A Metaheuristic Based Approach for the Customer-Centric Perishable Food Distribution Problem

The CNRST has awarded H. El Raoui an excellence scholarship. D. Pelta acknowledges support from projects TIN2017-86647-P (Spanish Ministry of Economy, Industry, and Competitiveness. Including FEDER funds) and PID2020-112754GB-I00 (Spanish Ministry of Science and Innovation).High transportation costs and poor quality of service are common vulnerabilities in various logistics networks, especially in food distribution. Here we propose a many-objective Customercentric Perishable Food Distribution Problem that focuses on the cost, the quality of the product, and the service level improvement by considering not only time windows but also the customers’ target time and their priority. Recognizing the difficulty of solving such model, we propose a General Variable Neighborhood Search (GVNS) metaheuristic based approach that allows to efficiently solve a subproblem while allowing us to obtain a set of solutions. These solutions are evaluated over some non-optimized criteria and then ranked using an a posteriori approach that requires minimal information about decision maker preferences. The computational results show (a) GVNS achieved same quality solutions as an exact solver (CPLEX) in the subproblem; (b) GVNS can generate a wide number of candidate solutions, and (c) the use of the a posteriori approach makes easy to generate different decision maker profiles which in turn allows to obtain different rankings of the solutions.CNRSTSpanish Ministry of Economy, Industry, and Competitiveness TIN2017-86647-PEuropean Commission TIN2017-86647-PSpanish Government PID2020-112754GB-I0

The Role of Metaheuristics as Solutions Generators

Optimization problems are ubiquitous nowadays. Many times, their corresponding computational models necessarily leave out of consideration several characteristics and features of the real world, so trying to obtain the optimum solution can not be enough for a problem solving point of view. The aim of this paper is to illustrate the role of metaheuristics as solutions’ generators in a basic problem solving framework. Metaheuristics become relevant in two modes: firstly because every run (in the case of population based techniques) allows to obtain a set of potentially good solutions, and secondly, if a reference solution is available, one can set up a new optimization problem that allows to obtain solutions with similar quality in the objectives space but maximally different structure in the design space. Once a set of solutions is obtained, an example of an a posteriori analysis to rank them according with decision maker’s preferences is shown. All the problem solving framework steps, emphasizing the role of metaheuristics are illustrated with a dynamic version of the tourist trip design problem (for the first mode), and with a perishable food distribution problem (for the second one). These examples clearly show the benefits of the problem solving framework proposed. The potential role of the symmetry concept is also exploredProject PID2020-112754GB-I00 from MCINAEI/10.13039/ 501100011033

A knowledge graph approach for state-of-the-art implementation of industrial factory movement tracking system

Digital sensing technologies are essential for realizing Industry 4.0, as they enhance productivity, assist with real-time decision-making, and provide flexibility and agility in manufacturing factories. However, implementing these technologies can be a significant challenge due to the need to consider various factors in manufacturing factories, such as heterogeneous equipment, fragmented knowledge, customization requirements, multiple alternative technologies, and the substantial costs involved in the trial-and-error process. A Knowledge Graph (KG) approach is proposed to streamline the implementation of the factory movement tracking system. The KG approach utilizes a knowledge representation reference model that integrates manufacturing objective, activity, resource, environment, factory movement, data, infrastructure, and decision support system. This reference model aids in classifying key phrases extracted from research abstracts and establishing knowledge relationships among them. A synthesized KG, created by analyzing thirty research abstracts, has correctly answered search queries about implementing the factory movement tracking system. This approach establishes a pathway for developing a software system to support movement tracking implementation through automatic interpretation, reasoning, and suggestions

Coupling soft computing, simulation and optimization in supply chain applications : review and taxonomy

Supply chain networks are typical examples of complex systems. Thereby, making decisions in such systems remains a very hard issue. To assist decision makers in formulating the appropriate strategies, robust tools are needed. Pure optimization models are not appropriate for several reasons. First, an optimization model cannot capture the dynamic behavior of a complex system. Furthermore, most common practical problems are very constrained to be modeled as simple tractable models. To fill in the gap, hybrid optimization/simulation techniques have been applied to improve the decision-making process. In this paper we explore the near-full spectrum of optimization methods and simulation techniques. A review and taxonomy were performed to give an overview of the broad field of optimization/simulation approaches applied to solve supply chain problems. Since the possibilities of coupling them are numerous, we launch a discussion and analysis that aims at determining the appropriate framework for the studied problem depending on its characteristics. Our study may serve as a guide for researchers and practitioners to select the suitable technique to solve a problem and/or to identify the promising issues to be further explored

Data-driven Discovery of Manufacturing Processes and Performance from Worker Localisation

Digital sensing technologies are essential for realizing Industry 4.0, as they enhance productivity, assist with real-time decision-making, and provide flexibility and agility in manufacturing factories. However, implementing these technologies can be a significant challenge due to the need to consider various factors in manufacturing factories, such as heterogeneous equipment, fragmented knowledge, customization requirements, multiple alternative technologies, and the substantial costs involved in the trial-and-error process. A Knowledge Graph (KG) approach is proposed to streamline the implementation of the factory movement tracking system. The KG approach utilizes a knowledge representation reference model that integrates manufacturing objective, activity, resource, environment, factory movement, data, infrastructure, and decision support system. This reference model aids in classifying key phrases extracted from research abstracts and establishing knowledge relationships among them. A synthesized KG, created by analyzing thirty research abstracts, has correctly answered search queries about implementing the factory movement tracking system. This approach establishes a pathway for developing a software system to support movement tracking implementation through automatic interpretation, reasoning, and suggestions

Agent based simulation of workers' behaviours around hazard areas in manufacturing sites

Rewards for risk taking behaviour by workers (if accidents do not occur) can be realised in the form of increased productivity or worker idle time. However, frequent unsafe behaviours of workers inevitably results in accidents and an associated loss in productivity. Workers' behaviour towards safety is influenced by management, who can encourage or discourage risk taking behaviour. In this paper, we explore the relationship between the perceived reward by individual workers who expose themselves to hazards and a management response in the form of inspections to monitor and address inappropriate behaviours. We conduct this study by developing an Agent Based Simulation Model, where workers are required to learn paths within a factory exposed to hazardous areas, with inspectors randomly moving around the factory to correct inappropriate behaviour if noticed. We assume workers are maximising their anticipated reward as they learn routes through the factory. This agent based model is used to explore the impact of inspection frequency and reward perception (i.e. parameters which can be influenced by management) on the number of workplace accident. The results demonstrated that the proposed model is a valuable tool to assist the management in predicting the potential safety improvement from safety management practices focusing on safety inspections, and changing workers perceptions

Towards more accurate iris recognition system by using hybrid approach for feature extraction along with classifier

Iris recognition become one of the most accurate and reliable steadfast human biometric recognition system of the decad. This paper presents an accurate framework for iris recognition system using hybrid algorithm in preprocess and feature extraction section. The proposed model for iris recognition with significant feature extraction was divided into three main levels. First level is having pre-processing steps which are necessary for the desired tasks. Our model deploys on three types of datasets such as UBIRIS, CASIA, and MMU and gets optimal results for performing activity. At last, perform matching process with decision based classifier for iris recognition with acceptance or rejection rates. Experimental based results provide for analysis according to the false receipt rate and false refusal amount. In the third level, the error rate will be checked along with some statistical measures for final optimal results. Constructed on the outcome the planned method provided the most efficient effect as compared to the rest of the approach

Hierarchical ensemble deep learning for data-driven lead time prediction

This paper focuses on data-driven prediction of lead times for product orders based on the real-time production state captured at the arrival instants of orders in make-to-order production environments. In particular, we consider a sophisticated manufacturing system where a large number of measurements about the production state are available (e.g. sensor data). In response to this complex prediction challenge, we present a novel ensemble hierarchical deep learning algorithm comprised of three deep neural networks. One of these networks acts as a generalist, while the other two function as specialists for different products. Hierarchical ensemble methods have previously been successfully utilised in addressing various multi-class classification problems. In this paper, we extend this approach to encompass the regression task of lead time prediction. We demonstrate the suitability of our algorithm in two separate case studies. The first case study uses one of the largest manufacturing datasets available, the Bosch production line dataset. The second case study uses synthetic datasets generated from a reliability-based model of a multi-product, make-to-order production system, inspired by the Bosch production line. In both case studies, we demonstrate that our algorithm provides high-accuracy predictions and significantly outperforms selected benchmarks including the single deep neural network. Moreover, we find that prediction accuracy is significantly higher in the synthetic dataset, which suggests that there is complexity (i.e. subtle interactions) in industrial manufacturing processes that are not easily reproduced in artificial models

Design of a serious game for safety in manufacturing industry using hybrid simulation modelling : towards eliciting risk preferences

Conventional methods used to elicit risk-taking preferences have demonstrated significant disparities with real-world behaviours, compromising the validity of the data collected. Serious gaming (SG) provides a high potential to bridge this gap. This paper presents a serious game as a novel approach to elicit risk-preference in an industrial manufacturing context, focusing on the game-design and implementation using hybrid simulation modelling. The developed SG serves as a tool for conducting incentivized experiments aimed at assessing human behaviour towards risk, to inform policy recommendations. The game incorporates two influential factors in shaping risk-taking behaviour in a manufacturing environment, namely the social learning and production pressure and use a variety of game mechanics to promote the players' motivation and engagement. A usability study was conducted with 10 participants using the Usability Scale System (SUS) to identify problems in the usability of the game. Results have shown that our game has a good usability

Using Worker Position Data for Human-Driven Decision Support in Labour-Intensive Manufacturing

This paper provides a novel methodology for human-driven decision support for capacity allocation in labour-intensive manufacturing systems. In such systems (where output depends solely on human labour) it is essential that any changes aimed at improving productivity are informed by the workers’ actual working practices, rather than attempting to implement strategies based on an idealised representation of a theoretical production process. This paper reports how worker position data (obtained by localisation sensors) can be used as input to process mining algorithms to generate a data-driven process model to understand how manufacturing tasks are actually performed and how this model can then be used to build a discrete event simulation to investigate the performance of capacity allocation adjustments made to the original working practice observed in the data. The proposed methodology is demonstrated using a real-world dataset generated by a manual assembly line involving six workers performing six manufacturing tasks. It is found that, with small capacity adjustments, one can reduce the completion time by 7% (i.e., without requiring any additional workers), and with an additional worker a 16% reduction in completion time can be achieved by increasing the capacity of the bottleneck tasks which take relatively longer time than others