Comparison of solution approaches for the train load planning problem in seaport terminals

In this paper the train load planning problem arising in a seaport container terminal is considered. This problem consists in determining the optimal assignment of containers to wagon slots in order to maximize the train utilization and, at the same time, to minimize unproductive operations in the terminal. Different solution approaches based on a mathematical programming model are compared. The best solution procedure, satisfactory both in terms of quality of the obtained solutions and for the computational times, is identified through extensive experimental tests. This procedure could be included in a planning tool to be used in real seaport terminals.     

An integer programming approach to the bloodmobile routing problem

Every day, a blood center must determine a set of locations among a group of potential sites to route their vehicles for blood collection so as to avoid shortfalls. In this study, a vehicle routing problem is modeled using an integer programming approach to simultaneously identify number of bloodmobiles to operate and minimize the distance travelled. Additionally, the model is extended to incorporate uncertainty in blood potentials and variable durations in bloodmobile visits. Optimal routings are determined using CPLEX solver and branch-and-price algorithm. Results show that proposed algorithm solve the problem to optimality up to 30 locations within 3600 s.     

Forecasting passenger load for a fixed planning horizon

This paper presents a forecasting system for predicting the number of boarding passengers on each of the next N departure dates of a particular flight leg using as input the booking levels made for the next N departure dates of that flight leg. A forecasting system that gives good estimates of future boardings of a particular flight leg is an important and essential tool for route management in the airline industry. The proposed forecasting system consists of a Kalman filter, which optimally integrates a large class of reservation measurements from a variety of sources and at arbitrary times, which is the case of the data, considered here. The accuracy of the predictions for a case study proved to be promising.     

A three-stage stochastic facility routing model for disaster response planning

This paper presents a three-stage mixed-integer stochastic programming model for disaster response planning, considering the opening of local distribution facilities, initial allocation of supplies, and last mile distribution of aid. The vehicles available for transportation, the state of the infrastructure and the demand of the potential beneficiaries are considered as stochastic elements. Extensive computational testing performed on realistic instances shows that the solutions produced by the stochastic programming model are significantly better than those produced by a deterministic expected value approach.     

The planning and real-time adjustment of courier routing and scheduling under stochastic travel times and demands

This study develops a planning and a real-time adjustment model to plan courier routes and schedules in an urban area and to adjust the planned routes in actual operations. The stochastic travel time aspect of the problem has been addressed at both the planning stage and the operation stage. A heuristic is developed to efficiently solve the stochastic real-time adjustment model and a simulation-based evaluation method is also developed to compare the performance of the proposed models. The test results, related to an international express company’s operations, show the good performance of the proposed models.     

A less-than-truckload carrier collaboration planning problem under dynamic capacities

This paper addresses the deterministic dynamic single carrier collaboration problem for the small- to medium-sized less-than-truckload (LTL) industry. It is formulated as a binary (0–1) multi-commodity minimum cost flow problem and solved using a branch-and-cut algorithm. Its inherent network structure is exploited to generate the lower bounds to the branch-and-cut algorithm using the network simplex method and by relaxing the integrality constraints. Results from numerical experiments indicate inherent trade-offs at the higher degrees of collaboration between waiting for more affordable collaborative capacity and incurring higher holding costs. They also suggest that collaborating LTL carriers experience increased capacity utilization.     

Railway crew capacity planning problem with connectivity of schedules

We study a tactical level crew capacity planning problem in railways which determines the minimum required crew size in a region while both feasibility and connectivity of schedules are maintained. We present alternative mathematical formulations which depend on network representations of the problem. A path-based formulation in the form of a set-covering problem along with a column-and-row generation algorithm is proposed. An arc-based formulation of the problem is solved with a commercial linear programming solver. The computational study illustrates the effect of schedule connectivity on crew capacity decisions and shows that arc-based formulation is a viable approach.     

A GRASP algorithm for the container stowage slot planning problem

This work presents a generalization of the Slot Planning Problem which raises when the liner shipping industry needs to plan the placement of containers within a vessel (stowage planning). State-of-the-art stowage planning relies on a heuristic decomposition where containers are first distributed in clusters along the vessel. For each of those clusters a specific position for each container must be found. Compared to previous studies, we have introduced two new features: the explicit handling of rolled out containers and the inclusion of separations rules for dangerous cargo. We present a novel integer programming formulation and a Greedy Randomized Adaptive Search Procedure (GRASP) to solve the problem. The approach is able to find high-quality solution within 1 s. We also provide comparison with the state-of-the-art on an existing and a new set of benchmark instances.     

Solving an integrated operational transportation planning problem with forwarding limitations

In integrated operational transportation planning (IOTP) problems, the traditional vehicle routing problem is extended by using external resources for the fulfillment of transportation requests. IOTP is getting more complex when the choice of the fulfillment mode is limited for some requests. In this paper, an existing column generation-based heuristic for IOTP is extended by two strategies for handling forwarding limitations. The computational experiments indicate that one of the extended versions of the heuristic outperforms all previous approaches in literature. Further on, the impact of forwarding limitations on different location structures and on the size of the private fleet is analyzed.     

A strategic planning model for the railway system accident rescue problem

The present study examines the location of emergency rescue problems for urban ambulance and railway emergency systems. The proposed model considers probabilistic rescue demand, independent busy fractions of ambulances, and the corresponding risk levels in railway segments. We formulate the proposed model using fuzzy multi-objective programming and solve it using a generic algorithm and a non-dominated sorting genetic algorithm-II. Computation results are analyzed by applying the model to a real-world Taiwan railway system. Analytical results demonstrate that a proper adjustment of the rescue resource location improves rescue effectiveness for railway rescue and urban medical service demand.     

A multi-objective sustainable load planning model for intermodal transportation networks with a real-life application

Growing importance of intermodal transportation necessitates modeling and solving load planning problems by taking into account various complex decisions simultaneously like transportation mode/service type selection, load allocation, and outsourcing. This paper presents a mixed-integer mathematical programming model for a multi-objective, multi-mode and multi-period sustainable load planning problem by considering import/export load flows to satisfy transport demands of customers and many other related issues. Several multiple objective optimization procedures are utilized in order to handle conflicting objectives simultaneously under crisp and fuzzy decision making environments. A real-life case study is also performed to present application and usefulness of the proposed model.     

A scenario planning approach for the flood emergency logistics preparation problem under uncertainty

This paper aims to develop a decision-making tool that can be used by government agencies in planning for flood emergency logistics. In this article, the flood emergency logistics problem with uncertainty is formulated as two stochastic programming models that allow for the determination of a rescue resource distribution system for urban flood disasters. The decision variables include the structure of rescue organizations, locations of rescue resource storehouses, allocations of rescue resources under capacity restrictions, and distributions of rescue resources. By applying the data processing and network analysis functions of the geographic information system, flooding potential maps can estimate the possible locations of rescue demand points and the required amount of rescue equipment. The proposed models are solved using a sample average approximation scheme. Finally, a real example of planning for flood emergency logistics is presented to highlight the significance of the proposed model as well as the efficacy of the proposed solution strategy.     

A novel modeling approach for the fleet deployment problem within a short-term planning horizon

This paper is concerned with model development for a short-term fleet deployment problem of liner shipping operations. We first present a mixed integer nonlinear programming model in which the optimal vessel speeds for different vessel types on different routes are interpreted as their realistic optimal travel times. We then linearize the proposed nonlinear model and obtain a mixed integer linear programming (MILP) model that can be efficiently solved by a standard mixed integer programming solver such as CPLEX. The MILP model determines the optimal route service frequency pattern and take into account the time window constraints of shipping services. Finally, we report our numerical results and performance of CPLEX on randomly generated instances.     

Adaptive large neighborhood search heuristics for the vehicle routing problem with stochastic demands and weight-related cost

The vehicle routing problem (VRP) with stochastic demands and weight-related cost is an extension of the VRP. Although some researchers have studied the VRP with either stochastic demands or weight-related cost, the literature on this problem is quite limited. We adopt the a priori optimization to tackle this problem and propose a dynamic programming to compute the expected cost of each route. We develop the adaptive large neighborhood search heuristics equipped with several approximate methods for the problem. To evaluate our heuristics, we generate 84 test instances. Computational results demonstrate the performance of our heuristics and can serve as benchmarks for future researchers.     

The strategic berth template problem

A marine container terminal operator may have a situation with excessive calling requests to be served especially when some new service contracts are under consideration. For this situation, we propose a strategic berth template problem (BTPS) that selects the ships among the requesting ones to be served and arrange their berth-windows within a limited planning horizon. The BTPS employs the subgradient optimization procedure, which is an improved version of the procedure that the authors developed for the operational berth allocation problem. A wide variety of numerical experiments indicate the improved subgradient procedure works well for the BTPS.     

The generalized bin packing problem

In the Generalized Bin Packing Problem (GBPP), given two sets of compulsory and non-compulsory items characterized by volume and profit and a set of bins with given volume and cost, we want to select the subset of profitable non-compulsory items to be loaded together with the compulsory ones into the appropriate bins in order to minimize the total net cost. Lower and upper bounds to the GBPP are given. The results of extensive computational experiments show that the proposed procedures are efficient and the bounds are tight.     

The generalized bin packing problem

In the Generalized Bin Packing Problem (GBPP), given two sets of compulsory and non-compulsory items characterized by volume and profit and a set of bins with given volume and cost, we want to select the subset of profitable non-compulsory items to be loaded together with the compulsory ones into the appropriate bins in order to minimize the total net cost. Lower and upper bounds to the GBPP are given. The results of extensive computational experiments show that the proposed procedures are efficient and the bounds are tight.     

2-Stage stochastic programming approach for hub location problem under uncertainty: A case study of air network of Iran

In this paper 2-stage stochastic programming has been developed for formulating stochastic uncapacitated multiple allocation hub location problem. This problem is studied under three cases. The first, stochastic demand, the second, stochastic transportation cost and the third, integrated stochastic, which is compounded of first and second cases. A case of air network in Iran is used to evaluate proposed formulations and computational results obtained by GAMS are presented. The results show that considering uncertainty into formulation could cause in different solutions.     

The congested multicommodity network design problem

This paper studies a version of the fixed-charge multicommodity network design problem where in addition to the traditional costs of flow and design, congestion at nodes is explicitly considered. The problem is initially modeled as a nonlinear integer programming formulation and two solution approaches are proposed: (i) a reformulation of the problem as a mixed integer second order cone program to optimally solve the problem for small to medium scale problem instances, and (ii) an evolutionary algorithm using elements of iterated local search and scatter search to provide upper bounds. Extensive computational results on new benchmark problem instances and on real case data are presented.     

The benefit of advance load information for truckload carriers

This paper models and measures the profit improvement trucking companies can achieve by collaborating with their clients to obtain advance load information (ALI). The main approach is to formulate a comprehensive and flexible mixed integer mathematical model and implement it in a dynamic rolling horizon context. The findings illustrate that access to the second and the third day ALI can improve the profit by averages of 22% and 6%, respectively. We also found that the impact of ALI depends on radius of service and trip length but is statistically independent of load density and fleet size.