Heuristic Simulated Annealing Research Articles

An innovative simulation algorithm for optimising the cost of communal water supply systems using EPANET

In this work, the EPANET software is used to parametrize the design, simulate and optimise the overall cost of building and running a reliable, sustainable and efficient communal Water Supply System. Considering a model community of forty houses with an average of six occupants per home, the developed optimization algorithm using the simulated annealing heuristics approach minimizes the total cost of continuous water distribution to the community through a system of pipe network in a way that satisfies the critical hydraulic rules and constraints for efficient water distribution. Thus, the controlled variables of the simulated system include volumetric flow, velocity and head loss. To realize the desired goal, the prototyped communal water distribution system was first simulated by water free fall i.e., only gravity feed under a peak demand flow condition. This facilitated the identification and localization of head-loss zones. Subsequently, virtual pump stations of various capacities were optimally introduced as boosters at identified nodal points to overcome critical head losses. The response of the system to varying capacities of the booster pumps was then used to analyse and determine the optimum capacity of the pump. Simulation outcome showed that the optimum least cost design for a sixtyyear system’s life cycle is achieved by using a combination of gravity and 5 hp (3.73 kW) pump with optimum pipe diameter ranging from 60 - 150mm.

Solving the Flying Sidekick Traveling Salesman Problem by a Simulated Annealing Heuristic

This study investigates the flying sidekick traveling salesman problem (FSTSP), in which a truck and an unmanned aerial vehicle work together to make deliveries. This study develops a revised mixed-integer linear programming (MILP) model for the FSTSP. The revised MILP model performs better than the existing model. Due to the FSTSP’s high complexity, we propose an effective heuristic based on simulated annealing (SA) to solve the problem. The novelty of the proposed SA heuristic lies in the new solution representation, which not only determines the visiting sequence of customers but also the service type of customers and rendezvous positions. Another feature of the proposed SA is a new operator specifically designed for the FSTSP. To evaluate the performance of the proposed SA heuristic, we conduct a comprehensive computational study where we fine-tune the parameters of the SA heuristic and compare the performance of the SA heuristic with several state-of-the-art algorithms including hybrid genetic algorithm (HGA) and iterated local search (ILS) in solving existing FSTSP benchmark instances. The results indicate that the proposed SA heuristic outperforms ILS and is statistically competitive with HGA. It obtains best-known solutions for all small FSTSP instances and 29 best-known solutions for the 60 large FSTSP instances, including 20 new best-known solutions.

Laser 3D tightly coupled mapping method based on visual information

PurposeSimultaneous localization and map building (SLAM), as a state estimation problem, is a prerequisite for solving the problem of autonomous vehicle motion in unknown environments. Existing algorithms are based on laser or visual odometry; however, the lidar sensing range is small, the amount of data features is small, the camera is vulnerable to external conditions and the localization and map building cannot be performed stably and accurately using a single sensor. This paper aims to propose a laser three dimensions tightly coupled map building method that incorporates visual information, and uses laser point cloud information and image information to complement each other to improve the overall performance of the algorithm.Design/methodology/approachThe visual feature points are first matched at the front end of the method, and the mismatched point pairs are removed using the bidirectional random sample consensus (RANSAC) algorithm. The laser point cloud is then used to obtain its depth information, while the two types of feature points are fed into the pose estimation module for a tightly coupled local bundle adjustment solution using a heuristic simulated annealing algorithm. Finally, the visual bag-of-words model is fused in the laser point cloud information to establish a threshold to construct a loopback framework to further reduce the cumulative drift error of the system over time.FindingsExperiments on publicly available data sets show that the proposed method in this paper can match its real trajectory well. For various scenes, the map can be constructed by using the complementary laser and vision sensors, with high accuracy and robustness. At the same time, the method is verified in a real environment using an autonomous walking acquisition platform, and the system loaded with the method can run well for a long time and take into account the environmental adaptability of multiple scenes.Originality/valueA multi-sensor data tight coupling method is proposed to fuse laser and vision information for optimal solution of the positional attitude. A bidirectional RANSAC algorithm is used for the removal of visual mismatched point pairs. Further, oriented fast and rotated brief feature points are used to build a bag-of-words model and construct a real-time loopback framework to reduce error accumulation. According to the experimental validation results, the accuracy and robustness of the single-sensor SLAM algorithm can be improved.

Data-driven trajectory-based analysis and optimization of airport surface movement

Driven by the recent deployment of equipment and the availability of data, aircraft trajectories on the surface can be tracked continuously, and the detailed analysis of surface operations is enabled to measure airport operational performance and prepare for collaborative decision making. This paper proposes a hybrid approach combining traffic analysis and optimization to address the airport surface movement problem. Important metrics of surface management such as runway utilization, taxi routes, taxi times are extracted and characterized based on Automatic Dependent Surveillance-Broadcast (ADS-B) messages. After recognizing the congestion bottleneck, a trajectory-based optimization model is proposed, and an adapted simulated annealing heuristic is applied to solve the related problem. Two concepts for optimizing surface operations: arrival taxi reroute and departure controlled pushback are evaluated. The proposed approach is illustrated for Beijing Capital International airport. Computational experiments show that a mean taxi-in time reduction of 5.1 min is achieved for the rerouting arrivals, and the mean taxi-out time is reduced by 3.7 min with gate holding strategy. This data-driven approach allows advanced characterization of complex surface operations and enables collaborative decision assistance tools for managing surface movement efficiently.

Two-Stage Vehicle Routing Optimization for Logistics Distribution Based on HSA-HGBS Algorithm

Aiming at the problems of complex urban road network, low efficiency of logistics distribution, and the difficulty of large-scale logistics distribution area division and routing planning, this paper proposes a two-stage logistics distribution vehicle routing optimization (VRP) method based on the establishment of a multi-factor complex road network constrained logistics distribution mathematical model. Considering the complex traffic elements and road network topological structure in logistics and distribution, in the first stage, a heuristic simulated annealing (HSA) distribution region partitioning algorithm is proposed with the objective of balancing vehicle task load to divide the urban logistics distribution network under complex road networks, so as to reduce the area scale and path search cost. In the second stage of route decision making, aiming at minimizing the total cost of logistics distribution, combining the VRP problem with complex road network conditions, a heuristic path search method combined with complex road network model constraints is proposed. In this stage, a hybrid genetic beam search(HGBS) algorithm is used to plan the path nodes, reduce the randomness of the model in the initial search for paths by heuristic genetic algorithms, then combine with Beam graph search methods to reduce the space and time used for the search, and use optimization algorithms to improve the accuracy of independent sub-region routing optimization and the rationality of overall physical distribution route selection. Finally, the proposed method is validated in this paper with two practical cases. The experimental results show that the two-stage decision-making algorithm proposed in this paper has certain advantages in partitioning schemes, minimizing total cost and iteration times. Through comparison, the optimization ability of this method for logistics distribution networks is proved.

Scheduling Multiple Groups of Jobs for a Multi-Line Steel Hot Rolling Mill

This work addresses the optimal scheduling for a multi-line steel hot rolling mill with two parallel production lines. Groups of products are manufactured at one of these lines in an alternate sequence. An optimal schedule minimizes the sum of the required setup times between all products and optimally fills up retooling times necessary at one line by processing a group of products at the other line. This requires both sequence optimization and, for some groups, the optimal selection of jobs.The optimization problem is a combination of traveling salesman and orienteering problems and considers all groups simultaneously. To iteratively solve the combined problem, an algorithm consisting of a heuristic simulated annealing and a local search procedure is employed. The effectiveness of the algorithm is analyzed in a case study.

Design of a two-echelon freight distribution system in an urban area considering third-party logistics and loading–unloading zones

This research examines the problem of designing a two-echelon freight distribution system in a dense urban area that considers third-party logistics (TPL) and loading–unloading zones (LUZs). The proposed system takes advantage of outsourcing the last mile deliveries to a TPL provider and utilizing LUZs as temporary intermediate facilities instead of using permanent intermediate facilities to consolidate freight. A mathematical model and a simulated annealing (SA) algorithm are developed to solve the problem. The efficiency and effectiveness of the proposed SA heuristic are verified by testing it on existing benchmark instances. Computational results show that the performance of the proposed SA is comparable with that of another state-of-the-art algorithm. The model and algorithm are then used to design a two-echelon freight distribution system in Taipei City, Taiwan. Results of the case study indicate that the proposed model and algorithm provide a better distribution system.

Joint bit and power loading for adaptive MIMO OFDM VLC systems

AbstractVisible light communication (VLC) is a short‐range wireless access technology based on the dual use of illumination structure. The frequency‐selective characteristics of VLC channels motivate the use of orthogonal frequency division multiplexing (OFDM). In addition, the presence of multiple light sources in most indoor spaces makes multiple‐input–multiple‐output (MIMO) communication techniques a natural solution for VLC systems. In this paper, we investigate the design of an adaptive MIMO OFDM system. Specifically, we propose adaptive bit and power loading for a direct current–biased OFDM VLC system with MIMO mode switching between repetition coding and spatial multiplexing. We formulate the adaptive algorithm design as an optimization problem where we aim to maximize spectral efficiency through the proper selection of modulation order, power level, and MIMO mode, while satisfying a targeted bit error rate. To solve the underlying NP‐hard problems, we use a simulated annealing heuristic. We present Monte Carlo simulation results for a typical indoor setting and illustrate the performance improvements through link adaptation.

A Solution Method Combining Simulated Annealing and Graph-based Heuristics for Operational Planning and Scheduling Benchmark Problems in an Automatic Picking System

A Solution Method Combining Simulated Annealing and Graph-based Heuristics for Operational Planning and Scheduling Benchmark Problems in an Automatic Picking System

Rectangle blanket problem: Binary integer linear programming formulation and solution algorithms

A rectangle blanket is a set of non-overlapping axis-aligned rectangles, used to approximately represent the two-dimensional image of a shape. The use of a rectangle blanket is a widely considered strategy for speeding-up the computations in many computer vision applications. Since neither the rectangles nor the image have to be fully covered by the other, the blanket becomes more precise as the non-overlapping area of the image and the blanket decreases. In this work, we focus on the rectangle blanket problem, which involves the determination of an optimum blanket minimizing the non-overlapping area with a given image subject to an upper bound on the total number of rectangles the blanket can include. This problem has similarities with rectangle covering, rectangle partitioning and cutting/packing problems. The image replaces an irregular master object by an approximating set of smaller axis-aligned rectangles. The union of these rectangles, namely, the rectangle blanket, is neither restricted to remain entirely within the master object, nor required to cover the master object completely. We first develop a binary integer linear programming formulation of the problem. Then, we introduce four methods for its solution. The first one is a branch-and-price algorithm that computes an exact optimal solution. The second one is a new constrained simulated annealing heuristic. The last two are heuristics adopting ideas available in the literature for other computer vision related problems. Finally, we realize extensive computational tests and report results on the performances of these algorithms.

A Simulated Annealing Heuristic for the Capacitated Green Vehicle Routing Problem

This research studies the capacitated green vehicle routing problem (CGVRP), which is an extension of the green vehicle routing problem (GVRP), characterized by the purpose of harmonizing environmental and economic costs by implementing effective routes to meet any environmental concerns while fulfilling customer demand. We formulate the mathematical model of the CGVRP and propose a simulated annealing (SA) heuristic for its solution in which the CGVRP is set up as a mixed integer linear program (MILP). The objective of the CGVRP is to minimize the total distance traveled by an alternative fuel vehicle (AFV). This research conducts a numerical experiment and sensitivity analysis. The results of the numerical experiment show that the SA algorithm is capable of obtaining good CGVRP solutions within a reasonable amount of time, and the sensitivity analysis demonstrates that the total distance is dependent on the number of customers and the vehicle driving range.

A Hybrid Simulated Annealing Heuristic for Multistage Heterogeneous Fleet Scheduling with Fleet Sizing Decisions

This paper deals with multistage heterogeneous fleet scheduling with fleet sizing decisions (MHFS-FSD). This MHFS-FSD attempts to integrate vehicles allocation and fleet sizing decisions considering the vehicle routing of multiple vehicle types. The problem is formulated as mixed integer programming model. The matrix formulation denoting vehicle allocation scheme is explored according to the characteristic of this problem. Generating vehicle allocation scheme with greedy heuristic procedure (VA-GHP) as initial solution of problem is presented. The USP-IVA method to update the initial solution generated by VA-GHP approach is developed. And then, incorporating VA-GHP and USP-IVA into simulated annealing algorithm, a novel heuristic called HSAH-GHP&IVA is proposed. Finally, some experiments are designed to test the proposed heuristic and the results show that the heuristic can generate reasonably good solutions in short CPU times.

Using Simulated Annealing to Improve the Information Dissemination Network Structure of a Foreign Animal Disease Outbreak Response

AbstractCommunication is an integral part of emergency response, and improving the information dissemination network for crisis communication can save time, resources, and lives. In a foreign animal disease (FAD) outbreak, timeliness of detection and response are critical. An outbreak of foot-and-mouth disease, a particularly significant FAD, could cripple the agriculture economy. This research uses communication data from a FAD response exercise in Kansas to develop a reliable crisis communication network model, contributing a general method for creating an information dissemination network from empirical communication data. We then introduce a simulated annealing heuristic that identifies an alternative network structure that minimizes the time for information to reach all response participants. The resultant network structure reduces overall information transmission time by almost 90% and reveals actionable observations for improving FAD response communication. We find that not only can a crisis communication network be improved significantly, but also that the quantitative results support qualitative observations from early in the data extraction process. This paper adds original methods to the literature and opens the door for future quantitative work in the area of crisis communication and emergency response.

A simulated annealing heuristic for the online symmetric traveling salesman problem

An online tour is created in online manner, and some nodes are decided to traverse those will be fixed and unchangeable for the next times. The exact values of costs are not available for decision maker; however, they are symmetric and satisfying the triangular inequality. A discrete time Markov chain is established in online policy times by permutation of some unfixed nodes. Then, a simulated annealing heuristic is applied to select the best state. The competitive analysis of the proposed method shows the remarkable competitive ratio 1.4203ρ – 0.4203 against the previous ones, where the initial obtained solution is ρ-approximated. The experimental results reveal improvement capability of the algorithm for limited iterations.

Hub interdiction problem variants: Models and metaheuristic solution algorithms

Hub location problem (HLP) is one of the strategic problems encountered in designing transportation and telecommunication networks. Regardless of the considered objective in design of hub networks, such as cost minimization or service level maximization, the located hubs can be subject to natural or intentional disruptions after installation. In this paper, we address the multiple allocation p-hub median, p-hub maximal covering, and p-hub center problems under intentional disruptions. In each case, the problem is considered as a Stackelberg game where the leader locates p hubs to optimize his/her objective function, whereas the follower tries to identify and interdict r hubs that their loss would diminish the network performance the most. Bilevel and single level mathematical formulations are presented to model the problem from the leader’s and the follower’s perspectives. Furthermore, efficient Simulated Annealing (SA) heuristics are proposed for solving the problems. Extensive computational experiments show the capability of the proposed SA algorithms to obtain the optimal solutions in short computational times. Some managerial insights are also derived based on the obtained numerical results.

Allocation and Vehicle Routing for Evacuation Operations: A Model and a Simulated Annealing Heuristic

AbstractIn case of a disaster such as an earthquake, one of the major problems encountered is rescuing the wounded from the rubble. They need to be transferred to clinics and hospitals for addition...

Mechanical modeling and characterization of meniscus tissue using flat punch indentation and inverse finite element method.

In this paper, to characterize the mechanical properties of meniscus by considering its local microstructure, a novel nonlinear poroviscoelastic Finite Element (FE) model has been developed. To obtain the mechanical response of meniscus, indentation experiments were performed on bovine meniscus samples. The ramp-relaxation test scenario with different depths and preloads was designed to capture the mechanical characteristics of the tissue in different regions of the medial and lateral menisci. Thereafter, a FE simulation was performed considering experimental conditions. Constitutive parameters were optimized by solving a FE-based inverse problem using the heuristic Simulated Annealing (SA) optimization algorithm. These parameters were ranged according to previously reported data to improve the optimization procedure. Based on the results, the mechanical properties of meniscus were highly influenced by both superficial and main layers. At low indentation depths, a high percentage relaxation (p < 0.01) with a high relaxation rate (p < 0.05) was obtained, due to the poroelastic and viscoelastic nature of the superficial layer. Increasing both penetration depth and preload level involved the main layer response and caused alterations in hyperelastic and viscoelastic parameters of the tissue, such that for both layers, the shear modulus was increased (p < 0.01) while the rate and percentage of relaxation were decreased (p < 0.01). Results reflect that, shear modulus of the main layer in anterior region is higher than central and posterior sites in medial meniscus. In contrast, in lateral meniscus, posterior side is stiffer than central and anterior sides.

Capacitated Next Controller Placement in Software Defined Networks

Software defined networking shifts the control plane of forwarding devices to one or more external entities known as controllers. Determining the optimal location of controllers in the network and the assignment of switches to them is widely known as controller placement problem. In case of controller failures, the switches are disconnected from the controller until they are reassigned to other active controllers with enough spare capacity. However, there is a significant upsurge in the worst case latency after the reassignment due to lack of planning for controller failures. In this paper, we propose a controller placement strategy that not only considers reliability and capacity of controllers but also plans ahead for controller failures to avoid repeated administrative intervention, drastic increase in latency and disconnections. It is formulated as a mixed integer linear program. The objective is to minimize the maximum, for all switches, of the sum of the latency from the switch to the nearest controller with enough capacity (first reference controller) and the latency from the first reference controller to its closest controller with enough capacity (second reference controller). We also proposed a generalized model which can be used to minimize the average latency and extended it for multiple controller failures. Furthermore, we presented a simulated annealing heuristic that efficiently solves the problem on large scale networks. The proposed formulation and heuristic are evaluated on various networks from the Internet Topology Zoo. Simulation results show that our proposed method performs better than the controller placement that does not plan ahead for failures.

Load Balancing Scheme with PTM Mode for Multicast Services Between MBSFN

In long term evolution (LTE) networks, the multicast services can be transmitted by single frequency network (SFN) mode or point-to-multipoint (PTM) mode, and the unicast services are delivered with point-to-point (PTP) mode. To avoid the network congestion in the LTE networks with the multicast and unicast mixed services, a load balancing (LB) scheme with PTM mode is proposed to minimize the demanded radio resources of the maximum load cell. The system model of the demanded radio resources is based on the signal to interference noise ratio (SINR) of multicast services with SFN mode and PTM mode. Then the minimization problem is solved by means of simulated annealing (SA) heuristics. It selects the optimal transmission mode between SFN and PTM for multicast services. Simulation results show that the proposed LB needs less demanded radio resources of the maximum load cell than SFN mode for all the multicast services.

Hybrid cyclicity: Combining the benefits of cyclic and non-cyclic timetables

We propose a new type of timetable that would combine both the regularity of the cyclic timetables and the flexibility of the non-cyclic ones. In order to do so, several combinations of the two timetables are considered. The regularity is incorporated in their design and the flexibility is evaluated using the passenger satisfaction (in monetary units). Each of the tested timetables is constructed using the Passenger Centric Train Timetabling Problem (PCTTP), that is solved using a simulated annealing heuristic. Note that the PCTTP, unlike the traditional Train Timetabling Problem (TTP), does not take into account the conflicts among trains. The aim of the PCTTP is to design such timetables that the passengers’ satisfaction is maximized and it remains the aim of the TTP to remove any potential conflicts. The performance of each of the considered timetables is assessed on the real network of Israeli Railways. The results of the case study show that our proposed hybrid cyclic timetable can provide the benefits of the cyclic and the non-cyclic timetable simultaneously. This timetable consists of 75% of cyclic trains (securing the regularity of the service) and of 25% of non-cyclic trains (deployed as supplementary trains during the peak hours and capturing the demand fluctuation). The level of the passenger satisfaction of the hybrid cyclic timetable is similar to the level of the non-cyclic one, which has about 18.5% of improvement as compared to the purely cyclic one.