Minimum cost path problem for Plug-in Hybrid Electric Vehicles

We introduce a practically important and theoretically challenging problem: finding the minimum cost path for PHEVs in a road network with refueling and charging stations. We show that this problem is NP-complete and present a mixed integer quadratically constrained formulation, a discrete approximation dynamic programming heuristic, and a shortest path heuristic as solution methodologies. Practical applications of the problem in transportation and logistics, considering specifically the long-distance trips, are discussed in detail. Through extensive computational experiments, significant insights are provided. In addition to the charging infrastructure availability, a driver’s stopping tolerance arises as another critical factor affecting the transportation costs.     

Incorporating inventory control decisions into a strategic distribution network design model with stochastic demand

In this paper, we propose a simultaneous approach to incorporate inventory control decisions––such as economic order quantity and safety stock decisions––into typical facility location models, which are used to solve the distribution network design problem. A simultaneous model is developed considering a stochastic demand, modeling also the risk pooling phenomenon. We present a non-linear-mixed-integer model and a heuristic solution approach, based on Lagrangian relaxation and the sub-gradient method. In a numerical application, we found that the potential cost reduction, compared to the traditional approach, increases when the holding costs and/or the variability of demand are higher.     

The capacitated plant location problem with customers and suppliers matching

This paper introduces a new problem called the capacitated plant location problem with customer and supplier matching (CLCSM). The product distribution from plants to customers and the material supply from suppliers to plants are considered together. We merge a distribution trip and a supply trip into one triangular trip for saving allocation cost. Vehicles from plants visit a customer and a supplier for each trip. We provide a heuristic solution procedure based on Lagrangian relaxation. Computational results indicate that the proposed heuristic solution procedure is shown to be efficient yielding optimal or near-optimal solutions for randomly generated instances.     

Optimal traffic calming: A mixed-integer bi-level programming model for locating sidewalks and crosswalks in a multimodal transportation network to maximize pedestrians’ safety and network usability

We study the effect that installing sidewalks and crosswalks, as traffic calming facilities, has on the safety and usability of a transportation network with automobile, public transit and walking as modes of transportation. A mathematical programming model is proposed for this problem whose objective is to minimize the safety hazard for pedestrians and the total transportation cost of the network. We utilize a customized greedy heuristic and a simulated annealing algorithm for solving the problem. The computational results indicate that installing sidewalks and crosswalks at proper locations can reduce the overall transportation cost and improve pedestrians’ safety.     

Optimal placement of multiple types of detectors under a small vessel attack threat to port security

We focus on a threat scenario where a terrorist would utilize a small vessel to attack a maritime target. We consider how to place multiple types of detectors to protect maritime targets from such an attack. Detectors are not perfectly reliable. The resulting detector placement problem is formulated as a nonlinear binary integer program such that the expected damage cost caused by the small vessel attack is minimized. Two exact algorithms and a greedy adding heuristic are proposed. Moreover, we conduct a detailed computational study and provide a case study in New York Harbor.     

Berth scheduling by customer service differentiation: A multi-objective approach

In this paper the discrete and dynamic berth allocation problem is formulated as a multi-objective combinatorial optimization problem where vessel service is differentiated upon based on priority agreements. A genetic algorithms based heuristic is developed to solve the resulting problem. A number of numerical experiments showed that the heuristic performed well in solving large, real life instances. The heuristic provided a complete set of solutions that enable terminal operators to evaluate various berth scheduling policies and select the schedule that improves operations and customer satisfaction. The proposed algorithm outperformed a state of the art metaheuristic and provided improved results when compared to the weighted approach.     

A heuristic approach for solving an integrated gate reassignment and taxi scheduling problem

Capacity limitation of airport ground operation is one of the major limiting factors in air traffic operation. The congestion on the gate and taxiway causes severe delay and propagate effect on the flight schedule. This paper considers the problem of integrated gate reassignment and taxiway scheduling, in which complex constraints related to runway restriction, gate allocation and taxiway conflict are all incorporated when determining the schedule. To solve this problem, we propose a novel heuristic approach. First, all possible aircraft schedules are enumerated by disretizing the waiting time along the path. Then, the cost is evaluated for each schedule and the conflict detection is conducted to generate constraint sets. Finally, we propose a set partition model, in which each decision variable denotes a candidate schedule that takes into account the possible constraints when generated. This method is compared with a sequential method that solves gate reassignment and taxiway scheduling problem separately. Computational results highlight the strength of our method.     

A hybrid population heuristic for the heterogeneous vehicle routing problems

The heterogeneous vehicle routing problem (HVRP) plays an important role in supply chain logistics. Two variants of HVRP are treated in this paper: one with fixed and variable costs (HVRPFD), and the other with only variable cost (HVRPD). A hybrid population heuristic that is able to solve both variants is proposed, in which a population of solutions are progressively evolved by crossovers and local searches. Computational results on a set of eight benchmark test problems from literature show that the proposed heuristic produces excellent solutions in short computing times.     

A hybrid parallel genetic algorithm for yard crane scheduling

This paper aims at postulating a novel strategy in terms of yard crane scheduling. In this study, a dynamic scheduling model using objective programming for yard cranes is initially developed based on rolling-horizon approach. To resolve the NP-complete problem regarding the yard crane scheduling, a hybrid algorithm, which employs heuristic rules and parallel genetic algorithm (PGA), is then employed. Then a simulation model is developed for evaluating this approach. Finally, numerical experiments on a specific container terminal yard are used for system illustration. Computational results suggest that the proposed method is able to solve the problem efficiently.     

Integrated recovery of aircraft and passengers after airline operation disruption based on a GRASP algorithm

This paper considers the integrated recovery of both aircraft routing and passengers. A mathematical model is proposed based on both the flight connection network and the passenger reassignment relationship. A heuristic based on a GRASP algorithm is adopted to solve the problem. A passenger reassignment solution is demonstrated to be optimal in each iteration for a special case. The effectiveness of the heuristic is illustrated through experiments based on synthetic and real-world datasets. It is shown that the integrated recovery of flights and passengers can decrease both the recovery cost and the number of disrupted passengers.     

A large neighborhood search heuristic to establish an optimal ad-hoc hubbing strategy in the wake of a large-scale airport outage

Abrupt airport outages can cause diversions and fuel-critical situations for flights, leading to costly passenger misconnections. We develop a large neighborhood search heuristic to optimize the rerouting of flights bound for a disrupted airport to a hub airport that is not disrupted, with the goal of accommodating passengers on existing flights departing the non-disrupted hub. The objective of the heuristic is to identify and reroute flights to the ad-hoc hub(s) – non-disrupted hub airport(s) – that minimize the sum of passenger travel time and wait time. We minimize the passenger cost as the sum of passenger travel time to the diversion airport and wait time for a connecting flight at the ad-hoc hub airport, subject to on-board fuel and diversion airport capacity constraints. We use the heuristic to determine how a coordinated traffic management strategy could have diverted flights immediately following a real-world airport outage.     

Hub location problems in transportation networks

In this paper we propose a 4-index formulation for the uncapacitated multiple allocation hub location problem tailored for urban transport and liner shipping network design. This formulation is very tight and most of the tractable instances for MIP solvers are optimally solvable at the root node. While the existing state-of-the-art MIP solvers fail to solve even small size instances of problem, our accelerated and efficient primal (Benders) decomposition solves larger ones. In addition, a very efficient greedy heuristic, proven to be capable of obtaining high quality solutions, is proposed. We also introduce fixed cost values for Australian Post (AP) dataset.     

Model and algorithm for an unpaired pickup and delivery vehicle routing problem with split loads

This paper addresses the routing problem with unpaired pickup and delivery with split loads. An interesting factor of our problem is that the quantity and place for pickup and delivery are decision variables in the network. We develop an easy-to-implement heuristic in order to gain an efficient and feasible solution quickly. Then, a local search algorithm based on the variable neighborhood search (VNS) method is developed to improve the performance of the heuristic. Computational results show that the proposed VNS method is able to obtain an optimal or near optimal solution in reasonable time for the formulated problem.     

Vector network equilibrium problems with capacity constraints of arcs

In this paper, we study a (weak) vector equilibrium principle with capacity constraints of arcs and common arcs in some different paths. We obtain some necessary and sufficient conditions for a (weak) vector minimum cost flow. By virtue of a (weak) Δ-equilibrium principle, we also derive some necessary and sufficient conditions for a weak vector equilibrium flow.     

A location-routing problem with disruption risk

This paper considers a location-routing problem in a supply-chain network with a set of producer–distributors that produce a single commodity and distribute it to a set of customers. The production capacity of each producer–distributor varies randomly due to a variety of possible disruptions, and the vehicles involved in the distribution system are disrupted randomly. The goal is to determine the location, allocation and routing decisions that minimize the annual cost of location, routing and disruption, under one of the moderate, cautious or pessimistic risk-measurement policies. Exact formulations and an efficient heuristic are presented for the problem.     

Resolving splits in location/allocation modeling: a heuristic procedure for transit center decisions

A heuristic procedure is developed to assign buses to transit centers (garages) in such a way that all the buses on a particular route are assigned to a single transit center. This research builds on an optimal mixed integer programming location/allocation model that splits the bus assignments when capacity limitations were reached at a transit center. The heuristic procedure adopts a two-step process: namely, assignment of all buses of a route to a unique transit center, then switching of routes to alternative transit centers to enforce capacity limitations. The procedure is shown to still provide cost savings over current locations and allocations for the Vancouver Regional Transit System (VRTS), Canada's largest urban transit network.     

Managing severe airspace flow programs: The Airlines’ side of the problem

This paper presents a heuristic-based approach for minimizing airlines’ schedule disruptions and operation costs associated with severe airspace flow programs. It considers primary decisions made by flight dispatchers such as flight slot substitution and rerouting outside the boundaries of the flow-constrained area. A two-stage heuristic is developed. In the first, a linear approximation of the problem is used to screen inefficient routing and slot substitution alternatives. The second stage examines possible solution improvements through trading flight assignments for every pair of conflicting routes. A genetic algorithm is developed and used to benchmark the performance of the two-stage heuristic. In the algorithm, flight route and slot allocation schemes are modeled as chromosomes. The fitness of these chromosomes measures the magnitude of schedule disruption and overall operating cost. A set of experiments that compare the performance of the two heuristics considering airspace flow programs with different levels of severity is presented.     

The generalized lock scheduling problem: An exact approach

The present paper introduces an integrated approach to solving the generalized lock scheduling problem. Three interrelated sub problems can be discerned: ship placement, chamber assignment and lockage operation scheduling. In their turn, these are closely related to the 2D bin packing problem, the assignment problem and the (parallel) machine scheduling problem respectively. In previous research, the three sub problems mentioned were considered separately, often using (heuristic) interaction between them to obtain better solutions. A mixed integer linear programming model is presented and applied to instances from both inland locks and locks in a tide independent port. The experiments show that small instances incorporating a wide range of real-life constraints can be solved to optimality.     

Comparing heuristic algorithms of the planar storage location assignment problem

This paper discusses the newly defined planar storage location assignment problem (PSLAP). We develop a mathematical programming model and GA-based and dynamic PSLAP heuristic algorithms for the solving procedure. Using the testing set, we compare the performance of GA-based and dynamic PSLAP heuristic algorithms. The mathematical programming model is utilized as a comparison criterion. The comparison results demonstrate that the dynamic PSLAP heuristic algorithm performs better than the other solving procedures. In addition, we describe simulation experiments conducted to investigate the effects of stock yard layout and production schedule instability on the operation of the block stock yard.     

Robust planning and disruption management in roll-on roll-off liner shipping

This paper presents different strategies for handling disruptions in fleet deployment in roll-on roll-off liner shipping, which basically consists of assigning a fleet of vessels to predefined voyages at minimum cost. A new mathematical model of the problem is presented, including a set of robust planning strategies, such as adding slack and rewarding early arrivals. To solve real-life instances a rolling horizon heuristic is proposed. A computational study, where we also propose some recovery planning strategies, is conducted, and simulation results show that adding robustness significantly reduces the actual cost of the plan and the total delays of the voyages.