Multi-start Simulated Annealing Research Articles

A multi-start simulated annealing strategy for Data Lake Organization Problem

The Data Lake Organization Problem consists of optimized data navigation structures generation to reduce the user’s time exploring all available data. The goal is to find a data organization that maximizes the expected probability of table discovery during user navigation. For this problem, we propose a simulated annealing metaheuristic and compare it with the Organize literature solution on benchmark instances. The instances are Socrata Open Data Lake samples with varying topics and open data from government entities worldwide. To validate our proposal, we performed a statistical analysis using a non-parametric test, which confirmed the dominance of our proposition over the state-of-the-art. Our proposal was more efficient and increased the expected probability of table discovery up to 15%. Thus, our strategy can find better solutions in the benchmarks evaluated even without exhaustively analyzing all of them and more effectively exploring the space of solutions.

Integrating distributed disassembly line balancing and vehicle routing problem in supply chain: Integer programming, constraint programming, and heuristic algorithms

Today, the fast-changing demands of customers force manufacturers to adapt their systems to the variability. As a result of the recent advances in technology and transport systems, most manufacturers have begun to employ more than one distributed facility to respond rapidly to the demands of their customers. In addition, with the emergence of Industry 4.0, information exchange between systems at the same and different levels has become relatively easy. Therefore, effective management of distributed facilities by integrating processes at different levels in the supply chain can provide a significant advantage in adapting the system to customer demand dynamics. Furthermore, recycling waste products and using them in new products have become essential for environmentally friendly production. Therefore, this paper introduces integrated distributed disassembly line balancing and vehicle routing problem first time in the literature. Since the distributed disassembly centers with routing decisions of the vehicles from these centers to the factories have not been considered before, the proposed integrated study will contribute to both industry and the literature. The contribution is not only limited to the proposed integrated problem. Also, novel solution methodologies, mixed-integer linear programming, mixed-integer non-linear programming, and constraint programming models are developed to solve the problem. Besides the mathematical models, a multi-start simulated annealing algorithm is also proposed to overcome the large-size instances due to the complexity of the proposed integrated problem. The comprehensive computational analysis demonstrates that the proposed methods are very competitive in providing good-quality solutions for the problem.

An improved memetic algorithm to solve the energy-efficient distributed flexible job shop scheduling problem with transportation and start-stop constraints.

In the current global cooperative production environment, modern industries are confronted with intricate production plans, demanding the adoption of contemporary production scheduling strategies. Within this context, distributed manufacturing has emerged as a prominent trend. Manufacturing enterprises, especially those engaged in activities like automotive mold production and welding, are facing a significant challenge in managing a significant amount of small-scale tasks characterized by short processing times. In this situation, it becomes imperative to consider the transportation time of jobs between machines. This paper simultaneously considers the transportation time of jobs between machines and the start-stop operation of the machines, which is the first time to our knowledge. An improved memetic algorithm (IMA) is proposed to solve the multi-objective distributed flexible job shop scheduling problem (MODFJSP) with the goal of minimizing maximum completion time and energy consumption. Then, a new multi-start simulated annealing algorithm is proposed and integrated into the IMA to improve the exploration ability and diversity of the algorithm. Furthermore, a new multiple-initialization rule is designed to enhance the quality of the initial population. Additionally, four improved variable neighborhood search strategies and two energy-saving strategies are designed to enhance the search ability and reduce energy consumption. To verify the effectiveness of the IMA, we conducted extensive testing and comprehensive evaluation on 20 instances. The results indicate that, when faced with the MODFJSP, the IMA can achieve better solutions in almost all instances, which is of great significance for the improvement of production scheduling in intelligent manufacturing.

Vehicle routing problems with drones equipped with multi-package payload compartments

The vehicle routing problem with drones (VRP-D) consists of designing combined truck-drone routes and schedules to serve a set of customers with specific requests and time constraints. In this paper, VRP-D is extended to include a fleet of drones equipped with multi-package payload compartments to serve more customers on a single trip. Moreover, a drone can return to a truck, different from the one from which it started, to swap its depleted battery and/or to pick up more packages. This problem, denoted as VRP-D equipped with multi-package payload compartments (VRP-D-MC), aims to maximize total profit. In this work, an adaptive multi-start simulated annealing (AMS-SA) metaheuristic algorithm is proposed to efficiently solve this problem. Experimental results show that the proposed algorithm outperforms the current state-of-the-art algorithms for VRP-D in terms of solution quality. Extensive analyses have been conducted to provide managerial insights. The analyses carried out show (i) the benefits of using drones equipped with different compartment configurations, (ii) the incremental total profit obtainable using a combined truck-drone fleet rather than a fleet of trucks, (iii) the benefit of swapping drone battery while picking up the items to deliver, and (iv) the impact of the packages load on the consumption energy of battery drone. It is also demonstrated that the different intensification and diversification mechanisms adopted improve the convergence of the traditional SA.

Minimizing total completion time in the no-wait jobshop scheduling problem using a backtracking metaheuristic

This study focused on the no-wait jobshop scheduling problem (NWJSP), which is NP-hard in the strong sense. A new benchmarking algorithm, named backtracking multi-start simulated annealing (BMSA), is presented in this study for minimizing the total completion time in NWJSPs. To effectively and efficiently find (near-) optimal schedules, the simulated annealing algorithm is strengthened by integrating the multi-start and backtracking mechanisms that enable the search processes to escape from the local optimum. The performance of the BMSA algorithm is assessed by comparing its experimental results to those of the best available metaheuristics on three well-known benchmark problem sets. Computational results and statistical analyses confirm that the BMSA algorithm significantly outperforms these benchmark metaheuristics. This study mainly contributes to providing an innovative approach for solving this highly intractable scheduling problem, which is also worth applying to solve a practical NWJSP.

Traffic Prediction-Enabled Energy-Efficient Dynamic Computing Resource Allocation in CRAN Based on Deep Learning

Due to the greatly increased bandwidth of 5G networks compared with that of 4G networks, the power consumption brought by baseband signal processing of 5G networks is much higher, which inevitably raises the operation expenditures. Cloud Radio Access Network (CRAN) is widely adopted in 5G networks, which splits the traditional base stations into Remote Radio Heads (RRHs) and Baseband Units (BBUs), which are equipped with computing resource for baseband signal processing. The number of required BBUs varies due to the fluctuation of wireless traffic of RRHs. Hence, fixed computing resource allocation might waste power. This paper investigates energy-efficient dynamic computing resource allocation in CRAN by predicting the wireless traffic of RRHs and allocating computing resource based on the prediction results aiming at using fewest BBUs to minimize power consumption. For wireless traffic prediction, a novel method based on two-dimensional CNN LSTM model with temporal aggregation is proposed. By treating the wireless traffic data as images, this model could extract spatial correlation from these data to improve accuracy. Moreover, the problem of dynamic computing resource allocation in CRAN is formulated as an offline four-constraint bin packing problem, considering both uplink and downlink baseband signal processing capacities of BBUs and Common Public Radio Interface (CPRI) bandwidths. For solving this problem, a Multi-start Simulated Annealing (MSA) algorithm is proposed. Simulation results demonstrate that the proposed method for wireless traffic prediction could outperform the state-of-the-art deep learning models. In addition, the proposed MSA algorithm could achieve lower power consumption than the state-of-the-art heuristic algorithms.

New benchmark algorithms for No-wait Flowshop Group Scheduling Problem with Sequence-Dependent Setup Times

Setup operations and waiting time between production procedures are prime examples of non-value-adding activities that can be alleviated through well-informed production decisions. Application of the No-wait Flowshop Group Scheduling Problems with Sequence-Dependent Setup Times (NWFGSP_SDST) as an optimization tool helps maintain the setup and waiting times at their minimum when optimizing advanced production systems. This research contributes to the understudied literature of Group Scheduling Problems (GSP), developing two metaheuristics, a Revised Multi-start Simulated Annealing (RMSA) and a local search-based variant (RMSALS), to solve the NWFGSP_SDST problem. Yielding the best-found solution in more than 99.7 percent of the benchmark instances, it is shown that RMSA is superior to the existing state-of-the-art algorithms developed to solve the NWFGSP_SDST problem. Besides, about 80 percent of the best-found solutions were further improved by RMSALS with the statistical analysis confirming that RMSALS reduced the total completion time obtained by RMSA at the expense of longer computational time. Overall, this research explored scheduling as an operational strategic tool facilitating lean production.

Novel hybrid algorithm for Team Orienteering Problem with Time Windows for rescue applications

Robots for rescue operations after a disaster are an interesting and challenging research problem that has the potential to save lives and reduce economic losses after a disaster. We developed TOPTWR, an extension of the popular TOPTW model, to model the issues in task allocation for teams of rescue robots. Our hybrid algorithm is based on a team of heterogeneous humanoid robots trying to optimize five objectives (task rewards, task completion time, total energy, maximum energy consumption for a single robot, and missed deadline penalties). A common approach to solve these kinds of problems are multi-objective evolutionary algorithms (MOEAs), but their major disadvantage is that they cannot deal with dynamic environments easily. This paper presents an efficient solution for TOPTWR by combining MOEAs with learning algorithms. A novel Extended Multi-Start Simulated Annealing Iterated Local Search (EMSAILS) operator using a modern state-of-the-art NSGA-III algorithm is proposed. In addition, we applied Q-Learning to learn the likely changes in the environment and how to react to them. This algorithm, HMO-TOPTWR-NSGA-III (HMO-N-L), uses an artificial neural network (ANN) as a function approximator to make the huge state and action spaces tractable. This paper includes a thorough empirical evaluation demonstrating the effectiveness of the multi-objective algorithm in both static and dynamic environments. The evaluation shows that the proposed algorithm reduces the error by up to 42% against three state-of-the-art approaches to TOPTW (HMO-N, MSA, and IPI).

Stochastic multi-objective integrated disassembly-reprocessing-reassembly scheduling via fruit fly optimization algorithm

Remanufacturing end-of-life (EOL) products is an important approach to yield great economic and environmental benefits. A remanufacturing process usually contains three shops, i.e., disassembly, processing and assembly shops. EOL products are dissembled into multiple components in a disassembly shop. Reusable components are reprocessed in a processing shop, and reassembled into their corresponding products in an assembly shop. To realize an overall optimization, we have to integrate them together when making decisions. In practice, a decision-maker usually has to optimize multiple criteria such as cost-related and service-oriented objectives. Additionally, we cannot accurately acquire the detail of EOL products due to their various usage processes. Therefore, multi-objective and uncertainty need to be considered simultaneously in an integrated disassembly-reprocessing-reassembly scheduling process. This work investigates a stochastic multi-objective integrated disassembly-reprocessing-reassembly scheduling problem to achieve the expected makespan and total tardiness minimization. To handle this problem, this work develops a multi-objective discrete fruit fly optimization algorithm incorporating a stochastic simulation approach. Its search techniques are designed according to this problem’s features from five aspects, i.e., solution representation, heuristic decoding rules, smell-searching, vision-searching, and genetic-searching. Simulation experiments are conducted by adopting twenty-five instances to verify the performance of the proposed approach. Nondominated sorting genetic algorithm II, bi-objective multi-start simulated annealing method, and hybrid multi-objective discrete artificial bee colony are chosen for comparisons. By analyzing the results with three performance metrics, we can find that the proposed approach performs better on all the twenty-five instances than its peers. Specifically, it outperforms them by 6.45%–9.82%, 6.91%–17.64% and 1.19%–2.76% in terms of performance, respectively. The results confirm that the proposed approach can effectively and efficiently tackle the investigated problem.

Solving no-wait job-shop scheduling problems using a multi-start simulated annealing with bi-directional shift timetabling algorithm

This paper presents a multi-start simulated annealing with bi-directional shift timetabling (MSA-BST) algorithm for solving the no-wait jobshop scheduling problem (NWJSP). To enhance the quality of the solution, a bi-directional shift timetabling procedure that allows delay timetabling and consequently enlarges the search space is embedded into the multi-start simulated annealing algorithm. The performance of the proposed MSA-BST algorithm is evaluated with respect to the makespan by comparing its computational results to those of the best-to-date meta-heuristic algorithm, the hybrid artificial bee colony (HABC) algorithm, in the literature. Experimental results that were obtained by applying the proposed algorithm to a set of benchmark problems in the literature indicate that it significantly outperforms HABC. The main contribution of this study is to provide a novel and effective alternative method for solving the complex NWJSP.

Simultaneous Multi-Start Simulated Annealing for Capacitated Vehicle Routing Problem

A new metaheuristic algorithm is proposed for Capacitated Vehicle Routing Problem. CVRP is one of the fundamental problems in combinatorial optimization that deals with transport route minimization. The algorithm combines Simulated Annealing, multi-start and simultaneous computing techniques. A series of computational tests are conducted on several CVRP benchmarks and near-optimal solutions are obtained. The results indicate superior performance compared with Simulated Annealing

Solving multi-objective team orienteering problem with time windows using adjustment iterated local search

One of the problems tourism faces is how to make itineraries more effective and efficient. This research has solved the routing problem with the objective of maximizing the score and minimizing the time needed for the tourist’s itinerary. Maximizing the score means collecting a maximum of various kinds of score from each destination that is visited. The profits differ according to whether those destinations are the favorite ones for the tourists or not. Minimizing time means traveling time and visiting time in the itinerary being kept to a minimum. Those are small case with 16 tourism destinations in East Java, and large case with 56 instances consists of 100 destinations each from previous research. The existing model is the Team Orienteering Problem with Time Window (TOPTW), and the development has been conducted by adding another objective, minimum time, become Flexible TOPTW. This model guarantees that an effective itinerary with efficient timing to implement will be produced. Modification of Iterated Local Search (ILS) into Adjustment ILS (AILS) has been done by replacing random construction in the early phase with heuristic construction, continue with Permutation, Reserved and Perturbation. This metaheuristic method will address this NP-hard problem faster than the heuristic method because it has better preparation and process. Contributing to this research is a multi-objective model that combines maximum score and minimum time, and a metaheuristics method to solve the problem faster and effectively. There are calibration parameter with 17 instances of 100 destinations each, small case test using Mixed Integer Linear Programming, and large case test comparing AILS with Multi-Start Simulated Annealing (MSA), Simulated Annealing (SA), Artificial Bee Colony (ABC), and Iterated Local Search. The result shows that the proposed model will provide itinerary with less number of visited destination 4.752% but has higher total score 8.774%, and 3836.877% faster, comparing with MSA, SA, and ABC. While AILS is compared with ILS, it has less visited destination 5.656%, less total score 56.291%, and faster 375.961%. Even though AILS has more efficient running time than other methods, it needs improvement in algorithm to create better result.

Integration of geostatistical realizations in data assimilation and reduction of uncertainty process using genetic algorithm combined with multi-start simulated annealing

This paper introduces a new methodology, combining a Genetic Algorithm (GA) with multi-start simulated annealing to integrate Geostatistical Realizations (GR) in data assimilation and uncertainty reduction process. The proposed approach, named Genetic Algorithm with Multi-Start Simulated Annealing (GAMSSA), comprises two parts. The first part consists of running a GA several times, starting with certain number of geostatistical realizations, and the second part consists of running the Multi-Start Simulated Annealing with Geostatistical Realizations (MSSAGR). After each execution of GA, the best individuals of each generation are selected and used as starting point to the MSSAGR. To preserve the diversity of the geostatistical realizations, a rule is imposed to guarantee that a given realization is not repeated among the selected individuals from the GA. This ensures that each Simulated Annealing (SA) process starts from a different GR. Each SA process is responsible for local improvement of the best individuals by performing local perturbation in other reservoir properties such as relative permeability, water-oil contact, etc. The proposed methodology was applied to a complex benchmark case (UNISIM-I-H) based on the Namorado Field, located in the Campos Basin, Brazil, with 500 geostatistical realizations and other 22 attributes comprising relative permeability, oil-water contact, and rock compressibility. Comparisons with a conventional GA algorithm are also shown. The proposed method was able to find multiple solutions while preserving the diversity of the geostatistical realizations and the variability of the other attributes. The matched models found by the GAMSSA method provided more reliable forecasts when compared with the matched models found by the GA.

Particle swarm optimisation algorithm and multi-start simulated annealing algorithm for scheduling batches of parts in multi-cell flexible manufacturing system

This paper considers the problem of scheduling batches of parts in a multi-cell flexible manufacturing system (MCFMS) with sequence dependent batch setup time. The goal is to find the best sequence of batches and hence to minimise the makespan. Two mathematical models are developed namely: batch availability model and job availability model. As the problem is known to be NP-hard, particle swarm optimisation (PSO) algorithm and multi-start simulated annealing (MSA) algorithm are proposed to solve the problem. The proposed algorithms are validated by testing the benchmark problems available in the literature. In addition to that, 80 problems with various sizes have been generated at random and then the performance of the proposed MSA and PSO algorithms are compared with CPLEX solver. The experimental results show that MSA provides better solution compared with PSO, the same solution as CPLEX and very close to the lower bound value provided by CPLEX.

Particle swarm optimisation algorithm and multi-start simulated annealing algorithm for scheduling batches of parts in multi-cell flexible manufacturing system

This paper considers the problem of scheduling batches of parts in a multi-cell flexible manufacturing system (MCFMS) with sequence dependent batch setup time. The goal is to find the best sequence of batches and hence to minimise the makespan. Two mathematical models are developed namely: batch availability model and job availability model. As the problem is known to be NP-hard, particle swarm optimisation (PSO) algorithm and multi-start simulated annealing (MSA) algorithm are proposed to solve the problem. The proposed algorithms are validated by testing the benchmark problems available in the literature. In addition to that, 80 problems with various sizes have been generated at random and then the performance of the proposed MSA and PSO algorithms are compared with CPLEX solver. The experimental results show that MSA provides better solution compared with PSO, the same solution as CPLEX and very close to the lower bound value provided by CPLEX.

A cooperative GPU-based Parallel Multistart Simulated Annealing algorithm for Quadratic Assignment Problem

GPU hardware and CUDA architecture provide a powerful platform to develop parallel algorithms. Implementation of heuristic and metaheuristic algorithms on GPUs are limited in literature. Nowadays developing parallel algorithms on GPU becomes very important. In this paper, NP-Hard Quadratic Assignment Problem (QAP) that is one of the combinatorial optimization problems is discussed. Parallel Multistart Simulated Annealing (PMSA) method is developed with CUDA architecture to solve QAP. An efficient method is developed by providing multistart technique and cooperation between threads. The cooperation is occurred with threads in both the same and different blocks. This paper focuses on both acceleration and quality of solutions. Computational experiments conducted on many Quadratic Assignment Problem Library (QAPLIB) instances. The experimental results show that PMSA runs up to 29x faster than a single-core CPU and acquires best known solution in a short time in many benchmark datasets.

A new methodology for history matching combining iterative discrete Latin Hypercube with multi-start simulated annealing

This paper introduces a new method for history matching, combining the Iterative Discrete Latin Hypercube (IDLHC) with multi-start Simulated Annealing methods. The proposed method, named IDLHCSA, combines the potential of the IDLHC in finding good matched models while preserving the diversity of solutions and the potential of the SA with local control in finding local (refined) solutions. The IDLHCSA was applied in two cases. The first is a simple reservoir model used as proof of concept. The second is a complex benchmark case (UNISIM-I-H) based on the Namorado field, located at the Campos Basin, Brazil. The robustness and effectiveness of the proposed method are demonstrated by comparison with other consolidated methods. It is demonstrated here that, when compared to other methodologies, the proposed method is more effective in finding multiple solutions for the history matching problem while maintaining solution diversity. The production forecast is analyzed and the predictive capacity of the matched models is assessed. The paper reveals that obtaining good matched models does not ensure reliable forecasts. The proposed method was able to find matched models which provided more reliable forecasts when compared to other methods.

Discrete optimization via gradient-based adaptive stochastic search methods

ABSTRACTGradient-based Adaptive Stochastic Search (GASS) is a new stochastic search optimization algorithm that has recently been proposed. It iteratively searches promising candidate solutions through a population of samples generated from a parameterized probabilistic model on the solution space, and updates the parameter of the probabilistic model based on a direct gradient method. Under the framework of GASS, we propose two discrete optimization algorithms: discrete Gradient-based Adaptive Stochastic Search (discrete-GASS) and annealing Gradient-based Adaptive Stochastic Search (annealing-GASS). In discrete-GASS, we transform the discrete optimization problem into a continuous optimization problem on the parameter space of a family of independent discrete distributions, and apply a gradient-based method to find the optimal parameter, such that the corresponding distribution has the best capability to generate optimal solution(s) to the original discrete problem. In annealing-GASS, we use a Boltzmann distribution as the parameterized probabilistic model, and propose a gradient-based temperature schedule that changes adaptively with respect to the current performance of the algorithm. We show convergence of both discrete-GASS and annealing-GASS under appropriate conditions. Numerical results on several benchmark optimization problems and the traveling salesman problem indicate that both algorithms perform competitively against a number of other algorithms, including model reference adaptive search, the cross-entropy method, and multi-start simulated annealing with different temperature schedules.

Multi-Objective Parallel Variable Neighborhood Search for Energy Consumption Scheduling in Blocking Flow Shops

Blocking flow shop scheduling problem has been extensively studied because of its widespread industrial applications. However, the existing research mostly aims at makespan or total flow time minimization and ignores the criterion for energy saving. This paper investigates the blocking flow shop scheduling problem with both makespan and energy consumption criteria. First, the multi-objective model of blocking flow shop scheduling is formulated in consideration of machine energy consumed in blocking and idle time. Then, a multi-objective parallel variable neighborhood search (MPVNS) algorithm is proposed to solve this problem. An improved Nawaz–Enscore–Ham-based heuristic is developed to generate initial solutions, and a variable neighborhood search is designed to explore these solutions in parallel. Furthermore, an insertion-based pareto local search method is embedded to enhance the exploitation of the algorithm. Finally, in order to validate its effectiveness, the MPVNS is compare with other two effective multi-objective metaheuristics by computational experiments based on well-known benchmark instances. The experimental results illustrate that the proposed algorithm is superior to non-dominated sorting genetic algorithm (II) and bi-objective multi-start simulated annealing algorithm in terms of set coverage and hypervolume measures.

Single allocation p-hub median location and routing problem with simultaneous pick-up and delivery

We introduce the single allocation p-hub median location and routing problem with simultaneous pick-up and delivery based on observations from real life hub networks. The aim of our problem is to minimize the cost of transferring the flow between hubs and routing the flow in the network. We propose several mixed integer programming formulations and two heuristic approaches based on multi start simulated annealing and ant colony system to solve these problems. Extensive results demonstrate that using our methods good solutions can be found despite the computationally challenging nature of the problem.