Simulated Annealing Metaheuristic Research Articles

A multiproduct economic order quantity model with simulated annealing application

PurposeThis paper aims to present a set of five models for the economic order quantity problem. Four models solve problems for a single product: incremental discounts with or without backorders and all-unit discounts with or without backorders, and the last model solves problems for the multiproduct case.Design/methodology/approachA basic integer non-linear model with binary variables is presented, and its flexible structure allows for all five models to be utilised with minor modifications for adaptation to individual situations. The multiproduct model takes into consideration the work of Chopra and Meindl (2012), who studied two types of product aggregations: full and adaptive. To find optimal or near-optimal solutions for the multiproduct case, the authors propose a simulated annealing metaheuristic application. Numerical examples are presented to improve the comprehension of each model, and the authors also present the efficiency of the simulated annealing algorithm through an example that aggregates 50 products, each one with different discount schemes and some allowing backorders.FindingsOur model proved to be efficient at finding optimal or near optimal solutions even when confronted with mathematical complexities such as the allowance of backorders and incremental discounts. Originality/valueFinally our model can process a mix of products with different discount schemes at the same time, and the simulated annealing metaheuristics could find optimal or near optimal solutions with very few iterations.

Planning the reverse logistic for recyclable solid waste by a mathematical model and a simulated annealing metaheuristic

Selective collection has grown over the last few years and this is leading to an increase of the reverse logistics costs paid by the city hall. Thus, a better planning of the reverse logistics may reduce costs by minimising the number of trucks needed and the total distance travelled. This paper proposes a mathematical model and a modified simulated annealing metaheuristic based on the vehicle routing problem with multiple trips to plan the reverse logistics for the selective collection. The mathematical model was solved by CPLEX 12.6 and the SA was developed in C. In order to test both of them, they were applied to the selective collection in five Brazilian cities that use a volunteer delivery stations (VDS) located at roads and squares. SA performed the planning of selective collection in a relatively low computational time and achieved a better plan than the ones done manually by the planners.

Planning the reverse logistic for recyclable solid waste by a mathematical model and a simulated annealing metaheuristic

Planning the reverse logistic for recyclable solid waste by a mathematical model and a simulated annealing metaheuristic

An Integrated Expert System for Linear Scheduling Heavy Earthmoving Operations

Heavy earthmoving operations are repetitive in nature and vulnerable to time-related restraints and uncertainties. Therefore, at the conceptual stage, scheduling these operations can take a linear form, known as linear schedule or line of balance (LOB). In such type of work, generating a preliminary line of balance for variable sequencing of activities is crucial. In this paper, an integrated expert system for determining preliminary linear schedules for heavy earthmoving operations at the conceptual stage is presented. The proposed system incorporates numerous factors that influence the analysis of earthmoving operations, which include geological and topographical parameters used to determine productivity rates at the conceptual stage. Also, the proposed system is capable of automatically generating a line of balance based on a stochastic scheduling technique via the metaheuristic simulated annealing intelligent approach to incorporate randomness and uncertainties in performing the associated activities. A parametric analysis is conducted in order to quantify the system’s degree of accuracy. An actual case project is then utilized to illustrate its numerical capabilities. Generating accurate linear schedules for heavy earthmoving operations at the conceptual design stage is anticipated to be of major significance to infrastructure project stakeholders, engineers, and construction managers by detecting schedule’s conflicts early in order to enhance overall operational logistics.

Monitoring olive oils quality and oxidative resistance during storage using an electronic tongue

Monitoring olive oils oxidative stability and quality parameters (free acidity, peroxide values, K232 and K270 extinction coefficients) is needed to guarantee that, during storage, their levels remain within the legal thresholds enabling their commercialization as high-value extra-virgin olive oils. Physicochemical levels are assessed using time-consuming routine analytical reference techniques. In this work, the feasibility of a novel approach that merges an electronic tongue and chemometric tools, for monitoring extra-virgin olive oils’ quality along one year of storage at dark or exposed to light is discussed. The results confirmed that physicochemical parameters varied with the storage lighting conditions and more significantly with time. Also, multiple linear regression models, using sub-sets of 22–28 sensors selected with a meta-heuristic simulated annealing algorithm, allow evaluating the storage time-evolution of olive oils’ peroxide values, extinction coefficients and oxidative stabilities with satisfactory accuracy (R2 ≥ 0.98 and ≥ 0.96, for leave-one-out and repeated K-fold cross-validation procedures, respectively). The capability of monitoring, in a single electrochemical assay, legal required quality parameters of olive oils, decreases considerable the analysis time and cost, allowing checking the compliance of extra-virgin olive oil quality with labeling. So, the use of electronic tongues for extra-virgin olive oil shelf-life assessment could be envisaged.

The dynamic vehicle allocation problem with application in trucking companies in Brazil

This paper deals with the dynamic vehicle allocation problem (DVAP) in road transportation of full truckloads between terminals. The DVAP involves multi-period resource allocation and consists of defining the “movements” of a fleet of vehicles that transport goods between terminals with a wide geographical distribution. These movements may be of fully laden vehicles, unladen vehicles for repositioning or vehicles held at a terminal to meet future demands. Emphasis is given to the characterization of the problem in real situations, the mathematical modeling of the problem and the use of exact and heuristic methods to solve it, including GRASP and simulated annealing metaheuristics. Results based on a case study of a transportation company in Brazil are presented and analyzed, showing that the approach can be effective in supporting practical decisions.

Modelo matemático e meta-heurística Simulated Annealing para o problema de alocação de berços com múltiplas cargas

Ocorre no Brasil um forte crescimento do comércio exterior na área de granéis, como minério de ferro e agrícolas que são exportados pelos portos. Assim, metodologias que auxiliem o planejamento da operação dos portos são importantes. Este artigo propõe um modelo matemático aplicado a um porto de granel que difere dos demais por três itens: 1) berços operam mais de um tipo de carga e com taxas de operação diferentes para cada um, 2) certas cargas não são operadas em todos os berços, 3) o tempo de operação é dependente do berço e da carga. O modelo proposto elabora a sequência de atendimento dos navios em cada berço e foi implementado no CPLEX 12.6. Como soluções ótimas são difíceis de serem alcançadas, é proposta também uma meta-heurística Simulated Annealing (SA). Para avaliação do modelo e do SA, foram realizados testes com dados reais do Complexo Portuário de Tubarão.

Scheduling of Job Resources in Multiunit Projects with the Use of Time I Cost Criteria

Abstract This paper presents a model of scheduling of multiunit construction project based on an NP-hard permutation flow shop problem, in which the considered criterion is the sum of the costs of the works’ execution of the project considering the time of the project as a constraint. It is also assumed that each job in the units constituting the project may be realized in up to three different ways with specific time and cost of execution. The optimization task relies on solving the problem with two different decision variables: the order of execution of units (permutation) and a set of ways to carry out the works in units. The task presented in the paper is performed with the use of a created algorithm which searches the space of solutions in which metaheuristic simulated annealing algorithm is used. The paper presents a calculation example showing the applicability of the model in the optimization of sub-contractors’ work in the construction project.

Throughput-Driven Partitioning of Stream Programs on Heterogeneous Distributed Systems

Graph partitioning is an important problem in computer science and is of NP-hard complexity. In practice it is usually solved using heuristics. In this article we introduce the use of graph partitioning to partition the workload of stream programs to optimise the throughput on heterogeneous distributed platforms. Existing graph partitioning heuristics are not adequate for this problem domain. In this article we present two new heuristics to capture the problem space of graph partitioning for stream programs to optimise throughput. The first algorithm is an adaptation of the well-known Kernighan-Lin algorithm, called KL-Adapted (KLA), which is relatively slow. As a second algorithm we have developed the Congestion Avoidance (CA) partitioning algorithm, which performs reconfiguration moves optimised to our problem type. We compare both KLA and CA with the generic meta-heuristic Simulated Annealing (SA). All three methods achieve similar throughput results for most cases, but with significant differences in calculation time. For small graphs KLA is faster than SA, but KLA is slower for larger graphs. CA on the other hand is always orders of magnitudes faster than both KLA and SA, even for large graphs. This makes CA potentially useful for re-partitioning of systems during runtime.

Routes visualization: Automated placement of multiple route symbols along a physical network infrastructure

This paper tackles the representation of routes carried by a physical network infrastructure on a map. In particular, the paper examines the case where each route is represented by a separate colored linear symbol offset from the physical network segments and from other routes—as on public transit maps with bus routes offset from roads. In this study, the objective is to automate the placement of such route symbols while maximizing their legibility, especially at junctions. The problem is modeled as a constraint optimiza- tion problem. Legibility criteria are identified and formalized as constraints to optimize, while focusing on the case of hiking routes in a physical network composed of roads and pedestrian paths. Two solving methods are tested, based on backtracking and simulated annealing meta-heuristics respectively. Encouraging results obtained on real data are pre- sented and discussed.

A new approach for multi-criteria scheduling

In this paper we propose a new approach, recommended for solving certain class of multi-criteria scheduling problems, with the use of cloud exploration of the solution space supported by fine-grained parallel computing. The approach allow us to approximate Pareto front more accurately than other known algorithms in competitive time. To show and check advantageous properties of the proposed approach, the new solution algorithm, called VESA, was implemented for the case of bi-criteria flow shop scheduling problem and tested against a number of high-quality benchmarks known in the literature. Vector processing technologies are used to enhance the efficiency of solution search and acceptance rates for the extended simulated annealing metaheuristic. Results are compared using, among others, the independent Hyper-Volume Indicator (IH) measure. Computer test of VESA confirms excellent approximation of the Pareto front.

Sensory intensity assessment of olive oils using an electronic tongue

Olive oils may be commercialized as intense, medium or light, according to the intensity perception of fruitiness, bitterness and pungency attributes, assessed by a sensory panel. In this work, the capability of an electronic tongue to correctly classify olive oils according to the sensory intensity perception levels was evaluated. Cross-sensitivity and non-specific lipid polymeric membranes were used as sensors. The sensor device was firstly tested using quinine monohydrochloride standard solutions. Mean sensitivities of 14±2 to 25±6mV/decade, depending on the type of plasticizer used in the lipid membranes, were obtained showing the device capability for evaluating bitterness. Then, linear discriminant models based on sub-sets of sensors, selected by a meta-heuristic simulated annealing algorithm, were established enabling to correctly classify 91% of olive oils according to their intensity sensory grade (leave-one-out cross-validation procedure). This capability was further evaluated using a repeated K-fold cross-validation procedure, showing that the electronic tongue allowed an average correct classification of 80% of the olive oils used for internal-validation. So, the electronic tongue can be seen as a taste sensor, allowing differentiating olive oils with different sensory intensities, and could be used as a preliminary, complementary and practical tool for panelists during olive oil sensory analysis.

Modelling the Problem of Production Scheduling for Reconfigurable Manufacturing Systems

Companies are gradually moving towards reconfigurable manufacturing systems (RMS) to achieve both changeable functionality of flexible manufacturing systems (FMS) and scalable capacity of dedicated manufacturing lines (DML) to the extent possible. Despite this expected trend in manufacturing, there is a dearth of literature on scheduling for RMS; papers in the production scheduling literature still mainly focus on either DML or FMS. This paper tackles the problem of scheduling production operations in RMS. After explicitly defining different aspects of the problem, a mathematical model is developed to formally model the problem. Using the model and commercial software of operations research, the small instances of the problem are solved for optimality. To effectively solve large instances of the problem, different simulated annealing metaheuristics are developed. Using numerical experiments, the model and simulated annealing algorithms are evaluated for performance.

Uniform parallel machine scheduling problems with a truncation sum-of-logarithm-processing-times-based learning effect

In internet manufacturing, a manufacturing cloud usually deals with multiple requests at the same time. Similar problems may appear if a manufacturing cloud is equipped with some kind of intelligence and can learn to process a later job more quickly. This research considers the problem of scheduling jobs on uniform parallel machines to minimise total weighted tardiness (TWT) under a truncation sum-of-logarithm-processing-times-based learning effect. In the proposed learning model, the actual job processing time is a function that depends not only on the processing times of the jobs already processed but also on a control parameter. An iterated local search (ILS) has been proposed to solve this problem. Computational results of applying the ILS to some cases show that the proposed ILS outperforms other metaheuristics (simulated annealing and ant colony optimisation) in terms of TWT. The present research also analyses the impacts of truncation learning effects on objective values.

Evaluation of healthy and sensory indexes of sweetened beverages using an electronic tongue

Overconsumption of sugar-sweetened beverages may increase the risk of health problems and so, the evaluation of their glycemic load and fructose-intolerance level is essential since it may allow establishing possible relations between physiologic effects of sugar-rich beverages and health. In this work, an electronic tongue was used to accurately classify beverages according to glycemic load (low, medium or high load) as well to their adequacy for people suffering from fructose malabsorption syndrome (tolerable or not): 100% of correct classifications (leave-one-out cross-validation) using linear discriminant models based on potentiomentric signals selected by a meta-heuristic simulated annealing algorithm. These results may be partially explained by the electronic tongue’s capability to mimic the human sweetness perception and total acid flavor of beverages, which can be related with glycemic load and fructose-intolerance index. Finally, the E-tongue was also applied to quantify, accurately, healthy and sensory indexes using multiple linear regression models (leave-one-out cross-validation: Radj>0.99) in the following dynamic ranges: 4.7<glycemic load≤30; 0.4<fructose intolerance index≤1.5; 32<sweetness perception<155; 1.3<total acid flavor, gL−1<8.3; and, 5.8<well-balanced flavor≤74. So, the proposed electronic tongue could be used as a practical, fast, low-cost and green tool for beverage’s healthy and sensory evaluation.

Multiuser Detection For DS-CDMA Systems Using Honeybees Mating Optimization Algorithm

The future wireless mobile communication systems will be required to support high-speed transmission rate and high quality of service. Direct sequence code division multiple access (DS-CDMA) is an important scheme for high-rate wireless communication. The capacity of DS-CDMA can be impaired by two problems; near-far effect and multiple-access interference (MAI). The use of conventional-matched filter detector for multiple users in DS-CDMA fails to combat any of these problems. The performance degradation caused by MAI can be overcome using multiuser detection (MUD). The use of maximum likelihood (ML) sequence estimation detector provides excellent results, but involves high computational complexity. In this paper, we propose a new meta-heuristic approach for MUD using honeybees mating optimization (HBMO) algorithm to detect the user bits based on the ML decision rule for DS-CDMA systems in additive white-Gaussian noise and flat Rayleigh fading channels. In order to improve the solutions generated by the HBMO, a second meta-heuristic method simulated annealing is used. By computer simulations, the bit error rate performance and the complexity curves show that the proposed HBMO-SA MUD is capable of outperforming the other conventional detectors and genetic algorithm detector.

Single-cultivar extra virgin olive oil classification using a potentiometric electronic tongue

Label authentication of monovarietal extra virgin olive oils is of great importance. A novel approach based on a potentiometric electronic tongue is proposed to classify oils obtained from single olive cultivars (Portuguese cvs. Cobrançosa, Madural, Verdeal Transmontana; Spanish cvs. Arbequina, Hojiblanca, Picual). A meta-heuristic simulated annealing algorithm was applied to select the most informative sets of sensors to establish predictive linear discriminant models. Olive oils were correctly classified according to olive cultivar (sensitivities greater than 97%) and each Spanish olive oil was satisfactorily discriminated from the Portuguese ones with the exception of cv. Arbequina (sensitivities from 61% to 98%). Also, the discriminant ability was related to the polar compounds contents of olive oils and so, indirectly, with organoleptic properties like bitterness, astringency or pungency. Therefore the proposed E-tongue can be foreseen as a useful auxiliary tool for trained sensory panels for the classification of monovarietal extra virgin olive oils.

The dynamic replica placement problem with service levels in content delivery networks: a model and a simulated annealing heuristic

Dynamically assigning copies (replicas) of internet files to regionally placed servers is a crucial planning problem for content delivery network providers. Their goal is to minimize placement, storage and delivery costs while fulfilling service-level agreements. Although there is considerable work on simplified versions of this problem such as the static case, the dynamic problem with all three cost types and service-level constraints has yet not been considered in the literature. This paper provides a mixed integer programming (MIP) formulation for the problem as well as a simulated annealing metaheuristic. A computational study is employed to assess the simulated annealing heuristic and the MIP employing a state-of-the-art solver.

Makespan optimization in a single-machine scheduling problem with dynamic job ready times—Complexity and algorithms

An industrial problem encountered in steel mills is considered in the paper. The problem is formulated as a single-processor scheduling problem with a dynamic model of the task release date. In this model, a task is released for processing when its preprocessing is in the final state. The instantaneous rate of the change of the preprocessing state (from the initial to the final one) depends on the amount of resource allotted to the task. The resource available for task preprocessing is renewable and upper bounded. The problem consists in establishing the task sequence and the resource allocation that minimize the makespan. This problem has already been considered in the literature, but its complexity has not been formally proved. The proof of NP-hardness by polynomial reduction from the partition problem is presented in this paper. Two metaheuristics (simulated annealing and tabu search) are proposed to solve the problem. Both are tested, and the results are analyzed.

Parallel machine scheduling to minimize the makespan with sequence dependent deteriorating effects

A new unrelated parallel machine scheduling problem with deteriorating effect and the objective of makespan minimization is presented in this paper. The deterioration of each machine (and therefore of the job processing times) is a function of the sequence of jobs that have been processed by the machine and not (as considered in the literature) by the time at which each job is assigned to the machine or by the number of jobs already processed by the machine. It is showed that for a single machine the problem can be solved in polynomial time, whereas the problem is NP-hard when the number of machines is greater or equal than two. For the last case, a set of list scheduling algorithms and simulated annealing meta-heuristics are designed and the effectiveness of these approaches is evaluated by solving a large number of benchmark instances.