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Warehouses are key elements of supply chain networks, and great attention is paid to increase their efficiency. Highly volatile space requirements are enablers of innovative resource sharing concepts, where warehouse capacities are traded on online platforms. In this context, our paper introduces the on-demand warehousing problem from the perspective of platform providers. The objective prioritises demand-supply matching with maximisation of the number of transactions. If there is a tie, the secondary objective maximises the number of suppliers matched with at least one customer and the number of customers that have matches within a specific threshold with respect to the minimum achievable cost. Besides the mathematical integer programming formulation, a myopic list-based heuristic and an efficient matheuristic approach are presented and benchmarked against the performance of a commercial optimisation solver. The impact of several parameters on the platform's objective is analysed. A particularly relevant finding is that the pricing flexibility on the demand side does not necessarily imply higher payments to the supply side. All data instances are made available publicly to encourage more researchers to work on this timely and challenging topic. [ABSTRACT FROM AUTHOR]

Given the growing importance of cold chains and the need to promote sustainable processes, energy efficiency in refrigerated transports is investigated at operational level. The Refrigerated Routing Problem is defined, involving multi-drop deliveries of palletised unit loads of frozen food from a central depot to clients. The objective is to select the route with minimum fuel consumption for both traction and refrigeration. The problem formulation considers speed variation due to traffic congestion phenomena, as well as decreasing load on board along the route as successive clients are visited. Transmission load for exposure of the vehicle to outdoor temperatures and infiltration load at door opening are modelled, taking into account outdoor conditions varying along the day and the year. The resulting multi-period problem is modelled and solved by means of Constraint Programming. Test scenarios come from a real local network for frozen bread dough distributed to supermarkets. Results show how fuel minimisation leads to the selection of different routes in comparison to the traditional total travel distance or time objectives. Energy savings are affected by demand distribution among the clients, departure time, number of visits per tour, seasonality and location of the delivery network. [ABSTRACT FROM AUTHOR]

We propose a general model for the problem of planning and scheduling steelmaking and casting activities obtained by combining common features and constraints of the operations from a real plant and the literature. For tackling the problem, we develop a simulated annealing approach based on a solution space made of job permutations, which uses as submodule a chronological constructive procedure that assigns processing times and resources to jobs. Our technique, properly tuned in a statistically principled way, is able to find good solutions for a large range of different settings and horizons. In addition, it outperforms both a greedy procedure and a constraint‐based solver developed for comparison purposes on almost all instances. Finally, we have collected several real‐world instances that we make available on the web along with the solution validator and our best results. [ABSTRACT FROM AUTHOR]

The thesis defense timetabling problem consists in composing the suitable committee for a set of defense sessions and assigning each graduation candidate to one of the sessions. In this work, we define the problem formulation that applies to some Italian universities and we provide three alternative solution methods, based on Integer Programming, Constraint Programming and Local Search, respectively. We also develop a principled instance generator, in order to expand the set of available instances. We perform an experimental analysis and we compare our solvers among themselves, using a testbed composed of both real-world and artificial instances. Even though there is no dominant method, the outcome is that Integer Programming gives the best average results, with Local Search being second, and Constraint Programming last on our testbed. All data is available on the web for verification and future comparisons. [ABSTRACT FROM AUTHOR]

In recent years, cold food chains have shown an impressive growth, mainly due to customers life style changes. Consequently, the transportation of refrigerated food is becoming a crucial aspect of the chain, aiming at ensuring efficiency and sustainability of the process while keeping a high level of product quality. The recently defined Refrigerated Routing Problem (RRP) consists of finding the optimal delivery tour that minimises the fuel consumption for both the traction and the refrigeration components. The total fuel consumption is related, in a complex way, to the distance travelled, the vehicle load and speed, and the outdoor temperature. All these factors depend, in turn, on the traffic and the climate conditions of the region where deliveries take place and they change during the day and the year. The original RRP has been extended to take into account also the total driving cost and to add the possibility to slow down the deliveries by allowing arbitrarily long waiting times when this is beneficial for the objective function. The new RRP is formulated and solved as both a Mixed Integer Programming and a novel Constraint Programming model. Moreover, a Local Search metaheuristic technique (namely Late Acceptance Hill Climbing), based on a combination of different neighborhood structures, is also proposed. The results obtained by the different solution methods on a set of benchmarks scenarios are compared and discussed. [ABSTRACT FROM AUTHOR]

We proposed models and metaheuristic search methods to solve complex real-world problems in the domain of computational logistics. In particular we focused on routing and loading problems, which have several practical applications in transportation, cutting, packing, and scheduling.We extended the problem formulations available in the literature to include several practical constraints, and we designed effective metaheuristic approaches based on large composite neighborhood structures. In addition, we made available to the community a set of challenging real-world instances provided by industrial partners. [ABSTRACT FROM AUTHOR]