Simulated Annealing Metaheuristic Research Articles

Model for Predicting Maize Crop Yield on Small Farms Using Clusterwise Linear Regression and GRASP

Planting a crop involves several key steps: resource assessment, crop selection, crop rotation, planting schedules, soil preparation, planting, care, and harvesting of crops. In this context, estimating the productivity of a crop based on available information, such as expected climatic conditions and agricultural practices, helps farmers reduce the uncertainty of their investment. In Colombia, maize is the fourth most important crop in the country. Significant efforts are required to improve productivity in traditional and technified production systems. In this sense, this research proposes and evaluates an approach called Clusterwise Linear Regression (CLR) to predict the crop maize yield in small farms, considering data on climate, soil, fertilization, and management practices, among others. To develop the CLR model, we conducted the following steps: data collection and preparation, clustering using k-means, cluster optimization with Greedy Random Adaptive Search Procedure (GRASP), and performance evaluation. The cluster optimization process allows the identification of clusters with similar characteristics and generates multiple linear regression models with mixed variables that explain the yield of the farms on each cluster. The Simulated Multiple Start Annealing (MSSA) metaheuristics were also evaluated, but the results of GRASP were the best. The results indicate that the proposed CLR approach is more effective than the linear and nonlinear algorithms mentioned in the literature, such as multiple lasso linear regression, random forests, XGBoost, and support vector machines. These algorithms achieved an accuracy of 70%. However, with the new CLR model, a significantly improved accuracy of 87% was achieved with test data. The clusters’ studies revealed key factors affecting crop yield, such as fertilization, drainage, and soil type. This transparency is a benefit over black-box models, which can be harder to interpret. This advancement can allow farmers to make better decisions about the management of their crops.

Configuration of multi‐shaper Time‐Sensitive Networking for industrial applications

AbstractIEEE 802.1 Time‐Sensitive Networking (TSN) has proposed several shapers, for example, time‐aware shaper (TAS, 802.1Qbv), asynchronous traffic shaping (ATS, 802.1Qcr), credit‐based shaper (CBS, 802.1Qav), and cyclic queuing and forwarding (CQF, 802.1Qch). The shapers have their advantages and disadvantages and can be used in isolation or in combination to address the varied timing requirements of industrial application streams. There is very limited work on how to analyse and configure shaper combinations. The authors are interested in the configuration optimisation of multi‐shaper TSN networks, targeting the TAS + CBS, TAS + ATS, and TAS + Multi‐CQF combinations. The authors first propose multi‐shaper integration approaches, focusing on a novel iterative delay analysis for TAS + ATS, an approach to integrate TAS and CQF by placing constraints on TAS scheduling as well as the TAS and CBS integration. We formulate the combinatorial optimisation problem of configuring multi‐shaper TSN networks, which consists, for example, of the routing of streams, the assignment of streams to the egress port queues, and the synthesis of gate control lists for TAS. Then, the authors propose a solution based on a simulated annealing metaheuristic. The proposed solutions are evaluated on large realistic test cases, up to tens of thousands of streams and devices.

Development of a dynamic traffic microsimulator for on-demand transit operations within an integrated modelling system

ABSTRACT This study develops an intelligent dynamic agent-based microsimulation (iDAMS) module for on-demand transit (ODT) operations within an integrated transport, land-use and energy (iTLE) model. A real-time optimization component within the iDAMS is formulated by the utilization of travelling salesman problem and simulated annealing metaheuristics that perform dynamic passenger-vehicle assignment. It simulates ODT operations for meeting the 24-hour auto-trip demand of Halifax, Canada, to compare the performance of the proposed system with personal cars (PCs). The optimization objectives are to determine the optimal fleet size and seat capacity that satisfies maximum trip requests while minimizing waiting time, travel time and vehicle kilometres travelled (VKT). Simulation results indicate that the ODT system can deliver service similar to PCs in Halifax while decreasing cost and emissions (13% reduction in VKT). The tools developed in this research will provide transit planners ability to conduct ODT scenario simulation and test system performance in real time.

Solving an Unrelated Parallel Machines Scheduling Problem with machine- and job-dependent setups and precedence constraints considering Support Machines

Single-stage production planning problems are common in the academic literature and in real-world environments. One of the least studied scenarios in the literature for this environment is that with Unrelated Parallel Machines. In this work, this type of problem is inspired by a real station within the wind tower production process. The problem presents characteristics that have already been studied, such as mandatory precedences and setup times that depend on the machine and the job. However, a new and real feature is presented: the existence of “support machines”, which are machines that can continue to work but cannot complete jobs due to reduced capacity for some operational reason. In this case, instead of abandoning their work, support machines can still be used to assist other machines by performing partial jobs before handing them over to full-capacity machines for completion. This innovative concept of support machines, never before presented in this context of production planning, introduces a unique approach to dealing with reduced-capacity machines without sacrificing their operational potential. A Mixed-Integer Linear Programming (MILP) model is formulated to mathematically represent this problem.This work explores the impact of these support machines on production planning. For this purpose, Tabu Search and Simulated Annealing metaheuristics have been adapted for their solution, and a novel Constructive Heuristic has been developed based on the real and manual process currently performed in the aforementioned factory. These three algorithms are run and compared on a real database in order to minimise the makespan. Their analysis shows that although the use of support machines generally gives positive results, an improvement is not achieved in all cases. Furthermore, contrary to what might be expected, the use of full-capacity machines in the role of support machines sometimes improves completion times.

Hybrid Forex prediction model using multiple regression, simulated annealing, reinforcement learning and technical analysis

Foreign exchange market refers to the market in which currencies from around the world are traded. It allows investors to buy or sell a currency of their choice. Forex interests several categories of stakeholders, such as companies that carry out international contracts, large institutional investors, via the main banks, which carry out transactions on this market for speculative purposes. One of the most important aspects in the Forex market is knowing when to invest by buying, selling, and this through the recorded trend of a currency pair, but given the characteristics of the Forex market namely its chaotic, noisy and not stationary nature, prediction becomes a big challenge for traders when it comes to predicting accuracy. This paper aims to predict the right action to be taken at a certain moment through the development of a model that combines multiple techniques such multiple regression, simulated annealing meta-heuristics, reinforcement learning and technical indicators.

A dynamic control method for extended arrival management using enroute speed adjustment and route change strategy

The continuous growth of air traffic leads to congestion in the surrounding area of airport. The optimization of the air traffic in this area requires the improvement of management strategies, especially for the arrival air traffic. SESAR has proposed the concept of Extended-Arrival MANagement (E-AMAN), which aims to plan the arrival streams from an earlier stage in order to achieve delay absorption and earlier planning in the en-route phases of flights. Based on this concept, we address a dynamic/on-line air traffic control problem to provide control decisions for flights in order to achieve safety and efficiency during arrival. The dynamic feature is realized by periodically updating the flight information for aircraft trajectory predictions. Sub-problems corresponding to each information update are established. The objective of this problem is to minimize the weighted sum of route congestion and the conflict resolution workload in the enroute segment, and the number of conflicts in the TMA (Terminal Maneuvering Area). A dynamic weight assignment approach is applied for ensuring that as an aircraft get closer to the TMA, its weight for TMA metrics increases accordingly. A case study based on the arrival traffic of the Paris Charles de Gaulle (CDG) airport is investigated by using a meta-heuristic simulated annealing algorithm combined with the rolling horizon approach. Final results are analyzed in terms of the reduction of concerned safety issues, final control decision distribution, and the time transferred through enroute decision change.

Optimal Area Polygonization by Triangulation and Visibility Search

The aim of the “CG:SHOP Challenge 2019” was to generate optimal area polygonizations of a planar point set. We describe here the algorithm that won the challenge. It is a two-phase algorithm based on the node-insertion move technique, which comes from the TSP. In the first phase, we use constrained triangulations to check efficiently the simplicity of the generated polygonizations. In the second phase, we perform visibility searches to be able to generate a wider variety of polygonizations. In both phases, the simulated annealing metaheuristic is implemented to approach the optimum.

A novel constraint programming and simulated annealing for disassembly line balancing problem with AND/OR precedence and sequence dependent setup times

This study considers recycling the products to reuse or reproduce the precious parts of these products to provide environmental sustainability and for companies to adapt to competitive conditions. These recycling processes include the dismantling of the products using the disassembly lines. Disassembly line balancing problems contribute to these processes by ensuring that products are disassembled efficiently and at minimum cost. Therefore, these problems have started to attract more attention in the literature. This study considers the disassembly line balancing problem, with sequence-dependent setup time and complex AND/OR precedence relations between tasks. The managerial impacts highlighted by the proposed problem are of great importance both from environmental and industrial sustainability due to the recycling process of the life-threatening wastes. The study aims to disassemble the products using a minimum number of workstations. Mixed-integer linear programming and constraint programming models have been developed for the considered problem in question. As far as is known, the sequence-dependent disassembly line balancing problem, which has complex precedence relations, has not been studied in the literature before, and the constraint programming model developed is also novel. The study also compares the constraint programming model with/without an initial solution. In addition, a simulated annealing metaheuristic is proposed to solve the problem, and its results are compared with the results of the models. Setup times were generated for well-known instances obtained from the literature. The results obtained using these instances showed that the constraint programming model successfully solved the problem under consideration.

Vehicle routing problems based on Harris Hawks optimization

The vehicle routing problem (VRP) is one of the challenging problems in optimization and can be described as combinatorial optimization and NP-hard problem. Researchers have used many artificial intelligence techniques in order to try to solve this problem. Among these techniques, metaheuristic algorithms that can perform random search are the most promising because they can be used to find the right solution in the shortest possible time. Therefore, in this paper, the Harris hawks optimization (HHO) algorithm was used to attempt to solve the VRP. The algorithm was applied to 10 scenarios and the experimental results revealed that the HHO had a strong ability to check for and find the best route as compared to other metaheuristic algorithms, namely, simulated annealing and artificial bee colony optimization. The comparison was based on three criteria: minimum objective function obtained, minimum number of iterations required and satisfaction of capacity constraints. In all scenarios, the HHO showed clear superiority over the other methods.

Area Optimal Polygonization Using Simulated Annealing

We describe a practical method to find near-optimal solutions for the area-optimal simple polygonization problem: Given a set of points S in the plane, the objective is to find a simple polygon of minimum or maximum area defined by S . Our approach is based on the celebrated metaheuristic Simulated Annealing. The method consists of a modular pipeline of steps, where each step can be implemented in various ways and with several parameters controlling it. We have implemented several different algorithms and created an application that computes a polygon with minimal (or maximal) area. We experimented with the various algorithmic options and with the controlling parameters of each algorithm to tune up the pipeline. Then, we executed the application on each of the benchmark instances, exploiting a grid of servers, to obtain near optimal results.

Intrachain Exciton Motion Can Compete with Interchain Hopping in Conjugated Polymer Films with a Strong J-Aggregate Property.

Understanding exciton diffusion properties in organic semiconductor films is crucial for organic solar cells because excitons need to diffuse to an electron donor/acceptor interface to dissociate into charges. We previously found that singlet excitons generated in the thin films of a novel naphthobisoxadiazole-based low-bandgap polymer PNOz4T exhibit two-dimensional exciton diffusion characteristics along the backbone and π-stacking directions owing to the HJ-aggregate property of PNOz4T. However, the diffusion constants along these directions could not be determined owing to the difficulty of data analysis. Herein, we present a detailed analysis based on a simulated annealing metaheuristic. We found that intrachain exciton motion can be faster than interchain hopping. On the basis of temperature dependence measurements, we found that exciton diffusion is more favorable at lower temperatures because the coherent component partly contributes to exciton motion.

A mathematical model and a simulated annealing algorithm for unequal multi-floor dynamic facility layout problem based on flexible Bay structure with elevator consideration

PurposeAs far as the authors know, no research has already been carried out on the multi-floor dynamic facility layout problem (MF-DFLP) in the continuous form regarding the flexible bay structure, the number and the variable location of the elevator. Therefore, the present paper models the given problem and attempts to find a sub-optimal solution for it using a meta-heuristic simulated annealing (SA) algorithm.Design/methodology/approachThe efficient use of resources has always been a prominent matter for decision-makers. Many reasons including land use, construction considerations and proximity of departments have led to the design of multi-floor facilities. On the other hand, their fast-evolving environment calls for dynamic planning. Therefore, in this paper, a model and the SA algorithm for MF-DFLP are presented.FindingsAfter presenting a mathematical model, the problem was solved precisely in a small size using the GAMS software. Also, a near-optimal solution method using a SA meta-heuristic algorithm is suggested and the proposed algorithm was run in the MATLAB software. To evaluate the presented model and the proposed solution, some test cases were considered in two aspects. The first aspect was the test cases that are newly generated in small, medium and large sizes to compare the exact optimal solution with the results of the meta-heuristic algorithm. Eight test cases with small sizes were solved using the GAMS software, the optimum solutions were obtained in a reasonable time, and the cost of their solutions was equal to that of the SA algorithm. Eight test cases with medium sizes were run in the GAMS software with the time limit of 80,000 s, and the SA algorithm had performed better for these test cases. Two test cases were also considered in large size that GAMS could not solve them, whereas the SA algorithm successfully found a proper solution for each. The second aspect included the test cases from the literature. The result showed that suggested algorithm is more capable of finding best solutions than compared algorithms.Originality/valueIn this paper, an unequal area MF-DFLP was studied in a continuous layout form in which the location and number of the elevators were considered to be variable, and the layouts were considered with flexible bay structure. These conditions were investigated for the first time.

Two-level optimization approach to tree-level forest planning

Laser scanning and individual-tree detection are used increasingly in forest inventories. As a consequence, methods that optimize forest management at the level of individual trees will be gradually developed and adopted. The current study proposed a hierarchical two-level optimization method for tree-level planning where the cutting years are optimized at the higher level. The lower-level optimization allocates the trees to the cutting events in an optimal way. The higher-level optimization employed differential evolution whereas the lower-level problem was solved with the simulated annealing metaheuristic. The method was demonstrated with a 30 ​m ​× ​30 ​m sample plot of planted Larix olgensis . The baseline case maximized the net present value as the only management objective. The solution suggested heavy thinning from above and a rotation length of 62 years. The baseline problem was enhanced to mixed stands where species diversity was used as another management objective. The method was also demonstrated in a problem that considered the complexity of stand structure, in addition to net present value. The objective variables that were used to measure complexity were the Shannon index (species diversity), Gini index (tree size diversity), and the index of Clark and Evans, which was used to describe the spatial distribution of trees. The article also presents a method to include natural advance regeneration in the optimization problem and optimize the parameters of simulated annealing simultaneously with the cutting years. The study showed that optimization approaches developed for forest-level planning can be adapted to problems where treatment prescriptions are required for individual trees.

A probabilistic model based optimization for aircraft scheduling in terminal area under uncertainty

Air traffic management relies strongly on the accuracy of prediction, while uncertainties such as weather, navigation accuracy, pilot operations, etc. may weaken the performance of predictive tools and cause potential safety issues or reduced capacity. Among all controlled airspace, the Terminal Maneuvering Area (TMA) is one of the most complex areas in which flight safety can be easily affected by unpredictable disturbances. This paper addresses an aircraft scheduling problem under uncertainty with the aim of providing a robust schedule for arrival flights. Uncertainty quantification and propagation along the routes are realized in a trajectory model that formulates the time information as random variables. Conflict detection and resolution are performed at waypoints of a predefined network based on the predicted time information from the trajectory model. By minimizing the expected number of conflicts, consecutively operated flights can be well separated. Apart from the proposed model, two other models - the deterministic model and a model that incorporates separation buffers - are presented as benchmarks. A meta-heuristic simulated annealing algorithm combined with a time decomposition sliding window is proposed for solving a case study of the Paris Charles de Gaulle (CDG) airport. Further, a simulation framework based on the Monte-Carlo approach is implemented so as to evaluate the optimized solutions obtained from the three models. Statistical comparison among the results shows instability of the model that incorporates the separation buffers, in contrast, the proposed model has absolute advantages in both stability and the conflict absorbing ability when uncertainty arises.

CAN LINEAR PROGRAMMING ASSIST METAHEURISTICS IN FOREST PRODUCTION PLANNING PROBLEM?

The planning of forest production requires the adoption of mathematical models to optimize the utilization of available resources. Hence, studies involving the improvement of decision-making processes must be performed. Herein, we evaluate an alternative method for improving the performance of metaheuristics when they are applied for identifying solutions to problems in forest production planning. The inclusion of a solution obtained by rounding the optimal solution of linear programming to a relaxed problem is investigated. Such a solution is included in the initial population of the clonal selection algorithm, genetic algorithm, simulated annealing, and variable neighborhood search metaheuristics when it is used to generate harvest and planting plans in an area measuring 4,210 ha comprising 120 management units with ages varying between 1 and 6 years. The same algorithms are executed without including the solutions mentioned in the initial population. Results show that the performance of the clonal selection algorithm, genetic algorithm, and variable neighborhood search algorithms improved significantly. Positive effects on the performance of the simulated annealing metaheuristic are not indicated. Hence, it is concluded that rounding off the solution to a relaxed problem is a good alternative for generating an initial solution for metaheuristics.

Scheduling problems on a new setting of flexible flowshops: ℓ-Machine proportionate flowshops

The classical flexible flowshop () environment consists of -stages in series with -machines in parallel at each stage, where each job has to undergo a particular series of actions. In this paper we consider a similar, yet different, and still a challenging real-life setting, that, to the best of our knowledge, has not been studied to date. We assume that there are -sets in parallel in which each of the sets is an -machine proportionate flowshop. In this new setting, each job can be processed on each one of the sets, but once a set is chosen, the job must be processed on all of its machines, in first-in-first-out method. We study several fundamental scheduling measures such as makespan, maximum tardiness with common due-date, total tardiness with common due-date, and total load. Moreover, we consider optional job-rejection and focus on minimising the total completion time. All problems are shown to be NP-hard, and pseudo-polynomial dynamic programming (DP) solution algorithms and Simulated Annealing (SA) metaheuristics are provided. The efficiency of all our proposed algorithms is validated through an extensive numerical study.

Optimization of the Multi-Facility Location Problem Using Widely Available Office Software

Multi-facility location problem is a type of task often solved (not only) in logistics. It consists in finding the optimal location of the required number of centers for a given number of points. One of the possible solutions is to use the principle of the genetic algorithm. The Solver add-in, which uses the evolutionary method, is available in the Excel office software. It was used to solve the benchmark in 4 levels of difficulty (from 5 centers for 25 points to 20 centers for 100 points), and one task from practice. The obtained results were compared with the results obtained by the metaheuristic simulated annealing method. It was found that the results obtained by the evolutionary method are sufficiently accurate. Their accuracy depends on the complexity of the task and the performance of the HW used. The advantage of the proposed solution is easy availability and minimal requirements for user knowledge.

DESIGN OF WASTE VEGETABLE OIL COLLECTION NETWORKS APPLYING VEHICLE ROUTING PROBLEM AND SIMULTANEOUS PICKUP AND DELIVERY

The growth of industrialization in Mexico has caused an increase in the demand for materials to satisfy the consumption of goods and services of a growing population. Given this scenario, there is a rise of the residual generation with affectations on the ecosystem and population health. Hence, the objective of this research was to design a network for waste vegetable oil collection based on vehicle routing problem with simultaneous pickup and delivery, starting from a distribution centre to 49 restaurants, as the generation sources of waste vegetable oil. The Vehicle Routing Problem Simultaneous Pickup and Delivery with Time Windows was the variant used as a vehicle routing method to solve the problem. The free software VPRPD was the tool used to solve the vehicle routing problem with simultaneous pickup and delivery that allowed to specify time restrictions. This software uses the simulated annealing metaheuristics in its syntax. As a result, it was obtained a total of 8 networks, for a vehicle capacity utilization of 70 percent in the 6 t vehicle and 46 percent in the 8 t vehicle.

Route Planning for Agricultural Machines with Multiple Depots: Manure Application Case Study

Capacitated field operations involve input/output material flows where there are capacity constraints in the form of a specific load that a vehicle can carry. As such, a specific normal-sized field cannot be covered in one single operation using only one load, and the vehicle needs to get serviced (i.e., refilling) from out-of-field facilities (depot). Although several algorithms have been developed to solve the routing problem of capacitated operations, these algorithms only considered one depot. The general goal of this paper is to develop a route planning tool for agricultural machines with multiple depots. The tool presented consists of two modules: the first one regards the field geometrical representation in which the field is partitioned into tracks and headland passes; the second one regards route optimization that is implemented by the metaheuristic simulated annealing (SA) algorithm. In order to validate the developed tool, a comparison between a well-known route planning approach, namely B-pattern, and the algorithm presented in this study was carried out. The results show that the proposed algorithm outperforms the B-pattern by up to 20.0% in terms of traveled nonworking distance. The applicability of the tool developed was tested in a case study with seven scenarios differing in terms of locations and number of depots. The results of this study illustrated that the location and number of depots significantly affect the total nonworking traversal distance during a field operation.

Reconstruction of structural anomalies out of seismic measurements by means of a non-deterministic full waveform inversion approach for application in mechanized tunneling

A full waveform inversion may be utilized as a promising tool to invert the properties of a spatial model out of seismic waveforms, but in turn may claim a high computational effort if the inversion method is not effective. Unscented hybrid simulated annealing, which is developed for an application in mechanized tunneling, is an optimization method that combines the metaheuristic simulated annealing with the unscented Kalman filter. The method needs a priori knowledge in the form of a user-defined parametrization, but therefore may invert the structure effectively and precisely. In this work, the authors apply the novel method on laboratory-generated seismic data. In a first step, three concrete plates with different structural anomalies are constructed. Seismic data is acquired and implemented into the optimization algorithm to invert the positions, shapes, and the material parameters of the structural anomalies. The authors find that the algorithm performs well on the generated data, reconstructing the structural anomalies with a satisfying precision during a low number of forward simulations.