Archived Multi-objective Simulated Annealing Research Articles

Using common redundancy components for suppliers in a supply chain network design problem considering energy costs and environmental effects

Improving the reliability of factories and suppliers through appropriate allocation of redundancy components is crucial in responding to customer demand. However, if individual factories independently buy and allocate redundancy components, huge investments will be required. To optimize the reliability level and costs of the supply chain network, the present study allocates these components to suppliers based on customer demand. While allocating redundancy components to factories improves the chain's reliability, it increases energy consumption and greenhouse gas emissions. Therefore, a balance between cost, reliability, and pollution needs to be established. The study considers a three-level supply chain, including the supplier (factories), distributor, and retailer, with stochastic and normally distributed demand assumed for each retailer. To address demand fluctuations, the risk pooling effect is implemented. As the problem belongs to the class of NP-hard problems, the study develops three multi-objective meta-heuristic algorithms, namely Non-dominated Sorting Genetic Algorithm II (NSGAII), Archived Multi-Objective Simulated Annealing (AMOSA), and Multi-Objective Artificial Bee Colony (MOABC) to solve it. The performance of these algorithms is evaluated using the results of the complete enumeration method. The comparison results based on six multi-objective performance metrics show that the MOABC algorithm has better performance in finding high-quality solutions in less time, while the NSGAII algorithm provides more diverse Pareto optimal solutions.

CLUSTMOSA: Clustering for GPS trajectory data based on multi-objective simulated annealing to develop mobility application

Mobility analysis is the core idea of many applications such as vehicle navigation, trajectory analysis, POI recommendation, and traffic flow analysis. These applications collect huge spatio-temporal information represented as trajectories of a moving object such as a vehicle or people using Global Positioning System enabled devices. Various techniques are evolved to process, manage and extract useful information from trajectories. Among these techniques, clustering plays an important and integral role in developing various mobility applications. Popular traditional clustering techniques such as DBSCAN, K-means, OPTICS, hierarchical clustering, and DJ-clustering are used for this purpose. However, these techniques suffer from major issues such as entrapping in local optima and being less effective in varying densities. Further, these methods have low search capability in search space, work upon single criteria optimization, and are less scalable for the big dataset. To overcome these issues, a new multi-objective criterion-based evolutionary clustering termed CLUSTMOSA is proposed. It exploits the search capability of archived multi-objective simulated annealing (AMOSA) to cluster the dataset. It stabilizes the exploratory and exploitative behavior of the solution. In this paper, three clustering evaluation metrics are simultaneously exploited as objective functions of CLUSTMOSA. Also, a new segmentation method is presented using bearing measurement for trajectory data. It helps to eliminate multiple waypoints localized over the straight roads and prevents multiple cluster formations for the same segment. To investigate the performance, the proposed CLUSTMOSA, along with a new segmentation method using bearing measurement is compared with the state-of-art methods of trajectory data mining. The extensive experiments and analysis prove the superiority of our clustering model over state-of-art approaches.

Optimization of the sampling design for multiobjective soil mapping using the multiple path SSA (MP-SSA) method

Spatial sampling is important for soil surveys and mapping, and the optimization of the sampling design is a hot topic. Most often in soil sampling, multiple purposes are usually involved and corresponding objectives need to be optimized as much as possible. In such cases, balanced optimization is needed to produce the best compromised solutions that reach the maximum common interest, but not to generate a well-spread Pareto front. To solve this problem, the multiple path spatial simulated annealing (MP-SSA) was developed by extending the classic SSA. It can synchronously optimize multiobjective functions of different types and magnitudes by setting one annealing path for each objective, and designing a voting and annealing mechanism. To illustrate the difference and performance for MP-SSA, it was compared with the archived multiobjective simulated annealing (AMOSA) and Non-dominated Sorting Genetic Algorithm II (NSGA-II), both aiming at generating well-spread Pareto front, in two case studies with hypothetical data and actual soil heavy metal data. The results show that the MP-SSA is more efficient in generating the best compromised solutions, and is an efficient and promising tool for balanced multiobjective optimization for spatial sampling design when all objectives need to be optimized as much as possible.

Floating marine debris mitigation by vessel routing modeling and optimization considering carbon emission and travel time

Macro-marine debris becomes a global thorny issue to human beings and people are keeping trying every means to mitigate the risk of it. In this paper, we propose to use vessel and develop optimal vessel routing network to collect macro-marine debris on the nearshore surface. Due to the integrated force of winds and ocean currents, debris constantly changes floating location in the ocean. Advanced remote sensing technology and General National Oceanic and Atmospheric Administration (NOAA) Operational Modeling Environment (GNOME) software are employed to identify debris locations and track the drifting trajectory, respectively. In order to balance collecting efficiency and vessel emission reduction, we propose a bi-objective mixed integer nonlinear programming model for vessel routing to minimize travel time and carbon emission, considering time window at debris location, vessel capacity, low/medium/high vessel speed, and cost including carbon tax. A novel pheromone heuristic adaptive large neighborhood search (PHALNS ) algorithm combined with archived multi-objective simulated annealing (AMOSA) mechanism is developed to solve the proposed model. The Yangtze River Estuary region is taken as a numerical example to verify the proposed model and algorithm. Six criterions are introduced to evaluate the best collection time, and the proposed algorithm is also compared with NSGA II algorithm. The results show that low carbon emission and cost effective could be achieved simultaneously, and vessel speed has larger impact than the route scheme.

A simulation-based optimisation framework for process plan generation in reconfigurable manufacturing systems (RMSs) in an uncertain environment

ABSTRACT Reconfigurable manufacturing system (RMS) is a manufacturing paradigm which is proven to be time and cost-effectively adaptable to a wide range of market changes due to its customisable capacity and functionality. In this paper, a two-step framework is proposed for the process plan generation in RMS. The first step, aimed at solving the multi-objective part-family Single-Unit Process Plan (SUPP) generation problem, involves a comparative approach using three metaheuristics, namely: The Non-Dominated Sorting Genetic Algorithm (NSGA-II), the Archived Multi-Objective Simulated Annealing (AMOSA) and the Multi-Objective Particle Swarm Optimisation (MOPSO) as well as a simulation-based optimisation method. The second step is designed to solve the multi-objective part-family Multi-Unit Process Plan (MUPP) generation problem with unpredictable demands in different periods using a combination of the answers of the algorithms in step 1. The number of units is also optimised using the NSGA-II. Finally, a novel heuristic algorithm named Designed Periods Algorithm (DPA) is proposed in the second step to meet the unpredictable demands in different periods. To illustrate the applicability of the framework, an example is presented, the results of which have shown the superiority of the MUPP over the SUPP in response to unpredictable demands according to the periods designed by DPA.

Multi-objective Simulated Annealing Variants to Infer Gene Regulatory Network: A Comparative Study.

Gene Regulatory Network (GRN) is formed due to mutual transcriptional regulation within a set of protein coding genes in cellular context of an organism. Computational inference of GRN is important to understand the behavior of each gene in terms of change in its protein production rate (expression level). As Recurrent Neural Network (RNN) is efficient in GRN modeling, a bi-objective RNN formulation has been applied here. Based on Archived Multi Objective Simulated Annealing (AMOSA), four algorithms, namely, AMOSA Revised (AMOSAR), Modified Freezing based AMOSA (AMOFSA), Tabu based AMOSA (AMOTSA) and Modified Freezing and Tabu based AMOSA (AMOFTSA) have been proposed and applied to RNN (treated as GRN) for parameter learning taking four gene expression time series datasets. Comparative studies on the performance of the algorithms (based on each dataset) have been made in terms of the number of GRNs obtained in the final non-dominated front and the performance metrics, namely, recall, precision and f1 score. Two proposed variants, namely, AMOFSA and AMOTSA have been found competitive in performance. Experimental observations and statistical analysis show that, modified algorithms are better than AMOSAR and the state-of-the-art algorithms in respect of the above-mentioned metrics.

An Improved Parallelized Multi-Objective Optimization Method for Complex Geographical Spatial Sampling: AMOSA-II

Complex geographical spatial sampling usually encounters various multi-objective optimization problems, for which effective multi-objective optimization algorithms are much needed to help advance the field. To improve the computational efficiency of the multi-objective optimization process, the archived multi-objective simulated annealing (AMOSA)-II method is proposed as an improved parallelized multi-objective optimization method for complex geographical spatial sampling. Based on the AMOSA method, multiple Markov chains are used to extend the traditional single Markov chain; multi-core parallelization technology is employed based on multi-Markov chains. The tabu-archive constraint is designed to avoid repeated searches for optimal solutions. Two cases were investigated: one with six typical traditional test problems, and the other for soil spatial sampling optimization applications. Six performance indices of the two cases were analyzed—computational time, convergence, purity, spacing, min-spacing and displacement. The results revealed that AMOSA-II performed better which was more effective in obtaining preferable optimal solutions compared with AMOSA and NSGA-II. AMOSA-II can be treated as a feasible means to apply in other complex geographical spatial sampling optimizations.

An Adaptive Surrogate Assisted CE-QUAL-W2 Model Embedded in Hybrid NSGA-II_ AMOSA Algorithm for Reservoir Water Quality and Quantity Management

The Meimeh dam construction is a project, planned to provide sustainable livelihoods, social, and economic developments in the Meimeh River Basin, Ilam, Iran. However, due to the high concentration of TDS in the Meimeh River and its tributaries, river impoundment and water storage can be harmful to the Meimeh Reservoir. The upstream inflow control and the reservoir operation management in a selective withdrawal scheme (SWS) were used to mitigate the potential environmental degradation of Meimeh Reservoir’s low water quality. CE-QUAL-W2 and WEAP (Water Evaluation and Assessment Programming) models were employed to evaluate the effects of various upstream saline inflow control scenarios. The analysis indicated that the diversion of the Siyoul tributary flow rate in the Meimeh River could result in lower violations of Total Dissolved Solids (TDS) concentrations and better water supply satisfaction. Then, the optimal reservoir operation management strategies in a SWS were derived in the best upstream inflow control scenario. The adaptive surrogate-assisted WQSM (water quality simulation model), coupled to hybrid NSGA-II_AMOSA (Non-dominated Sorting Genetic Algorithm-II_Archived Multi-Objective Simulated Annealing) algorithm, has been applied to derive the suitable reservoir operation strategies in SWS, improve the water supply satisfaction and alleviate the adverse effects of reservoir outflow TDS violations. The performances of the best water quality and supply scenarios have been compared with the scenario based on the standard operation policy (SOP) in the Meimeh Reservoir. The results show that most violations of TDS criteria occur during the peak agricultural seasons and significant water deficit in some dry years happens in the SOP.

A Multi-Objective Model for Minimizing Makespan and Total Travel Time in Put Wall Based Picking Systems

In this study, a multi-objective order batching and picker routing model for minimising the total travel time and makespan in a put wall-based picking system is proposed. A put wall is a hardware composed of containers that is usually used as a sorting station. Put walls usually have limited capacity and can accommodate only a part of received orders at a time. Thus, in put wall-based picking systems, order batching and picker routing should be considered along with the scheduling of the orders on the put wall. To solve the problem, a genetic algorithm (GA), a coevolutionary genetic algorithm (COGA) and an archived multi-objective simulated annealing (AMOSA) are proposed. It is found that GA and COGA offer a more diverse set of solutions to the problem compared to AMOSA.

A New Bi-Level Planning Approach to Find Economic and Reliable Layout for Large-Scale Wind Farm

This paper presents a bi-level planning approach to find an optimal layout for a large-scale wind farm (WF) from economic and reliability perspectives. The upper level of the planning approach is formulated as constrained multi-objective optimization (MOO) to obtain the optimal WF layout, where two conflicting objectives are optimized simultaneously. Two objectives for MOO are i) yearly gross energy production and ii) annual loan repayment (for cable and land area of the WF), where the first objective is maximized and the second objective is minimized. Jensen's wake model is used to calculate the wake effect, and to evaluate the multiple wake effect within a WF, a coordinate-transformation-based approach has been adopted in this study. An efficient hybrid multi-objective metaheuristic algorithm, which is based on nondominated sorting multi-objective particle swarm optimization and improved archive multi-objective simulated annealing, has been proposed to find the optimized WF layout. The superiority of the Pareto front obtained by the proposed algorithm for the large-scale WF layout is supported by its comparison with different powerful and well-established MOO algorithms. In the lower level of the proposed approach, economic and reliability analyses of the solutions (WF layout), obtained in the Pareto front by the proposed hybrid algorithm, have been done. In this context, two performance metrics, a) generation economy index and b) expected wind energy generation (EWEG), have been considered. A sequential Monte Carlo simulation has been developed to calculate the EWEG considering random failure of both wind turbines and cables in this paper. The most suitable WF layout is selected by trading off between economy and reliability using a fuzzy decision-making approach.

Machine layout design problem under product family evolution in reconfigurable manufacturing environment: a two-phase-based AMOSA approach

Reconfigurable manufacturing systems (RMSs) are designed to manufacture a specific product, incorporating the scalability to other products in the same family. This ability is based primarily on the reconfiguration capabilities offered by reconfigurable machines tools (RMTs). This paper addresses one of the most important aspects related to the reactivity of RMSs. More specifically, it considers the relations, which link the conceived system with two important environments: its logical environment, i.e., the product family (products that share similarities) in which the RMS can evolve, and its physical environment, i.e., the physical workshop that implements this RMS. We study the machine layout problem by considering the product family evolution where two sub-problems are addressed. The first sub-problem concerns the evolution of the product, in the same family, towards new products to meet the evolutions and the requirements of the customers. The second sub-problem deals with the machine layout problem based on the results of the first sub-problem. For this, our two-phase-based approach combines the well-known metaheuristic, archived multi-objective simulated annealing (AMOSA), with an exhaustive search–based heuristic to determine the best machine layout for all the selected machines of the product family. The developed approach is based on the initially generated process plans of products (in the product family) for the RMS design under performance metrics. Moreover, the proposed layout must, at its best, respect both the constraints imposed by the generated process plans and those depicting the available location in the shop floor where machines can be placed. An illustrative numerical example is proposed to demonstrate the applicability of our approach.

Multi-objective multi-pollutant waste load allocation model for rivers using coupled archived simulated annealing algorithm with QUAL2Kw

Abstract A simulation-optimization approach is a suitable tool in waste load allocation problems when considering competing objectives and complex pollutant fate and transport processes in water bodies. Here, an archived multi-objective simulated annealing (AMOSA) algorithm is developed to determine various decision variables related to multi-pollutant waste load allocation (MPWLA) problems. The developed AMOSA algorithm has been coupled to QUAL2Kw in order to derive optimal MPWLA programs in Gheshlagh River, Kordestan, Iran. Minimizing wastewater treatment plant (WWTP) costs, improving the EquityMeasure, and enhancing water quality index (WQI) of the river have been considered as objective functions of MPWLA problems. The applied WQI integrates various water quality parameters (biochemical oxygen demand (BOD), dissolved oxygen (DO), NH4-N, NO3-N, PO4-P, total suspended solids (TSS), and Coliform) in monitoring stations along the river. Results show in the scenario with the best EquityMeasure, higher pollutant removal rates have been allocated to Sanandaj WWTP effluent and pollutant point source No. 7 (creek of landfill leachate) due to their greater contributions to Gheshlagh River contamination. Owing to high pollutant load effluents and unsuitable background conditions in Gheshlagh River, more specific studies show that the water quality index may not be improved over 0.22, no matter how much cost is incurred or equity is sacrificed.

A New Hybrid Approach for Machine Layout Design Under Family Product Evolution for Reconfigurable Manufacturing Systems

Reconfigurable manufacturing system (RMS) is a new paradigm that answers many of the challenges that the market nowadays imposes. In this paper, we address one of the most important aspects related to the reactivity of RMSs. We consider the relations, which link the conceived system with two important environments: its logical environment, i.e., the product family in which the RMS can evolve, and its physical environment, i.e., the physical workshop that implements this RMS. More specifically, we study the machine layout problem by considering the family product evolution where two sub-problems are considered. The first sub-problem concerns the evolution of the product, in the same family, towards new products to meet the evolutions and the requirements of the customers. The second sub-problem deals with the machine layout problem based on the results of the first sub-problem. We develop an approach which combines the well-known metaheuristic, archived multi-objective simulated annealing (AMOSA), with an exhaustive search-based heuristic to determine the best machine layout for all the selected machines of our product family. The approach is based on the initially generated process plans of products (in the product family) for the RMS design under performance metrics. The proposed layout must at its best, respects both the constraints imposed by the generated process plans and those depicting the available location in the shop floor where machines can be placed. To show the applicability of our approach, we present a simple numerical example and discuss the obtained numerical results.

Multi-objective sustainable process plan generation in a reconfigurable manufacturing environment: exact and adapted evolutionary approaches

Achieving competitiveness in nowadays manufacturing market goes through being cost and time-efficient as well as environmentally harmless. Reconfigurable manufacturing system (RMS) is a paradigm that is able to meet these challenges due to its scalability and integrability. In this paper, we aim to solve the multi-objective sustainable process plan generation problem in a reconfigurable environment. In addition to the total production cost and the completion time, we use the amount of greenhouse gases (GHG) emitted during the manufacturing process as a sustainability criterion. We propose an iterative multi-objective integer linear programming (I-MOILP) approach and its comparison with adapted versions of the two well-known evolutionary algorithms, respectively, the Archived Multi-Objective Simulated Annealing (AMOSA) and the Non-dominated Sorting Genetic Algorithm (NSGA-II). Moreover, we study the influence of the probabilities of genetic operators on the convergence of the adapted NSGA-II. To illustrate the applicability of the three approaches, an example is presented and obtained numerical results analysed.

A Bi-Objective RNN Model to Reconstruct Gene Regulatory Network: A Modified Multi-Objective Simulated Annealing Approach.

Gene Regulatory Network (GRN) is a virtual network in a cellular context of an organism, comprising a set of genes and their internal relationships to regulate protein production rate (gene expression level) of each other through coded proteins. Computational Reconstruction of GRN from gene expression data is a widely-applied research area. Recurrent Neural Network (RNN) is a useful modeling scheme for GRN reconstruction. In this research, the RNN formulation of GRN reconstruction having single objective function has been modified to incorporate a new objective function. An existing multi-objective meta-heuristic algorithm, called Archived Multi Objective Simulated Annealing (AMOSA), has been modified and applied to this bi-objective RNN formulation. Executing the resulting algorithm (called AMOSA-GRN) on a gene expression dataset, a collection (termed as Archive) of non-dominated GRNs has been obtained. Ensemble averaging has been applied on the archives, and obtained through a sequence of executions of AMOSA-GRN. Accuracy of GRNs in the averaged archive, with respect to gold standard GRN, varies in the range 0.875 - 1.0 (87.5 - 100 percent).

Modularity assessment in reconfigurable manufacturing system (RMS) design: an Archived Multi-Objective Simulated Annealing-based approach

Enhancing productivity, reducing inaccuracy and avoiding time waste at changeover are considered major drivers in manufacturing system design. One of the emerging paradigms concerned with these characteristics is reconfigurable manufacturing systems (RMSs). The high responsiveness and performance efficiencies of RMS make it a convenient manufacturing paradigm for and flexible enabler of mass customization. The RMS offers customized flexibility and a variety of alternatives as features thanks to its reconfigurable machine tool (RMT). These machines represent a major component of RMS and are based on an adjustable, modular and reconfigurable structure. Hence, the system modularity is of great importance. This paper outlines a multi-objective approach to optimize the RMS design. Three objectives are considered: the maximization of the system modularity, the minimization of the system completion time and the minimization of the system cost. We developed a modularity-based multi-objective approach that uses an adapted version of the “Archived Multi-Objective Simulated Annealing” (AMOSA) method to solve the optimization problem by selecting from a set of candidate machines the most suitable ones. Implemented, the decision maker can use a multi-objective decision making tool based on the well-known “Technique for Order of Preference by Similarity to Ideal Solution” (TOPSIS) to choose the best solution in the Pareto front according to his preferences. We demonstrated the applicability of the proposed approach through an illustrative example and an analysis of the obtained numerical results.

Bi-objective reliable location-inventory-routing problem with partial backordering under disruption risks: A modified AMOSA approach

In this paper, we present a reliable model of multi-product and multi-period Location-Inventory-Routing Problem (LIRP) considering disruption risks. An inventory system with stochastic demand in which the supply of the product is randomly disrupted in distribution centers, is considered in this paper. Partial backordering is used in case stock out occurs by considering the probability of the confronting defects in distribution centers in time of disruption. We presented a bi-objective mixed-integer nonlinear programming (MINLP) model. The first objective minimizes the locating, routing and transportation costs and inventory components which consist of ordering, holding and partial backordering costs. The second objective is to minimize the total failure costs related to disrupted distribution centers that leads to reliability of the supply chain network. Because of NP-hardness of the proposed model, we modified Archived Multi-Objective Simulated Annealing (AMOSA) meta-heuristic algorithm to solve the bi-objective problem in large scales and compared the results with three other algorithms. To improve performance of the algorithms Taguchi method is used to tune parameters. Finally, several numerical examples are generated to evaluate solution methods and five multi-objective metrics are calculated to compare results of the algorithms.

An Archived Multi-Objective Simulated Annealing Algorithm for Vehicle Routing Problem with Time Windows

An Archived Multi-Objective Simulated Annealing Algorithm for Vehicle Routing Problem with Time Windows

An Efficient Automatic Bat Clustering Algorithm for Multi Objective Optimization

In data mining the clustering techniques is used for grouping a set of physical or abstract objects into similar objects. In this process, k-means algorithm is a major role to group the similar objects. The major issue of this algorithm is the user gives the number of clusters in priori as k value where as the final clustering results is ineffective. To avoid such a problem a new Multi Objective (MO) method Bat Modified Clustering Multi-Objective Optimization (BATMClustMOO) is proposed. This algorithm is a combination of Archived Multi-Objective Simulated Annealing (AMOSA) and Bat algorithm (BA) is suggested which can partition the data into a suitable number of clusters k and then find the best cluster centroid automatically. The AMOSA acts as the local search and BA acts as the global search to fix the number of clusters and cluster centroid. Each cluster is splitted into many small hyper spherical sub clusters and the centroid of all small sub-clusters is fixed into a string that comprises the entire clustering. In order to verify the performance of the proposed algorithm the different benchmark datasets are taken from UCI repository. The experimental results show the proposed method is better than the existing methods.

Multiobjective Simulated Annealing-Based Clustering of Tissue Samples for Cancer Diagnosis.

In the field of pattern recognition, the study of the gene expression profiles of different tissue samples over different experimental conditions has become feasible with the arrival of microarray-based technology. In cancer research, classification of tissue samples is necessary for cancer diagnosis, which can be done with the help of microarray technology. In this paper, we have presented a multiobjective optimization (MOO)-based clustering technique utilizing archived multiobjective simulated annealing(AMOSA) as the underlying optimization strategy for classification of tissue samples from cancer datasets. The presented clustering technique is evaluated for three open source benchmark cancer datasets [Brain tumor dataset, Adult Malignancy, and Small Round Blood Cell Tumors (SRBCT)]. In order to evaluate the quality or goodness of produced clusters, two cluster quality measures viz, adjusted rand index and classification accuracy ( % CoA) are calculated. Comparative results of the presented clustering algorithm with ten state-of-the-art existing clustering techniques are shown for three benchmark datasets. Also, we have conducted a statistical significance test called t-test to prove the superiority of our presented MOO-based clustering technique over other clustering techniques. Moreover, significant gene markers have been identified and demonstrated visually from the clustering solutions obtained. In the field of cancer subtype prediction, this study can have important impact.