Branch-Price-and-Cut Algorithms for the Pickup and Delivery Problem with Time Windows and Last-in-First-Out Loading

This paper proposes models and algorithms for the pickup and delivery vehicle routing problem with time windows and last-in-first-out (LIFO) loading constraints (PDPTWL). The LIFO loading rule ensures that no handling is required prior to unloading an item from a vehicle: a linear stack loading structure is maintained and an item can only be delivered if it is the last one in the stack. Three exact branch-price-and-cut algorithms are proposed for this problem. The first incorporates the LIFO constraints in the master problem. The second one handles the LIFO constraints directly in the shortest path pricing problem. It applies a dynamic programming algorithm relying on an ad hoc dominance criterion. The third algorithm is a hybrid between the first two methods. Known valid inequalities are adapted to the PDPTWL and the impact of different path relaxations on the total computation time is investigated. Computational results obtained on instances derived from known instances of the pickup and delivery problem with time windows (PDPTW) are reported. </jats:p

Evaluation of optimal charging station location for electric vehicles: an Italian case-study

Electric vehicles are accelerating the world transition to sustainableenergy. Nevertheless, the lack of a proper charging station infrastructure inmany real implementations still represents an obstacle for the spread of sucha technology. In this paper, we present a real-case application of optimizationtechniques in order to solve the location problem of electric charging stationsin the district of Biella, Italy. The plan is composed by several progressiveinstallations and the decision makers pursue several objectives that mightconflict each other. For this reason, we present an innovative framework basedon the comparison of several ad-hoc Key Performance Indicators (KPIs) forevaluating many different location aspects

Competition and cooperation in pickup and multiple delivery problems

Logistics is a highly competitive industry; large hauliers use their size to benefit from economies of scale while small logistics companies are often well placed to service local clients. To obtain economies of scale, small hauliers may seek to cooperate by sharing loads. This paper investigates the potential for cost savings and problems associated with this idea. We study dynamic scheduling of shared loads for real-world truck haulage in the UK and model it as a dynamic pickup and multiple delivery problem (PMDP). In partnership with Transfaction Ltd., we propose realistic cost and revenue functions to investigate how companies of different sizes could cooperate to both reduce their operational costs and to increase profitability in a number of different scenarios