivery times and between actual and shortest possible ride times.…”

A Comparison of Environmental Impacts of Hard Time Windows and Semi Soft Time Windows Using Exact Routing Solution

“…Constraint (10) is a time windows constraint specifying that if a vehicle travels from i to j, service at j cannot start earlier than that at i. Constraint (11) specifies the service start time at all vertices that must be within their specified time windows [ai, bi]. Note that the constraint (10) is the linearized form of the original non-linear constraint (13). (13) For the VRPSSTW, the ESPPRC is extended to the Elementary Shortest Path Problem with Resource Constraints and Late Arrival Penalties by replacing the constraint (11) with constraint (14) and by replacing the cost cif by time dependent cost c'ij calculated as (15), where si represents the service start time at j and cl represents the unit late arrival penalty cost.…”