Route-based transportation network design

Given shipment demand and driving regulations, a consolidation carrier has to make decisions on how to route both shipments and drivers at minimal cost. The traditional way to formulate and solve these problems is through the use of two-step models. This thesis presents a heuristic algorithm to solve an integrated model that can provide superior solutions. The algorithm combines a slope scaling initialization phase and tabu search to find high-quality solutions. The performance of the proposed heuristic is benchmarked against a commercial solver and these results indicate that the proposed method is able to produce better quality solutions for the similar solution time

A scheme for determining vehicle routes based on Arc-based service network design

In freight transportation, less-than-truckload carriers often need to assign each vehicle a cyclic route so that drivers can come back home after a certain period of time. However, the Node-Arc model for service network design addresses decisions on each arc and does not determine routes directly, although the vehicle balancing constraint ensures that the number of outgoing vehicles equals the number of incoming vehicles at each node. How to transform the optimized service network into a set of vehicle routes remains an important problem that has not yet been studied. In this paper, we propose a three-phase scheme to address this problem. In the first stage, we present an algorithm based on the depth-first search to find all of the different cyclic routes in a service network design solution. In the second stage, we propose to prune poor cyclic routes using real-life constraints so that a collection of acceptable vehicle routes can be obtained before route assignment. Some of the pruning can also be done in the first stage to speed up the proposed algorithm. In the third stage, we formulate the problem of selecting a set of cyclic routes to cover the entire network as a weighted set covering problem. The resulting model is formulated as an integer program and solved with IBM ILOG CPLEX solver. Experimental results on benchmark instances for service network design indicate the effectiveness of the proposed scheme which gives high-quality solutions in an efficient way

A Stochastic Discrete Optimization Model for Multimodal Freight Transportation Network Design

Since the freight transport mostly incorporates several modes and transshipments, the uncertainty issue becomes more and more important. This uncertainty can be influenced by several factors, which essentially affect the transportation cost. This paper presents a stochastic discrete optimization model for designing multimodal freight transportation network, which includes the decision of transshipment location and mode choice. Taking different direction from the previous researches, the model takes into account the variation effect of traffic flow, capacity and loading/unloading productivity to the freight cycle time which directly influences the transportation cost. A Monte Carlo simulation is then applied to propagate those variations by following the observed distribution. A combinatorial optimization model is also incorporated to find the right combination of transportation alternatives, where genetic local search (GLS) is invoked. Its applicability is then tested to an actual multimodal network, which consists of sea and land transportation modes and several transshipment terminals

Hybrid metaheuristics for the accessibility windows assembly line balancing problem level 2 (AWALBP-L2)

This paper addresses an assembly line balancing problem in which the length of the workpieces is larger than the width of the workstations. The problem differs from traditional variants of assembly line balancing in the sense that only a portion of the workpiece, or portions of two consecutive workpieces, can be reached from any workstation. Consequently, at any stationary stage of the cycle, each workstation can only process a portion of the tasks, namely, those which are inside the area of a workpiece that is reachable from the workstation. The objective is to find a (cyclic) movement scheme of the workpieces along the line and a task assignment to stationary stages of the production process, while minimizing the cycle time. We propose three hybrid approaches of metaheuristics and mathematical programming - one based on simulated annealing and the other two based on tabu search, relying on different neighborhood definitions. The two former approaches make use of a classical neighborhood, obtained by applying local changes to a current solution. The latter approach, in contrast, draws ideas from the corridor method to define a corridor around the current solution, via the imposition of exogenous constraints on the solution space of the problem. An extensive computational experiment is carried out to test the performance of the proposed approaches, improving the best results published to date.Postprint (author's final draft

Soft clustering-based scenario bundling for a progressive hedging heuristic in stochastic service network design

We present a method for bundling scenarios in a progressive hedging heuristic (PHH) applied to stochastic service network design, where the uncertain demand is represented by a finite number of scenarios. Given the number of scenario bundles, we first calculate a vector of probabilities for every scenario, which measures the association strength of a scenario to each bundle center. This membership score calculation is based on existing soft clustering algorithms such as Fuzzy C-Means (FCM) and Gaussian Mixture Models (GMM). After obtaining the probabilistic membership scores, we propose a strategy to determine the scenario-to-bundle assignment. By contrast, almost all existing scenario bundling methods such as K-Means (KM) assume before the scenario-to-bundle assignment that a scenario belongs to exactly one bundle, which is equivalent to requiring that the membership scores are Boolean values. The probabilistic membership scores bring many advantages over Boolean ones, such as the flexibility to create various degrees of overlap between scenario bundles and the capability to accommodate scenario bundles with different covariance structures. We empirically study the impacts of different degrees of overlap and covariance structures on PHH performance by comparing PHH based on FCM/GMM with that based on KM and the cover method, which represents the state-of-the-art scenario bundling algorithm for stochastic network design. The solution quality is measured against the lower bound provided by CPLEX. The experimental results show that, GMM-based PHH yields the best performance among all methods considered, achieving nearly equivalent solution quality in a fraction of the run-time of the other methods