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2 pages, 174 KiB

Open AccessEditorial

Special Issue: “The Planning and Scheduling of Manufacturing Systems”

by Paolo Renna

Appl. Sci. 2022, 12(22), 11713; https://doi.org/10.3390/app122211713 - 18 Nov 2022

Cited by 2 | Viewed by 869

Abstract

The “Fourth Industrial Revolution” (alternatively known as “Industry 4 [...] Full article

(This article belongs to the Special Issue Planning and Scheduling of Manufacturing Systems)

Research

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27 pages, 2896 KiB

Open AccessArticle

Impact of Unreliable Subcontracting on Production and Maintenance Planning Considering Quality Decline

by Héctor Rivera-Gómez, Joselito Medina-Marin, Francisca Santana-Robles, Oscar Montaño-Arango, Irving Barragán-Vite and Gabriel Cisneros-Flores

Appl. Sci. 2022, 12(7), 3379; https://doi.org/10.3390/app12073379 - 26 Mar 2022

Cited by 5 | Viewed by 1680

Abstract

Manufacturing systems face several disturbances during production, such as sudden failures, defects, and unreliable subcontractors that reduce their production capacity. Currently, subcontracting represents an efficient alternative to support production decisions. The novelty of the study was the development of a new integrated model [...] Read more.

Manufacturing systems face several disturbances during production, such as sudden failures, defects, and unreliable subcontractors that reduce their production capacity. Currently, subcontracting represents an efficient alternative to support production decisions. The novelty of the study was the development of a new integrated model that properly coordinates production, subcontracting, and maintenances strategies in the context of stochastic uncertainty, quality deterioration, and random subcontracting availability. Such a set of characteristics has not been addressed before in the literature. A simulation–optimization approach was proposed to address such a stochastic model. A numerical case study was performed as an illustration of the approach and a comprehensive sensitivity analysis was performed to analyze the impact of several costs. Furthermore, the effect of the availability of the subcontractor and the producer was analyzed. The main finding of the study showed that the integrated model led to significant economic cost savings compared to other approaches that address such policies in isolation. The results also indicated that quality deterioration had a strong impact on the subcontracting rate and that the proposed joint control policy adequately coordinated these three key functions. The level of subcontracting participation was directly defined by its availability and the subcontracting cost. Full article

(This article belongs to the Special Issue Planning and Scheduling of Manufacturing Systems)

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14 pages, 617 KiB

Open AccessArticle

Production Planning Problem of a Two-Level Supply Chain with Production-Time-Dependent Products

by Jun-Hee Han, Ju-Yong Lee and Bongjoo Jeong

Appl. Sci. 2021, 11(20), 9687; https://doi.org/10.3390/app11209687 - 17 Oct 2021

Cited by 5 | Viewed by 1972

Abstract

This study considers a production planning problem with a two-level supply chain consisting of multiple suppliers and a manufacturing plant. Each supplier that consists of multiple production lines can produce several types of semi-finished products, and the manufacturing plant produces the finished products [...] Read more.

This study considers a production planning problem with a two-level supply chain consisting of multiple suppliers and a manufacturing plant. Each supplier that consists of multiple production lines can produce several types of semi-finished products, and the manufacturing plant produces the finished products using the semi-finished products from the suppliers to meet dynamic demands. In the suppliers, different types of semi-finished products can be produced in the same batch, and products in the same batch can only be started simultaneously (at the same time) even if they complete at different times. The purpose of this study is to determine the selection of suppliers and their production lines for the production of semi-finished products for each period of a given planning horizon, and the objective is to minimize total costs associated with the supply chain during the whole planning horizon. To solve this problem, we suggest a mixed integer programming model and a heuristic algorithm. To verify performance of the algorithm, a series of tests are conducted on a number of instances, and the results are presented. Full article

(This article belongs to the Special Issue Planning and Scheduling of Manufacturing Systems)

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38 pages, 590 KiB

Open AccessArticle

Model Predictive Control for Flexible Job Shop Scheduling in Industry 4.0

by Philipp Wenzelburger and Frank Allgöwer

Appl. Sci. 2021, 11(17), 8145; https://doi.org/10.3390/app11178145 - 02 Sep 2021

Cited by 11 | Viewed by 2155

Abstract

In the context of Industry 4.0, flexible manufacturing systems play an important role. They are designed to provide the possibility to adapt the production process by reacting to changes and enabling customer specific products. The versatility of such manufacturing systems, however, also needs [...] Read more.

In the context of Industry 4.0, flexible manufacturing systems play an important role. They are designed to provide the possibility to adapt the production process by reacting to changes and enabling customer specific products. The versatility of such manufacturing systems, however, also needs to be exploited by advanced control strategies. To this end, we present a novel scheduling scheme that is able to flexibly react to changes in the manufacturing system by means of Model Predictive Control (MPC). To introduce flexibility from the start, the initial scheduling problem, which is very general and covers a variety of special cases, is formulated in a modular way. This modularity is then preserved during an automatic transformation into a Petri Net formulation, which constitutes the basis for the two presented MPC schemes. We prove that both schemes are guaranteed to complete the production problem in closed loop when reasonable assumptions are fulfilled. The advantages of the presented control framework for flexible manufacturing systems are that it covers a wide variety of scheduling problems, that it is able to exploit the available flexibility of the manufacturing system, and that it allows to prove the completion of the production problem. Full article

(This article belongs to the Special Issue Planning and Scheduling of Manufacturing Systems)

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16 pages, 943 KiB

Open AccessArticle

Multi-Objective Production Scheduling of Perishable Products in Agri-Food Industry

by Fatma Tangour, Maroua Nouiri and Rosa Abbou

Appl. Sci. 2021, 11(15), 6962; https://doi.org/10.3390/app11156962 - 28 Jul 2021

Cited by 3 | Viewed by 1706

Abstract

This paper deals with dynamic industry scheduling problem in agri-food production. The decision-making study in this paper is articulated around the management of perishable products under constrained resources. The scheduling of logistics operations is considered at the operational level. Two metaheuristics are proposed [...] Read more.

This paper deals with dynamic industry scheduling problem in agri-food production. The decision-making study in this paper is articulated around the management of perishable products under constrained resources. The scheduling of logistics operations is considered at the operational level. Two metaheuristics are proposed to solve dynamic scheduling under perturbations. The uncertainty sources considered in this study are the expiration date of product components and production delays. The proposed Genetic Algorithm (GA) and the Ant Colony Optimization Algorithm (ACO) take into consideration two objective functions: minimizing the makespan and reducing the number of perishable products. The algorithms are tested on a flow-shop agri-food system. Full article

(This article belongs to the Special Issue Planning and Scheduling of Manufacturing Systems)

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16 pages, 1228 KiB

Open AccessArticle

Development of a Steel Plant Rescheduling Algorithm Based on Batch Decisions

by David García-Menéndez, Henar Morán-Palacios, Eliseo P. Vergara-González and Vicente Rodríguez-Montequín

Appl. Sci. 2021, 11(15), 6765; https://doi.org/10.3390/app11156765 - 23 Jul 2021

Cited by 2 | Viewed by 2418

Abstract

During the steelmaking and continuous casting process in the steel plant, it is common to encounter delays that affect initial planning. Furthermore, continuous casting machines themselves can lose much of their performance in the event of closure of one or more of their [...] Read more.

During the steelmaking and continuous casting process in the steel plant, it is common to encounter delays that affect initial planning. Furthermore, continuous casting machines themselves can lose much of their performance in the event of closure of one or more of their casting strands. The situation that is generated, far from being a planning problem, forces consideration of a vision of cost analysis when deciding changes in the planned sequences. This study presents a detailed analysis of the different circumstances that can cause strands closures or sequence breaks, their consequences and the different options available to minimize losses. Finally, an algorithm capable of analyzing the workshop situation and making the most favorable decision to optimize production is proposed, analyzed and compared with the efficiency of the original scheduling method in a real steel plant. The new algorithm proves its efficiency in all situations, with a time-saving average of 26.41 min per decision taken. Full article

(This article belongs to the Special Issue Planning and Scheduling of Manufacturing Systems)

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16 pages, 1480 KiB

Open AccessArticle

A Data-Driven Robust Scheduling Method Integrating Particle Swarm Optimization Algorithm with Kernel-Based Estimation

by Peng Zheng, Peng Zhang, Ming Wang and Jie Zhang

Appl. Sci. 2021, 11(12), 5333; https://doi.org/10.3390/app11125333 - 08 Jun 2021

Cited by 3 | Viewed by 2053

Abstract

The assembly job shop scheduling problem (AJSSP) widely exists in the production process of many complex products. Robust scheduling methods aim to optimize the given criteria for improving the robustness of the schedule by organizing the assembly processes under uncertainty. In this work, [...] Read more.

The assembly job shop scheduling problem (AJSSP) widely exists in the production process of many complex products. Robust scheduling methods aim to optimize the given criteria for improving the robustness of the schedule by organizing the assembly processes under uncertainty. In this work, the uncertainty of process setup time and processing time is considered, and a framework for the robust scheduling of AJSSP using data-driven methodologies is proposed. The framework consists of obtaining the distribution information of uncertain parameters based on historical data and using a particle swarm optimization (PSO) algorithm to optimize the production schedule. Firstly, the kernel density estimation method is used to estimate the probability density function of uncertain parameters. To control the robustness of the schedule, the concept of confidence level is introduced when determining the range of uncertain parameters. Secondly, an interval scheduling method constructed using interval theory and a customized discrete PSO algorithm are used to optimize the AJSSP with assembly constraints. Several computational experiments are introduced to illustrate the proposed method, and these were proven effective in improving the performance and robustness of the schedule. Full article

(This article belongs to the Special Issue Planning and Scheduling of Manufacturing Systems)

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28 pages, 3019 KiB

Open AccessArticle

An Integrated Model of Production, Maintenance, and Quality Control with Statistical Process Control Chart of a Supply Chain

by Zied Hajej, Aime C. Nyoungue, Aminu S. Abubakar and Kammoun Mohamed Ali

Appl. Sci. 2021, 11(9), 4192; https://doi.org/10.3390/app11094192 - 05 May 2021

Cited by 15 | Viewed by 3565

Abstract

This article investigates integrated maintenance, production, and product quality control policy for a supply chain consisting of a single machine producing only one type of product, a main storage warehouse, and multi-purchases warehouses. The variation of the production rate and its use over [...] Read more.

This article investigates integrated maintenance, production, and product quality control policy for a supply chain consisting of a single machine producing only one type of product, a main storage warehouse, and multi-purchases warehouses. The variation of the production rate and its use over time impact the manufacturing system’s degradation degree. Hence, the machine is subject to a random failure that directly affects the quality of the products. The goal of this study is to establish an optimal production and delivery planning with inventory management considering the production, holding, and delivery costs, and then an appropriate maintenance strategy, considering the influence of the production rate on the system degradation. Also, we provide a quality control policy to reduce the proportion of non-compliant products by using the statistical process control chart to forecast production. Forecasting the production aims to satisfy the varying demands during a finite horizon under service and quality levels. Numerical examples are presented to justify the effectiveness of the suggested strategy. Full article

(This article belongs to the Special Issue Planning and Scheduling of Manufacturing Systems)

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17 pages, 2646 KiB

Open AccessArticle

Multimodal Optimization of Permutation Flow-Shop Scheduling Problems Using a Clustering-Genetic-Algorithm-Based Approach

by Pan Zou, Manik Rajora and Steven Y. Liang

Appl. Sci. 2021, 11(8), 3388; https://doi.org/10.3390/app11083388 - 09 Apr 2021

Cited by 12 | Viewed by 2189

Abstract

Though many techniques were proposed for the optimization of Permutation Flow-Shop Scheduling Problem (PFSSP), current techniques only provide a single optimal schedule. Therefore, a new algorithm is proposed, by combining the k-means clustering algorithm and Genetic Algorithm (GA), for the multimodal optimization of [...] Read more.

Though many techniques were proposed for the optimization of Permutation Flow-Shop Scheduling Problem (PFSSP), current techniques only provide a single optimal schedule. Therefore, a new algorithm is proposed, by combining the k-means clustering algorithm and Genetic Algorithm (GA), for the multimodal optimization of PFSSP. In the proposed algorithm, the k-means clustering algorithm is first utilized to cluster the individuals of every generation into different clusters, based on some machine-sequence-related features. Next, the operators of GA are applied to the individuals belonging to the same cluster to find multiple global optima. Unlike standard GA, where all individuals belong to the same cluster, in the proposed approach, these are split into multiple clusters and the crossover operator is restricted to the individuals belonging to the same cluster. Doing so, enabled the proposed algorithm to potentially find multiple global optima in each cluster. The performance of the proposed algorithm was evaluated by its application to the multimodal optimization of benchmark PFSSP. The results obtained were also compared to the results obtained when other niching techniques such as clearing method, sharing fitness, and a hybrid of the proposed approach and sharing fitness were used. The results of the case studies showed that the proposed algorithm was able to consistently converge to better optimal solutions than the other three algorithms. Full article

(This article belongs to the Special Issue Planning and Scheduling of Manufacturing Systems)

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Review

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28 pages, 2394 KiB

Open AccessEditor’s ChoiceReview

A Literature Review of Energy Efficiency and Sustainability in Manufacturing Systems

by Paolo Renna and Sergio Materi

Appl. Sci. 2021, 11(16), 7366; https://doi.org/10.3390/app11167366 - 10 Aug 2021

Cited by 46 | Viewed by 11384

Abstract

Climate change mitigation, the goal of reducing CO₂ emissions, more stringent regulations and the increment in energy costs have pushed researchers to study energy efficiency and renewable energy sources. Manufacturing systems are large energy consumers and are thus responsible for huge greenhouse [...] Read more.

Climate change mitigation, the goal of reducing CO₂ emissions, more stringent regulations and the increment in energy costs have pushed researchers to study energy efficiency and renewable energy sources. Manufacturing systems are large energy consumers and are thus responsible for huge greenhouse gas emissions; for these reasons, many studies have focused on this topic recently. This review aims to summarize the most important papers on energy efficiency and renewable energy sources in manufacturing systems published in the last fifteen years. The works are grouped together, considering the system typology, i.e., manufacturing system subclasses (single machine, flow shop, job shop, etc.) or the assembly line, the developed energy-saving policies and the implementation of the renewable energy sources in the studied contexts. A description of the main approaches used in the analyzed papers was discussed. The conclusion reports the main findings of the review and suggests future directions for the researchers in the integration of renewable energy in the manufacturing systems consumption models. Full article

(This article belongs to the Special Issue Planning and Scheduling of Manufacturing Systems)

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