We extend the classical transportation problem from linear optimization and introduce several competing forwarders. This results in a noncooperative game which is commonly known as generalized Nash equilibrium problem. We show the existence of Nash equilibria and present numerical methods for their efficient computation. Furthermore, we discuss several equilibrium selection concepts that are ap...

The senior transportation problem (STP) is an optimization problem in which a fixed fleet of volunteer-operated, heterogeneous vehicles from multiple depots must satisfy as many elderly door-to-door transportation requests as possible within a fixed time horizon. All requests consist of a oneto-one pickup and delivery with time windows. Moreover, as the clients are seniors, a maximum ride time ...

The present work investigates a Dynamic Vehicle Allocation problem (DVAP) which is faced by major forwarding companies active in road transportation. A company owning a limited fleet of vehicles wants to maximize its operational profit over an infinite horizon divided into equal periods (days). The profit stems from revenues for transporting full truckloads (FTL) and from costs derived from wai...

abstract one of the basic assumptions in hub covering problems is considering the covering radius as an exogenous parameter which cannot be controlled by the decision maker. practically and in many real world cases with a negligible increase in costs, to increase the covering radii, it is possible to save the costs of establishing additional hub nodes. change in problem parameters during the pl...

Green Supply Chain Management (GSCM) is one of the recent innovations for the enhancement of capabilities of Supply Chain Management. In this research, we aim to study the various activities of the Supply Chain processes of the various Indian Manufacturing Industries i.e. both SME’s {Small Manufacturing Enterprises} & Large Scale Industries & finds how much ecofriendly they are (i.e. how much %...

We study the online transportation problem. We show that the halfopt-competitive ratio for the greedy algorithm is (min(m; logC)), where m is the number of server sites, and C is the total number of servers. We also present an algorithm Balance that is a modi cation of the greedy algorithm and that has a halfopt-competitive ratio of O(1).

Consider a transportation problem with sets of sources and sinks. There are profits and prices on the edges. The goal is to maximize the profit while meeting the following constraints; the total flow going out of a source must not exceed its capacity and the total price of the incoming flow on a sink must not exceed its budget. This problem is closely related to the generalized flow problem. We...