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The profitable tour problem (PTP) is a well-known NP-hard routing problem searching for a tour visiting a subset of customers while maximizing profit as the difference between total revenue collected and traveling costs. PTP is known to be solvable in polynomial time when special structures of the underlying graph are considered. However, the computational complexity of the corresponding probabilistic generalizations is still an open issue in many cases. In this paper, we analyze the probabilistic PTP where customers are located on a tree and need, with a known probability, for a service provision at a predefined prize. The problem objective is to select a priori a subset of customers with whom to commit the service so to maximize the expected profit. We provide a polynomial time algorithm computing the optimal solution in $O(n^2)$, where $n$ is the number of nodes in the tree.

Consolidation of loose packages into transport units is a fundamental activity offered by logistics service-providers. Moving the transport units instead of loose packages is faster (with one movement only, multiple packages are loaded instead of having one load operation for each package), safer (chances of damage and loss is reduced) and cheaper. One of the typical objective of consolidation problems is the minimization of the number of transport units used, e.g. containers. In air transportation, however, transport units have multiple aspects which concur in the calculation of the cost and thus optimization in the number and characteristics of the transport units is required. In this paper, we present the air transport unit consolidation problem where the aim is to determine how to consolidate loose packages in transport units with the goal of minimizing the corresponding cost. The problem is a variant of the three-dimensional bin packing problem where the objective function is formulated according to the way costs are calculated in the air transport business. In addition, side constraints are included to take into account specific requirements. We propose a heuristic algorithm which construct an initial feasible solution and then improves it through a local search algorithm. Computational tests on randomly generated instances show that the algorithm provides high quality solutions in a reasonable computing time. In addition, tests on real data show that it improves solutions found in practice by the company providing the data.

Among the most important variants of the traveling salesman problem (TSP) are those relaxing the constraint that every locus should necessarily get visited, rather taking into account a revenue (prize) for visiting customers. In the Profitable Tour Problem (PTP), we seek for a tour visiting a subset of customers while maximizing net gain (profit) as difference between total revenue collected from visited customers and incurred traveling costs. The metric TSP can be modeled as a PTP with large revenues. As such, PTP is well-known to be NP-hard and also APX-hardness follows. Nevertheless, PTP is solvable in polynomial time on particular graph structures like lines, trees and circles. Following recent emphasis on robust optimization, and motivated by current flourishing of retail delivery services, we study the Probabilistic Profitable Tour Problem (PPTP), the generalization of PTP where customers will show up with a known probability, in their respective loci,only after the tour has been planned. Here, the selection of customers has to be made a priori, before knowing if a customer will actually submit his request or will not. While the tour has to be designed without this knowledge, revenues will only be collected from customers who will require the service. The objective is to maximize the expected net gain obtained by visiting only the customers that show up. We provide a polynomial time algorithm computing and characterizing the space of optimal solutions for the special case of the PPTP where customers are distributed on a line.

Many cities and towns offer to citizens a Bike-Sharing System (BSS). When a company starts the service, multiple decisions have to be taken at strategic, tactical, and operational level. When the service is in place, it is often necessary to adapt these decisions to changed conditions. In this paper, starting from the experience gained in the real-case of Bicimia in Brescia, Italy, we present a simulation framework to support decisions in the design or revision of a BSS, including the shifts of the operators, the number of vehicles and their capacity, the sizing of the stations, and the sizing of the fleet of bikes.

The advances in information and communication technology are changing theway people move. Companies that offer demand-responsive transportation serviceshave the opportunity to reduce their costs and increase their revenues throughcollaboration, while at the same time reducing the environmental impact of theiroperations. We consider the case of companies, offering a shared taxi service, thatare involved in horizontal collaboration and present mixed integer programmingmodels for the optimization of their routes that embed constraints aimed at bal-ancing the workload exchange. These constraints bound the imbalance in terms oftraveled time and/or served customers to be less than thresholds agreed in advanceby the companies. We also present a heuristic algorithm and show the benefits ofthe collaboration.

We study a number of variants of an abstract scheduling problem inspired by the scheduling of reclaimers in the stockyard of a coal export terminal. We analyze the complexity of each of the variants, providing complexity proofs for some and polynomial algorithms for others. For one, especially interesting variant, we also develop a constant factor approximation algorithm., Comment: 26 pages