This paper proposes a solution approach for the multicommodity capacitated fixed-charge network design problem with uncertain demand modeled as a two-stage stochastic program. The proposed learning-based matheuristic combines heuristic search techniques with mathematical programming. It provides a systematic approach to identifying structures of good-quality solutions by gradually considering scenarios and their influences on design decisions. Extensive computational experiments illustrate the efficiency of the proposed matheuristic in obtaining high-quality solutions with limited computational efforts.

City logistics seeks to optimize the distribution of goods in urban areas by developing new business models. Such models are not only centered on cost reduction, but also account for reducing the negative impact resulting from city logistics activities. Therefore, environmental aspects or congestion are important factors as well. Through consolidation of goods of different shipper-consignee pairs, the utilization of urban vehicles is improved and the total kilometers traveled within the city can be reduced. In the literature, inbound and outbound traffic are treated separately. This, however, results in empty urban vehicle traffic and reduces utilization of the system. Therefore, we consider a city logistics system that simultaneously accounts for both inbound and outbound demand. We consider a two-tier system, where the inbound goods are transported from external zones to satellites from where the final distribution is performed. The outbound demands are shipped via satellites to the external zones. To analyze the impact of considering both flows, we define and compare key performance indicators, like the urban vehicle utilization and number of vehicles. Numerical analyses are performed on different network structures and demand patterns. The results show the importance of combining both flows within one system. Moreover, we give insights on how different key performance indicators vary depending on the network and demand scenario.

Abstract We consider the stochastic fixed‐charge capacitated multicommodity network design (S‐CMND) problem with uncertain demand. We propose a two‐stage stochastic programming formulation, where design decisions make up the first stage, while recourse decisions are made in the second stage to distribute the commodities according to observed demands. The overall objective is to optimize the cost of the first‐stage design decisions plus the total expected distribution cost incurred in the second stage. To solve this formulation, we propose a metaheuristic framework inspired by the progressive hedging algorithm of Rockafellar and Wets. Following this strategy, scenario decomposition is used to separate the stochastic problem following the possible outcomes, scenarios, of the random event. Each scenario subproblem then becomes a deterministic CMND problem to be solved, which may be addressed by efficient specialized methods. We also propose and compare different strategies to gradually guide scenario subproblems to agree on the status of design arcs and aim for a good global design. These strategies are embedded into a parallel solution method, which is numerically shown to be computationally efficient and to yield high‐quality solutions under various problem characteristics and demand correlations. © 2011 Wiley Periodicals, Inc. NETWORKS, 2011.

Addressing multi-attribute, ldquorichrdquo combinatorial optimization problems in a comprehensive manner presents significant methodological and computational challenges. In this paper, we present an integrative multi-thread cooperative optimization framework that can simultaneously deal with multiple dimensions of a rich problem. We present the basic concepts and detail the design and operating principles of the methodology. We illustrate the framework on a rich combinatorial problem, an extended version of the vehicle routing problem with the duration and capacity constraints as well as time windows, multiple periods and multiple depots.

This paper shows how local branching can be used to accelerate the classical Benders decomposition algorithm. By applying local branching throughout the solution process, one can simultaneously improve both the lower and upper bounds. We also show how Benders feasibility cuts can be strengthened or replaced with local branching constraints. To assess the performance of the different algorithmic ideas presented in this hybrid solution approach, extensive computational experiments were performed on two families of network design problems. Numerical results clearly illustrate their benefits.