Optimal distance tolls under congestion pricing and continuously distributed value of time

This paper addresses the optimal distance-based toll design problem for cordon-based congestion pricing schemes. The optimal distance tolls are determined by a positive and non-decreasing toll-charge function with respect to the travel distance. Each feasible toll-charge function is evaluated by a probit-based SUE (Stochastic User Equilibrium) problem with elastic demand, asymmetric link travel time functions, and continuously distributed VOT, solved by a convergent Cost Averaging (CA) method. The toll design problem is formulated as a mixed-integer mathematical programming with equilibrium constraints (MPEC) model, which is solved by a Hybrid GA (Genetic Algorithm)–CA method. Finally, the proposed models and algorithms are assessed by two numerical examples.     

Estimation of interregional input–output table using hybrid algorithm of the RAS method and real-coded genetic algorithm

In this paper, we propose a method for improving the accuracy of the estimation of interregional input–output tables, by combining the RAS method and the real-coded Genetic Algorithm (GA); these are simple representative methods for the estimation of an interregional input–output table. By comparing the performance evaluation results obtained using the proposed method, the RAS method, and Simulated Annealing, we verified that the combination of the genetic algorithm and the RAS method can enhance the estimation accuracy of an interregional input–output table. In addition, performance is further enhanced by adjusting GA parameters.     

Optimal assignment of airport baggage unloading zones to outgoing flights

The outbound airport baggage handling system (BHS) consists of a set of unloading zones (chutes) which are assigned to outgoing flights. Airport baggage operations have inherent uncertainties such as flight delays and varying number of bags. In this paper, the chute assignment problem is modeled as a Stochastic Vector Assignment Problem (SVAP) and multiple extensions are presented to incorporate the various design needs of the airport. A real airport outbound BHS is presented. This case study also guided the optimization models’ design process. The performance of the optimization models is compared with the methods used in practice and literature.     

A Green Vehicle Routing Problem

A Green Vehicle Routing Problem (G-VRP) is formulated and solution techniques are developed to aid organizations with alternative fuel-powered vehicle fleets in overcoming difficulties that exist as a result of limited vehicle driving range in conjunction with limited refueling infrastructure. The G-VRP is formulated as a mixed integer linear program. Two construction heuristics, the Modified Clarke and Wright Savings heuristic and the Density-Based Clustering Algorithm, and a customized improvement technique, are developed. Results of numerical experiments show that the heuristics perform well. Moreover, problem feasibility depends on customer and station location configurations. Implications of technology adoption on operations are discussed.     

The continuous Berth Allocation Problem: A Greedy Randomized Adaptive Search Solution

In this research, continuous and dynamic Berth Allocation Problem (BAP) is studied to minimize the total weighted flow time. Different from previous studies on continuous BAP, an efficient method is proposed to address the problem to identify the possible locations for next vessel in the Time-space diagram. Then two versions of Greedy Randomized Adaptive Search Procedure (GRASP) are developed to search for near optimal solutions. Both small and large scale numerical experiments are tested to examine the effectiveness of the proposed GRASPs by comparison with CPLEX and stochastic beam search, respectively.     

A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges

This paper presents several heuristics for a variation of the vehicle routing problem in which the transportation fleet is composed of electric vehicles with limited autonomy in need for recharge during their duties. In addition to the routing plan, the amount of energy recharged and the technology used must also be determined. Constructive and local search heuristics are proposed, which are exploited within a non deterministic Simulated Annealing framework. Extensive computational results on varying instances are reported, evaluating the performance of the proposed algorithms and analyzing the distinctive elements of the problem (size, geographical configuration, recharge stations, autonomy, technologies, etc.).     

A robust mathematical model and heuristic algorithms for integrated aircraft routing and scheduling,with consideration of fleet assignment problem

One of the most important airline's products is to determine the aircraft routing and scheduling and fleet assignment. The key input data of this problem is the traffic forecasting and allocation that forecasts traffic on each flight leg. The complexity of this problem is to define the connecting flights when passengers should change the aircraft to reach the final destination. Moreover, as there exists various types of uncertainties during the flights, finding a solution which is able to absorb these uncertainties is invaluable. In this paper, a new robust mixed integer mathematical model for the integrated aircraft routing and scheduling, with consideration of fleet assignment problem is proposed. Then to find good solutions for large-scale problems in a rational amount of time, a heuristic algorithm based on the Simulated Annealing (SA) is introduced. In addition, some examples are randomly generated and the proposed heuristic algorithm is validated by comparing the results with the optimum solutions. The effects of robust vs non-robust solutions are examined, and finally, a hybrid algorithm is generated which results in more effective solution in comparison with SA, and Particle Swarm Optimization (PSO).     

A novel mathematical model for a multi-period,multi-product optimal ordering problem considering expiry dates in a FEFO system

One of the main challenges of retail units is to determine the order quantities of different types of products, each with a specific expiry date, so that the system cost including shortage cost is minimized. We study a new multi-product multi-period replenishment problem for a First Expired-First Out (FEFO) based warehouse management system. The proposed nonlinear model is first converted to a linear one and then solved by applying two evolutionary algorithms: the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), in which design parameters are set using Taguchi method. Computational results demonstrate the applicability of the proposed model for perishable items and comparing the results shows the efficiency of the proposed metaheuristics as well.     

Branch-and-price algorithm for the location-routing problem with time windows

This study proposes a branch-and-price algorithm to solve the Location-Routing Problem with Time Windows (LRPTW) which has never been attempted with the exact solutions before. The problem is solved by the simplex algorithm in the master problem and elementary shortest path problems with resource constraint corresponding to column generation in the subproblem until only the non-negative reduced cost columns remain. The proposed algorithm can solve many testing instances effectively. The computational results and the effect of time windows are also compared and discussed.     

Criteria selection and multi-objective optimization of aircraft landing problem

Inspired by the similarities of the aircraft landing problem (ALP) and the single machine scheduling problem, we propose a criteria selection method that has been used successfully in the single machine scheduling problem to determine appropriate objective functions of ALP. First, for four different types of criteria—min-max, min-sum, completion time related, and due-dates related criteria—their corresponding physical meanings in ALP are elaborated. Then, a criteria selection method is proposed to determine several appropriate criteria, which are taken as the multi-objective while modeling ALP. Different solution algorithms, including Imperialist Competitive Algorithm (ICA), are adopted to solve the multi-objective ALP. Finally, the performance of the proposed model and method are evaluated using a set of benchmark instances. The computational results demonstrate the efficiency of our approach for solving ALP, which can simultaneously improve punctual performance, enhance runway utilization, reduce air traffic controller workload, and maintain equity among airlines.     

Optimizing a bi-objective inventory model of a three-echelon supply chain using a tuned hybrid bat algorithm

This paper presents a bi-objective VMI problem in a single manufacturer-single vendor multi-retailer (SM-SV-MR) supply chain, which a redundancy allocation problem is incorporated. In the hybridized problem, a manufacturer produces a single item using several machines that work in series, and stores it in a warehouse to replenish one vendor who delivers it to several retailers using the shortest possible route. A novel meta-heuristic, called hybrid bat algorithm (HBA), with calibrated parameters is utilized to find a near-optimum solution. To show the efficiency of HBA, the results are compared to the ones using the traditional BA and a genetic algorithm.     

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.     

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.     

Multi-criteria decision-making for complex bundling configurations in surface transportation of air freight

Airlines typically carry out freight transportation in a hub and spoke structure, where the movements between the outstations and the hub are served by trucks. To transport freight efficiently, air carriers must consider bundling options for shipments that are delivered at outstations and have to be moved to the hub. There are three options when it comes to bundling freight: on ‘through unit load devices’ (T-ULD) (all freight for the same flight at the hub), on ‘mixed unit load devices’ (M-ULD) (freight for different flights at the hub) and loose freight in trucks. The optimal freight bundling configuration for carriers, taking into account their main KPIs (key performance indicators), is unknown. This research formulates the problem as a multi-criteria decision-making (MCDM) problem, allowing carriers to decide which configuration is optimal for a given outstation. The selected KPIs (cost, (un)loading time, and quality) are formulated as mathematical functions. A new MCDM, called best worst method (BWM), is then used to identify the best configuration with respect to the three KPIs. The proposed methodology is applied to KLM Cargo to identify the best configuration for the selected outstations that supply freight to the KLM hub at Schiphol Airport. This case study shows that there are different optimal freight bundling configurations for different outstations and that trucking costs and freight handling tariffs are among the key factors in deciding which configuration is optimal.     

二次指派问题的蚁群算法研究

蚁群算法是受自然界蚂蚁觅食过程中,基于信息素的最短路径搜索食物行为启发,提出的一种智能优化算法。在采用蚁群算法求解二次指派问题中,针对蚁群算法存在的过早收敛问题,使用距离及流量作为启发式信息并引入局部优化,对蚁群算法的结果加以改进,计算机仿真结果表明,蚁群算法对求解二次指派问题有较好的效果。     

基于连通可靠性的车辆路径问题

物流配送车辆路径问题(VRP)是一个NP-hard问题,很多求解方法仅考虑路网连通无穷大的情况,将其看成平均旅行时间问题,对于突发事件下造成路网拥堵情况下的车辆路径问题很少涉及,对此结合蚁群算法,对突发事件下各路段的连通可靠性进行比较后选择合适路段通过,用以提高路网的通行能力。     

基于遗传模拟退火算法的战时物流配送路径优化研究

综合考虑战时物流配送车辆路径问题（VRP）的多目标评价，提出多属性道路网络下战时物流配送的VRP算法，并建立完全分层优化模型。将进化算法与传统优化技术相结合，构造了模型的两层求解算法，第一层采用遗传算法和模拟退火算法混合的GASA算法，第二层采用枚举法。并以成品燃油配送为例进行了实验，结果表明算法较标准遗传算法更有效。     

A model for a multi-size inland container transportation problem

In the multi-size Inland Container Transportation Problem (mICT) trucks are able to transport up to two 20-foot or one 40-foot container at a time along routes with various pickup and delivery locations. A mixed-integer linear program for the mICT is presented using two alternative objective functions: minimization of the total travel distance and minimization of the total operation time of the trucks. The presented model is tested on instances which vary in size. Computational experiments show that by means of the presented model small problem instances can be solved optimally.     

Combining Lagrangian heuristic and Ant Colony System to solve the Single Source Capacitated Facility Location Problem

The facility location problems have been applied extensively in practice. We describe a Multiple Ant Colony System (MACS) to solve the Single Source Capacitated Facility Location Problem (SSCFLP). Lagrangian heuristics have been shown to produce good solutions for the SSCFLP. A hybrid algorithm, which combines Lagrangian heuristic and Ant Colony System (ACS), LH–ACS, is developed for the SSCFLP. The performance of the proposed methods are tested on two sets of benchmark instances and compared with other heuristic algorithms in the literature. The computational results indicate that both MACS and LH–ACS are effective and efficient for the SSCFLP and competitive with other well-known algorithms.