A novel cross-docking EOQ-based model to optimize a multi-item multi-supplier multi-retailer inventory management system

Nowadays, the retail industry accounts for a large share of the world’s economy. Cross-docking is one of the most effective and smart inventory management systems used by retail companies to respond to demands efficiently. In this study, the aim is to develop a novel cross-docking EOQ-based model for a retail company. By considering a two-stage inventory procurement process, a new multi-item, multi-supplier, multi-retailer EOQ model is developed to minimize the total inventory costs. In the first stage, the required items are received from suppliers and are held in a central warehouse. In the second stage, these items are delivered to several retail stores. The total inventory costs include four main parts, i.e., holding costs at the central warehouse, holding costs at the retail stores, fixed ordering costs from the suppliers, and fixed ordering costs from the central warehouse. The optimal inventory policy is obtained by analyzing extrema, and a numerical example is used to confirm the efficiency of the proposed model. Based on the obtained results, it is evident that the proposed model produces the optimal policy for the cross-docking system. Furthermore, the model enables managers to analyze the effects of key factors on the costs of the system. Based on the obtained results, the annual demand of each retailer, the ordering cost by the central warehouse, the ordering cost at each retail store, and the holding cost at each retail store have a direct impact on the optimal cost. Furthermore, it is not possible to describe the effects of the holding cost at the central warehouse on the optimal cost of the system generally

Multi-project Optimal Scheduling Considering Reliability and Quality Within the Construction Supply Chain: A Hybrid Genetic Algorithm

Although the construction industry, especially because of its relationship with other economic sectors, is one of the most important sectors that plays a key role in a country's economic growth, the construction supply chain has been considered less attention. Therefore, construction supply chain network design is of great importance for not only the companies but also governments. Thus, presenting an original mixed integer linear programming model, this paper introduces an optimal framework for a multi-project multi-resource multi-supplier construction supply chain network design for large construction companies with a decentralized procurement strategy. The main objective is to design a reliable supply chain model based on the quality of projects under the certain predefined budget, considering the entire supply chain as a single entity. Using a bi-objective approach to formulate the chain and the Lp-metric approach to solve the problem, make it possible to obtain a single-objective structural framework to reliability-quality trade-off consideration. To solve the problem in small and medium scales, GAMS software is employed, and a hybrid algorithm based on Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm is developed to solve the large-scaled problem. The results show the capability of the model to attain optimal size of the chain as well as the quality-reliability trade-off considering a pre-specified budget. And, to the best of authors knowledge this is the first to obtain such a structured integrated framework in the construction supply chain

DATA REPLICATION IN DISTRIBUTED SYSTEMS USING OLYMPIAD OPTIMIZATION ALGORITHM

Achieving timely access to data objects is a major challenge in big distributed systems like the Internet of Things (IoT) platforms. Therefore, minimizing the data read and write operation time in distributed systems has elevated to a higher priority for system designers and mechanical engineers. Replication and the appropriate placement of the replicas on the most accessible data servers is a problem of NP-complete optimization. The key objectives of the current study are minimizing the data access time, reducing the quantity of replicas, and improving the data availability. The current paper employs the Olympiad Optimization Algorithm (OOA) as a novel population-based and discrete heuristic algorithm to solve the replica placement problem which is also applicable to other fields such as mechanical and computer engineering design problems. This discrete algorithm was inspired by the learning process of student groups who are preparing for the Olympiad exams. The proposed algorithm, which is divide-and-conquer-based with local and global search strategies, was used in solving the replica placement problem in a standard simulated distributed system. The 'European Union Database' (EUData) was employed to evaluate the proposed algorithm, which contains 28 nodes as servers and a network architecture in the format of a complete graph. It was revealed that the proposed technique reduces data access time by 39% with around six replicas, which is vastly superior to the earlier methods. Moreover, the standard deviation of the results of the algorithm's different executions is approximately 0.0062, which is lower than the other techniques' standard deviation within the same experiments

Industry 5.0 implications for inclusive sustainable manufacturing: an evidence-knowledge-based strategic roadmap

Despite the hype surrounding Industry 5.0 and its importance for sustainability, the micro-mechanisms through which this agenda can lead to socio-environmental values are largely understudied. The present study strived to address this knowledge gap by developing a strategic roadmap that outlines how Industry 5.0 can boost sustainable manufacturing. The study first conducted a content-centric literature review and identified 12 functions through which Industry 5.0 can inclusively boost sustainable manufacturing. The study further developed a strategic roadmap that identified the complex contextual relationships among the functions and explained how they should be synergistically leveraged to maximize their contribution to sustainability. Results reveal that value network integration, sustainable technology governance, sustainable business model innovation, and sustainable skill development are the most driver and tangible implications of Industry 5.0 for sustainable manufacturing. Alternatively, renewable integration and manufacturing resilience are among the most dependent and hard-to-reach sustainable functions of Industry 5.0, and their materialization requires major strategic collaboration among stakeholders. The strategic roadmap outlines how Industry 5.0 stakeholders can leverage the technological and functional constituents of this agenda to promote sustainable manufacturing inclusively

Using a Process Approach to Pandemic Planning: A Case Study

The purpose of this article was to demonstrate the difference between a pandemic plan’s textual prescription and its effective processing using graphical notation. Before creating a case study of the Business Process Model and Notation (BPMN) of the Czech Republic’s pandemic plan, we conducted a systematic review of the process approach in pandemic planning and a document analysis of relevant public documents. The authors emphasized the opacity of hundreds of pages of text records in an explanatory case study and demonstrated the effectiveness of the process approach in reengineering and improving the response to such a critical situation. A potential extension to the automation and involvement of SMART technologies or process optimization through process mining techniques is presented as a future research topic

Sport Performance Analysis with a Focus on Racket Sports: A Review

Athletes, both professional and amateur, are always looking for ways to improve their performance. With the introduction and increasing availability of modern technologies and smart devices arose the need to measure and analyze performance, but likewise, the use of these innovations as a competitive advantage also arose. Scientific publications reflect the wide range of available approaches and technologies, as well as the growing interest in various sports. As a result, we concentrated on a systematic review of publications that presented performance analysis tools and methods in all sports, with a final focus on racket sports. Clarivate Analytics’ Web of Science (WoS) and Elsevier Inc.’s SCOPUS databases were searched for 1147 studies that conducted performance analysis and sports research and were published in English. The data in the systematic review are current, up until 18 May 2021. A general review was performed on 759 items, and then 65 racket sports publications were thoroughly scrutinized. We concentrated on performance data, data collection and analysis tools, performance analysis methods, and software. We also talked about performance prediction. In performance research, we have identified specific approaches for specific sports as well as key countries. We are also considering expanding performance analysis in to E-sports in the future

Fuzzy Mathematical Programming and Self-Adaptive Artificial Fish Swarm Algorithm for Just-in-Time Energy-Aware Flow Shop Scheduling Problem With Outsourcing Option

Flow shop scheduling (FSS) problem constitutes a major part of production planning in every manufacturing organization. It aims at determining the optimal sequence of processing jobs on available machines within a given customer order. In this article, a novel biobjective mixed-integer linear programming (MILP) model is proposed for FSS with an outsourcing option and just-in-time delivery in order to simultaneously minimize the total cost of the production system and total energy consumption. Each job is considered to be either scheduled in-house or to be outsourced to one of the possible subcontractors. To efficiently solve the problem, a hybrid technique is proposed based on an interactive fuzzy solution technique and a self-adaptive artificial fish swarm algorithm (SAAFSA). The proposedmodel is treated as a single objectiveMILP using a multiobjective fuzzy mathematical programming technique based on the e-constraint, and SAAFSA is then applied to provide Pareto optimal solutions. The obtained results demonstrate the usefulness of the suggested methodology and high efficiency of the algorithm in comparison with CPLEX solver in different problem instances. Finally, a sensitivity analysis is implemented on the main parameters to study the behavior of the objectives according to the real-world conditions

An Integration of Neural Network and Shuffled Frog-Leaping Algorithm for CNC Machining Monitoring

This paper addresses Acoustic Emission (AE) from Computer Numerical Control (CNC) machining operations. Experimental measurements are performed on the CNC lathe sensors to provide the power consumption data. To this end, a hybrid methodology based on the integration of an Artificial Neural Network (ANN) and a Shuffled Frog-Leaping Algorithm (SFLA) is applied to the data resulting from these measurements for data fusion from the sensors which is called SFLA-ANN. The initial weights of ANN are selected using SFLA. The goal is to assess the potency of the signal periodic component among these sensors. The efficiency of the proposed SFLA-ANN method is analyzed compared to hybrid methodologies of Simulated Annealing (SA) algorithm and ANN (SA-ANN) and Genetic Algorithm (GA) and ANN (GA-ANN)

Sustainable fuzzy multi-trip location-routing problem for medical waste management during the COVID-19 outbreak

The performance of waste management system has been recently interrupted and encountered a very serious situation due to the epidemic outbreak of the novel Coronavirus (COVID-19). To this end, the handling of infectious medical waste has been particularly more vital than ever. Therefore, in this study, a novel mixed-integer linear programming (MILP) model is developed to formulate the sustainable multi-trip location-routing problem with time windows (MTLRP-TW) for medical waste management in the COVID-19 pandemic. The objectives are to concurrently minimize the total traveling time, total violation from time windows/service priorities and total infection/environmental risk imposed on the population around disposal sites. Here, the time windows play a key role to define the priority of services for hospitals with a different range of risks. To deal with the uncertainly, a fuzzy chance-constrained programming approach is applied to the proposed model. A real case study is investigated in Sari city of Iran to test the performance and applicability of the proposed model. Accordingly, the optimal planning of vehicles is determined to be implemented by the municipality, which lakes 19.733 h to complete the processes of collection, transportation and disposal. Finally, several sensitivity analyses are performed to examine the behavior of the objective functions against the changes of controllable parameters and evaluate optimal policies and suggest useful managerial insights under different conditions. (C) 2020 Elsevier B.V. All rights reserved