Multi-start Simulated Annealing Algorithm Research Articles

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.

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.

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.

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.

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 row facility layout using multi-start simulated annealing

In the single row facility layout problem (SRFLP), we are given a set of n facilities, their lengths, and a flow cost matrix. The problem asks to arrange the facilities along a straight line so as to minimize the sum of the products of the flow costs and center-to-center distances between facilities. We develop a multi-start simulated annealing (MSA) algorithm for solving this problem. The algorithm employs two move types, pairwise interchanges of facilities and insertions. We propose O(n)-time procedures for computing gains of both types of moves. They make our algorithm very fast compared to the traditional approaches, which are based on a straightforward procedure for obtaining the move gain in O(n2) time. When the temperature during the cooling process drops to a certain level, the computation is accelerated with the use of an additional technique which is reminiscent of a local search algorithm (it takes O(1) time to compute the move gain and O(n2) time to perform the required calculations when a move is accepted). We report computational results for SRFLP instances of size up to 1000 facilities. The results demonstrate the superiority of the MSA algorithm over the state-of-the-art methods. The source code implementing MSA is made publicly available as a benchmark for future comparisons.

Multi-start simulated annealing heuristic for the location routing problem with simultaneous pickup and delivery

The location routing problem with simultaneous pickup and delivery (LRPSPD) is a new variant of the location routing problem (LRP). The objective of LRPSPD is to minimize the total cost of a distribution system including vehicle traveling cost, depot opening cost, and vehicle fixed cost by locating the depots and determining the vehicle routes to simultaneously satisfy the pickup and the delivery demands of each customer. LRPSPD is NP-hard since its special case, LRP, is NP-hard. Thus, this study proposes a multi-start simulated annealing (MSA) algorithm for solving LRPSPD which incorporates multi-start hill climbing strategy into simulated annealing framework. The MSA algorithm is tested on 360 benchmark instances to verify its performance. Results indicate that the multi-start strategy can significantly enhance the performance of traditional single-start simulated annealing algorithm. Our MSA algorithm is very effective in solving LRPSPD compared to existing solution approaches. It obtained 206 best solutions out of the 360 benchmark instances, including 126 new best solutions.

Solving the team orienteering problem using effective multi-start simulated annealing

The team orienteering problem (TOP) is known as an NP-complete problem. A set of locations is provided and a score is collected from the visit to each location. The objective is to maximize the total score given a fixed time limit for each available tour. Given the computational complexity of this problem, a multi-start simulated annealing (MSA) algorithm which combines a simulated annealing (SA) based meta-heuristic with a multi-start hill climbing strategy is proposed to solve TOP. To verify the developed MSA algorithm, computational experiments are performed on well-known benchmark problems involving numbers of locations ranging between 42 and 102. The experimental results demonstrate that the multi-start hill climbing strategy can significantly improve the performance of the traditional single-start SA. Meanwhile, the proposed MSA algorithm is highly effective compared to the state-of-the-art meta-heuristics on the same benchmark instances. The proposed MSA algorithm obtained 135 best solutions to the 157 benchmark problems, including five new best solutions. In terms of both solution quality and computational expense, this study successfully constructs a high-performance method for solving this challenging problem.

Solving bus terminal location problems using evolutionary algorithms

Bus terminal assignment with the objective of maximizing public transportation service is known as bus terminal location problem (BTLP). We formulate the BTLP, a problem of concern in transportation industry, as a p-uncapacitated facility location problem (p-UFLP) with distance constraint. The p-UFLP being NP-hard (Krarup and Pruzan, 1990), we propose evolutionary algorithms for its solution. According to the No Free Lunch theorem and the good efficiency of the distinctive preserve recombination (DPX) operator, we design a new recombination operator for solving a BTLP by new evolutionary and memetic algorithms namely, genetic local search algorithms (GLS). We also define the potential objective function (POF) for the nodes and design a new mutation operator based on POF. To make the memetic algorithm faster, we estimate the variation of the objective function based on POF in the local search as part of an operator in memetic algorithms. Finally, we explore numerically the performance of nine proposed algorithms on over a thousand randomly generated problems and select the best two algorithms for further testing. The comparative studies show that our new hybrid algorithm composing the evolutionary algorithm with the GLS outperforms the multistart simulated annealing algorithm.

Solving the Multiple Competitive Facilities Location and Design Problem on the Plane

A continuous location problem in which a firm wants to set up two or more new facilities in a competitive environment is considered. Other facilities offering the same product or service already exist in the area. Both the locations and the qualities of the new facilities are to be found so as to maximize the profit obtained by the firm. This is a global optimization problem, with many local optima. In this paper we analyze several approaches to solve it, namely, three multistart local search heuristics, a multistart simulated annealing algorithm, and two variants of an evolutionary algorithm. Through a comprehensive computational study it is shown that the evolutionary algorithms are the heuristics that provide the best solutions. Furthermore, using a set of problems for which the optimal solutions are known, only the evolutionary algorithms were able to find the optimal solutions for all the instances. The evolutionary strategies presented in this paper can be easily adapted to handle other continuous location problems.

Coal and Conflict: A Study of Industrial Relations at Collieries.W. H. Scott , Enid Mumford , L. C. McGivering , J. M. Kirkby

Previous articleNext article No AccessBook ReviewsCoal and Conflict: A Study of Industrial Relations at Collieries. W. H. Scott , Enid Mumford , L. C. McGivering , J. M. Kirkby Louis KriesbergLouis Kriesberg Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by American Journal of Sociology Volume 70, Number 1Jul., 1964 Article DOIhttps://doi.org/10.1086/223760 Views: 1Total views on this site Citations: 7Citations are reported from Crossref Copyright 1964, 1965, University of ChicagoPDF download Crossref reports the following articles citing this article:Krzysztof Walkowiak, Michal Kucharzak, Pawel Kopec, Andrzej Kasprzak ILP model and algorithms for restoration of anycast flows in Elastic Optical Networks, (Nov 2014): 102–108.https://doi.org/10.1109/RNDM.2014.7014938Xiao-yan Xing, Yi-yong Xiao, Ren-qian Zhang Parameter selection optimization for parametric cost estimation based on Simulated Annealing, (Oct 2010): 231–236.https://doi.org/10.1109/ICIEEM.2010.5646651Zhufang Wang, Donghong Cui A Hybrid Algorithm Based on Genetic Algorithm and Simulated Annealing for Solving Portfolio Problem, (Jul 2009): 106–109.https://doi.org/10.1109/BIFE.2009.34Zhengbing Hu, Rabah Moh'd, Al Shboul The Application of Ant Colony Optimization Technique (ACOT) for Employees Selection and Training, (Apr 2009): 497–502.https://doi.org/10.1109/DBTA.2009.172Jonas C. P. Yu, H. M. Wee, K. J. Wang An integrated three-echelon supply chain model for deteriorating items via simulated annealing method, (Jul 2008): 3921–3926.https://doi.org/10.1109/ICMLC.2008.4621088Humberto Brandao De Oliveira, Germano Vasconcelos, Guilherme Alvarenga A Multi-Start Simulated Annealing Algorithm for the Vehicle Routing Problem with Time Windows, (Oct 2006): 24–24.https://doi.org/10.1109/SBRN.2006.4C. Muller, E.H. Magill, P. Prosser, D.G. Smith Artificial intelligence in telecommunications, (): 883–887.https://doi.org/10.1109/GLOCOM.1993.318205

The Backboard Wiring Problem: A Placement Algorithm

The Backboard Wiring Problem: A Placement Algorithm