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Supply Chain (SC) Optimization is a key activity in today's industry with the goal of increasing operational efficiency, reducing costs, and improving customer satisfaction. Traditional optimization methods often struggle to effectively use resources while handling complex and dynamic Supply chain networks. This paper introduces a novel biomimetic metaheuristic algorithm called the Wombat Optimization Algorithm (WOA) for supply chain optimization. This algorithm replicates the natural behaviors observed in wombats living in the wild, particularly focusing on their foraging tactics and evasive maneuvers towards predators. The theory of WOA is described and then mathematically modeled in two phases: (i) exploration based on the simulation of wombat movements during foraging and trying to find food and (ii) exploitation based on simulating wombat movements when diving towards nearby tunnels to defend against its predators. The effectiveness of WOA in addressing optimization challenges is assessed by handling the CEC 2017 test suite across various problem dimensions, including 10, 30, 50, and 100. The findings of the optimization indicate that WOA demonstrates a strong ability to effectively manage exploration and exploitation, and maintains a balance between them throughout the search phase to deliver optimal solutions for optimization problems. A total of twelve well-known metaheuristic algorithms are called upon to test their performance against WOA in the optimization process. The outcomes of the simulations reveal that WOA outperforms the other algorithms, achieving superior results across most benchmark functions and securing the top ranking as the most efficient optimizer. Using a Wilcoxon rank sum test statistical analysis, it has been proven that WOA outperforms other algorithms significantly. WOA is put to the test with twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems to showcase its ability to solve real-world optimization problems. The results of the simulations demonstrate that WOA excels in real-world applications by delivering superior solutions and outperforming its competitors. [ABSTRACT FROM AUTHOR]

Traditional delivery route planning faces challenges in reducing logistics costs and improving customer satisfaction with growing customer demand and complex road traffic, especially in uncertain supply chain environment. To address these challenges, we introduce an innovative two-phase delivery route planning method integrating edge intelligence technology. The novelty of our approach lies in utilizing edge computing devices to monitor real-time changes in road conditions and dynamically adjust delivery routes, thereby providing an effective solution for efficient and flexible logistics. Initially, we construct a mixed-integer programming model that minimizes the total cost under constraints such as customer destinations and time windows. Subsequently, in the cloud-edge collaborative mode, edge computing devices are utilized to collect real-time road conditions and transmit it to the cloud server. The cloud server comprehensively considers customer demand and road condition changes and employs adaptive genetic algorithms and A-star algorithms to adjust the delivery routes dynamically. Finally, comprehensive experiments are conducted to validate the effectiveness of our method. The results demonstrate that our approach can promptly respond to changes in customer demands and road conditions and flexibly plan the optimal delivery routes, thereby significantly reducing overall costs and enhancing customer satisfaction. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop a multi-objective mathematical programming model for the trade-off of time, cost, and quality in the project-scheduling problem (PSP) by taking priorities and resource constraints as well as activity preemption into account. First, a small-sized problem instance that was a sub-project of an oil and gas construction project was used for te validation of the proposed model and algorithm. Subsequently, considering the sensitivity, complexity, and importance of oil and gas projects, the proposed model was implemented in a large-sized oil and gas construction project. Considering the NP-hardness of this problem, the NSGA-II metaheuristic algorithm was used to deal with the time, cost, and quality trade-off problem. Finally, a sensitivity analysis was implemented on the three main parameters of time, cost, and quality to investigate the effects of changes on the results. The findings show that the proposed model is more sensitive to cost changes, so an increase in project costs leads to a drastic change in the values of other objective functions. [ABSTRACT FROM AUTHOR]

This paper introduces the airport shuttle bus scheduling problem (ASBSP) as a new practical scheduling variant. In this problem, a number of identical vehicles that have a specific number of available seats provides transfer service between the airport and the city centre. After making a transfer in one direction, the vehicle can either make a new transfer in the opposite direction depending on the availability and the schedule of the passengers or make an empty return to make a new transfer in the same direction. The vehicles can wait in either location until their next transfer. The passengers have certain time windows for the transfer in relation to their flight times and operational rules to satisfy customer satisfaction. This is a profit-seeking service where transfer requests can also be rejected. The ASBSP aims to prepare a daily schedule for the available vehicles and to assign passengers to these vehicles with the objective of maximising the total profit. This paper presents two alternative mixed integer programming formulations and proposes two valid inequalities to get better bounds. Furthermore, it develops a hybrid metaheuristic that integrates multi-start, simulated annealing and large neighbourhood search for its solution. Extensive computational experiments on real-life benchmark instances have been made to test the performances of the formulations and the hybrid metaheuristic. Furthermore, the impacts of several problem parameters including the number of vehicles, vehicle capacity, transfer fee, transportation time and allowable passenger waiting times on the problem complexity and results have been investigated. [ABSTRACT FROM AUTHOR]

In this era of trying to get more information from data, demand forecasting plays a very important role for companies. Companies implement many of their plans based on these forecasts. However, forecasting demand is not always easy. Demand is divided into many different classes depending on its structure. One of them is the problem of intermittent demand. Estimating intermittent demand is a more difficult problem than estimating series with low variability and no null demand. When working with intermittent demands, either a good demand forecast or a good inventory policy is required. Determining an efficient inventory policy is important in terms of meeting customer demand and customer satisfaction. In this study, an inventory lower bound and upper bound are calculated to balance the inventory cost of intermittent demand and the lost sale cost. For this purpose, 7 different test data with intermittent demand structure were studied. A mathematical model is proposed to calculate the cost with the upper and lower inventories under intermittent demand. A feasible solution could not be obtained with the proposed model, and a fitness function for the relevant model was proposed. This function was run on test data using genetic algorithm (GA) and particle swarm optimization (PSO). The results and solution times of GA and PSO were compared. In this way, the variability of demand and the difference between arrival times of demands were met with minimal cost. [ABSTRACT FROM AUTHOR]

The last years have seen a rapid growth of the takeaway delivery market, which has provided a lot of jobs for deliverymen. However, increasing numbers of takeaway orders and the corresponding pickup and service points have made order selection and path planning a key challenging problem to deliverymen. In this paper, we present a problem integrating order selection and delivery path planning for deliverymen, the objective of which is to maximize the revenue per unit time subject to maximum delivery path length, overdue penalty, reward/penalty for large/small number of orders, and high customer scoring reward. Particularly, we consider uncertain order ready time and customer satisfaction level, which are estimated based on historical habit data of stores and customers using a machine-learning approach. To efficiently solve this problem, we propose a hybrid evolutionary algorithm, which adapts the water wave optimization (WWO) metaheuristic to evolve solutions to the main order selection problem and employs tabu search to route the delivery path for each order selection solution. Experimental results on test instances constructed based on real food delivery application data demonstrate the performance advantages of the proposed algorithm compared to a set of popular metaheuristic optimization algorithms. [ABSTRACT FROM AUTHOR]

User-centered logistics that aim at customer satisfaction are gaining importance because of growing e-commerce and home deliveries. Customer satisfaction can be strongly increased by offering narrow delivery time windows. However, there is a tradeoff for the logistics provider because user-friendly delivery time windows might decrease operational flexibility. Against this background, we study the vehicle routing problem with multiple time windows (VRPMTW) that determines a set of optimal routes such that each customer is visited once within one out of several time windows. We present a large neighborhood search–based metaheuristic for the VRPMTW that contains a dynamic programming component to optimally select a time window for each customer on a route, and we present computationally efficient move descriptors for all search operators. We evaluate the performance of our algorithm on the Belhaiza instance set for the objectives of minimizing traveled distance and duration. For the former objective, we provide new best-known solutions for 9 of 48 instances, and for the latter, we provide new best-known solutions for 13 of 48 instances. Overall, our algorithm provides the best average solution quality over the full benchmark set among all available algorithms. Furthermore, we design new benchmark instances that reflect planning tasks in user-centered last-mile logistics. Based on these, we present managerial studies that show the benefit of our algorithm for practitioners and allow to derive insights on how to offer time windows to customers. We show that offering multiple time windows can be economically beneficial for the logistics service providers and increases customer flexibility simultaneously. [ABSTRACT FROM AUTHOR]

Resource-constrained product general assembly lines with complex processes face significant challenges in delivering orders on time. Accurate and efficient resources allocation of assembly lines remain a critical factor for punctual order delivery, full use of resources and associated customer satisfaction in complex production systems. In order to quickly solve the order-based dynamic resource allocation problem, in this paper a metamodel-based, multi-response optimization method is proposed for a complex product assembly line, which has the characteristics of order-based production, long working time of processes, multiple work area re-entry and restricted operator quantity. Considering the complexity of the assembly line and the uncertainty of orders, the correlation between system performance indicators and resource parameters is investigated. Multiple metamodels are constructed by the Response Surface Methodology to predict and optimize the system performance. The adequacy of the constructed metamodels is verified and validated based on the bootstrap resampling method. Under the condition of ensuring the throughput demand of the assembly line, the desirability function is applied to simultaneously optimize the multi-response, and the resource allocation solution is generated. The method in this paper can be used to rapidly adjust the resource configuration of the assembly line when considering the order changes. [ABSTRACT FROM AUTHOR]

Today, upon the higher internet usage and the Covid-19 pandemic, the use of omni-channel distribution has experienced significant growth. The shopping experience in omni-channel distributions is influenced by the physical environment of the buyer, delivery time, and the cost of production to distribution of the goods which have a significant impact on customer loyalty and customer satisfaction. The lack of comprehensive studies in this field, and the number of constant variables in most of the available studies in the literature, especially uncertainty-laden demand, illustrate the significance of this study. After a related literature review and experts' interviews, based on omni-channel Approach, all important factors influencing time, cost, and customer satisfaction have been included within a multi-objective mathematical model. Thus, defining constraints and decision variables, the objective functions have been solved within two new meta-heuristic algorithms, namely MOGWO and NSGA-II. Besides, these algorithms have been validated using NPS, DM, MID, and SNS indices. Upon comparing the outputs of these two algorithms and inserting 30 numerical instances, it has been shown that the MOGWO method has a stronger Pareto frontier and organized scattering for Pareto solutions. However, averagely, the NSGA-II algorithm produces fewer and more values compared with the first and second objectives, respectively. [ABSTRACT FROM AUTHOR]

Over the years cloud computing has seen significant evolution in terms of improvement in infrastructure and resource provisioning. However the continuous emergence of new applications such as the Internet of Things (IoTs) with thousands of users put a significant load on cloud infrastructure. Load balancing of resource allocation in cloud-oriented IoT is a critical factor that has a significant impact on the smooth operation of cloud services and customer satisfaction. Several load balancing strategies for cloud environment have been proposed in the past. However the existing approaches mostly consider only a few parameters and ignore many critical factors having a pivotal role in load balancing leading to less optimized resource allocation. Load balancing is a challenging problem and therefore the research community has recently focused towards employing machine learning-based metaheuristic approaches for load balancing in the cloud. In this paper we propose a metaheuristics-based scheme Data Format Classification using Support Vector Machine (DFC-SVM), to deal with the load balancing problem. The proposed scheme aims to reduce the online load balancing complexity by offline-based pre-classification of raw-data from diverse sources (such as IoT) into different formats e.g. text images media etc. SVM is utilized to classify “n” types of data formats featuring audio video text digital images and maps etc. A one-to-many classification approach has been developed so that data formats from the cloud are initially classified into their respective classes and assigned to virtual machines through the proposed modified version of Particle Swarm Optimization (PSO) which schedules the data of a particular class efficiently. The experimental results compared with the baselines have shown a significant improvement in the performance of the proposed approach. Overall an average of 94% classification accuracy is achieved along with 11.82% less energy 16% less response time and 16.08% fewer SLA violations are observed. [ABSTRACT FROM AUTHOR]

Background: In this study, the food delivery problem faced by a food company is discussed. There are seven different regions where the company serves food and a certain number of customers in each region. The time of requesting food for each customer varies according to the shift situation. This type of problem is referred to as a vehicle routing problem with time windows in the literature and the main aim of the study is to minimize the total travel distance of the vehicles. The second aim is to determine which vehicle will follow which route in the region by using the least amount of vehicle according to the desired mealtime. Methods: In this study, genetic algorithm methodology is used for the solution of the problem. Metaheuristic algorithms are used for problems that contain multiple combinations and cannot be solved in a reasonable time. Thus in this study, a solution to this problem in a reasonable time is obtained by using the genetic algorithm method. The advantage of this method is to find the most appropriate solution by trying possible solutions with a certain number of populations. Results: Different population sizes are considered in the study. 1000 iterations are made for each population. According to the genetic algorithm results, the best result is obtained in the lowest population size. The total distance has been shortened by about 14% with this method. Besides, the number of vehicles in each region and which vehicle will serve to whom has also been determined. This study, which is a real-life application, has provided serious profitability to the food company even from this region alone. Besides, there have been improvements at different rates in each of the seven regions. Customers' ability to receive service at any time has maximized customer satisfaction and increased the ability to work in the long term. Conclusions: The method and results used in the study were positive for the food company. However, the metaheuristic algorithm used in this study does not guarantee an optimal result. Therefore, mathematical models or simulation models can be considered in terms of future studies. Besides, in addition to the time windows problem, the pickup problem can also be taken into account and different solution proposals can be developed. [ABSTRACT FROM AUTHOR]

• Enhances energy optimization by categorizing devices, considering satisfaction, refining approaches. • Integrates user satisfaction, grid loads, and energy costs for a robust optimization framework. • Proposes WOA for appliance scheduling, offering practical and effective near-optimal solutions. The increasing proliferation of energy-intensive devices in urban smart homes presents a pressing challenge to the stability of electrical systems, manifesting as the potential for peak load issues, unexpected disruptions, and a consequential decline in economic returns. This research is dedicated to the meticulous optimization of energy consumption, achieved through the implementation of intelligent scheduling protocols for household devices, all while factoring in their distinct characteristics and user preferences. The primary objective of this paper is to introduce a holistic engineering framework tailored to analyze the intricate dynamics of household energy consumption behaviors, with a particular emphasis on the community-level context prevalent in smart urban environments. In addressing the inherent intricacy and nonlinearity inherent in multi-objective challenges within this domain, our proposal revolves around the deployment of a specialized whale optimization algorithm. This algorithm offers a powerful tool to efficiently navigate the complexities of optimization problems, yielding solutions that approach near-optimality. Empirical findings stemming from our experimentation robustly affirm the efficacy of our proposed algorithm. They indicate substantial reductions in energy expenditure, heightened levels of customer satisfaction, and a more equitably balanced load distribution throughout the primary grid. These outcomes, in turn, contribute significantly to the overarching enhancement of global power system performance, all the while aligning harmoniously with the socio-economic considerations central to urban energy transition endeavors. [ABSTRACT FROM AUTHOR]

In software incremental development methodology, the product develops in several releases. In each release, one set of the requirements is suggested for development. The development team must select a subset of the proposed requirements for development in the next release such that by consideration the limitation of the problem provides the highest satisfaction to the customers and the lowest cost to the company. This problem is known as the next release problem. In complex projects where the number of requirements is high, development teams cannot choose an optimized subset of the requirements by traditional methods, so an intelligent algorithm is required to help in the decision-making process. The main contributions of this study are fivefold: (1) The customer satisfaction and the cost of every requirement are determined by use of fuzzy numbers because of the possible changing of the customers' priorities during the product development period; (2) An improved approximate approach is suggested for summing fuzzy numbers of different kinds, (3) A new metaheuristic algorithm namely the Binary Artificial Algae Algorithm is used for choosing an optimized subset of requirements, (4) Experiments performed on two fuzzy datasets confirm that the resulted subsets from the suggested algorithm are free of human mistake and can be a great guidance to development teams in making decisions. [ABSTRACT FROM AUTHOR]

In incremental software development approaches, the product is developed in various releases. In each release, a set of requirements is proposed for the development. Usually, due to lack of funds, lack of time and dependency between requirements, there is no possibility to develop all the required requirements. There are two conflicting objectives for choosing an optimal subset of the requirements: increasing customer satisfaction and reducing development costs. This problem is known as the next release problem (NRP) and is categorized as an NP-hard problem. Unlike the standard version of the NRP, we formulate this problem as a restricted multi-objective optimization problem. There exist metaheuristic algorithms for solving this problem performed as serials. In this paper, we introduce a parallel algorithm based on the master–slave model in order to improve the quality of the solutions. Based on the criteria of multi-objective problems, the quality of the obtained solution is compared with several metaheuristic algorithms. Two scenarios and two different datasets are used for experiments. Results indicate that the proposed method in the first scenario would highly improve the quality of solutions. Moreover, the method reduces execution time significantly through improvement in the quality of the solution in the second scenario. [ABSTRACT FROM AUTHOR]

This article proposes a new multi-objective model for a lot-sizing and scheduling problem (LSSP) under uncertainty. The model considers economic aspects, reliability and quality inspection, and customer satisfaction and behaviour in designing the LSSP. A utility function is applied to increase customer satisfaction and maximize responsiveness. In addition, the adaptive neuro-fuzzy inference system is employed to address uncertain demands. The presented model uses a fuzzy c-means clustering method to assess customers' behaviour. A hybrid multi-objective metaheuristic algorithm, comprised of the multi-objective red deer algorithm and parallel non-dominated sorting genetic algorithm-II, is applied to solve the model efficiently. The results obtained from experiments on several problem instances show the superiority of the proposed metaheuristic algorithm over other algorithms, such as multi-objective particle swarm optimization, used in this article. Finally, a real case study is presented to show the applicability of the model, and several analyses are implemented to extend managerial insights. [ABSTRACT FROM AUTHOR]

Mobile commerce (m-commerce) contributes to increasing the popularity of electronic commerce (e-commerce), allowing anybody to sell or buy goods using a mobile device or tablet anywhere and at any time. As demand for e-commerce increases tremendously, the pressure on delivery companies increases to organise their transportation plans to achieve profits and customer satisfaction. One important planning problem in this domain is the multi-vehicle profitable pickup and delivery problem (MVPPDP), where a selected set of pickup and delivery customers need to be served within certain allowed trip time. In this paper, we proposed hybrid clustering algorithms with the greedy randomised adaptive search procedure (GRASP) to construct an initial solution for the MVPPDP. Our approaches first cluster the search space in order to reduce its dimensionality, then use GRASP to build routes for each cluster. We compared our results with state-of-the-art construction heuristics that have been used to construct initial solutions to this problem. Experimental results show that our proposed algorithms contribute to achieving excellent performance in terms of both quality of solutions and processing time. [ABSTRACT FROM AUTHOR]

Optimization and prediction of customer satisfaction in the shipping industry impacts immensely upon strategic planning and consequently on the targeted market share of a corporation. In shipping industry, accurate measures of customer satisfaction are usually very cumbersome to elaborate. In this work we aim to reveal the most effective optimization methods, employing artificial intelligence approaches such as rough sets, neural networks, advanced classification methods as well as multi-criteria analysis under a comparative framework vis-à-vis their forecasting performance. [ABSTRACT FROM AUTHOR]

Customer satisfaction and focusing on the most profitable customers are the key elements to achieving a sustainable competitive advantage in the current business environment. The mixed-model assembly line (MMAL) is an efficient type of manufacturing system that has been used increasingly over recent years to satisfy diverse customer demands. Product sequencing in an MMAL system has a huge effect on the performance of the firm in meeting customer demand and, therefore, on customer satisfaction. This study aims to address a sequencing problem with respect to recency, frequency and monetary factors. A two-stage framework is developed to achieve this goal. First, the prioritized orders based on the preference ranking organization method for enrichment of evaluations (PROMETHEE) are categorized into high-priority and normal-priority orders. Then, a bi-objective mathematical programming model is developed to determine the optimum sequence of products to minimize the total cost, as well as to maximize levels of customer satisfaction. After validation of the proposed model, owing to the NP-hard nature of this problem, two multi-objective metaheuristics are applied to tackle the second stage of this study. Several test problems are presented to evaluate the performance of these algorithms. The results show the superiority of one solution approach over the other. [ABSTRACT FROM AUTHOR]

We develop a two-phase approach to solving the capacitated routing problem (CVRP) with stochastic demand. A nonlinear chance-constrained optimization model is solved to determine delivery quantities, and a tabu search metaheuristic is used to determine vehicle routes. The goal of this research is to assure that a logistics company would satisfy the demands of customers with a high probability, while minimizing the overall transportation cost. We introduce the concept of premium customers, who are guaranteed a higher level of service. We show that our chance-constrained method has some strategic advantages over the CVRP with recourse approach. We examine the possibility of the logistics company charging customers selectively with an additional service fee to assure a high level of service. Moreover, we provide managerial insight on when the best time is to pay for the premium membership. We present computational results on commonly studied small to large-scale CVRP instances. A simulation study is conducted to explore the performance of the proposed chance-constrained approach using the CVRP with recourse. We conclude that our chance-constrained CVRP model could serve a logistics company well when resource costs and service guarantees are of concern. [ABSTRACT FROM AUTHOR]

There are many reasons for the growing interest in developing new product projects for any firm. The most embossed reason is surviving in a highly competitive industry which the customer tastes are changing rapidly. A well-managed supply chain network can provide the most profit for firms due to considering new product development. Along with profit, customer satisfaction and production of new products are goals which lead to a more efficient supply chain. As new products appear in the market, the old products could become obsolete, and then phased out. The most important parameter in a supply chain which considers new and developed products is the time that developed and new products are introduced and old products are phased out. With consideration of the factors noted above, this study proposes to design a tri-objective multi-echelon multi-product multi-period supply chain model, which incorporates product development and new product production and their effects on supply chain configuration. The supply chain under consideration is assumed to consist of suppliers, manufacturers, distributors and customer groups. In terms of overcoming NP-hardness of the proposed model and in order to solve the complicated problem, a non-dominated sorting genetic algorithm is employed. As there is no benchmark available in the literature, the non-dominated ranking genetic algorithm is developed to validate the results obtained and some test problems are provided to show the applicability of the proposed methodology and evaluate the performance of the algorithms. [ABSTRACT FROM AUTHOR]

Preserving the integrity, safety and freshness of perishables can improve customer satisfaction and enhance delivery firm's competitiveness. Since perishables will lose their nutritional value and freshness during delivery, carrying out efficient transportation is of vital importance. This paper studies a supplier that delivers perishable goods to customers in a multiple-time-period planning horizon while simultaneously considering transportation cost and customer satisfaction. The supplier must decide specific vehicle routes of the delivery task. In addition, time windows, which have not been addressed in previous bi-objective multiple-time-period studies, are considered in this situation. The perishable delivery problem is modeled as a bi-objective vehicle routing problem with multiple time periods, aiming to minimize the transportation costs and maximize customer satisfaction by minimizing the loss of perishable freshness. A combined VNS and SA metaheuristic algorithm based on the ε -constraint method with several improvements is developed to solve this problem. A Pareto frontier reflects the trade-off between transportation costs and customer satisfaction. The computational results on a series of randomly generated instances validate the model and the effectiveness of the proposed algorithm, and some managerial insights are proposed from sensitivity analysis. [ABSTRACT FROM AUTHOR]

• A three-level optimization framework for the optimal scheduling of grid-connected and Islanded microgrids to mitigate power losses and maximize loadability. • Introduces a stochastic mixed-integer non-linear problem with the objective of enhancing microgrid loadability and optimizing daily operating costs. • The uncertain parameters related to renewable power generation, load demand, and power loss, voltage limit have been considered as constrains in formulation. • The impact of optimal placement of distributed generation sources and demand-side management on microgrid loadability is investigated. • Demonstrates the efficacy of the proposed framework on enhanced version of the IEEE-33 bus and IEEE 69-bus distribution network based microgrid test system for evaluation. • Simultaneously the customer satisfaction index of the proposed test system is evaluated for different seasonal load profiles. • A comparative analysis of the simulation results with popular eight different metaheuristic algorithms is also illustrated and operating cost per day for both modes of operation is presented. This research work presents the tri-level optimization framework for the optimal scheduling of grid-connected and autonomous microgrids to diminish power losses and maximize loadability. Since the network's voltage profile depends on the loading level, the flexible load shaping-based demand-side management strategy is incorporated to investigate its impact on microgrid loadability. With the consideration of uncertain parameters related to renewable power generation, load demand, and power loss, voltage limit constraints, the resultant problem is formulated as a stochastic mixed-integer non-linear problem to enhance microgrid loadability and optimize daily operating costs. The interdependency of demand side management program and microgrid loadability is investigated. The seasonal load profiles covering the weekend and weekday loads in winter, summer, and spring/fall seasons are examined in this research work. The enhanced versions of the distribution networks IEEE-33 and IEEE-69 based microgrid test systems are chosen to evaluate the proposed framework in both off-grid and autonomous modes of operation. Simultaneously, the overall customer satisfaction index is evaluated and improved according to the seasonal load profiles winter weekday, winter-weekend, summer-weekday, summer-weekend, spring-weekday, and spring-weekend by 8.68%, 7.97%, 16.7%, 19.62%, 17.14%, 20.50% respectively. The recently reported Whale Optimization Algorithm is adopted to solve the proposed optimization problem, and the obtained simulation results are validated by comparing them with popular metaheuristic algorithms. The computational burden on the utility is reduced for optimal scheduling of grid-integrated microgrid to extract maximum power by maintaining network voltage profile. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new mathematical model for Capacitated Inventory Routing Problem (CIRP), which considers fresh delivery of perishable products to the customers' location; otherwise, a reduction in products' demand may occur. Therefore, we attempt to plan delivering the process of products at the right time to avoid extra inventory, causing expiration of products. This leads to the dissatisfaction of customers which causes decrease in their demands. Moreover, we address global concerns about environmental issues and impacts of transportation on environment, so we aim to minimize fuel costs to reduce costs and pollution related to the CO2 emission. CIRPs is an NP-hard problem which needs a long run time for large-scale problems. To tackle this type of problem in an acceptable time, we introduce a hybrid algorithm combining Genetic Algorithm (GA) and Simulated Annealing (SA) Algorithm. Some experiments are conducted and the results show the efficiency of our proposed algorithm. [ABSTRACT FROM AUTHOR]

We present a general framework for solving a real-world multimodal home-healthcare scheduling (MHS) problem from a major Austrian home-healthcare provider. The goal of MHS is to assign home-care staff to customers and determine efficient multimodal tours while considering staff and customer satisfaction. Our approach is designed to be as problem-independent as possible, such that the resulting methods can be easily adapted to MHS setups of other home-healthcare providers. We chose a two-stage approach: in the first stage, we generate initial solutions either via constraint programming techniques or by a random procedure. During the second stage, the initial solutions are (iteratively) improved by applying one of four metaheuristics: variable neighborhood search, a memetic algorithm, scatter search and a simulated annealing hyper-heuristic. An extensive computational comparison shows that the approach is capable of solving real-world instances in reasonable time and produces valid solutions within only a few seconds. [ABSTRACT FROM AUTHOR]

This study proposed a novel hybrid metaheuristic that hybridized the particle swarm optimization, simulated annealing and variable neighborhood search to solve the hybrid flowshop scheduling with sequence-dependent setup times (SDST). To address the realistic assumptions of the proposed problem, three additional traits were added to the scheduling problem. These include SDST, position-dependent learning effects (LEs), and the consideration of tardiness together with earliness penalties as objective function. According to the best of our knowledge, this problem has never been investigated in the hybrid flowshop. Considering position-dependent LEs, it is assumed that the learning process reflects a decrease in the process and setup times as a function of the number of repetitions of the production of a same operation in a same stage because in many realistic situations, the more time you practice, the better LE you obtain. To evaluate the performance of the suggested method, the hybrid simulated annealing metaheuristic (HSA) presented recently is investigated for comparison purposes and computational experiments are performed on standard test problems. Results show that the suggested method performs better than the HSA for various test problems. [ABSTRACT FROM AUTHOR]

The classic Traveling Salesman Problem (TSP) only considers the costs involved in the routes and does not differentiate products or customers. Logistic companies face conflict between operational costs, customers with different categories of products, and customer satisfaction, which is directly related to delivery time. This paper presents a new mathematical model for a TSP with variable costs and priority prizes, taking into account the customer's product and preference values. This problem is denoted as the Multicommodity Traveling Salesman Problem with Priority Prizes (MTSPPP). Two versions of the Biased Random-Key Genetic Algorithm (BRKGA) are proposed to solve medium and large instances of the MTSPPP. Computational tests were performed, using modified instances based on classical TSP instances. The proposed methods have proved to be efficient in solving the MTSPPP. [ABSTRACT FROM AUTHOR]