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Maintenance planning aims to improve the reliability of assets, prevent the occurrence of asset failures, and reduce maintenance costs associated with downtime of assets and maintenance resources (such as spare parts and workforce). Thus, effective maintenance planning is instrumental in ensuring high asset availability with the minimum cost. Nevertheless, to find such optimal planning is a nontrivial task due to the (i) complex and usually nonlinear inter-relationship between different planning decisions (e.g., inventory level and workforce capacity), and (ii) stochastic nature of the system (e.g., random failures of parts installed in assets). To alleviate these challenges, we study a joint maintenance planning problem by considering several decisions simultaneously, including workforce planning, workforce training, and spare parts inventory management. We develop a hybrid solution algorithm (DRLSA ) that is a combination of Double Deep Q-Network based Deep Reinforcement Learning (DRL) and Simulated Annealing (SA) algorithms. In each episode of the proposed algorithm, the best solution found by DRL is delivered to SA to be used as an initial solution, and the best solution of SA is delivered to DRL to be used as the initial state. Different from the traditional SA algorithms where neighborhood structures are selected only randomly, the DRL part of DRLSA learns to choose the best neighborhood structure to use based on experience gained from previous episodes. We compare the performance of the proposed solution algorithm with several well-known meta-heuristic algorithms, including Simulated Annealing, Genetic Algorithm (GA), and Variable Neighborhood Search (VNS). Further, we also develop a Machine Learning (ML) algorithm (i.e., K-Median) as another benchmark in which different properties of spare parts (e.g., failure rates, holding costs, and repair rates) are used as clustering features for the ML algorithm. Our study reveals that the DRLSA finds the optimal solutions for relatively small-size instances, and it has the potential to outperform traditional meta-heuristic and ML algorithms. [ABSTRACT FROM AUTHOR]

This work presents two solution methods for the m-machine robust permutation flow shop problem with processing time uncertainty. The goal is to minimize the makespan of the worst-case scenario by utilizing an approach based on budgeted uncertainty, in which only a subset of operations will reach their worst-case processing time values. To obtain efficient solutions to this problem, we first extend an existing two-machine worst-case procedure, based on dynamic programming, generalizing it to m machines. The worst-case calculation is then incorporated into two proposed solution methods: an exact column-and-constraint generation algorithm and a GRASP metaheuristic. Based on experiments with four sets of literature-based instances, empirical results demonstrate the ability of the GRASP to efficiently produce an optimal or near-optimal solution in most cases. [ABSTRACT FROM AUTHOR]

The main resources in software projects are human resources equipped with various skills, which makes software development a typical intelligence-intensive process. Therefore, effective human resource scheduling is indispensable for the success of software projects. The aim of software project scheduling is to assign the right employee to the right activity at the right time. Uncertainty is inevitable in software development, which further complicates the scheduling of projects. We investigate the software project scheduling problem with uncertain activity durations (SPSP-UAD) and aim at obtaining effective scheduling policies for the problem. We present and transform a scenario-based non-linear chance-constrained stochastic programming model into an equivalent linear programming model. To solve the NP-hard SPSP-UAD efficiently, we develop a hybrid meta-heuristic TLBO-GA that combines the teaching–learning-based optimization algorithm (TLBO) and the genetic algorithm (GA). Our TLBO-GA is also equipped with some problem-specific operators, such as population initialization, rows exchange and local search. We use simulation to evaluate the scheduling policies obtained by our algorithms. Extensive computational experiments are conducted to evaluate the performance of our TLBO-GA in comparison to the exact solver CPLEX and four existing meta-heuristic algorithms. The comparative results reveal the effectiveness and efficiency of our TLBO-GA. Our TLBO-GA provides an extensible and adaptive automated scheduling decision support tool for the software project manager in the complex and uncertain software development environment. [ABSTRACT FROM AUTHOR]

The execution of a project is often distributed among multiple sites. The planning of such a project includes selecting a specific site for the execution of each of the project's activities and allocating the available resource units to the execution of these activities over time. While some resource units are available at a certain site only, others can be moved across sites. Given the spatial distance between sites, transportation times arise if a resource unit must be transported from one site to another or if the output of an activity must be transported to another site. This planning problem has been introduced in recent literature as the multi-site resource-constrained project scheduling problem. We present a continuous-time model and devise a matheuristic for this planning problem. The continuous-time model uses, among others, binary variables to impose a sequence between activities assigned to the same resource units. In the matheuristic, the binary restrictions on these variables are initially relaxed and iteratively restored for the subset of activities scheduled in the current iteration. We compare the performance of the continuous-time model and the matheuristic to the performance of a discrete-time model and several metaheuristics from the literature using two sets of test instances from the literature. Both the continuous-time model and the matheuristic derive on average superior solutions in shorter average running times than the reference approaches. [ABSTRACT FROM AUTHOR]

The location planning of relief distribution centres (DCs) is crucial in humanitarian logistics as it directly influences the disaster response and service to the affected victims. In light of the critical role of facility location in humanitarian logistics planning, the study proposes a two-stage relief distribution location problem. The first stage of the model determines the minimum number of relief DCs, and the second stage find the optimal location of these DCs to minimize the total cost. To address a more realistic situation, restrictions are imposed on the coverage area and capacity of each DCs. In addition, for optimally solving this complex NP-hard problem, a novel two-phase algorithm with exploration and exploitation phase is developed in the paper. The first phase of the algorithm i.e., exploration phase identifies a near-optimal solution while the second phase i.e. exploitation phase enhances the solution quality through a close circular proximity investigation. Furthermore, the comparative analysis of the proposed algorithm with other well-known algorithms such as genetic algorithm, pattern search, fmincon, multistart and hybrid heuristics is also reported and computationally tested from small to large data sets. The results reveal that the proposed two-phase algorithm is more efficient and effective when compared to the conventional metaheuristic methods. [ABSTRACT FROM AUTHOR]

In this paper we study the performance of a two-stage approach to scheduling under uncertainty making use of sequences of groups of permutable operations. Given a sample set of uncertainty realization scenarios, the goal is to compute a sequence of groups of permutable operations representing a partial scheduling decision in the first-stage, that yields the best possible score in the second-stage, when, for a specific scenario, a full operation sequence is obtained via a second-stage decision policy. This approach is described for a single-machine problem and the jobshop problem with stochastic and robust optimization, as well as several commonly studied objectives. We propose new constraint programming models as well as a genetic algorithm meta-heuristic to compute such two-stage solutions. We also investigate a warm-start scheme to work around the difficult search space of sequences of permutable operations. Experiments are carried out to characterize when this two-stage approach yields better results. We also compare the introduced methods with existing ones. Theoretical extensions of the known methods are also described and evaluated. [ABSTRACT FROM AUTHOR]

In this paper, a new responsive-green-cold vaccine supply chain network during the COVID-19 pandemic is developed for the first time. According to the proposed network, a new multi-objective, multi-period, multi-echelon mathematical model for the distribution-allocation-location problem is designed. Another important novelty in this paper is that it considers an Internet-of-Things application in the COVID-19 condition in the suggested model to enhance the accuracy, speed, and justice of vaccine injection with existing priorities. Waste management, environmental effects, coverage demand, and delivery time of COVID-19 vaccine simultaneously are therefore considered for the first time. The LP-metric method and meta-heuristic algorithms called Gray Wolf Optimization (GWO), and Variable Neighborhood Search (VNS) algorithms are then used to solve the developed model. The other significant contribution, based on two presented meta-heuristic algorithms, is a new heuristic method called modified GWO (MGWO), and is developed for the first time to solve the model. Therefore, a set of test problems in different sizes is provided. Hence, to evaluate the proposed algorithms, assessment metrics including (1) percentage of domination, (2) the number of Pareto solutions, (3) data envelopment analysis, and (4) diversification metrics and the performance of the convergence are considered. Moreover, the Taguchi method is used to tune the algorithm's parameters. Accordingly, to illustrate the efficiency of the model developed, a real case study in Iran is suggested. Finally, the results of this research show MGO offers higher quality and better performance than other proposed algorithms based on assessment metrics, computational time, and convergence. [ABSTRACT FROM AUTHOR]

In recent years, the need for emergency resources has dramatically increased and it has caused an overcrowding problem for the emergency department (ED). Solving this problem by increasing medical resources is either impractical or infeasible. Thus, this manuscript develops a multi-objective mathematical model to allocate medical resources for the emergency department (ED). The optimal resource allocation is exploited by using some meta-heuristic algorithms, i.e., fast and elitism non-dominated sorting genetic algorithm (NSGA II), non-dominated sorting particle swarm algorithm (NSPSO), and non-dominated sorting differential evolution (NSDE). Thereafter, a dynamic simulation model, which embeds the solutions from the resource allocation model in the simulation process, is constructed. Each feasible solution from the three meta-heuristic algorithms is simulated to estimate the performance of the resources allocated in terms of the average service level and staff utilization. The results show that the performance of the NSGAII, where the average service level and staff utilization for the current resources are 0.844 and 0.751, respectively, is better than those of NSPSO and NSDE. Besides, the number of medical staff gives a significant effect on the service level and utilization while the number of beds only impacts staff utilization. The simulation model can find out that the best combination of the number of staff and the number of beds is from 1 to 10 staffs and 1–6 beds to maximize the utilization and service level. [ABSTRACT FROM AUTHOR]

This paper presents a new multi-objective mathematical model for the hub location and routing problem under uncertainty in flows, costs, times, and number of job opportunities. This model aims at minimizing the total transportation cost consisting of routing and fixed cost and maximizing the employment and regional development as social responsibility. An M/M/c/K queue system is applied to estimate the waiting time at hub nodes and maximize the responsiveness. Also, a fuzzy queuing method is applied to model the uncertainties in this network. A powerful evolutionary meta-heuristic algorithm based on fuzzy invasive weed optimization, variable neighborhood search, and game theory is developed to solve the introduced model and obtain near-optimal Pareto solutions. Many experiments as well as a real transportation case-study show the superiority of the proposed approaches compared to the state-of-the-art algorithm. [ABSTRACT FROM AUTHOR]

In the context of simulation-based optimisation, this paper reviews recent work related to the role of metaheuristics, matheuristics (combinations of exact optimisation methods with metaheuristics), simheuristics (hybridisation of simulation with metaheuristics), biased-randomised heuristics for 'agile' optimisation via parallel computing, and learnheuristics (combination of statistical/machine learning with metaheuristics) to deal with NP-hard and large-scale optimisation problems in areas such as transport and logistics, manufacturing and production, smart cities, telecommunication networks, finance and insurance, sustainable energy consumption, health care, military and defence, e-marketing, or bioinformatics. The manuscript provides the main related concepts and updated references that illustrate the applications of these hybrid optimisation–simulation–learning approaches in solving rich and real-life challenges under dynamic and uncertainty scenarios. A numerical analysis is also included to illustrate the benefits that these approaches can offer across different application fields. Finally, this work concludes by highlighting open research lines on the combination of these methodologies to extend the concept of simulation-based optimisation. [ABSTRACT FROM AUTHOR]

We propose a novel heuristic approach, sequential solution method (SSM), for the efficient solution of Continuous Facility Layout Problems (CFLPs). The proposed SSM approach is compared with exact solution methods as well as Genetic Algorithm (GA) and Simulated Annealing (SA) metaheuristic algorithms. We also improved the metaheuristic approaches based on approximating the facility coordinates with the coordinates of the Center of the Smallest Rectangle (CSR) that covers all facilities in the solution. The proposed SSM approach is a recursive heuristic based on the exact solutions of reduced layout problems. Instead of solving the original CFLP with many variables, SSM first generates subproblems (facility clusters) of smaller sizes using a clustering model and then sequentially solves layout subproblems where non-member facilities locations are constrained. Based on an experimental study, we report that the proposed SSM substantially outperforms exact approaches and meta-heuristic approaches and hence provide an alternative approach for efficiently solving large CFLP instances. [ABSTRACT FROM AUTHOR]

Every year, natural disasters such as earthquakes, floods, volcanos, etc. cause millions of victims. So a quick response to these disasters is vital to reduce their negative consequences. Vehicle routing models can make important contributions to faster response, and thus, save lives. This paper proposes a vehicle routing problem to deliver relief resources from origins to destinations in response to disasters. For this purpose, a multi-period, multi-depot, multi-trip mixed-integer linear programming model is developed. Minimizing the sum of arrival times is considered as a service-based objective function for the increase of the survival rate. For the first time, the problem is solved using a grouping metaheuristic algorithm. Then its performance is compared with two other grouping algorithms. To evaluate the solution method, the algorithms are implemented on various test problems and compared statistically. Additionally, to show the validity of the model, sensitivity analyses are performed and managerial insights are given. [ABSTRACT FROM AUTHOR]

As a multi-criteria decision-making (MCDM) problem, supplier selection plays a key role in achieving the objectives of a supply chain system. Multiple strategic, operational, quantitative, and qualitative criteria influence the supplier selection process. A wide spectrum of criteria have been introduced, classified, and used by researchers and practitioners to evaluate the suppliers' performance; however, measuring and employing all of these criteria is impractical in real-world scenarios due to the budget, time, and information limitations. In this study, a decision support system (DSS) is developed, which helps managers to select a set of most effective criteria for the supplier selection process. This DSS is a threefold integration of MCDM and simulation and optimization. In this framework, the MCDM module incorporates a combination of criteria to select the suppliers. Then, a simulation model is used to evaluate the performance of the supply chain system considering the selected suppliers. Based on the simulation results, a multi-objective metaheuristic algorithm is utilized to find the ideal combinations of the criteria to maximize the supply chain system performance. [ABSTRACT FROM AUTHOR]

In this paper we introduce a bi-objective location-allocation problem for Unmanned Aircraft Systems (UASs) operating in a hostile environment. The objective is to find the locations to deploy UASs and assign Unmanned Aerial Vehicles to regions for surveillance. One of the objectives is to maximize search effectiveness, while the second is the minimization of the threats posed to the UASs. These two objectives are in conflict, because they are affected differently by the proximity between the UAS locations and the target regions. First, we have formulated this problem as a mixed integer nonlinear program. Next, we have developed its linearization which can be solved by a commercial optimizer for small-scale problem instances. To solve large-scale problems, we have adopted a well-known metaheuristic for multi-objective problems, namely the elitist non-dominated sorting genetic algorithm. We have also developed a hybrid approach, which has proven to be more effective than each approach alone. [ABSTRACT FROM AUTHOR]

The foreign exchange market significantly affects international trade, making accurate exchange rate predictions essential for investors, businesses, and government organizations. In this study, we propose an AI-driven ensemble model called the multi-model dynamic prediction system (MMDPS) to enhance exchange rate forecasting accuracy, prediction direction consistency, and prediction robustness. To that end, we implemented a series of measures. First, the submodels of MMDPS apply fuzzy prediction, chaos prediction, deep learning, and linear time-series forecasting methods to enhance the information extraction capability of MMDPS. Second, we introduced an innovative hybrid wavelet denoising method to enhance the ability of MMDPS to handle noise in exchange rate data. Third, we employed a novel multi-objective metaheuristic optimization algorithm to determine the optimal combination weights, optimizing across the dimensions of predictive accuracy and directional consistency. This approach further enhances the generalization capability and predictive robustness of MMDPS. Finally, we incorporated into MMDPS multiple variables that are closely related to exchange rates and can rapidly reflect market changes, such as panic indices and commodity futures prices, to enhance the model’s predictive robustness against uncertainty shocks. The predictive performance of MMDPS was validated using 15 datasets with varying frequencies. The results of our experiments demonstrate that MMDPS significantly outperforms the benchmark models in terms of prediction accuracy, consistency in prediction direction, and predictive robustness. Furthermore, MMDPS can significantly enhance investor returns and mitigate investment risks, which underscore its critical application value. [ABSTRACT FROM AUTHOR]

Sports timetabling is a complex and challenging problem. The latest open benchmark dataset for the sport timetabling problem is from the International Timetabling Competition (ITC) 2021. Due to its complexity, only a few approaches have successfully generated feasible solutions for the problems in this dataset, as reported in scientific literature. To the best of our knowledge, there is only one study in the literature that has successfully generated feasible solutions for all 45 problems in the dataset. In this paper, we propose our novel efficient algorithm based on the Iterated Local Search algorithm to solve the ITC 2021 benchmark dataset. Unlike prior successful approaches that combined metaheuristics with an exact approach, our proposed approach is solely metaheuristic. Our contribution includes the design of strategies for both perturbation and local search phases, coupled with the integration of shuffling strategies. The experimental results show that our proposed algorithm is remarkably successful in generating feasible solutions for all 45 problems present in the ITC 2021 dataset. [ABSTRACT FROM AUTHOR]

In this work, we propose a new variant of construct, merge, solve, and adapt (CMSA), which is a recently introduced hybrid metaheuristic for combinatorial optimization. Our newly proposed variant, named reinforcement learning CMSA (RL-CMSA), makes use of a reinforcement learning (RL) mechanism trained online with data gathered during the search process. In addition to generally outperforming standard CMSA, this new variant proves to be more flexible as it does not require a greedy function for the evaluation of solution components at each solution construction step. We present RL-CMSA as a general framework for enhancing CMSA by leveraging a simple RL learning process. Moreover, we study a range of specific designs for the employed learning mechanism. The advantages of the introduced CMSA variant are demonstrated in the context of the far from most string and minimum dominating set problems, showing the improvement in performance and simplicity with respect to standard CMSA. In particular, the best performing RL-CMSA variant proposed is statistically significantly better than the standard algorithm for both problems, obtaining 1.28% and 0.69% better results on average respectively. [ABSTRACT FROM AUTHOR]

Noisy intermediate-scale quantum computers are now readily available, motivating many researchers to experiment with Variational Quantum Algorithms. Among them, the Quantum Approximate Optimization Algorithm is one of the most popular one studied by the combinatorial optimization community. In this tutorial, we provide a mathematical description of the class of Variational Quantum Algorithms, assuming no previous knowledge of quantum physics from the readers. We introduce precisely the key aspects of these hybrid algorithms on the quantum side (parametrized quantum circuit) and the classical side (guiding function, optimizer). We devote a particular attention to QAOA, detailing the quantum circuits involved in that algorithm, as well as the properties satisfied by its possible guiding functions. Finally, we discuss the recent literature on QAOA, highlighting several research trends. [ABSTRACT FROM AUTHOR]

Mobility as a Service (MaaS) transforms urban transportation from car ownership to subscription-based models. A key factor for the success of MaaS is accurately predicting users’ Willingness to Pay (WTP) for various subscription packages, enhancing their adoption and satisfaction. This paper employs a “smart predict-then-optimize” framework, where the weekly, annual, and monthly MaaS subscription models are formulated as online, offline, and hybrid online-offline mobility resource allocation problems, respectively. We develop a group method of data handling (GMDH)-driven self-adaptive harmony search (SAHS) algorithm to solve the proposed mobility resource allocation problems effectively. Initially, GMDH-type neural networks predict users’ WTP using their historical travel data, such as travel distance and service time, and socio-demographic characteristics, including inconvenience tolerance and travel delay budget; then these predicted WTP values are fed into the weekly, annual, and monthly mobility resource allocation problems, respectively. Comprehensive numerical experiments based on a simulated dataset demonstrate the robust prediction performance of the GMDH neural network across weekly, monthly, and annual subscription models, as well as the effectiveness of the GMDH-driven SAHS algorithm in managing resource allocation for these models. Our numerical findings highlight that the monthly subscription model strikes an optimal balance, combining the flexibility of the weekly model with the strategic depth of the annual model. This study proposes three distinct MaaS subscription models and a data-driven metaheuristic algorithm to customize MaaS offerings to user needs. [ABSTRACT FROM AUTHOR]

Vaccination is the most effective strategy for battling infectious diseases, breaking the disease transmission chain, and achieving herd immunity. Implementing vaccination for the whole population requires an integrated vaccine supply chain network that considers sustainability and resiliency in the network. For this purpose, in this research, a location-allocation-inventory-distribution problem in the sustainable and resilient vaccine supply chain network, considering mix-and-match vaccine regimens against SARS-CoV-2, is designed. The mix-and-match-based vaccination to reach robust immunization, increase vaccination effectiveness, and more resilience to cope with shortages is applied. In addition, three pillars of sustainability, to minimize distribution network costs, vaccine disposal impact, and greenhouse gas emissions, in terms of economic and environmental, and maximizing job creation, demand satisfaction, and vaccination effectiveness to ensure social sustainability, are developed. Also, scenario-based optimization is presented to meet the inevitable disruptions and breakdowns, such as the supply capacity of suppliers and uncertain amounts of vaccine demand, which depends on the previous type of vaccine injected, and robust stochastic programming is used to handle uncertainties. To solve the proposed model, efficient meta-heuristic algorithms, including the genetic algorithm (GA) and variable neighborhood search (VNS), are applied. In addition, a new hybrid algorithm called H-GAVNS based on the GA and VNS is developed in this research to discover near-optimal results. Finally, a case study of the COVID-19 vaccine in Iran’s environment is presented to confirm the accuracy of the presented model. The outcomes show that uncertainties in the real world and sustainability and resiliency aspects are well managed and responded to by the designed model. [ABSTRACT FROM AUTHOR]

This work studies Heterogeneous Capacitated Clustering Problems (HCCP) which are variations of the Capacitated Clustering Problem (CCP). They are applied to the layout of IT-Teams in software factory. In the real problem, there are workstations placed along the software factory where they are disposed in a pattern the individuals working in the same project form a group that would be geographically close to each other. The groups are heterogeneous in the number of individuals. The general problem discussed here consider a given number of individuals with attributes (weight and coordinates in Euclidean space), and wishes to determine minimum dissimilar clusters constrained to a given maximum capacity for each cluster. The groups are formed to achieve a specific objective of just forming feasible capacitated compact groups and/or also including the position of the group manager. The models here presented and evaluated consider a new generalized version of the heterogeneous capacitated median problem (gHCMP) which extracts the medians from the set of individuals; a new formulation is introduced for the min-max diameter heterogeneous capacitated clustering problem; a new formulation for the minimum group distance heterogeneous capacitated clustering problem; and a new formulation using the HCCCP. We also discuss ways to obtain the best results from the proposed formulations using adequate solvers for each problem; and it is presented a metaheuristic framework for the HCCCP that solves close to optimality all the real instances here tested. We also present the results obtained from the formulations and metaheuristic related to instances that represent real situations about organizing the layout of two Brazilian software factories. The proposed gHCMP model outperform the solutions obtained for the evaluated instances also using the other models. [ABSTRACT FROM AUTHOR]

This work aims to evaluate and propose matheuristics for the Distinguishing String Selection Problem (DSSP) and the Distinguishing Substring Selection Problems (DSSSP). Heuristics based on mathematical programming have already been proposed for String Selection problems in the literature and we are interested in adopting and testing different approaches for those problems. We proposed two matheuristics for both the DSSP and DSSSP by combining the Variable Neighbourhood Search (VNS) metaheuristic and mathematical programming. We compare the linear relaxation, lower bounds found through the branch-and-bound technique, and the matheuristics in three different groups of instances. Computational experiments show that the Basic Core Problem Algorithm (BCPA) finds overall better results for the DSSP. However, it was unable to provide any solutions for some hard DSSSP instances in a reasonable time limit. The two matheuristics based on the VNS have their own niche related to the different groups of instances. They found good solutions for the DSSSP while the BCPA failed. All the obtained data are available in our repository. [ABSTRACT FROM AUTHOR]

This article addresses the one-commodity pickup and delivery traveling salesman problem (1-PDTSP), which is a generalization of the well-known traveling salesman problem. The 1-PDTSP aims to find a Hamiltonian tour in which a set of supply points (pickup locations), demand points (delivery locations) are visited and, the total traveled distance is minimized. We propose a hybrid metaheuristic based on multi-start evolutionary local search and variable neighborhood descent to solve the 1-PDTSP. To test the performance of our algorithm, we solve instances with up to 500 nodes available in the literature and we demonstrate that our approach is able to provide competitive results when comparing to other existing strategies. Since a direct application of the 1-PDTSP arises as the bicycle repositioning problem, we also use our metaheuristic algorithm to solve a set of real-case instances based on EnCicla, the bicycle sharing system in the Aburrá Valley (Antioquia, Colombia). [ABSTRACT FROM AUTHOR]

Transportation of hazardous materials (Hazmat) is one of the most important aspects of transportation planning, which poses several risks to the physical and social environment. These risks have become one of the most important concerns of planners, and as a result, any considerations that can reduce costs and environmental risks are important. Since establishing an intermodal transportation network by different modes of transportation have a key role in transportation planning of hazmat, and lack of proper decision-making may impose high costs and endanger society, in this research, for the first time, the problem of routing and intermodal terminal location is considered simultaneously. This problem is formulated as a mixed-integer nonlinear programming problem for load management of regular and hazmat freights by the railroad intermodal transportation that considers congestion in multiple states and determines the appropriate equipment capacity. In order to validate the proposed model with the objective functions of minimizing the total transportation risk and operating costs, different test problems are solved by the elastic method. Due to the computational complexity of the problem, a metaheuristic algorithm, namely sub-population genetic algorithm (SPGA), is also developed to obtain near-optimal Pareto solutions. In addition, several experiments are provided to compare the SPGA and multi-objective particle swarm optimization, which has recently been presented in the literature. Finally, the obtained results are analyzed, and the efficiency of the algorithm is examined using some performance metrics. [ABSTRACT FROM AUTHOR]

This study proposes a scenario-based mixed-integer programming model to investigate the responsive-resilient inventory-location problem under uncertainty. The proposed model minimizes the total costs and makes decisions about the location, allocation, and inventory problems. The literature showed that simultaneous consideration of responsiveness and resilience measures has been ignored by the researchers. Hence, to fill this gap, this study considers responsiveness and resilience measures in the proposed model. Also, since the uncertainty exists in the nature of the research problem due to changes in the business environment, this paper applies the queuing theory and robust fuzzy stochastic optimization to cope with uncertainty. At first, the existed uncertainty in lead time and demand is tackled by employing the queuing theory, and some performance measures of the system are calculated. Then, the achieved results are incorporated into the fuzzy robust stochastic model. As the problem is an NP-Hard, this study develops several metaheuristic algorithms to solve the proposed model in a reasonable time. Then, the applicability of the proposed model and efficiency of the developed algorithms are shown by several numerical examples. Eventually, several sensitivity analyses are conducted on some important parameters of the model, and useful managerial insights are provided. [ABSTRACT FROM AUTHOR]

This work proposes a binary nonlinear bi-objective optimization model for the problem of planning the sustainable cultivation of crops. The solution to the problem is a planting schedule for crops to be cultivated in predefined plots, in order to minimize the possibility of pest proliferation and maximize the profit of this process. Biological constraints were also considered. Exact methods, based on the nonlinear model and on a linearization of that model were proposed to generate Pareto optimal solutions for the problem of sustainable cultivation, along with a metaheuristic approach for the problem based on a genetic algorithm and on constructive heuristics. The methods were tested using semi-randomly generated instances to simulate real situations. According to the experimental results, the exact methodologies performed favorably for small and medium size instances. The heuristic method was able to potentially determine Pareto optimal solutions of good quality, in a reduced computational time, even for high dimension instances. Therefore, the mathematical models and the methods proposed may support a powerful methodology for this complex decision-making problem. [ABSTRACT FROM AUTHOR]

Selecting a vegetation layer design goes along with determining its future irrigation need. Therefore, it is essential to take a design decision that is minimising the cumulated construction and irrigation costs in a given depreciation period. This contribution showcases a decision support approach using long term weathering time series and soil water balances with example data for turf soccer fields in six German regions. The approach relies on minimising both material and irrigation costs by modifying soil layer design parameters; here the layer thickness and therefore its water retention capacity. E.g. suggested layer thicknesses between 200 and 250 mm for Stuttgart lead over 10–40 year depreciation periods to estimated substrate and water cost savings between 90 and 194% in comparison to a standard substrate layer thickness of 80 mm. For practical applications, the presented theoretical approach needs to be adapted with the usable soil water storage capacity and relationships describing evapotranspiration for given substrate-turfgrass combinations. [ABSTRACT FROM AUTHOR]

Quantum Bridge Analytics relates to methods and systems for hybrid classical-quantum computing, and is devoted to developing tools for bridging classical and quantum computing to gain the benefits of their alliance in the present and enable enhanced practical application of quantum computing in the future. This is the second of a two-part tutorial that surveys key elements of Quantum Bridge Analytics and its applications. Part I focused on the Quadratic Unconstrained Binary Optimization (QUBO) model which is presently the most widely applied optimization model in the quantum computing area, and which unifies a rich variety of combinatorial optimization problems. Part II (the present paper) introduces the domain of QUBO-Plus models that enables a larger range of problems to be handled effectively. After illustrating the scope of these QUBO-Plus models with examples, we give special attention to an important instance of these models called the Asset Exchange Problem (AEP). Solutions to the AEP enable market players to identify exchanges of assets that benefit all participants. Such exchanges are generated by a combination of two optimization technologies for this class of QUBO-Plus models, one grounded in network optimization and one based on a new metaheuristic optimization approach called combinatorial chaining. This combination opens the door to expanding the links to quantum computing applications established by QUBO models through the Quantum Bridge Analytics perspective. We show how the modeling and solution capability for the AEP instance of QUBO-Plus models provides a framework for solving a broad range of problems arising in financial, industrial, scientific, and social settings. [ABSTRACT FROM AUTHOR]

In the context of big data, many scientific communities aim to provide efficient approaches to accommodate large-scale datasets. This is the case of the machine-learning community, and more generally, the artificial intelligence community. The aim of this article is to explain how data mining problems can be considered as combinatorial optimization problems, and how metaheuristics can be used to address them. Four primary data mining tasks are presented: clustering, association rules, classification, and feature selection. This article follows the publication of a book in 2016 concerning this subject (Dhaenens and Jourdan in Metaheuristics for big data, Wiley, Hoboken, 2016), and an article published in 4OR (Dhaenens and Jourdan in 4OR 17 (2):115–139, 2019); additionally, updated references and an analysis of the current trends are presented. [ABSTRACT FROM AUTHOR]

In response to the increasing demand for faster customer service and cost-effective solutions in competitive markets, many companies are exploring strategies and tools to streamline their services. One emerging approach involves the integration of drones with trucks, offering potential benefits such as reduced environmental impact and delivery time. This study focuses on the use of a single truck coordinating with multiple drones for postal package delivery. The drones are transported by the truck, and both vehicles are responsible for carrying out deliveries. To account for weather uncertainties, specifically wind direction and speed affecting drone travel time, a robust optimization model is developed to address the truck-drone routing problem. Additionally, a hybrid metaheuristic algorithm is proposed, combining Adaptive Large Neighborhood Search, Clarke and Wright Saving Algorithm, and Genetic Algorithm. The effectiveness of this algorithm is assessed through numerical experiments, including sensitivity analyses on key problem parameters. The findings demonstrate that the proposed model has practical applications in last-mile delivery services, while the algorithm provides near-optimal solutions within a reasonable timeframe (ALNS reaches the solutions 3500% faster than GAMS for small-sized problems in average). Also the results show that with the 100% increase in average distance between nodes in the network, the service time increases by more than 200%. [ABSTRACT FROM AUTHOR]

Dial-a-ride operations consist of door-to-door transportation systems designed for users with specific needs. Governments and companies offer such services, and due to the flexibility and service level required by the users, it is considerably more costly than public transportation, besides emitting higher levels of CO2\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$_2$$\end{document}. Hence, it is crucial to analyze alternatives to improve operational costs and efficiency without compromising the quality of the service. This paper introduces a variant for the dial-a-ride problem with private fleets and common carriers (DARP-PFCC) integrated with public transportation. Requests can be served by the private fleet, the common carrier, or by integrating them into the public transportation system. In this case, users are collected at the pickup locations and taken to bus stops. After the bus trip, other vehicles serve them from the bus stops to their final destination. Bus schedules must be considered when deciding on the best integration trip. As a methodology, we solve this extension of the DARP-PFCC with a metaheuristic and machine learning hybrid method by combining a biased random key genetic algorithm with the Q-Learning and local search heuristics (BRKGA-QL). This paper also introduces some improvements to this method, particularly with respect to population quality and diversity, thanks to a new mutation method in the classical crossover operator and deterministic rules for the learning process. Computational experiments on a new benchmark data set with realistic data from Québec City show that our BRKGA-QL outperforms its previous version. In addition, we provide a qualitative analysis for the DARP-PFCC, showing that the middle mile integration with public transportation can save up to 20% in operating costs, besides reducing the traveled distances of private vehicles and common carriers. [ABSTRACT FROM AUTHOR]

Virtual voice assistants have become ubiquitous in our modern lives and continue to evolve. With the proliferation of virtual assistants such as Alexa, Cortana, and Siri in households, it is important to go beyond the potential benefits and explore the factors that may lead to user anxiety and trauma. This study examines posts on social media to analyze sentiment toward virtual voice assistants and identify areas of concern that directly impact their use and potential to cause trauma or anxiety. To conduct this study, we collected and processed 13,578 Instagram, Facebook and X (Previously Twitter) posts using sentiment analysis and topic modeling with Python libraries. In addition, K-means clustering and particle swarm optimization (PSO) analysis were performed. The results show that the majority of posts (60.32%) express positive sentiments toward virtual voice assistants, while a significant percentage reflect negative or neutral sentiments. We identified ten major issues of concern related to the use of virtual assistants, including privacy and security, technology addiction and anxiety, cyberbullying, and technology-based trauma therapy. The scope for future research on virtual assistants is broad and includes areas such as privacy, ethical implications, and strategies to improve usability. [ABSTRACT FROM AUTHOR]

In this paper we present a hybrid metaheuristic approach called PVS (Progress and Verify Strategy) for the two-dimensional strip packing problem (2SPP). PVS relies on two procedures: a local search algorithm that delivers satisfying placements of the items on the horizontal axis, and an exact procedure that searches for the positions of the items on the vertical axis. This last one explores all the possibilities, starting with the most promising ones, and can be stopped at any moment. PVS follows a specific anytime strategy which continuously improves the current solution until it is provably optimal or a given time limit is reached. Experimental results show that the method is competitive on moderate-sized instances compared to the best known approaches. [ABSTRACT FROM AUTHOR]

This research proposes a differential evolution-based regression framework for forecasting one day ahead price of Bitcoin. The maximal overlap discrete wavelet transformation first decomposes the original series into granular linear and nonlinear components. We then fit polynomial regression with interaction (PRI) and support vector regression (SVR) on linear and nonlinear components and obtain component-wise projections. The sum of these projections constitutes the final forecast. For accurate predictions, the PRI coefficients and tuning of the hyperparameters of SVR must be precisely estimated. Differential evolution, a metaheuristic optimization technique, helps to achieve these goals. We compare the forecast accuracy of the proposed regression framework with six advanced predictive modeling algorithms- multilayer perceptron neural network, random forest, adaptive neural fuzzy inference system, standalone SVR, multiple adaptive regression spline, and least absolute shrinkage and selection operator. Finally, we perform the numerical experimentation based on—(1) the daily closing prices of Bitcoin for January 10, 2013, to February 23, 2019, and (2) randomly generated surrogate time series through Monte Carlo analysis. The forecast accuracy of the proposed framework is higher than the other predictive modeling algorithms. [ABSTRACT FROM AUTHOR]

In this paper we propose a hybrid metaheuristic based on Particle Swarm Optimization, which we tailor on a portfolio selection problem. To motivate and apply our hybrid metaheuristic, we reformulate the portfolio selection problem as an unconstrained problem, by means of penalty functions in the framework of the exact penalty methods. Our metaheuristic is hybrid as it adaptively updates the penalty parameters of the unconstrained model during the optimization process. In addition, it iteratively refines its solutions to reduce possible infeasibilities. We report also a numerical case study. Our hybrid metaheuristic appears to perform better than the corresponding Particle Swarm Optimization solver with constant penalty parameters. It performs similarly to two corresponding Particle Swarm Optimization solvers with penalty parameters respectively determined by a REVAC-based tuning procedure and an irace-based one, but on average it just needs less than 4% of the computational time requested by the latter procedures. [ABSTRACT FROM AUTHOR]

The global food insecurity, malnourishment and rising world hunger are the major hindrances in accomplishing the zero hunger sustainable development goal by 2030. Due to the continuous increment of wheat production in the past few decades, India received the second rank in the global wheat production after China. However, storage capacity has not been expanded with similar extent. The administrative bodies in India are constructing several capacitated silos in major geographically widespread producing and consuming states to curtail this gap. This paper presents a multi-period single objective mathematical model to support their decision-making process. The model minimizes the silo establishment, transportation, food grain loss, inventory holding, carbon emission, and risk penalty costs. The proposed model is solved using the variant of the particle swarm optimization combined with global, local and near neighbor social structures along with traditional PSO. The solutions obtained through two metaheuristic algorithms are compared with the optimal solutions. The impact of supply, demand and capacity of silos on the model solution is investigated through sensitivity analysis. Finally, some actionable theoretical and managerial implications are discussed after analysing the obtained results. [ABSTRACT FROM AUTHOR]

Modern-day logistics companies require increasingly shorter lead-times in order to cater for the increasing popularity of on-demand services. There is consequently an urgent need for fast scheduling algorithms to provide high quality, real-time implementable solutions. In this work we model a spare part delivery problem for an on-demand logistics company, as a variant of vehicle routing problem. For large delivery networks, the optimisation solution technique of column generation has been employed successfully in a variety of vehicle routing settings and is often used in combination with exact methods for solving problems with a large number of variables. Challenges may arise when the pricing subproblem is difficult to solve in a realistic period due to complex constraints or a large number of variables. The problem may become intractable when the network structure varies daily or is known with less certainty over longer period. In such instances, a high quality heuristic solution may be more preferable than an exact solution with excessive run time. We propose an improved version of column generation approach integrating an efficient genetic algorithm to obtain fast and high-quality solutions for a sustainable spare parts delivery problem. More specifically, we propose to retain the traditional column generation iterative framework, with master problem solved exactly, but with pricing subproblem solved using a metaheuristic. Computational results on a real dataset indicate that this approach yields improved solutions compared to the current best-case business-as-usual costs. It also substantially decreases the computational time; allowing for high-quality, tractable solutions to be obtained in few minutes. We propose to strike a balance between the practical and efficient solution aspects of metaheuristic algorithms, and the exact decomposition and iterative aspect of the column generation solution technique. [ABSTRACT FROM AUTHOR]

This paper proposes a local search method based on a large neighborhood to solve the static version of the problem defined for the Second International Nurse Rostering Competition (INRC-II). The search method, driven by a simulated annealing metaheuristic, uses a combination of neighborhoods that either change the assignments of a nurse or swap the assignments of two compatible nurses, for multiple consecutive days. Computational results on the set of competition instances show that our method has been able to improve on all previous approaches on some datasets, and to get close to the best ones in others. Best solutions, along with the datasets and the validation tool, are made available for future comparison. [ABSTRACT FROM AUTHOR]

The online version of the original article can be found at https://doi.org/10.1007/s10479-022-05005-7Correction to: Annals of Operations Researchhttps://doi.org/10.1007/s10479-022-05005-7This correction is published as several notations were overlooked by typesetter during proofing.Original article has been corrected.Publisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.By Fariba Goodarzian; Vikas Kumar and Peiman GhasemiReported by Author; Author; Author [Extracted from the article]

The sport teams grouping problem (STGP) concerns the assignment of sport teams to round-robin tournaments. The objective is to minimize the total travel distance of the participating teams while simultaneously respecting fairness constraints. The STGP is an NP-Hard combinatorial optimization problem highly relevant in practice. This paper investigates the performance of some complimentary optimization approaches to the STGP. Three integer programming formulations are presented and thoroughly analyzed: two compact formulations and another with an exponential number of variables, for which a branch-and-price algorithm is proposed. Additionally, a meta-heuristic method is applied to quickly generate feasible high-quality solutions for a set of real-world instances. By combining the different approaches' results, solutions within 1.7% of the optimum values were produced for all feasible instances. Additionally, to support further research, the considered STGP instances and corresponding solutions files were shared online. [ABSTRACT FROM AUTHOR]

In this paper, the multi-product facility location problem in a two-stage supply chain is investigated. In this problem, the locations of depots (distribution centres) need to be determined along with their corresponding capacities. Moreover, the product flows from the plants to depots and onto customers must also be optimised. Here, plants have a production limit whereas potential depots have several possible capacity levels to choose from, which are defined as multilevel capacities. Plants must serve customer demands via depots. Two integer linear programming (ILP) models are introduced to solve the problem in order to minimise the fixed costs of opening depots and transportation costs. In the first model, the depot capacity is based on the maximum number of each product that can be stored whereas in the second one, the capacity is determined by the size (volume) of the depot. For large problems, the models are very difficult to solve using an exact method. Therefore, a matheuristic approach based on an aggregation approach and an exact method (ILP) is proposed in order to solve such problems. The methods are assessed using randomly generated data sets and existing data sets taken from the literature. The solutions obtained from the computational study confirm the effectiveness of the proposed matheuristic approach which outperforms the exact method. In addition, a case study arising from the wind energy sector in the UK is presented. [ABSTRACT FROM AUTHOR]

This article considers the heterogeneous fleet vehicle routing problem, as a variant of a well-known transportation problem: the vehicle routing problem. In order to solve this particular routing problem, a variable neighborhood search with a threshold accepting mechanism is developed and implemented. The performance of the algorithm was compared to other algorithms and tested on datasets from the available literature. Computational results show that our proposed algorithm is competitive and generates new best solutions. [ABSTRACT FROM AUTHOR]

Hub disruption result in substantially higher transportation cost and customer dissatisfaction. In this study, first a mathematical model to design hub-and-spoke networks under hub failure is presented. For a fast and inexpensive recovery, the proposed model constructs networks in which every single demand point will have a backup hub to be served from in case of disruption. The problem is formulated as a mixed integer quadratic program in a way that could be linearized without significantly increasing the number of variables. To further ease the model' computational burden, indicator constraints are employed in the linearized model. The resulting formulation produced optimal solutions for small and some medium size instances. To tackle large problems, three efficient particle swarm optimisation-based metaheuristics which incorporate efficient solution representation, short-term memory and special crossover operator are proposed. We present the results for two scenarios relating to high and low probabilities of hub failures and provide managerial insight. The computational results, using problem instances with various sizes taken from CAB and TR datasets, confirm the effectiveness and efficiency of the proposed problem formulation and our new solution techniques. [ABSTRACT FROM AUTHOR]

In this paper, the NP-complete multiple level warehouse layout problem is addressed. The problem consists of assigning items to cells and levels with the objective of minimizing transportation costs. A worm optimization algorithm (WO) is introduced, based on the foraging behaviors of Caenorhabditis elegans (Worms), and its performance was assessed by comparing with a genetic algorithm (GA), ant colony optimization (ACO), and an exact solution (B&B) for small problems. The computational results reflected the superiority of WO in large problems, with a marginally better performance than ACO and GA in smaller ones, while solving the tested problems within a reasonable computational time. Furthermore, WO was able to attain most of the known optimal solutions. [ABSTRACT FROM AUTHOR]

The capacitated green vehicle routing problem is considered in this paper as a new variant of the vehicle routing problem. In this problem, alternative fuel-powered vehicles (AFVs) are used for distributing products. AFVs are assumed to have low fuel tank capacity. Therefore, during their distribution process, AFVs are required to visit alternative fuel stations (AFSs) for refueling. The design of the vehicle routes for AFVs becomes difficult due to the limited loading capacity, the low fuel tank capacity and the scarce availability of AFSs. Two solution methods, the two-phase heuristic algorithm and the meta-heuristic based on ant colony system, are proposed to solve the problem. The numerical experiment is performed on the randomly generated problem instances to evaluate the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

A major problem in communication networks is how to efficiently define the routing paths and to allocate bandwidths in order to satisfy a given collection of transmission requests. In this paper, we study this routing problem by modeling it as a bi-objective single path multicommodity flow problem (SMCFP). Two conflicting objective functions are simultaneously optimized: the delay and reliability of the generated paths. To tackle the complexity of this problem (NP-hard), most of the existing studies proposed approximate methods and metaheuristics as solution approaches. In this paper, we propose to adapt the augmented ϵ-constraint method in order to solve small sized instances of the bi-SMCFP. For large scale problems, we develop three metaheuristics: a multiobjective multi-operator genetic algorithm, an adaptive multiobjective variable neighborhood search and new hybrid method combining the ϵ-constraint with the evolutionary metaheuristic. The idea of the hybridization schema is to first use the metaheuristic to generate a good approximation of the Pareto front, then to enhance the quality of the solutions using the ϵ-constraint method to push them toward the exact Pareto front. An intelligent decomposition scheme is used to reduce the size of the search space before applying the exact method. Computational results demonstrate the efficiency of the proposed hybrid algorithm using instances derived from real network topology and other randomly generated instances. [ABSTRACT FROM AUTHOR]

This paper presents an integrated approach for short-term supply chain management (SCM) at a fast moving consumer goods production plant. The problem is to determine the production quantities, to provide a detailed production schedule, to trigger the relevant express deliveries of raw material, and to manage the distribution. We propose a linear integer model, which integrates all of these decisions within scheduling. To find high quality solutions in a reasonable amount of time, various solution methods are proposed, such as a greedy constructive heuristic, two tabu search metaheuristics, a basic variable neighborhood search and an enhanced one, which uses a variable shaking operator. Experiments on realistic instances show that the latter method is efficient and robust. This paper is a contribution to the SCM literature (indeed, only few references address the integration of short term decisions) and to the general metaheuristics field (as the variable neighborhood search paradigm is extended). [ABSTRACT FROM AUTHOR]

Two efficient neighborhood reduction schemes are proposed for the solution of the p-median problem on the plane. Their integration into a local search significantly reduces the run time with an insignificant deterioration in the quality of the solution. For completeness this fast local search is also embedded into one of the most powerful metaheuristic algorithms, which is a combination of a genetic algorithm and a new improvement algorithm, recently developed for this continuous location problem. Excellent results for instances with up to 1060 demand points with various values of p are reported. Eight new best known solutions for ten instances of a large problem with 3038 demand points and up to 500 facilities are also found. [ABSTRACT FROM AUTHOR]

In this article we introduce a new multiobjective optimizer based on a recently proposed metaheuristic algorithm named Variable Mesh Optimization (VMO). Our proposal (multiobjective VMO, MOVMO) combines typical concepts from the multiobjective optimization arena such as Pareto dominance, density estimation and external archive storage. MOVMO also features a crossover operator between local and global optima as well as dynamic population replacement. We evaluated MOVMO using a suite of four well-known benchmark function families, and against seven state-of-the-art optimizers: NSGA-II, SPEA2, MOCell, AbYSS, SMPSO, MOEA/D and MOEA/D.DRA. The statistically validated results across the additive epsilon, spread and hypervolume quality indicators confirm that MOVMO is indeed a competitive and effective method for multiobjective optimization of numerical spaces. [ABSTRACT FROM AUTHOR]

The NP-hard Bicluster Editing Problem (BEP) consists of editing a minimum number of edges of an input bipartite graph G in order to transform it into a vertex-disjoint union of complete bipartite subgraphs. Editing an edge consists of either adding it to the graph or deleting it from the graph. Applications of the BEP include data mining and analysis of gene expression data. In this work, we generate and analyze random bipartite instances for the BEP to perform empirical tests. A new reduction rule for the problem is proposed, based on the concept of critical independent sets, providing an effective reduction in the size of the instances. We also propose a set of heuristics using concepts of the metaheuristics ILS, VNS, and GRASP, including a constructive heuristic based on analyzing vertex neighborhoods, three local search procedures, and an auxiliary data structure to speed up the local search. Computational experiments show that our heuristics outperform other methods from the literature with respect to both solution quality and computational time. [ABSTRACT FROM AUTHOR]