Search Results (11)

In today’s perishable products industry, the importance of sustainability as a critical consideration has significantly increased. This study focuses on the design of a sustainable perishable product supply chain network (SPPSCN), considering the factors of economics cost, environmental impacts, and social responsibility. The proposed model is a comprehensive production–location–inventory problem optimization framework that addresses multiple objectives, echelons, products, and periods. To solve this complex problem, we introduce three hybrid metaheuristic algorithms: bat algorithm (BA), shuffled frog leaping algorithm (SFLA), and cuckoo search (CS) algorithm, all hybrid with variable neighbourhood search (VNS). Sensitivity to input parameters is accounted for using the Taguchi method to tune these parameters. Additionally, we evaluate and compare these approaches among themselves and benchmark their results against a reference method, a hybrid genetic algorithm (GA) with VNS. The quality of the Pareto frontier is evaluated by six metrics for test problems. The results highlight the superior performance of the bat algorithm with variable neighbourhood search. Furthermore, a sensitivity analysis is conducted to evaluate the impact of key model parameters on the optimal objectives. It is observed that an increase in demand has a nearly linear effect on the corresponding objectives. Moreover, the impact of extending raw material shelf life and product shelf life on these objectives is limited to a certain range. Beyond a certain threshold, the influence becomes insignificant. Full article

This research work addresses the integrated scheduling of jobs and flexible (non-systematic) maintenance interventions in permutation flowshop production systems. We propose a coordinated model in which the time intervals between successive maintenance tasks as well as their number are assumed to be non-fixed for each machine on the shopfloor. With such a flexible nature of maintenance activities, the resulting joint schedule is more practical and representative of real-world scenarios. Our goal is to determine the best job permutation in which flexible maintenance activities are properly incorporated. To tackle the NP-hard nature of this problem, an artificial bee colony (ABC) algorithm is developed to minimize the total production time (Makespan). Experiments are conducted utilizing well-known Taillard’s benchmarks, enriched with maintenance data, to compare the proposed algorithm performance against the variable neighbourhood search (VNS) method from the literature. Computational results demonstrate the effectiveness of the proposed algorithm in terms of both solution quality and computational times. Full article

Being able to infer the clonal evolution and progression of cancer makes it possible to devise targeted therapies to treat the disease. As discussed in several studies, understanding the history of accumulation and the evolution of mutations during cancer progression is of key importance when devising treatment strategies. Given the importance of the task, many methods for phylogeny reconstructions have been developed over the years, mostly employing probabilistic frameworks. Our goal was to explore different methods to take on this phylogeny inference problem; therefore, we devised and implemented three different metaheuristic approaches—Particle Swarm Optimization (PSO), Genetic Programming (GP) and Variable Neighbourhood Search (VNS)—under the Perfect Phylogeny and the Dollo-k evolutionary models. We adapted the algorithms to be applied to this specific context, specifically to a tree-based search space, and proposed six different experimental settings, in increasing order of difficulty, to test the novel methods amongst themselves and against a state-of-the-art method. Of the three, the PSO shows particularly promising results and is comparable to published tools, even at this exploratory stage. Thus, we foresee great improvements if alternative definitions of distance and velocity in a tree space, capable of better handling such non-Euclidean search spaces, are devised in future works. Full article

A matheuristic approach that combines a reduced variable neighbourhood search (rVNS) algorithm and a mathematical programming (MP) solver to solve a novel model for the districting problem in a public bicycle-sharing system is presented. The problem is modelled as an integer programming problem. While the rVNS algorithm aims to find a high-quality set of centres for the repositioning zones, the MP solver computes the optimal allocation network of the stations to the centres of the repositioning zones. We use a predefined grid to reduce the search space the rVNS needs to explore. The proposed approach obtains promising results for small and medium-sized instances, and is also able to handle large-sized models. Full article

The transit route network design problem (TRNDP) has long attracted research attention, with many metaheuristic approaches proposed for its solution. So far, and despite the promising performance of Variable Neighbourhood Search (VNS) variants for vehicle routing problems, the performance of the algorithm on the TRNDP remains unexplored. In this context, this study develops a VNS-based algorithm for the problem at hand. The performance of the algorithm is tested using benchmark networks used in bus transit network design and compared with some of the most recent and efficient methods from the literature. Results show that the algorithm yields superior results over existing implementations in short computational times. Full article

In this paper, a vehicle routing problem (VRP) model considering delivery time windows and variable service time is established for the delivery problem in community group purchasing. A solution model for an improved ant colony algorithm (ACA) is proposed by improving the initial feasible solution and the neighbourhood search mechanism of the ant colony algorithm. The algorithm of the improved ant colony and the commonly used algorithm are solved for real cases and publicly available benchmark datasets, respectively, for comparative analysis. The results show that the improved ACA has stronger optimization capability, faster convergence speed, and has advantages in solving VRPTW problems with variable service time. The computational efficiency is also improved by 41% over the genetic algorithm (GA) in the solution of the benchmark dataset, which provides a certain reference for solving the community group distribution problem. Full article

A synthetic review of the application of multi-objective optimization models to the design of climate-responsive buildings and neighbourhoods is carried out. The review focused on the software utilized during both simulation and optimization stages, as well as on the objective functions and the design variables. The hereby work aims at identifying knowledge gaps and future trends in the research field of automation in the design of buildings. Around 140 scientific journal articles, published between 2014 and 2021, were selected from Scopus and Web of Science databases. A three-step selection process was applied to refine the search terms and to discard works investigating mechanical, structural, and seismic topics. Meta-analysis of the results highlighted that multi-objective optimization models are widely exploited for (i) enhancing building’s energy efficiency, (ii) improving thermal and (iii) visual comfort, minimizing (iv) life-cycle costs, and (v) emissions. Reviewed workflows demonstrated to be suitable for exploring different design alternatives for building envelope, systems layout, and occupancy patterns. Nonetheless, there are still some aspects that need to be further enhanced to fully enable their potential such as the ability to operate at multiple temporal and spatial scales and the possibility of exploring strategies based on sector coupling to improve a building’s energy efficiency. Full article

The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required methods in the distributed network. However, a security issue exists and it is hard to run different types of tasks (e.g., security, delay-sensitive, and delay-tolerant tasks) of IoMT applications on heterogeneous computing nodes. This work proposes a new healthcare architecture for workflow applications based on heterogeneous computing nodes layers: an application layer, management layer, and resource layer. The goal is to minimize the makespan of all applications. Based on these layers, the work proposes a secure offloading-efficient task scheduling (SEOS) algorithm framework, which includes the deadline division method, task sequencing rules, homomorphic security scheme, initial scheduling, and the variable neighbourhood searching method. The performance evaluation results show that the proposed plans outperform all existing baseline approaches for healthcare applications in terms of makespan. Full article

This paper examines the behaviour of Bitcoin returns and those of several other cryptocurrencies in the pre and post period of the introduction of the Bitcoin futures market. We use the principal component-guided sparse regression (PC-LASSO) model to analyze several sample sizes for the pre and post periods. Besides the neighbourhood of the break time, the current period is also investigated as returns start to recover after some time. Search intensity is observed to be the most important variable for Bitcoin for all periods, whereas for the other cryptocurrencies there are other variables that seem more important in the pre period, while search intensity still stands out in the post period. Furthermore, GARCH analyses suggest that search intensity increases the volatility of Bitcoin returns more in the post period than it does in the pre period. Our empirical findings suggest that the top five cryptocurrencies are substitutes before the launch of Bitcoin futures. However, this effect is lost, and moreover, there are spillover effects on altcoins during both the post and the recovery period. We find a spillover effect of the introduction of bitcoin futures on altcoins and this effect seems to persist during the recovery period. Full article

During the last decade, Wireless sensor networks (WSNs) have attracted interest due to the excellent monitoring capabilities offered. However, WSNs present shortcomings, such as energy cost and reliability, which hinder real-world applications. As a solution, Relay Node (RN) deployment strategies could help to improve WSNs. This fact is known as the Relay Node Placement Problem (RNPP), which is an NP-hard optimization problem. This paper proposes to address two Multi-Objective (MO) formulations of the RNPP. The first one optimizes average energy cost and average sensitivity area. The second one optimizes the two previous objectives and network reliability. The authors propose to solve the two problems through a wide range of MO metaheuristics from the three main groups in the field: evolutionary algorithms, swarm intelligence algorithms, and trajectory algorithms. These algorithms are the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), Multi-Objective Artificial Bee Colony (MO-ABC), Multi-Objective Firefly Algorithm (MO-FA), Multi-Objective Gravitational Search Algorithm (MO-GSA), and Multi-Objective Variable Neighbourhood Search Algorithm (MO-VNS). The results obtained are statistically analysed to determine if there is a robust metaheuristic to be recommended for solving the RNPP independently of the number of objectives. Full article

In this paper, the approach focused on the variables involved in assessing the quality of a distributed generation system are reviewed in detail, for its investigation and research contribution. The aim to minimize the electric power losses (unused power consumption) and optimize the voltage profile for the power system under investigation. To provide this assessment, several experiments have been made to the IEEE 34-bus test case and various actual test cases with the respect of multiple Distribution Generation DG units. The possibility and effectiveness of the proposed algorithm for optimal placement and sizing of DG in distribution systems have been verified. Finally, four algorithms were trailed: simulated annealing (SA), hybrid genetic algorithm (HGA), genetic algorithm (GA), and variable neighbourhood search. The HGA algorithm was found to produce the best solution at a cost of a longer processing time. Full article