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.     

A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: A case study on San Francisco district

We propose a multi-depot location-routing model considering network failure, multiple uses of vehicles, and standard relief time. The model determines the locations of local depots and routing for last mile distribution after an earthquake. The model is extended to a two-stage stochastic program with random travel time to ascertain the locations of distribution centers. Small instances have been solved to optimality in GAMS. A variable neighborhood search algorithm is devised to solve the deterministic model. Computational results of our case study show that the unsatisfied demands can be significantly reduced at the cost of higher number of local depots and vehicles.     

Planning towing processes at airports more efficiently

This paper addresses the towing process of airplanes as part of the turnaround process. We introduce a VRP based MIP model which assigns different types of towing tractors to jobs with specified service time windows. The objective function minimizes operating costs subject to operational restrictions such as technical compatibility of tractor types with plane types. Our modeling approach allows for multiple depots as well as multiple trips. To solve the model we develop a column generation heuristic. Computational results show the superior behavior of the proposed heuristic compared to the original MIP formulation solved with CPLEX. In a case study we derive insights which support schedulers in their daily work. For this, we identify cost drivers and evaluate the efficiency of manual schedules in retrospect.     

The trade-off between fixed vehicle costs and time-dependent arrival penalties in a routing problem

This paper introduces the vehicle routing problem with soft time windows (VRPSTW) in which problem definition differs from ones previously defined in literature. Branch-and-price approach is employed, resulting in a set partitioning master problem and its new subproblem. Novel techniques are consequently developed to solve this new subproblem. Experimental results report the comparisons of these solution techniques under the branch-and-price framework. The VRPSTW solutions have further been compared to the state-of-the-art literature, signifying the superiority of the VRPSTW on this issue.     

A polynomial-time heuristic for the quay crane double-cycling problem with internal-reshuffling operations

One of great challenges in seaport management is how to handle containers under reshuffling, called reshuffles. Repositioning reshuffles in a bay (internal reshuffling) can improve the efficiency of quay cranes and help ports to reduce ship turn-around time. This paper studies the quay crane double-cycling problem with internal-reshuffling operations, and presents a fast solution algorithm. To reduce the number of operations necessary to turn around a bay of a vessel, the problem is first formulated as a new integer program. A polynomial-time heuristic is then developed. The analysis is made on the worst-case error bound of the proposed algorithm. Results are presented for a suite of combinations of problem instances with different bay sizes and workload scenarios. Comparisons are made between our algorithm and the start-of-the-art heuristic. The computational results demonstrate that our model can be solved more efficiently with CPLEX than the model proposed by Meisel and Wichmann (2010), and the proposed algorithm can well solve real-world problem instances within several seconds.     

The time dependent vehicle routing problem with time windows: Benchmark problems, an efficient solution algorithm, and solution characteristics

An algorithm that can tackle time dependent vehicle routing problems with hard or soft time windows without any alteration in its structure is presented. Analytical and experimental results indicate that average computational time increases proportionally to the number of customers squared. New replicable test problems that capture the typical speed variations of congested urban settings are proposed. Solution quality, time window perturbations, and computational time results are discussed as well as a method to study the impact of perturbations by problem type. The algorithm efficiency and simplicity is well suited for urban areas where fast running times may be required.     

Multiple sourcing under supplier failure risk and quantity discount: A genetic algorithm approach

This paper investigates an order allocation problem of a manufacturer/buyer among multiple suppliers under the risks of supply disruption. A mixed integer non-linear programming (MINLP) model is developed for order allocation considering different capacity, failure probability and quantity discounts for each supplier. We have shown that the formulated problem is NP-hard in nature and genetic algorithm (GA) approach is used to solve it. The model is illustrated through a numerical study and the result portrays that the cost of supplier has more influence on order quantity allocation rather than supplier’s failure probability.     

Hierarchical multi-objective evacuation routing in stadium using ant colony optimization approach

Evacuation planning is a fundamental requirement to ensure that most people can be evacuated to a safe area when a natural accident or an intentional act happens in a stadium environment. The central challenge in evacuation planning is to determine the optimum evacuation routing to safe areas. We describe the evacuation network within a stadium as a hierarchical directed network. We propose a multi-objective optimization approach to solve the evacuation routing problem on the basis of this hierarchical directed network. This problem involves three objectives that need to be achieved simultaneously, such as minimization of total evacuation time, minimization of total evacuation distance and minimal cumulative congestion degrees in an evacuation process. To solve this problem, we designed a modified ant colony optimization (ACO) algorithm, implemented it in the MATLAB software environment, and tested it using a stadium at the Wuhan Sports Center in China. We demonstrate that the algorithm can solve the problem, and has a better evacuation performance in terms of organizing evacuees’ space-time paths than the ACO algorithm, the kth shortest path algorithm and the second generation of non-dominated sorting genetic algorithm were used to improve the results from the kth shortest path algorithm.     

A metaheuristic approach to the reliable location routing problem under disruptions

This paper examines a reliable capacitated location–routing problem in which depots are randomly disrupted. Customers whose depots fail must be reinserted into the routes of surviving depots. We present a scenario-based mixed-integer programming model to optimize depot location, outbound delivery routing, and backup plans. We design a metaheuristic algorithm that is based on a maximum-likelihood sampling method, route-reallocation improvement, two-stage neighborhood search and simulated annealing. Numerical tests show that the heuristic is able to generate results that would keep operating costs and failure costs well balanced. Managerial insights on scenario identification, facility deployment and model simplification are drawn.     

An estimation of returns to scale of airport airsides under multiple optimal solutions in DEA

The returns to scale (RTS) nature of 37 Chinese airport airsides are investigated in this paper. Multiple optimal solutions in DEA (Data Envelopment Analysis) models may lead to error RTS estimation. To address this problem, we use the (Zhu and Shen, 1995) RTS method. The empirical study shows that all those airsides with two runways operate under decreasing RTS and those airsides with only one runway either operate in the area of increasing RTS or in the area of constant RTS.     

Vehicle routing optimization with soft time windows in a fuzzy random environment

This paper is concerned with a vehicle routing problem with soft time windows (VRPSTW) in a fuzzy random environment. Two objectives are considered: (1) minimize the total travel cost and (2) maximize the average satisfaction level of all customers. After setting up the model for the VRPSTW in a fuzzy random environment, the fuzzy random expected value concept is used to deal with the constraints and its equivalent crisp model is derived. The global–local–neighbor particle swarm optimization with exchangeable particles (GLNPSO-ep) is employed to solve the equivalent crisp model. A case study is also presented to illustrate the effectiveness of the proposed approach.     

Hybrid metaheuristic solutions to inventory location routing problem

This paper considers a supply chain network with multiple depots and geographically dispersed customers, each of which faces non-constant demand over a discrete planning horizon. The goal is to determine a set of depots to open, the delivery quantities to customers per period and the sequence in which they are replenished by a vehicle fleet such that the total system-wide cost is minimized. To solve it, first we construct a mixed integer program, and then propose a hybrid metaheuristic consisting of initialization, intensification and post-optimization. Results show that the proposed heuristic is considerably efficient and effective for many classical instances.     

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.     

Algebraic connectivity maximization of an air transportation network: The flight routes’ addition/deletion problem

A common metric to measure the robustness of a network is its algebraic connectivity. This paper introduces the flight routes addition/deletion problem and compares three different methods to analyze and optimize the algebraic connectivity of the air transportation network. The Modified Greedy Perturbation algorithm (MGP) provides a local optimum in an efficient iterative manner. The Weighted Tabu Search (WTS) is developed for the flight routes addition/deletion problem to offer a better optimal solution with longer computation time. The relaxed semidefinite programming (SDP) is used to set a performance upper bound and then three rounding techniques are applied to obtain feasible solutions. The simulation results show the trade-off among the Modified Greedy Perturbation, Weighted Tabu Search and relaxed SDP, with which we can decide the appropriate algorithm to adopt for maximizing the algebraic connectivity of the air transportation networks of different sizes. Finally a real air transportation network of Virgin America is investigated.     

Reducing the wasted transportation capacity of Personal Rapid Transit systems: An integrated model and multi-objective optimization approach

This study addresses the problem of wasted transportation capacity in Personal Rapid Transit (PRT) systems. We propose a two-tier transportation model that integrates PRT and capillary transportation systems. We study a related multi-objective empty vehicle redistribution problem that attempts to minimize empty movement and the number of vehicles used. Furthermore, we design a hybrid multi-objective genetic algorithm that integrates multiple crossover operators and linear programming techniques to solve the proposed problem. Evaluations indicate that our algorithm produces satisfactory results, and simulations confirm the efficiency of our proposed two-tier transportation system.     

Models and algorithms for multi-crane oriented scheduling method in container terminals

“Multi-crane oriented” is a scheduling method that yard trailers can be shared by different quay cranes. In this paper, two models for this problem are developed. The first one is a model for an inter-ship-based sharing method. In this model, yard trailers can be shared by quay cranes of different ships. To solve the model, a two-phase Tabu search algorithm is designed. The second one is a model for a ship-based sharing method. In this model, yard trailers can only be shared by quay cranes of the same ship. Q-learning algorithm is designed to solve the model. Numerical tests show that the “multi-crane oriented” method can decrease the yard trailers’ travel distance, reduce the disequilibrium of different working lines, and thus improve the operation efficiency in container terminals.     

Economic and environmental concerns in planning recyclable waste collection systems

This paper addresses the planning of recyclable waste collection systems while accounting for economic and environmental concerns. Service areas and vehicle routes are defined for logistics networks with multiple depots where different products are collected. The problem is modeled as a multi-product, multi-depot vehicle routing problem with two objective functions: distance and CO₂ emissions minimization. A decomposition solution method is developed and applied to a real case study. Six scenarios regarding different service areas and objective functions are studied. Savings of up to 22% in distance and 27% in CO₂ emissions are achieved, exceeding economic and environmental goals.     

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.     

用蚁群算法求解类TSP问题的研究

铁路运输调度问题能否很好解决对于铁路运输公司至关重要,旅行商问题(简称TSP)经常被用来研究运输调度问题。根据某化工集团铁路运输公司的生产实际,提出了“类TSP”问题,但由于“类TSP”和TSP有很大区别,以前求解TSP的优化算法不能直接用于“类TSP”的求解。利用蚁群算法是可以较好解决TSP的一类新型模拟进化算法,适应“类TSP”的要求,并通过“蚁后规则”和变异机制的引入,提出了一种改进的人工蚁群算法。计算机仿真结果表明该算法求解“类TSP”是行之有效的。     

Liner hub-and-spoke shipping network design

This paper proposes a liner hub-and-spoke shipping network design problem by introducing the concept of a main port, as well as some container shipping constraints such as multi-type container shipment and transit time constraints, which are seldom considered in the previous studies. It develops a mixed-integer programming model with nonconvex multi-linear terms for the proposed problem. An efficient genetic algorithm embedded with a multi-stage decomposition approach is developed to solve the model. Numerical experiments are carried out to assess the effectiveness of the proposed model and the efficiency of the proposed algorithm.