Hybrid Heuristic Method Research Articles

Integrated crew organization and work zone scheduling for network-wide daily road pavement rehabilitation

This study develops a new integer-programming model to address the network-wide daily road pavement rehabilitation scheduling problem. In the model, the crew organization and work zone schedule are jointly optimized daily, with the objective of minimizing both the operational cost and user travel time. A day-to-day traffic dynamics model is applied to capture the non-equilibrium traffic evolution against network supply variation over the planning horizon, which leads to a simulation-based optimization problem. To solve this challenging problem, a two-stage hybrid heuristic solution method is proposed. In the first stage, hybrid tabu search (TS) meta-heuristics are comparatively developed to identify a group of active crew work routes without time slacks. The obtained crew routes are then fed to the second stage for work zone scheduling via a discrete compass search algorithm. Some important findings are obtained from numerical experiments. First, crew routing (or crew organization) is the dominant decision in the studied problem, and a desirable work zone schedule encourages a crew to execute the assigned tasks continually. The findings can be used to develop simplified and efficient solution algorithms. Second, the hybrid TS meta-heuristics developed for crew routing exhibit superior performance compared to other solution methods. Finally, a well-defined model for the current problem should consider both user travel time and operation cost. Our model enables decision-makers to make an effective trade-off between these two objectives. An effective measure is suggested to evaluate the cost-effectiveness of budget investment decisions when budgets are limited.

Competitive inland port location and pricing problem: A perspective from the entering seaport

Competition among seaports has been becoming more and more fierce in current times, which has extended to the contest between transportation chains including seaports and their inland ports. Against this background, this paper studies competitive inland port location and pricing problem for an entering seaport under the condition that the incumbent competitive seaport has construct-ed inland transportation chains inside their overlapping hinterland. Specifically, this paper formulates a mixed-integer nonlinear program for the considered problem, in which we take packaged price and service time as influence factors for the inland transportation chains competition and characterize inland ports choice behaviors for shippers based on logit model. Additionally, this paper designs a hybrid heuristic method by integrating a genetic algorithm and an analytical method to solve location and pricing subproblems, respectively. Based on the computational results and sensitivity analysis, this paper provides some valuable suggestions on how to locate in-land ports and make price decisions for the new entering seaport.

Neural Network-Based Subway Regenerative Energy Optimization With Variable Headway Constraints

For subway regenerative energy optimization, the essential idea is to maximize the amount of regenerative energy absorption (AREA) between accelerating trains and braking trains by adjusting train timetable. The bottlenecks preventing the theoretical research from being used to industrial practice lie in the time-consuming simulation of AREA and the constant setting of headway threshold, which seriously reduces the computational efficiency and compresses the solution space. For addressing these issues, we formulate neural networks to speed up the AREA simulation, develop a bisection algorithm to dynamically adjust the headway threshold under moving block mechanism, and then establish a hybrid heuristic method integrating neural networks, genetic algorithm, variable neighborhood search and simulated annealing to determine the timetable, including dwell time distribution (DTD) at stations and headway time distribution (HTD) among consecutive trains. The effectiveness of the proposed method is confirmed by numerical experiments based on the real ATO data of Beijing Subway Changping Line. The results reveal that the neural networks could save 98.96% of the computational time at the expense of 1.76% of the accuracy loss, such that the hybrid heuristic algorithm could save 75.68% of the computational time. Benchmarked with the constant headway constraints used in literature, the variable headway constraints could achieve an average improvement of AREA by 8.94%. In addition, the joint optimization of DTD and HTD could increase the AREA by 15.28% on average, compared to the single optimization of HTD. This research is of great significance to improve the practicability of subway regenerative energy optimization algorithms.

An Optimal Layout Pattern-Based Solution Approach to the Extended Machine Layout Problem With Multirow Multicolumn Structure

The machine layout problem (MLP) is to determine the optimal location arrangement of machines in a production system, usually in order to minimize the total material transmission distance. This article considers a type of MLP in an automated manufacturing system with unequal-area machines, machine replicas, and taking multi-row multi-column layout structure. The aim is to determine the near-optimal machine layout and orbit layout simultaneously. We model the problem as a mixed-integer linear program by introducing the concept of orbit-based relative locations and using auxiliary binary variables, including relative location assignment variables, in-block relationship variables, and material routing variables. We further exploit the optimal layout patterns, based on which, when the relative locations of the machines and orbits are given, we can quickly derive the near-optimal absolute machine and orbit positions without solving a mixed-integer linear program. A two-layer hybrid heuristic method is proposed to solve the problem: the outer layer is a variable neighborhood search metaheuristic used to explore the solution space of the relative locations, while the inner layer is a recursive heuristic based on the optimal layout patterns. Experiments on instances of different sizes are conducted, and the effectiveness and efficiency of the proposed algorithms are verified. Note to Practitioners—Many manufacturing systems, for example, those in the semiconductor industry, use automated guided vehicles running on orbits to transfer materials between machines. The design of such a manufacturing system consists of determining the numbers of vertical and horizontal orbits, their positions, and the positions of all machines. It is not unusual for there to be hundreds of machines. Furthermore, machines of the same type can be deployed non-adjacently in an automated transfer context since materials can easily be transmitted to an arbitrary machine. The flexibility and complexity of the layout make this layout problem extremely difficult to solve. To the best of our knowledge, no dedicated study has been conducted on this issue. Additionally, state-of-the-art methodologies for related machine layout problems cannot be extended to this problem, partly because one needs to consider the material routing problem explicitly on the orbit network. In this study, we reveal the optimal layout patterns given the relative locations of machines and orbits, and we develop a recursive heuristic that is thousands of times faster than solving the corresponding mixed-integer linear program. Our proposed two-layer hybrid heuristics can solve problems with hundreds of machines, while the inner-layer problem’s precision is guaranteed to within 1%. We believe our algorithm can be used for machine and orbit layout design in flexible manufacturing systems where machine-to-machine material shipment is enables in a rectangular trasmission network.

Joint Optimization of Multi-Cycle Timetable Considering Supply-to-Demand Relationship and Energy Consumption for Rail Express

Rail expresses play a vital role in intracity and intercity transportations. For accommodating multi-source passenger traffic with different travel demand, while optimizing the energy consumption, we propose a multi-cycle train timetable optimization model and a decomposition algorithm. A periodized spatial-temporal network that can support the integrated optimization of passenger service satisfaction and energy consumption considering multi-cycles is studied as the basis of the modeling. Based on this, an integrated optimization model taking the planning of the train spatial-temporal path, cycle length and active lines as variables is proposed. Then, for solving the issues caused by the complex relationships among the cycle length, line and train spatial-temporal path in large-scale cases, a hybrid heuristic Lagrangian decomposition method is investigated. Numerical experiments under different passenger flow demand scenarios are performed. The results show that the more fluctuating the passenger flow is, the more obvious the advantage of a multi-cycle timetable is. For the scenario with two passenger flow peaks, compared to a single-cycle timetable, the demand satisfaction ratio of the multi-cycle timetable is 4.44% higher and the train vacancy rate is 11.49% lower. A multi-cycle timetable also saves 3.24 h running time and 15,553.6 kwh energy consumption compared to a single-cycle timetable. Large-scale real cases show that this advantage still exists in practice.

Dynamic bicycle relocation problem with broken bicycles

In response to the demand imbalance across stations with broken bicycles in a bicycle sharing system (BSS), this study proposes a novel decision problem that aims to determine the size of the fleet of relocation vehicles, the bicycle stations they are assigned to serve, and efficient adaptive routing plans to ensure a good level of bicycle inventory at each station and on a timely basis, by considering the broken bicycles in each station, which is referred to as the dynamic bicycle relocation problem with broken bicycle consideration (a.k.a DBRPB). Assuming that the numbers of broken bicycles and bicycle relocation demand at each bicycle station are independent random variables and will only be revealed upon the arrival of the relocation vehicle, the objective of the DBRPB is to maximize the expected total satisfied demand, comprising both relocation demand and broken bicycle demand, using the adaptive routing strategy while incorporating the deployment cost of the relocation vehicles. The relocation vehicle will adjust its relocation route after the actual demand is revealed, every time it visits a station. A tailored branch-and-price (B&P) approach is proposed to find the exact optimal solution of the DBRPB. To solve the pricing problem, a tailored Markov decision process (MDP) is formulated in the pricing problem of the B&P approach, to determine both the optimal value of the expected satisfied demand and the next station to visit, given the available information, including time, current station, the unordered set of unvisited stations and the bicycle inventory of the relocation vehicle. A hybrid heuristic method incorporating variable neighbourhood search (VNS) and partial optimization is further proposed to solve the large-scale problem. Numerical experiments using a randomly generated BSS network and the Nanjing BSS respectively are conducted to validate the efficiency and effectiveness of the proposed methodology as well as to obtain insights into the impacts of key parameters on the solution.

A stochastic programming approach to enhance the resilience of infrastructure under weather‐related risk

ABSTRACTThe presented methodology results in an optimal portfolio of resilience‐oriented resource allocation under weather‐related risks. The pre‐event mitigations improve the capacity of the transportation system to absorb shocks from future natural hazards, contributing to risk reduction. The post‐event recovery planning results in enhancing the system's ability to bounce back rapidly, promoting network resilience. Considering the complex nature of the problem due to uncertainty of hazards, and the impact of the pre‐event decisions on post‐event planning, this study formulates a nonlinear two‐stage stochastic programming (NTSSP) model, with the objective of minimizing the direct construction investment and indirect costs in both pre‐event mitigation and post‐event recovery stages. In the model, the first stage prioritizes a bridge group that will be retrofitted or repaired to improve the system's robustness and redundancy. The second stage elaborates the uncertain occurrence of a type of natural hazard with any potential intensity at any possible network location. The damaged state of the network is dependent on decisions made on first‐stage mitigation efforts. While there has been research addressing the optimization of pre‐event or post‐event efforts, the number of studies addressing two stages in the same framework is limited. Even such studies are limited in their application due to the consideration of small networks with a limited number of assets. The NTSSP model addresses this gap and builds a large‐scale data‐driven simulation environment. To effectively solve the NTSSP model, a hybrid heuristic method of evolution strategy with high‐performance parallel computing is applied, through which the evolutionary process is accelerated, and the computing time is reduced as a result. The NTSSP model is implemented in a test‐bed transportation network in Iowa under flood hazards. The results show that the NTSSP model balances the economy and efficiency on risk mitigation within the budgetary investment while constantly providing a resilient system during the full two‐stage course.

Optimal Power Flow Considering Global Voltage Stability Based on a Hybrid Modern Heuristic Technique

Integrating large renewable energy to grid complicates power systems’ steady-state operation which leads to the high risk of voltage instability due to its intermittent and stochastic properties. The steady-state voltage stability margin (VSM) can be measured by the smallest singular value (SSV) of the load flow Jacobian matrix. Unlike other voltage stability indices such as L-index which shows the probability of voltage collapse for a particular bus, namely, local stability index, SSV represents the global voltage stability and the smaller the value is, the riskier the whole system's stability is. Voltage stability constrained-optimal power flow (VSC-OPF) is an effective tool to stabilize system voltage. Yet VSC-OPF is a highly non-linear and non-convex problem because of AC power flow and implicit SSV constraints. Such challenge is tackled, in this work, by a novel hybrid heuristic method, differential evolutionary particle swarm optimization (DEEPSO) which can easily formulate explicit constraints and objective functions to obtain near-optimal solutions efficiently. Several case studies are conducted on the IEEE 30-bus system, and they demonstrate the effectiveness of the hybrid technique and presents some insights on voltage profiles under various system operating condition.

A Hybrid Genetic Algorithm for Service Caching and Task Offloading in Edge-Cloud Computing

Edge-cloud computing is increasingly prevalent for Internet-of-thing (IoT) service provisioning by combining both benefits of edge and cloud computing. In this paper, we aim to improve the user satisfaction and the resource efficiency by service caching and task offloading for edge-cloud computing. We propose a hybrid heuristic method to combine the global search ability of the genetic algorithm (GA) and heuristic local search ability, to improve the number of satisfied requests and the resource utilization. The proposed method encodes the service caching strategies into chromosomes, and evolves the population by GA. Given a caching strategy from a chromosome, our method exploits a dual-stage heuristic method for the task offloading. In the first stage, the dual-stage heuristic method pre-offloads tasks to the cloud, and offloads tasks whose requirements cannot be satisfied by the cloud to edge servers, aiming at satisfying as many tasks’ requirements as possible. The second stage re-offloads tasks from the cloud to edge servers, to get the utmost out of limited edge resources. Experimental results demonstrate the competitive edges of the proposed method over multiple classical and state-of-the-art techniques. Compared with five existing scheduling algorithms, our method achieves 11.3%–23.7% more accepted tasks and 1.86%–18.9% higher resource utilization.

A Particle Swarm Optimization With Lévy Flight for Service Caching and Task Offloading in Edge-Cloud Computing

Edge-cloud computing is an efficient approach to address the high latency issue in mobile cloud computing for service provisioning, by placing several computing resources close to end devices. To improve the user satisfaction and the resource efficiency, this paper focuses on the task offloading and service caching problem for providing services by edge-cloud computing. This paper formulates the problem as a constrained discrete optimization problem, and proposes a hybrid heuristic method based on Particle Swarm Optimization (PSO) to solve the problem in polynomial time. The proposed method, LMPSO, exploit PSO to solve the service caching problem. To avoid PSO trapping into local optimization, LMPSO adds lévy flight movement for particle updating to improve the diversity of particle. Given the service caching solution, LMPSO uses a heuristic method with three stages for task offloading, where the first stage tries to make full use of cloud resources, the second stage uses edge resources for satisfying requirements of latency-sensitive tasks, and the last stage improves the overall performance of task executions by re-offloaded some tasks from the cloud to edges. Simulated experiment results show that LMPSO has upto 156% better user satisfaction, upto 57.9% higher resource efficiency, and upto 155% greater processing efficiency, in overall, compared with other seven heuristic and meta-heuristic methods.

Flutter Derivatives Identification and Uncertainty Quantification for Bridge Decks Based on the Artificial Bee Colony Algorithm and Bootstrap Technique

This paper presents a novel parameter identification and uncertainty quantification method for flutter derivatives estimation of bridge decks. The proposed approach is based on free-decay vibration records of a sectional model in wind tunnel tests, which consists of parameter identification by a heuristic optimization algorithm in the sense of weighted least squares and uncertainty quantification by a bootstrap technique. The novel contributions of the method are on three fronts. Firstly, weighting factors associated with vertical and torsional motion in the objective function are determined more reasonably using an iterative procedure rather than preassigned. Secondly, flutter derivatives are identified using a hybrid heuristic and classical optimization method, which integrates a modified artificial bee colony algorithm with the Powell’s algorithm. Thirdly, a statistical bootstrap technique is used to quantify the uncertainties of flutter derivatives. The advantages of the proposed method with respect to other methods are faster and more accurate achievement of the global optimum, and refined uncertainty quantification in the identified flutter derivatives. The effectiveness and reliability of the proposed method are validated through noisy data of a numerically simulated thin plate and experimental data of a bridge deck sectional model.

The container drayage problem for heterogeneous trucks with multiple loads: A revisit

In this study, we revisit the container drayage problem for heterogeneous trucks with multiple loads in view of its practical significance. We first propose a tailored mixed-integer linear programming (MILP) model explicitly to reflect the reality of container drayage operations. To solve medium-scale problems more effectively, two relaxed MILP models are built to provide a solution that is close to optimal with less computational time. These two relaxed models are proven to work as intended and provide optimal solutions under certain conditions. Moreover, an effective hybrid heuristic method incorporating the cheapest feasible insertion mechanism and the variable neighborhood search scheme is designed to solve large-scale problems. Extensive numerical experiments demonstrate the scalability of the heuristic method in real-world applications.

Hybrid Heuristic Algorithm for Better Energy Optimization and Resource Utilization in Cloud Computing

Energy-efficient execution of the scientific workflow is a challenging task in cloud computing that demands high-performance computing to process growing datasets. Due to the interdependency of tasks in the scientific workflow applications, energy-efficient resource allocation is vital for large-scale applications running on heterogeneous physical machines. Thus, this paper proposes a hybrid heuristic algorithm based energy-efficient cloud computing service (HH-ECO) that offers a significant solution for resource allocation, task scheduling, and optimization of scientific workflows. To ensure the energy-efficient execution, the HH-ECO focuses on executing non-dominant workflow tasks through adaptive mutation and energy-aware migration strategy. HH-ECO adopts the chaotic based particle swarm optimization (C-PSO) principle to optimize the resource allocation, task scheduling, and resource migration by generating the global best plans without local convergence. C-PSO with adaptive mutation avoids the deterioration of global optima while finding the best host to place the virtual machine and ensures an appropriate resource allocation plan. By considering the workflow task precedence relationships during C-PSO based task scheduling, the novel hybrid heuristic method efficiently solves the multi-objective combinatorial optimization problem without dominance among the workflow tasks. The Cloudsim based simulation study delivers superior results compared to the existing methods such as the hybrid heuristic workflow scheduling algorithm (HHWS) and distributed dynamic VM management (DDVM). The proposed approach significantly improves the optimal makespan to 38.27% and energy conservation to 38.06% compared to the existing methods.

Simulation-Based Multi-Criteria Optimization of Parallel Heat Treatment Furnaces at a Casting Manufacturer

This paper presents the development and evaluation of a digital method for multi-criteria optimized production planning and control of production equipment in a case-study of an Austrian metal casting manufacturer. Increased energy efficiency is a major requirement for production enterprises, especially for energy intensive production sectors such as casting. Despite the significant energy-efficiency potential through optimized planning and the acknowledged application potential for sophisticated simulation-based methods, digital tools for practical planning applications are still lacking. The authors develop a planning method featuring a hybrid (discrete-continuous) simulation-based multi-criteria optimization (a multi-stage hybrid heuristic and metaheuristic method) for a metal casting manufacturer and apply it to a heat treatment process, that requires order batching and sequencing/scheduling on parallel machines, considering complex restrictions. The results show a ~10% global goal optimization potential, including traditional business goals and energy efficiency, with a ~6% energy optimization. A basic feasibility demonstration of applying the method to synchronize energy demand with fluctuating supply by considering flexible energy prices is conducted. The method is designed to be included in the planning loop of metal casting companies: receiving orders, machine availability, temperature data and (optional) current energy market price-data as input and returning an optimized plan to the production-IT systems for implementation.

CVaR-cardinality enhanced indexation optimization with tunable short-selling constraints

ABSTRACT Enhanced-index-funds have attracted considerable attention from investors over the last decade, which aims at outperforming a benchmark index while maintaining a similar risk level. In this article, we investigate an enhanced indexation methodology using Conditional Value-at-Risk (CVaR). In particular, we adopt CVaR of excess returns as risk measurement subject to cardinality constraint for controlling the tracking portfolio scale precisely and tunable short-selling constraints for adjusting the margin of each risky asset adaptively within the budget of short-selling. As the resulted model is a mixed 0–1 binary program, we propose an improved hybrid heuristic method, where a customized relax-round-polish is embedded to improve the quality of the iterative population. Computational results on five standard data sets from OR-library show that our proposed method is generally superior to the naive portfolio strategy and the CVaR-LASSO method in terms of the out-of-sample excess return, Sharpe ratio and maximum drawdown of the portfolio.

A routing model and solution approach for alternative fuel vehicles with consideration of the fixed fueling time

In this paper, we introduce the Vehicle Routing Problem for Alternative Fuel Vehicles with Fixed Fueling Time (VRPAFVFFT) to model decisions to be made with regards to the vehicle routes cons, dering fuel stations. A Mixed Integer Programming (MIP) model is proposed with improving methods, which includes an adaptive branch-and-cut algorithm. To solve large scale problems, we present a hybrid heuristic method, which combines an Adaptive Large Neighborhood Search (ALNS) with a local search and a MIP model. We conduct experiments to show the efficiency of our solving methods as well as insights from real world cases.

Adaptive method of detecting traffic anomalies in high-speed multi-service communication networks

In the paper, an adaptive hybrid heuristic (behavioral) method for detecting small traffic anomalies in high-speed multiservice communication networks, which operates in real time, is proposed and investigated. The relevance of this study is determined by the fact that network security management processes in high-speed multiservice communication networks need to be implemented in a mode close to real-time mode, as well as identifying possible network security threats in the early stages of the implementation of possible network attacks. The proposed method and algorithm belong to the class of adaptive methods and algorithms with preliminary training. The average relative error in estimating the evaluated traffic parameters does not exceed 10%, which is sufficient for the implementation of operational network management tasks. Anomalies of the expectation of traffic intensity and its dispersion are identified if their valuesexceed the normal values by 15% or more, which makes it possible to detect possible network attacks in the early phases of their implementation, for example, at the stage of scanning ports and interfaces of the attacked system. The procedure for detecting anomalous traffic behavior is implemented based on the Mamdani’s method of hierarchical fuzzy logical inference. A study of the proposed method for detecting anomalous behavior of network traffic showed its high efficiency.

SVR-RSM: a hybrid heuristic method for modeling monthly pan evaporation.

In the present study, a hybrid intelligent model called SVR_RSM, which was extracted using response surface method (RSM) combined by the support vector regression (SVR) approaches was applied for predicting monthly pan evaporation (Epan). This method is established based on two basic calibrating process using RSM and SVR. In the first process, an input data group with two different input variables are used to calibrate the RSM; hence, the calibrating data by RSM in the first process are applied as input database for calibrating the SVR in the second process. Results obtained using the proposed SVR_RSM was compared with those obtained using the RSM, SVR, and the well-known multilayer perceptron neural network (MLPNN) models. Climatic variables including maximum and minimum temperatures (Tmax, Tmin), wind speed (U2), and relative humidity (H%), and the periodicity represented by the month number (α) were selected for predicting the monthly Epan measured with the standard class A evaporation pan. Data was collected at six climatic stations located at the northern East of Algeria. The performances of the proposed models were compared using the RMSE, MAE, modified index of agreement (d), coefficient of correlation (R), and modified Nash and Sutcliffe efficiency (NSE). Using various input combination, the results show that the hybrid SVR_RSM model performed better than all the proposed models. Overall, better accuracy was observed when the model contained the periodicity (α), and it was demonstrated that the best accuracy was obtained using only Tmax and Tmin, coupled with the periodicity.

Variable neighborhood search-based methods for integrated hybrid flow shop scheduling with distribution

With the rapid development of make-to-order pattern including E-commerce and takeout and catering service in restaurants, the study of integrated scheduling and distribution receives more and more attentions. Based on a practical order picking and distribution system, a three-stage hybrid flow shop scheduling problem with distribution is studied. Each order is processed on the hybrid flow shop which consists of identical parallel machines with sequence-dependent setup times at stage 1, identical parallel machines at stage 2 and dedicated machines at stage 3, followed by a multi-trip traveling salesman problem with capacitated vehicles for customers of different destination areas. A mixed-integer linear programming model is formulated to minimize the maximum delivery completion time. A variable neighborhood search (VNS)-based method, a four-layered constructive heuristic method (denoted by $$CH_{VNS}$$) and a hybrid heuristic method (denoted by $$CONS_{VNS}$$) which combines the VNS method and the $$CH_{VNS}$$ method are developed to solve the problems with practical size. Computational experiments show the effectiveness and efficiency of the proposed methods.

Coordinated optimized scheduling of locks and transshipment in inland waterway transportation using binary NSGA‐II

AbstractTraffic congestion in inland waterways caused by insufficient throughput capacity of locks has become a compelling problem in developed inland shipping countries. In order to avoid excessive time wasted in waiting for lock service, it is suggested that some types of cargoes should be unloaded at the quays and transported by road/train to their destinations, which is called water–land transshipment. By this means, the ships are divided into two groups that either pass the lock or are transshipped at the quays, engendering the lock and water–land transshipment co‐scheduling (LWTC) problem. This paper focuses on the LWTC, where the roll‐on roll‐off ships, passenger ships, and general cargo ships that can be transshipped and other ships that can only pass the lock are considered in a lock‐quay co‐scheduling system. The LWTC problem is decomposed into an outer‐layer main 0‐1 optimization problem and two inner‐layer subproblems: lock scheduling and berth allocation. A multiobjective optimization model is proposed for the LWTC problem based on its two‐layer structure. To solve the LWTC problem, a hybrid heuristic method is proposed, where a modified binary nondominated sorting genetic algorithm II is proposed to solve the main problem, and the two subproblems are solved by specific heuristics. The proposed model and hybrid method are tested on instances extracted from historical data of traffic at Three Gorges Dam, the results of which demonstrate the feasibility of the model and the superiority of the proposed hybrid heuristic method over other comparisons.