Attended Home Delivery is a last-mile distribution paradigm in which the customer must be present at home to receive the goods in person.In this context, we study a vehicle routing and scheduling problem in which the customers' availability is given as a time-dependent probability profile, and the company incurs a penalty cost proportional to the probability of not finding the customer at home during the selected timeslot for delivery.Using an efficient Mixed-Integer Linear Programming formulation for the problem as a black-box tool and lexicographic optimization procedures, we develop an economic analysis to support the company in exploiting the tradeoff between basic optimization and the possibility of increasing the customer presence probabilities by paying additional costs.Managerial insights are derived from the change in the value and structure of optimal solutions under different budget levels allocated to improving customer availability profiles.This work has been supported by "ULTRAOPTYMAL -Urban Logistics and sustainable TRAnsportation: OPtimization under uncertainTY and MAchine Learning", a PRIN2020

Social Engagement is a novel business model transforming final users of a service from passive into active components.In this framework, people are contacted by a company and they are asked to perform tasks in exchange for a reward.This arises the complicated optimization problem of allocating the different types of workforce so as to minimize costs.We address this problem by explicitly modeling the behavior of contacted candidates through consolidated concepts from utility theory and proposing a chance-constrained optimization model aiming at optimally deciding which user to contact, the amount of the reward proposed, and how many employees to use in order to minimize the total expected costs of the operations.A solution approach is proposed and its computational efficiency is investigated through experiments.

In recent years, researchers have oriented their studies towards new technologies based on quantum physics that should resolve complex problems currently considered to be intractable.This new research area is called Quantum Computing.What makes Quantum Computing so attractive is the particular way with which quantum technology operates and the great potential it can offer to solve real-world problems.This work focuses on solving assignment-like combinatorial optimization problems by exploiting this novel computational approach.A case-study, denoted as the Seating Arrangement Optimization problem, is considered.It is modeled through the Quadratic Unconstrained Binary Optimization paradigm and solved through two tools made available by the D-Wave Systems company, QBSolv, and a quantum-classical hybrid system.The obtained experimental results are compared in terms of solution quality and computational efficiency.

Abstract The transshipment location–allocation problem consists of locating transshipment facilities (e.g., intermodal hubs) of a transportation network and allocating freight flows through them, from several origins to several destinations, to satisfy demand and supply constraints. The objective is to maximize the total net transportation utility given by the total shipping utility minus the total cost to locate the facilities. Moreover, flow synchronization at the facilities must also be ensured. Unfortunately, the flow synchronization depends on a broad set of unknown events, which could cause both unexpected reductions of the facility capacity and uncertain utility of handling operations. In this paper, we first want to evaluate how uncertainty on facility capacity and handling operations utility affects the transshipment location–allocation problem in terms of complexity, net gain, and optimal solutions. Moreover, we extend the problem from a single to a multi‐period setting to have a wider view of future scenarios realizations and consequently synchronize the flows by using different facilities on different periods. We propose a two‐stage stochastic programming formulation with recourse and analyze, over a ground set of instances, some well‐known economic indicators to derive managerial insights on the importance of addressing uncertainty for the problem. Finally, given the computational burden of solving the deterministic equivalent problem, we propose several heuristics based on progressive hedging and test their performance.

Online scheduling has been an attractive field of research for over three decades.Some recent developments suggest that Reinforcement Learning (RL) techniques have the potential to deal with online scheduling issues effectively.Driven by an industrial application, in this paper we apply four of the most important RL techniques, namely Q-learning, Sarsa, Watkins's Q(λ), and Sarsa(λ), to the online single-machine scheduling problem.Our main goal is to provide insights on how such techniques perform.The numerical results show that Watkins's Q(λ) performs best in minimizing the total tardiness of the scheduling process.

Electric vehicles are accelerating the world's transition to sustainable energy.Nevertheless, the lack of a proper charging station infrastructure in many real implementations still represents an obstacle for the spread of such a technology.In this paper, we present a real case application of optimization techniques in order to solve the location problem of electric charging stations in the district of Biella, Italy.The plan is composed by several progressive installations and decision makers pursue several objectives that might be in contrast.For this reason, we present an innovative framework based on the comparison of several ad-hoc Key Performance Indicators for evaluating many different aspects of a location solution.

We introduce a problem where a fleet of vehicles is available to visit suppliers offering various products at different prices and quantities, with the aim to select a subset of suppliers so to satisfy products demand at the minimum traveling and purchasing costs. Vehicles have a predefined capacity and pairs of products may be incompatible to be carried simultaneously on a same vehicle. We call this problem the multi‐vehicle traveling purchaser problem with pairwise incompatibility constraints. We show how a three‐index one‐commodity flow formulation for the problem is easy to implement with a common MILP solver, but highly nonefficient when solving large size instances. We concentrate on a formulation using connectivity constraints to exclude subtours and introduce a branch‐and‐cut framework using a preprocessing routine and the separation of different valid inequalities. We also propose a four‐step heuristic based on the solution of different subproblems and use it to provide an initial feasible solution. We run computational tests on a large set of instances with up to 50 suppliers, 100 products, and 20% of crossed incompatibility between products. Results show that two different streamlined versions of the proposed exact method largely outperform the plain solution by the commercial solver Cplex 12.3. Also, the heuristic approach is observed to be rather effective and efficient providing a valid solving alternative. © 2014 Wiley Periodicals, Inc. NETWORKS, Vol. 65(2), 139–154 2015