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.     

Scheduling technicians for planned maintenance of geographically distributed equipment

A real-world planned maintenance scheduling problem that exists at several business units within United Technologies Corporation (UTC) is addressed in this paper. The scheduling problem is formulated as a multiple tour maximum collection problem with time-dependent rewards and an adaptive memory tabu search heuristic is developed to solve it. The effectiveness of the proposed solution approach is examined using real-world problem instances supplied by UTC. Relevant upper bounds are derived for the application. Results of numerical experiments indicate that the proposed tabu search heuristic is able to obtain near optimal solutions for large-size (i.e., actual) problem instances in reasonable computation time.     

Runway capacity management – An empirical study with application to Doha International Airport

This paper examines a three-faceted approach for runway capacity management, based on the runway configuration, a chosen scheduling approach, and an aircraft separation standard. These factors prompt alternative runway settings that are encapsulated using a classical mixed-integer formulation. The optimal solution for each runway setting is compared against our proposed optimization-based heuristic. This integrated approach is applied to investigating the transition from the (Old) Doha International Airport to the New Doha International Airport. Our empirical study based on historical data reveals that the proposed heuristic consistently yields optimal or near-optimal schedules, with considerable savings in fuel cost and reductions in delays, while preserving the spirit of an FCFS sequencing policy.     

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.     

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 robust optimization approach to the integrated berth allocation and quay crane assignment problem

This paper investigates the integrated berth allocation and quay crane assignment problem in container terminals. A deterministic model is formulated by considering the setup time of quay cranes. However, data uncertainties widely exist, and it may cause the deterministic solution to be infeasible. To handle the uncertainties, a robust optimization model is established. Furthermore, to control the level of conservativeness, another robust optimization model with the price constraints is proposed. A genetic algorithm and an insertion heuristic algorithm are suggested to obtain near optimal solutions. Computational experiments indicate that the presented models and algorithms are effective to solve the problems.     

The Electric Traveling Salesman Problem with Time Windows

To minimize greenhouse gas emissions, the logistic field has seen an increasing usage of electric vehicles. The resulting distribution planning problems present new computational challenges.We address a problem, called Electric Traveling Salesman Problem with Time Windows. We propose a mixed integer linear formulation that can solve 20-customer instances in short computing times and a Three-Phase Heuristic algorithm based on General Variable Neighborhood Search and Dynamic Programming.Computational results show that the heuristic algorithm can find the optimal solution in most small-size instances within a tenth of a second and achieves goods solutions in instances with up to 200 customers.     

The simultaneous berth and quay crane allocation problem

This paper addresses efficient berth and crane allocation scheduling at a multi-user container terminal. First, we introduce a formulation for the simultaneous berth and crane allocation problem. Next, by employing genetic algorithm we develop a heuristic to find an approximate solution for the problem. The fitness value of a chromosome is obtained by crane transfer scheduling across berths, which is determined by a maximum flow problem-based algorithm based on a berth allocation problem solution defined by the chromosome. The results of numerical experiments show that the proposed heuristic is applicable to solve this difficult but essential terminal operation problem.     

A novel heuristic approach for solving aircraft landing problem with single runway

Nowadays, airlines administrations are more willing to utilize optimization tools to control air traffic due to considerable increases in volume of air transports. A challenging problem in the field of air traffic is how to optimally schedule landing time of aircrafts and assign them to different runways such that early and late landing costs are minimized. This problem is called aircraft landing problem (ALP). This paper proposes a novel decomposition based heuristic by solving two sub-problems for the ALP with single runway. In the first sub-problem, we apply the adaptive large neighborhood search (ALNS) algorithm to find a sequence of aircrafts. The solution found in the first sub-problem will be sent to the second sub-problem, to check for the feasibility of the solution using CPLEX solver. A set of benchmark problem are taken from the OR library for the purpose of comparison with other existing approaches. The computational results exhibit that the proposed algorithm is capable of finding the best known optimal solution for all the instances.     

Aircraft line maintenance scheduling and optimisation

With the advent of new technologies and more modern aircraft, many of the maintenance jobs traditionally scheduled for periodic block checks can now be performed in the ‘‘line maintenance” environment, i.e., during layovers between scheduled flights of an aircraft. This flexibility can be exploited to reduce maintenance costs and improve fleet utilisation of an airline. In this paper we introduce and study the Line Maintenance Scheduling Problem (LMSP). The LMSP assigns jobs to available maintenance opportunities, defined by aircraft routes, and sets the starting time for each job. Its objective is to minimise the deviation from this schedule with respect to given due dates for each task, without exceeding resource capacity at the airports at any moment. We formulate the LMSP as a mixed integer programming problem, and describe and compare two solution approaches for this problem: an integrated exact solution algorithm, which solves job assignment and timetabling simultaneously, and a sequential, heuristic approach. We tested our algorithms on a set of instances inspired on data provided by an industry partner. Our experiments show the applicability of both approaches on realistic settings: the exact approach was able to find the optimal solution for all instances, in less than 10 min on average. Our analysis also shows with an example that line maintenance can be more efficient when capacity is spatially spread, even if the total capacity is reduced.     

The optimization model for the location of maritime emergency supplies reserve bases and the configuration of salvage vessels

This paper studies the location–allocation–configuration problem of emergency resources in a maritime emergency system and it proposes a discrete nonlinear integer-programming model, which integrates the location, allocation and the configuration problem. The model is converted into a two-stage model keeping the calculation logic. It designs a hybrid heuristic algorithm and a genetic algorithm. The test results show that the hybrid heuristic algorithm is more efficient than the genetic algorithm, the sensitivity analysis studies the influence of some parameters to the final solution and the Uncertainty–Sensitivity justification tool is used to evaluate the assumptions.     

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.     

An effective genetic algorithm for the fleet size and mix vehicle routing problems

This paper studies the fleet size and mix vehicle routing problem (FSMVRP), in which the fleet is heterogeneous and its composition to be determined. We design and implement a genetic algorithm (GA) based heuristic. On a set of twenty benchmark problems it reaches the best-known solution 14 times and finds one new best solution. It also provides a competitive performance in terms of average solution.     

Incorporating location,routing and inventory decisions in supply chain network design

This paper for the first time presents a novel model to simultaneously optimize location, allocation, capacity, inventory, and routing decisions in a stochastic supply chain system. Each customer’s demand is uncertain and follows a normal distribution, and each distribution center maintains a certain amount of safety stock. To solve the model, first we present an exact solution method by casting the problem as a mixed integer convex program, and then we establish a heuristic method based on a hybridization of Tabu Search and Simulated Annealing. The results show that the proposed heuristic is considerably efficient and effective for a broad range of problem sizes.     

Heuristics for quay crane scheduling at indented berth

This paper discusses the quay crane scheduling problem at indented berth, an extension to the current quay crane scheduling problem in the field of container terminal operation. A mixed integer programming model by considering the unique features of the quay crane scheduling problem at indented berth is formulated. For solution, decomposition heuristic framework is developed and enhanced by Tabu search. To evaluate the performance of the proposed heuristic framework, a comprehensive numerical test is carried out and its results show the good quality of the proposed heuristic framework.     

A matheuristic approach to the air-cargo recovery problem under demand disruption

Air cargo transport is subject to unpredictable changes in expected demand, necessitating adjustments to itinerary planning to recover from such disruptions. We study a flight rescheduling problem to react to cargo demand disruptions in the short run. To increase flexibility, we consider two different cargo assignment policies. We propose a matheuristic approach to solve the problem that provides high-quality solutions in a short computational time, based on column generation in which each subproblem is solved using an ad-hoc heuristic. The approach is tested on demand disruption instances containing up to 75 air cargo orders with different penalty levels. The results show that the proposed method improves profit by 54% over the solution generated by a commercial MIP solver within a 1-h time limit, and by 15% over the solution with the routes fixed as in the original flight planning that only allows cargo to be re-routed. We also show that there exist incremental benefits in the range of 3–5% by allowing cargo for a given order to be transported by various aircraft.     

Optimal solution for a cargo revenue management problem with allotment and spot arrivals

We consider a single-leg cargo revenue management problem, in which a two-dimensional cargo capacity is sold through allotment contracts and in the spot market. Capacity sold on an allotment basis is guaranteed. We optimally solve the problem of determining how much of the total weight and volume capacity to sell on an allotment basis, by deriving a closed-form expression of the objective function. We provide numerical examples of industry-size problems and perform sensitivity analysis by changing some problem parameters. The sensitivity analysis illustrates the dependency of the optimal decisions on the spot and allotment booking types. The remaining capacity is then sold over a booking horizon in the spot market. Allotment bookings and spot requests can arrive any time over the booking horizon. Since some of the allotment bookings might not show up at departure, cargo carriers tend to overbook the remaining capacity allocated to spot requests. For these requests, we formulate a discrete-time dynamic capacity control model, to decide which of the spot requests to accept, based on the total weight and volume of the allotment show-ups and spot bookings accepted at the time of an arrival. We solve the exact dynamic programming model for medium-size industry problems. Since the booking policy based on critical booking levels or time periods is not optimal, we propose several heuristics to solve large industry problems and derive an upper bound on the value function. We test their performance via simulation against the optimal solution, the upper bound, and the first-come first-served policy, and recommend a heuristic that performs well in a wide variety of numerical cases. Finally, we show via simulation, that our model outperforms the one existing in the literature, for small and medium-size industry problems.     

Application of an alternative expected marginal seat revenue method (EMSRc) in unrestricted fare environments

We reintroduce an expected revenue maximization formulation for airline seat allocation. We present a numerical method to find the exact solution to the integer programing problem. We further show that when this method is applied to a nested fare structure, it constitutes a heuristic method which has far better performance in an unrestricted fare environment, where fare buckets are completely undifferentiated, compared to EMSRa, EMSRb and EMSRb-MR. With use of simulation, we show that this method can recapture a significant portion of the potential revenue loss when restrictions are removed, while its performance in a fully differentiated environment is only marginally inferior compared to other methods. This method is also applicable to hotels and cruise lines where not only are there fewer “fences” around different offered rates, but also there is a greater tendency for consumers to buy down since most bookings are fully refundable.     

Stochastic fleet deployment models for public bicycle rental systems

This paper presents two stochastic bike deployment (SBD) models that determine the optimal number of bicycles allocated to each station in a leisure-oriented public bicycle rental system with stochastic demands. The SBD models represent the stochastic demands using a set of scenarios with given probabilities. A multilayer bike-flow time-space network is constructed for developing the models, where each layer corresponds to a given demand scenario and effectively describes bicycle flows in the spatial and temporal dimensions. As a result, the models are formulated as the integer multi-commodity network flow problem, which is characterized as NP-hard. We propose a heuristic to efficiently obtain good quality solutions for large-size model instances. Test instances are generated using real data from a bicycle rental system in Taiwan to evaluate the performance of the models and the solution algorithm. The test results show that the models can help the system operator of a public bicycle system make effective fleet deployment decisions.     

城市间多模式交通旅客票价制定的博弈分析

各种交通方式要走向市场，运价改革是关键环节之一。通过对城市间多模式交通方式的经营博弈问题进行研究，以不同交通方式的票价作为决策变量，建立了经营者之间的广义均衡博弈模型，并表示为一个拟变分不等式问题。鉴于拟变分不等式求解比较困难，设计了基于混沌优化方法的启发式求解算法。