We address the stochastic scheduled service network design problem with quality targets and uncertainty on travel times. This important problem, raising in the tactical planning process of consolidation-based freight carriers, has been little studied up to now. We define the problem considering quality targets for on-time operation of services and delivery of demand loads to destinations. We introduce a two-stage mixed-integer stochastic model defined over a space-time network, with quality targets modeled through penalties. We also propose an effective progressive-hedging-based meta-heuristic, based on a partialdecomposition concept aiming to address the challenges raised by the presence of flow-distribution decisions in the first-stage problem and by the flow-related degeneracy particular to network design. The results of an extensive numerical experimentation emphasize the worthiness of the formulation, as well as the very good performance of the proposed meta-heuristic when compared to a well-known commercial solver.
The scope of this paper is to advance the investigation into the importance of introducing uncertainty in service network design (SND) formulations by examining the uncertainty of travel times, a phenomenon that has been little studied up to now. The topic of our research thus is the stochastic scheduled service network design problem with service-quality targets and uncertainty on travel times, an important problem raising in the tactical planning process of consolidation-based freight carriers. Quality-service targets relate to the on-time operation of services and delivery of commodity flows to destinations. The problem is formulated as a two-stage mixed-integer linear stochastic model defined over a space-time network, with service targets modelled through penalties. Its aim is to define a cost-efficient transportation plan such that the chosen quality-service targets are respected as much as possible over time. An extensive experimental campaign is proposed using a large set of random generated instances with the scope of enhancing the understanding of the relations between the characteristics of a service network and its robustness, in terms of respect of the service schedule and delivery due dates, given business-as-usual fluctuations of travel times. Several analyses are reported identifying the features that appear in stochastic solutions to hedge against or, at least, reduce the bad effects of travel time uncertainty on the performance of a service network.
The paper deals with a sequencing and routing problem originated by a real-world application context. The problem consists in defining the best sequence of locations to visit within a warehouse for the storage and/or retrieval of a given set of items during a specified time horizon, where the storage/retrieval location of an item is given. Picking and put-away of items are simultaneously addressed, by also considering some specific requirements given by the layout design and operating policies which are typical in the kind of warehouses under study. Specifically, the considered sequencing policy prescribes that storage locations must be replenished or emptied one at a time by following a specified order of precedence. Moreover, two fleet of vehicles are used to perform retrieving and storing operations, whose routing is restricted to disjoint areas of the warehouse. We model the problem as a constrained multicommodity flow problem on a space-time network, and we propose two Mixed-Integer Linear Programming formulations, whose primary goal is to minimize the time traveled by the vehicles during the time horizon. Since large-size realistic instances are hardly solvable within the time limit commonly imposed in the considered application context, a matheuristic approach based on a time horizon decomposition is proposed. Finally, we provide an extensive experimental analysis aiming at identifying suitable parameter settings for the proposed approach, and testing the matheuristic on particularly hard realistic scenarios. The computational experiments show the efficacy and the efficiency of the proposed approach.
The paper deals with a sequencing and routing problem originated by a real-world application context. The problem consists in defining the best sequence of locations to visit within a warehouse for the storage and/or retrieval of a given set of items during a specified time horizon, by considering some specific requirements and operating policies which are typical of the kind of warehouse under study. A fleet composed of both electric (i.e., equipped with a lithium-ion battery) and conventional (i.e., with internal combustion engine) forklifts is considered. We model the problem in terms of constrained multicommodity flows on a space-time network, and we extend a matheuristic approach proposed for the case of only conventional vehicles. Preliminary computational results are also presented.
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