Diversification-based learning simulated annealing algorithm for hub location problems

2019 ◽  
Vol 26 (6) ◽  
pp. 1995-2016
Author(s):  
Himanshu Rathore ◽  
Shirsendu Nandi ◽  
Peeyush Pandey ◽  
Surya Prakash Singh
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Convergence Rates ◽  
Location Problems ◽  
Computational Time ◽  
Data Sets ◽  
Learning Mechanisms ◽  
Hub Location ◽  
Content Type ◽  
Annealing Algorithm

Purpose The purpose of this paper is to examine the efficacy of diversification-based learning (DBL) in expediting the performance of simulated annealing (SA) in hub location problems. Design/methodology/approach This study proposes a novel diversification-based learning simulated annealing (DBLSA) algorithm for solving p-hub median problems. It is executed on MATLAB 11.0. Experiments are conducted on CAB and AP data sets. Findings This study finds that in hub location models, DBLSA algorithm equipped with social learning operator outperforms the vanilla version of SA algorithm in terms of accuracy and convergence rates. Practical implications Hub location problems are relevant in aviation and telecommunication industry. This study proposes a novel application of a DBLSA algorithm to solve larger instances of hub location problems effectively in reasonable computational time. Originality/value To the best of the author’s knowledge, this is the first application of DBL in optimisation. By demonstrating its efficacy, this study steers research in the direction of learning mechanisms-based metaheuristic applications.

A multiproduct economic order quantity model with simulated annealing application

2017 ◽  
Vol 12 (1) ◽  
pp. 119-142 ◽  
Author(s):  
Valdecy Pereira ◽  
Helder Gomes Costa
Keyword(s):  
Simulated Annealing ◽  
Design Methodology ◽  
Simulated Annealing Algorithm ◽  
Economic Order Quantity ◽  
Economic Order ◽  
Optimal Solutions ◽  
Order Quantity ◽  
Single Product ◽  
Content Type ◽  
Annealing Algorithm

Purpose This 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/approach A 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. Findings Our 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/value Finally 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.

PARAMETERS OPTIMIZE METHOD BASED ON GENETIC AND SIMULATED ANNEALING ALGORITHMS

2008 ◽  
Vol 22 (01) ◽  
pp. 169-181 ◽  
Author(s):  
JIAO-MIN LIU ◽  
JING-HONG WANG
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Initial Study ◽  
Direct Selection ◽  
Data Sets ◽  
Theoretical Support ◽  
Fuzzy Extension Matrix ◽  
Annealing Algorithm ◽  
Parameter Values

This paper gives an initial study on the comparison between Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA). Firstly, a new algorithm is presented. This method combines Genetic Algorithm and Simulated Annealing Algorithm, and it can be used to optimize the three parameters α, β and γ. It involes the rules that are extracted from Fuzzy Extension Matrix (FEM). These parameters play an important part in the entire process of rule extraction based on FEM. Secondly, it provides some theoretical support to the direct selection of the parameter values through experiments. Lastly, five data sets from the UCI Machine Learning centers are employed in the study. Experimental results and discussions are given.

scholarly journals A simulated annealing algorithm for the allocation of production resources in the food catering industry

2020 ◽  
Vol 122 (7) ◽  
pp. 2139-2158 ◽  
Author(s):  
Alessandro Tufano ◽  
Riccardo Accorsi ◽  
Riccardo Manzini
Keyword(s):  
Simulated Annealing ◽  
Food Safety ◽  
Food Quality ◽  
Simulated Annealing Algorithm ◽  
Production Plant ◽  
Content Type ◽  
Multi Objective ◽  
Catering Industry ◽  
Annealing Algorithm

PurposeThis paper addresses the trade-off between asset investment and food safety in the design of a food catering production plant. It analyses the relationship between the quality decay of cook-warm products, the logistics of the processes and the economic investment in production machines.Design/methodology/approachA weekly cook-warm production plan has been monitored on-field using temperature sensors to estimate the quality decay profile of each product. A multi-objective optimisation model is proposed to (1) minimise the number of resources necessary to perform cooking and packing operations or (2) to maximise the food quality of the products. A metaheuristic simulated annealing algorithm is introduced to solve the model and to identify the Pareto frontier of the problem.FindingsThe packaging buffers are identified as the bottleneck of the processes. The outcome of the algorithms highlights that a small investment to design bigger buffers results in a significant increase in the quality with a smaller food loss.Practical implicationsThis study models the production tasks of a food catering facility to evaluate their criticality from a food safety perspective. It investigates the tradeoff between the investment cost of resources processing critical tasks and food safety of finished products.Social implicationsThe methodology applies to the design of cook-warm production. Catering companies use cook-warm production to serve school, hospitals and companies. For this reason, the application of this methodology leads to the improvement of the quality of daily meals for a large number of people.Originality/valueThe paper introduces a new multi-objective function (asset investment vs food quality) proposing an original metaheuristic to address this tradeoff in the food catering industry. Also, the methodology is applied and validated in the design of a new food production facility.

Performance evaluation of a hybridized simulated annealing algorithm for flow shop scheduling under a dynamic environment

Kybernetes ◽  
2014 ◽  
Vol 43 (7) ◽  
pp. 1024-1039 ◽  
Author(s):  
Robin Kumar Samuel ◽  
P. Venkumar
Keyword(s):  
Simulated Annealing ◽  
Hybrid Algorithm ◽  
Simulated Annealing Algorithm ◽  
Flow Shop ◽  
Practical Implementation ◽  
Content Type ◽  
Production Logistics ◽  
Dynamic Conditions ◽  
Hybrid Simulated Annealing ◽  
Annealing Algorithm

Purpose – The purpose of this paper is to propose a hybrid-simulated annealing algorithm to address the lacunas in production logistics. The primary focus is laid on the basic understanding of the critical quandary occurring in production logistics, and subsequently research attempts are undertaken to resolve the issue by developing a hybrid algorithm. A logistics problem associated with a flow shop (FS) having a string of jobs which need to be scheduled on m number of machines is considered. Design/methodology/approach – An attempt is made here to introduce and further establish a hybrid-simulated annealing algorithm (NEHSAO) with a new scheme for neighbourhood solutions generation, outside inverse (OINV). The competence in terms of performance of the proposed algorithm is enhanced by incorporating a fast polynomial algorithm, NEH, which provides the initial seed. Additionally, a new cooling scheme (Ex-Log) is employed to enhance the capacity of the algorithm. The algorithm is tested on the benchmark problems of Carlier and Reeves and subsequently validated against other algorithms reported in related literature. Findings – It is clearly observed that the performance of the proposed algorithm is far superior in most of the cases when compared to the other conventionally used algorithms. The proposed algorithm is then employed to a FS under dynamic conditions of machine breakdown, followed by formulation of three cases and finally identification of the best condition for scheduling under dynamic conditions. Originality/value – This paper proposes an hybrid algorithm to reduce makespan. Practical implementation of this algorithm in industries would lower the makespan and help the organisation to increse their profit

Cleaning Uncertain Database with Aggregate Constraints Based on the Modified Simulated Annealing Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 1661-1664
Author(s):  
Ji Cheng Shan ◽  
Bin Liu ◽  
Qing Bao Liu
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Uncertain Data ◽  
Data Sets ◽  
Function Model ◽  
Object Function ◽  
Dirty Data ◽  
Efficiency And Effectiveness ◽  
Uncertain Database ◽  
Annealing Algorithm

In this paper, we investigate the problem how to clean uncertain data with aggregate constraints in order to reduce the uncertainty and clean the dirty data in uncertain data sets. We find the shortages by analyzing the existing model and methods for cleaning uncertain data with aggregate constraints. We modified the existing Object Function model in literature and designed an appropriate algorithm for our problem by studying the Modified Simulated Annealing algorithm. Our experiments verify the efficiency and effectiveness of our algorithm.

A Metaheuristic for the Pickup and Delivery Problem with Time Windows

2003 ◽  
Vol 12 (02) ◽  
pp. 173-186 ◽  
Author(s):  
Haibing Li ◽  
Andrew Lim
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Time Windows ◽  
Pickup And Delivery ◽  
Data Sets ◽  
Search Procedure ◽  
Delivery Problem ◽  
Pickup And Delivery Problem ◽  
Annealing Algorithm ◽  
Problem Instances

In this paper, we propose a metaheuristic to solve the pickup and delivery problem with time windows. Our approach is a tabu-embedded simulated annealing algorithm which restarts a search procedure from the current best solution after several non-improving search iterations. The computational experiments on the six newly-generated different data sets marked our algorithm as the first approach to solve large multiple-vehicle PDPTW problem instances with various distribution properties.

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