The multi-path Traveling Salesman Problem with stochastic travel costs

Given a set of nodes, where each pair of nodes is connected by several paths and each path shows a stochastic travel cost with unknown distribution, the multipath Traveling Salesman Problem with stochastic travel costs aims at finding an expected minimum Hamiltonian tour connecting all nodes. Under a mild assumption on the unknown probability distribution a deterministic approximation of the stochastic problem is given. The comparison of such approximation with a Montecarlo simulation shows both the accuracy and the eciency of the deterministic approximation, with a mean percentage gap around 2% and a reduction of the computational times of two orders of magnitude

Modeling the retail system competition

The retail system is a competitive environment and its transformations have a relevant socio-economic impact. In this context, it is important to represent customer-store interactions, and, to this end, literature mostly proposes logit models. It is well-known that these models present some behavioral and structural anomalies (e.g., the Independence-from-Irrelevant-Alternatives) making them hardly applicable to retail system analysis. In this paper, we show that even some alternative approaches (e.g. Nested-logit or Paired-Combinatorial logit models) do not suitably represent the competition between retail stores, and we present a new modeling framework. It aims at overcoming the above limits by two cooperating logit-based models: the first one analyzes customer-store interactions; the second model uses the interaction information to evaluate the impact of some major transformations. The framework has been integrated in a decision support system and used in real-life cases to determine the impact of new stores in some Italian regions

On a novel optimisation model and solution method for tactical railway maintenance planning

Within the ACEM-Rail project of the European Seventh Framework Programme new measurement and inspection techniques for monitoring the track condition are developed. By means of these new techniques the prediction of future track condition will be highly improved. To our knowledge mid-term maintenance planning is done for projects and preventive tasks, but predictions of the track condition are not incorporated into the planning process up to now. To efficiently utilise this new kind of information one task within the ACEM-Rail project is the development of methods for planning predictive maintenance tasks along with preventive and corrective ones in a mid-term planning horizon. The scope of the mid-term or tactical maintenance planning is the selection and combination of tasks and the allocation of tasks to time intervals where they will be executed. Thereby a coarse maintenance plan is determined that defines which tasks are combined together to form greater tasks as well as the time intervals for executing the selected tasks. This tactical plan serves as the base for booking future track possessions and for scheduling the maintenance tasks in detail. In this paper an algorithmic approach is presented which is able to react on dynamic and uncertain changes due to any track prediction updating. To this end optimisation algorithms are implemented within a rolling planning process, so it is possible to respond to updated information on track condition by adapting the tactical plan. A novel optimisation method is developed to generate cost effective and robust solutions by looking ahead into the future and evaluating different solutions in several scenario

The Multi-path Traveling Salesman Problem with Stochastic Travel Costs: Building Realistic Instances for City Logistics Applications

One of the main issues related to routing problems applied in an urban context with uncertainty related to the transportation costs is how to define realistic instances. In this paper, we overcome this issue, providing a standard methodology to extend routing instances from the literature incorporating real data provided by sensors networks. In order to test the methodology, we consider a routing problem specifically designed for City Logistics and Smart City applications, the multi-path Traveling Salesman Problem with stochastic travel costs, where several paths connect each pair of nodes and each path shows a stochastic travel cost with unknown distribution

An efficient meta-heuristic for multi-dimensional multi-container packing

Tech. Rep. CIRRELT-2011-14, CIRRELT, Montrea

Smart Steaming: A New Flexible Paradigm for Synchromodal Logistics

Slow steaming, i.e., the possibility to ship vessels at a significantly slower speed than their nominal one, has been widely studied and implemented to improve the sustainability of long-haul supply chains. However, to create an efficient symbiosis with the paradigm of synchromodality, an evolution of slow steaming called smart steaming is introduced. Smart steaming is about defining a medium speed execution of shipping movements and the real-time adjustment (acceleration and deceleration) of traveling speeds to pursue the entire logistic system’s overall efficiency and sustainability. For instance, congestion in handling facilities (intermodal hubs, ports, and rail stations) is often caused by the common wish to arrive as soon as possible. Therefore, smart steaming would help avoid bottlenecks, allowing better synchronization and decreasing waiting time at ports or handling facilities. This work aims to discuss the strict relationships between smart steaming and synchromodality and show the potential impact of moving from slow steaming to smart steaming in terms of sustainability and efficiency. Moreover, we will propose an analysis considering the pros, cons, opportunities, and risks of managing operations under this new policy

A Progressive Hedging Method for the Optimization of Social Engagement and Opportunistic IoT Problems

Due to the spread of the social engagement paradigm, several companies are asking people to perform tasks in exchange for a reward. The advantages of this business model are savings in economic and environmental terms. In previous works, it has been proved that the problem of finding the minimum amount of reward such that all tasks are performed is difficult to solve, even for medium-size realistic instances (if more than one type of person is considered). In this paper, we propose a customized version of the progressive hedging algorithm that is able to provide good solutions for large realistic instances. The proposed method reaches the goal of defining a procedure that can be used in real environments