单车场大规模车辆路径优化问题研究

针对传统优化技术在解决大规模车辆路径问题中存在的缺陷,提出了一种解决单车场大规模车辆路径优化问题的综合启发式算法。首先,采用Sweep技术将区域分解成几个子区。其次,设计了分区的禁忌搜索算法,并采用相邻区域综合优化技术,提高了算法的全局搜索能力。仿真试验表明,该算法能够有效解决大规模车辆路径优化问题。     

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.     

Arc routing problems to restore connectivity of a road network

After a disaster, restoring accessibility in the affected area is critical for response operations. We study two arc routing problems for clearing blocked roads. The first problem minimizes the time to reconnect the road network, while the second maximizes the total benefit gained by reconnecting network components within a time limit. For each problem, we develop a mixed integer programming formulation and two versions of a heuristic algorithm. We conduct computational experiments on Istanbul data and instances adapted from the literature. The heuristics achieve near-optimal or optimal solutions quickly in most of the tested instances.     

Warehouse reshuffling: Insights and optimization

Warehouse reshuffling is a reorganization strategy that consists of repositioning items by moving them sequentially. This study investigates how to optimize warehouse reshuffling and quantifies the effect of common assumptions. A mathematical programming formulation for the general warehouse reshuffling problem, the complexity of the problem, several heuristics based on the problem structure, a formal proof delimitating instances where double-handling can be a productive move, and managerial insights on the performance of reshuffling policies in various environments are presented. Experimental results suggest that the proposed heuristics improve upon a benchmark heuristic by relaxing how cycles are handled and incorporating double-handling.     

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).     

Routing and refueling plans to minimize travel time in alternative-fuel vehicles

A driver who drives an alternative-fuel vehicle (AFV) from an origin point to a destination point needs to consider how to get there (i.e., the routing problem), when to stop, and how and when to refuel (i.e., the refueling plan). In this study, models and algorithms are proposed that optimize a one-way-trip path such that the total travel time from the origin to the destination is minimized. The travel time consists of the setup time, the refueling time and the driving time. The setup time includes waiting for the AFV to be served at a refueling station and the preparation time of charging the machine. We categorized the problems into two types: (1) the refueling plan problem when the routing decision is given and (2) an integrated problem of routing and refueling. Another axis of categorization is when (1) setup time and refueling times are site-independent and (2) parameters are site-dependent. We propose optimal algorithms for site-independent problems and the integrated problem of routing and refueling planning with site-independent parameters. We also conduct experiments and sensitivity analyses for the site-dependent integrated problems of routing and refueling.     

Optimizing road network daily maintenance operations with stochastic service and travel times

This paper studies optimization methods for a routing problem encountered in daily maintenance operations of a road network. Stochastic service and travel times on road segments are considered. The problem is formulated as a variation of the capacitated arc routing problem (CARP). A chance-constrained programming model is firstly developed and solved by a branch-and-cut algorithm. A stochastic programming model with recourse is also proposed to take into account the recourse costs in case of route failure. The problem is solved by an adaptive large neighborhood search algorithm. The computational experiments demonstrate the effectiveness of the algorithm.     

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.     

Task selection and routing problems in collaborative truckload transportation

This paper introduces the task selection and routing problem in collaborative transportation in which a truckload carrier receives tasks from shippers and other partners and makes a selection between a private vehicle and an external carrier to serve each task. The objective is to minimize the variable and fixed costs for operating the private fleet plus the total costs charged by the external carrier. The mathematical formulation and the lower bound are established. A memetic algorithm is developed to solve the problem. The computational results show that the proposed algorithm is effective and efficient.     

Dynamic supply chain network design with capacity planning and multi-period pricing

This paper addresses a new problem in designing and planning a multi-echelon and multi-product supply chain network over a multi-period horizon in which customer zones have price-sensitive demands. Based on price-demand relationships, a generic method is presented to obtain price levels for products and then, a mixed-integer linear programming model is developed. Due to the problem intractability, a simulated annealing algorithm that uses some developed linear relaxation-based heuristics for capacity planning and pricing is presented. Numerical results demonstrate the significance of the model as well as the efficiency of the solution algorithm and linear relaxation-based heuristics.     

The split delivery vehicle routing problem with minimum delivery amounts

In the vehicle routing problem, a fleet of vehicles must service the demands of customers in a least-cost way. By allowing multiple vehicles to service the same customer (i.e., splitting deliveries), substantial savings in travel costs are possible. However, split deliveries are often an inconvenience to the customer who would prefer to have demand serviced in a single visit. We consider the vehicle routing problem in which split deliveries are allowed only if a minimum fraction of a customer’s demand is serviced by a vehicle. We develop a heuristic method for solving this problem and report computational results on a wide range of problem sets.     

Integrated inventory-transportation model by synchronizing delivery and production cycles

This paper develops an integrated single-vendor multi-buyer inventory-transportation synchronized supply chain model. In this paper, the decisions of truck assignment and routing are also considered and as a result, a series of vehicle routing problems (VRP) are required to be solved. Due to the highly complicated objective function and the NP-hard VRP problems, the problem cannot be solved analytically. Hence, meta-heuristics are proposed. By means of the numerical examples and a case study, the meta-heuristics developed are shown to be very effective in solving such comprehensive supply chain models, and the results so obtained are promising.     

The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion

The green vehicle routing and scheduling problem (GVRSP) aims to minimize green-house gas emissions in logistics systems through better planning of deliveries/pickups made by a fleet of vehicles. We define a new mixed integer liner programming (MIP) model which considers heterogeneous vehicles, time-varying traffic congestion, customer/vehicle time window constraints, the impact of vehicle loads on emissions, and vehicle capacity/range constraints in the GVRSP. The proposed model allows vehicles to stop on arcs, which is shown to reduce emissions up to additional 8% on simulated data. A hybrid algorithm of MIP and iterated neighborhood search is proposed to solve the problem.     

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.     

Relay network design in freight transportation systems

This paper provides techniques, heuristics, and algorithms for the location of a minimal number of relay points on a highway network that satisfies the driver-distance constraint––a driver leaving a distribution center does not travel for more than t miles before which he/she returns back to the originating point or rests before moving on further. Straight Route and Detour versions are considered. Empirical evaluations of the proposed heuristics on road networks are performed. The results indicate that the Straight Route version is computationally efficient but locates a larger number of relay points than the Detour versions.     

A column generation based approach for the Train Network Design Optimization problem

This paper investigates the Train Network Design Optimization problem arising from railroad industry which involves the integration of three inter-related decision sub-problems: train routing which is to identify origin, destination and itinerary for individual trains; block-to-train assignment detailing the block movements with trains and swaps between trains; and crew-to-train assignment specifying the crew services for train routes. A column generation based hierarchical approach with two stages is designed: the first stage generates a pool of promising train routes iteratively based on the crew segments by the column generation technique; and the second stage develops an integer linear programming model for the subsequent decisions including train route selection and block-to-train assignment. Numerical experiments with realistic test instances are conducted and the outcome demonstrates the capability of the proposed approach in solving the Train Network Design Optimization problem competently.     

A multi-start heuristic approach for the split-delivery vehicle routing problem with minimum delivery amounts

We propose a new multi-start solution approach for the split-delivery vehicle routing problem with minimum delivery amounts (SDVRP-MDA). Initial solutions are generated by both node-insertion and route-addition procedures with a single parameter to control the restart. These solutions are then improved by a variable neighborhood descent metaheuristic with a novel search operator inspired by node-ejection chains. We test the proposed approach with 32 benchmark instances for four different minimum delivery fractions. Using the proposed algorithm, out of 128 cases tested, we find 81 best known solutions and 34 new best solutions; overall, we find 43 new best solutions.     

An adaptive memory programming metaheuristic for the heterogeneous fixed fleet vehicle routing problem

This paper studies the heterogeneous fixed fleet vehicle routing problem (HFFVRP), in which the fleet is composed of a fixed number of vehicles with different capacities, fixed costs, and variable costs. Given the fleet composition, the HFFVRP is to determine a vehicle scheduling strategy with the objective of minimizing the total transportation cost. We propose a multistart adaptive memory programming (MAMP) and path relinking algorithm to solve this problem. Through the search memory, MAMP at each iteration constructs multiple provisional solutions, which are further improved by a modified tabu search. As an intensification strategy, path relinking is integrated to enhance the performance of MAMP. We conduct a series of experiments to evaluate and demonstrate the effectiveness of the proposed algorithm.     

A robust inventory routing policy under inventory inaccuracy and replenishment lead-time

In this paper, we study an inventory routing problem under replenishment lead-time and inventory inaccuracy, which exist extensively in distribution systems for fresh products, but are often ignored in existing research. To solve the problem, a robust inventory routing policy is developed in three steps. At first, we propose the methods of updating the probability of the current net inventory and predicting those in future periods. For each candidate route, we develop a Robust TQL Algorithm to optimize the replenishment time, replenishment quantity and replenishment stage length. Finally, a genetic algorithm-based method is developed to optimize the delivery route.     

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.