A metaheuristic for stochastic service network design

Hoff, Arild; Lium, Arnt-Gunnar; Løkketangen, Arne; Crainic, Teodor Gabriel

doi:10.1007/s10732-009-9112-8

Cited by 44 publications

(28 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, studies that model time dependencies, such as Andersen et al [2009b], and consolidation opportunities, such as Moccia et al [2011], frequently assume deterministic demand. Research that models uncertainty in the demand, such as Hoff et al [2010], is usually developed for different transportation services of a single transportation mode (e.g, truck), although services offered by LSPs are increasingly becoming intermodal.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, integrating stochasticity in these techniques and heuristics is not straightforward. Further designs, such as stochastic scenarios [Hoff et al, 2010], are necessary to model the variability in some of the DSND problem parameters. Still, the possible gains of incorporating stochasticity into DSND models and solution approaches has been acknowledged in practice [Lium et al, 2009] and an increasing number of studies have been performed in the last years to determine the value of exploiting stochastic information [Zuidwijk and Veenstra, 2015].…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Anticipatory freight selection in intermodal long-haul round-trips

Rivera

Mes

2017

Transportation Research Part E: Logistics and Transportation Re

View full text Add to dashboard Cite

We consider a Logistic Service Provider (LSP) that transports freight periodically in a long-haul round-trip. At the start of a round-trip, the LSP consolidates freights in a long-haul vehicle and delivers them to multiple destinations in a region. Within this region, the LSP picks up freights using the same vehicle and transports them back to the starting location. The same region is visited every period, independent of which freights were consolidated. Consequently, differences in costs between two periods are due to the destinations visited (for delivery and pickup of freights) and the use of an alternative transport mode. Freights have different time-windows and become known gradually over time. The LSP has probabilistic knowledge about the arrival of freights and their characteristics. Using this knowledge, the goal of the LSP is to consolidate freights in a way that minimizes the total costs over time. To achieve this goal, we propose the use of a look-ahead policy, which is computed using an Approximate Dynamic Programming (ADP) algorithm. We test our solution method using information from a Dutch LSP that transports containers daily, by barge, from the East of the country to different terminals in the port of Rotterdam, and back. We show that, under different instances of this real-life information, the use of an ADP policy yields cost reductions up to 25.5% compared to a benchmark policy. Furthermore, we discuss our findings for several network settings and state characteristics, thereby providing key managerial insights about look-ahead policies in intermodal long-haul round-trips.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Anticipatory freight selection in intermodal long-haul round-trips

Rivera

Mes

2017

Transportation Research Part E: Logistics and Transportation Re

View full text Add to dashboard Cite

show abstract

“…On the other hand, the few models which include time dependencies, such as [3], are developed for a single mode of transportation. The few models that include uncertainty in the demand, such as [6], are usually developed for the road transportation mode.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A disadvantage of most of these heuristics and mathematical programming techniques is that they are less suitable for stochastic settings. Further design such as stochastic scenarios [6] or probabilistic constraints is required to incorporate stochastic elements on these solution approaches. Nevertheless, the need and the benefits of introducing stochastic elements into DSND formulations have been widely recognized in practice [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Multi-period Freight Consolidation

Rivera

Mes

2015

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Logistic Service Providers (LSPs) offering hinterland transportation face the trade-off between efficiently using the capacity of longhaul vehicles and minimizing the first and last-mile costs. To achieve the optimal trade-off, freights have to be consolidated considering the variation in the arrival of freight and their characteristics, the applicable transportation restrictions, and the interdependence of decisions over time. We propose the use of a Markov model and an Approximate Dynamic Programming (ADP) algorithm to consolidate the right freights in such transportation settings. Our model incorporates probabilistic knowledge of the arrival of freights and their characteristics, as well as generic definitions of transportation restrictions and costs. Using small test instances, we show that our ADP solution provides accurate approximations to the optimal solution of the Markov model. Using a larger problem instance, we show that our modeling approach has significant benefits when compared to common-practice heuristic approaches.

show abstract

“…A mixed-integer programming model was developed with the objective of minimizing the expected cost over all scenarios. Hoff et al 27 is the continuation of Lium et al, 26 with the primary aim of developing efficient approaches that can solve large real-sized instances. A variable neighborhood search (VNS)-based approach was proposed.…”

Section: Introductionmentioning

confidence: 99%

A robust optimization model for green regional logistics network design with uncertainty in future logistics demand

Zhang

Huang

et al. 2015

Advances in Mechanical Engineering

View full text Add to dashboard Cite

This article proposes a new model to address the design problem of a sustainable regional logistics network with uncertainty in future logistics demand. In the proposed model, the future logistics demand is assumed to be a random variable with a given probability distribution. A set of chance constraints with regard to logistics service capacity and environmental impacts is incorporated to consider the sustainability of logistics network design. The proposed model is formulated as a two-stage robust optimization problem. The first-stage problem before the realization of future logistics demand aims to minimize a risk-averse objective by determining the optimal location and size of logistics parks with CO 2 emission taxes consideration. The second stage after the uncertain logistics demand has been determined is a scenario-based stochastic logistics service route choices equilibrium problem. A heuristic solution algorithm, which is a combination of penalty function method, genetic algorithm, and Gauss-Seidel decomposition approach, is developed to solve the proposed model. An illustrative example is given to show the application of the proposed model and solution algorithm. The findings show that total social welfare of the logistics system depends very much on the level of uncertainty in future logistics demand, capital budget for logistics parks, and confidence levels of the chance constraints.

show abstract

A metaheuristic for stochastic service network design

Cited by 44 publications

References 45 publications

Anticipatory freight selection in intermodal long-haul round-trips

Anticipatory freight selection in intermodal long-haul round-trips

Dynamic Multi-period Freight Consolidation

A robust optimization model for green regional logistics network design with uncertainty in future logistics demand

Contact Info

Product

Resources

About