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3 pages, 189 KiB

Open AccessEditorial

New Trends in Production and Operations Management

by Panagiotis Tsarouhas

Appl. Sci. 2023, 13(16), 9071; https://doi.org/10.3390/app13169071 - 08 Aug 2023

Viewed by 6013

Abstract

Operations Management includes the management of all company activities that support the input–output cycle [...] Full article

(This article belongs to the Special Issue New Trends in Production and Operations Management)

Research

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15 pages, 929 KiB

Open AccessArticle

Design Optimization of Stacked Pallet Load Units

by Piotr Sawicki and Hanna Sawicka

Appl. Sci. 2023, 13(4), 2153; https://doi.org/10.3390/app13042153 - 07 Feb 2023

Cited by 3 | Viewed by 1632

Abstract

The article deals with the problem of building stacked pallet load units consisting of at least two stackable pallet load units. Moreover, this article concerns the part of the flow of goods in distribution networks that is prepared at the place of initial [...] Read more.

The article deals with the problem of building stacked pallet load units consisting of at least two stackable pallet load units. Moreover, this article concerns the part of the flow of goods in distribution networks that is prepared at the place of initial assembly in the form of palletized loading units designed for the final receiver. Such a unit does not exceed the limits of permissible weight or height. The article proposes a single-criteria binary programming model in which the goal is to minimize the pallet spaces required to accommodate the constructed units. In addition to the classical parameters of acceptable weight and height of the units, the constraints also take into account the fragility of the goods placed on each unit, filling the top layer of each unit, and its height homogeneity. The model developed was verified on a test dataset, and the savings from the use of optimum construction of the stacked palletized cargo units were demonstrated through the conducted experiments. Full article

(This article belongs to the Special Issue New Trends in Production and Operations Management)

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17 pages, 1524 KiB

Open AccessArticle

Forecasting Steel Production in the World—Assessments Based on Shallow and Deep Neural Networks

by Balduíno César Mateus, Mateus Mendes, José Torres Farinha, António J. Marques Cardoso, Rui Assis and Lucélio M. da Costa

Appl. Sci. 2023, 13(1), 178; https://doi.org/10.3390/app13010178 - 23 Dec 2022

Cited by 4 | Viewed by 2114

Abstract

Forecasting algorithms have been used to support decision making in companies, and it is necessary to apply approaches that facilitate a good forecasting result. The present paper describes assessments based on a combination of different neural network models, tested to forecast steel production [...] Read more.

Forecasting algorithms have been used to support decision making in companies, and it is necessary to apply approaches that facilitate a good forecasting result. The present paper describes assessments based on a combination of different neural network models, tested to forecast steel production in the world. The main goal is to find the best machine learning model that fits the steel production data in the world to make a forecast for a nine-year period. The study is important for understanding the behavior of the models and sensitivity to hyperparameters of convolutional LSTM and GRU recurrent neural networks. The results show that for long-term prediction, the GRU model is easier to train and provides better results. The article contributes to the validation of the use of other variables that are correlated with the steel production variable, thus increasing forecast accuracy. Full article

(This article belongs to the Special Issue New Trends in Production and Operations Management)

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17 pages, 436 KiB

Open AccessArticle

A Stochastic Queueing Model for the Pricing of Time-Sensitive Services in the Demand-Sharing Alliance

by Jianpei Wen

Appl. Sci. 2022, 12(23), 12121; https://doi.org/10.3390/app122312121 - 27 Nov 2022

Cited by 2 | Viewed by 860

Abstract

The medical alliance has developed rapidly in recent years. This kind of alliance established by multiple hospitals can alleviate the imbalance of medical resources. We investigate the benefit of demand sharing between a hospital with large demand (HD) and another hospital with large [...] Read more.

The medical alliance has developed rapidly in recent years. This kind of alliance established by multiple hospitals can alleviate the imbalance of medical resources. We investigate the benefit of demand sharing between a hospital with large demand (HD) and another hospital with large supply (HS). Two hospitals are modeled as queueing systems with finite service rates. Both hospitals set prices to maximize the revenues by serving their time-sensitive patients. We adopt a cooperative game theoretic framework to determine when demand sharing is beneficial. We also propose an optimal allocation of this benefit through a commission fee, which makes the alliance stable. We find that demand sharing may not be beneficial even if HS has a low capacity utilization. Demand sharing becomes beneficial for both hospitals only when the idle service capacity of HS exceeds a threshold, which depends on the potential demand rate of the HS and the unit waiting cost of hospitals. Furthermore, if the idle service capacity of HS is smaller than another threshold, which depends on the potential demand of the two hospitals and the service capacity of HD, then the benefit of demand sharing will be independent of the service capacity and potential demand of HD. We also examine the effect of system parameters on revenue gains due to demand sharing. Full article

(This article belongs to the Special Issue New Trends in Production and Operations Management)

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12 pages, 9931 KiB

Open AccessArticle

Data Matrix Based Low Cost Autonomous Detection of Medicine Packages

by José Lima, Cláudia Rocha, Luísa Rocha and Paulo Costa

Appl. Sci. 2022, 12(19), 9866; https://doi.org/10.3390/app12199866 - 30 Sep 2022

Cited by 2 | Viewed by 1570

Abstract

Counterfeit medicine is still a crucial problem for healthcare systems, having a huge impact in worldwide health and economy. Medicine packages can be traced from the moment of their production until they are delivered to the costumers through the use of Data Matrix [...] Read more.

Counterfeit medicine is still a crucial problem for healthcare systems, having a huge impact in worldwide health and economy. Medicine packages can be traced from the moment of their production until they are delivered to the costumers through the use of Data Matrix codes, unique identifiers that can validate their authenticity. Currently, many practitioners at hospital pharmacies have to manually scan such codes one by one, a very repetitive and burdensome task. In this paper, a system which can simultaneously scan multiple Data Matrix codes and autonomously introduce them into an authentication database is proposed for the Hospital Pharmacy of the Centro Hospitalar de Vila Nova de Gaia/Espinho, E.P.E. Relevant features are its low cost and its seamless integration in their infrastructure. The results of the experiments were encouraging, and with upgrades such as real-time feedback of the code’s validation and increased robustness of the hardware system, it is expected that the system can be used as a real support to the pharmacists. Full article

(This article belongs to the Special Issue New Trends in Production and Operations Management)

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24 pages, 2948 KiB

Open AccessArticle

Risk Treatment for Energy-Oriented Production Plans through the Selection, Classification, and Integration of Suitable Measures

by Stefan Roth, Mirjam Huber, Johannes Schilp and Gunther Reinhart

Appl. Sci. 2022, 12(13), 6410; https://doi.org/10.3390/app12136410 - 23 Jun 2022

Cited by 3 | Viewed by 1439

Abstract

With rising electricity prices, industries are trying to exploit opportunities to reduce electricity costs. Adapting to fluctuating energy prices offers the possibility to save electricity costs without reducing the performance of the production system. Production planning and control play key roles in the [...] Read more.

With rising electricity prices, industries are trying to exploit opportunities to reduce electricity costs. Adapting to fluctuating energy prices offers the possibility to save electricity costs without reducing the performance of the production system. Production planning and control play key roles in the implementation of the adjustments. By taking into account the price forecasts for the electricity markets in addition to machine utilization, work in process, and throughput time, an energy-oriented production plan is set up. The electrical energy is procured based on this plan and the associated load profile. Deviations from the forecast and the purchased amount of electricity lead to high penalties, as they can destabilize the energy system. For manufacturing companies, this means that machine failures and other unexpected events must be dealt with in a structured manner to avoid these penalty costs. This paper presents an approach to selecting, classifying, and integrating suitable measures from existing risk treatment paths into the production schedule. The selection of measures is based on a hybrid multi-criteria decision-making method in which the three relevant criteria, namely, cost, energy flexibility, and risk reduction, are weighted by applying both an analytic hierarchy process and entropy, and they are then prioritized according to multi-attribute utility theory. In the following, the subdivision into preventive and reactive measures is made in order to choose between the modification of the original plan or the creation of backup plans. With the help of mathematical optimization, the measures are integrated into the production schedule by minimizing the cost of balancing energy. The approach was implemented in MATLAB^® and validated using a case study in the foundry industry. Full article

(This article belongs to the Special Issue New Trends in Production and Operations Management)

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14 pages, 2814 KiB

Open AccessArticle

Unfair and Risky? Profit Allocation in Closed-Loop Supply Chains by Cooperative Game Approaches

by Ting Zeng and Tianjian Yang

Appl. Sci. 2022, 12(12), 6245; https://doi.org/10.3390/app12126245 - 19 Jun 2022

Cited by 2 | Viewed by 1476

Abstract

Behavioral factors (i.e., risk aversion and fairness concern) are considered for profit allocation in a closed-loop supply chain. This paper studies a two-echelon closed-loop supply chain (CLSC) consisting of a risk-neutral manufacturer, a risk-averse fairness-neutral retailer, and a risk-neutral retailer having fairness concerns. [...] Read more.

Behavioral factors (i.e., risk aversion and fairness concern) are considered for profit allocation in a closed-loop supply chain. This paper studies a two-echelon closed-loop supply chain (CLSC) consisting of a risk-neutral manufacturer, a risk-averse fairness-neutral retailer, and a risk-neutral retailer having fairness concerns. Cooperative game analysis is used to characterize equilibriums under five scenarios: a centralized, a decentralized and three partially allied models. Analytical results confirm that even when factoring in retailers’ risk aversion and fairness concern, the centralized model still outperforms decentralized. This paper makes a numerical study on the effects of risk aversion and fairness concern on profit distribution under these five models. It reveals that the impact of the risk aversion parameter and fairness concern parameter is dynamic, not always positive or negative. These research results provide helpful insights for CLSC managers to find out available choices and feasible ways to achieve fair profit allocations. Full article

(This article belongs to the Special Issue New Trends in Production and Operations Management)

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15 pages, 833 KiB

Open AccessArticle

Optimization Model for Selective Harvest Planning Performed by Humans and Robots

by Ben Harel, Yael Edan and Yael Perlman

Appl. Sci. 2022, 12(5), 2507; https://doi.org/10.3390/app12052507 - 28 Feb 2022

Cited by 4 | Viewed by 2114

Abstract

This paper addresses the formulation of an individual fruit harvest decision as a nonlinear programming problem to maximize profit, while considering selective harvesting based on fruit maturity. A model for the operational level decision was developed and includes four features: time window constraints, [...] Read more.

This paper addresses the formulation of an individual fruit harvest decision as a nonlinear programming problem to maximize profit, while considering selective harvesting based on fruit maturity. A model for the operational level decision was developed and includes four features: time window constraints, resource limitations, yield perishability, and uncertainty. The model implementation was demonstrated through numerical studies that compared decisions for different types of worker and analyzed different robotic harvester capabilities for a case study of sweet pepper harvesting. The results show the influence of the maturity classification capabilities of the robot on its output, as well as the improvement in cycle times needed to reach the economic feasibility of a robotic harvester. Full article

(This article belongs to the Special Issue New Trends in Production and Operations Management)

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23 pages, 1267 KiB

Open AccessArticle

Human–Machine Systems Reliability: A Series–Parallel Approach for Evaluation and Improvement in the Field of Machine Tools

by Rosa Ma Amaya-Toral, Manuel R. Piña-Monarrez, Rosa María Reyes-Martínez, Jorge de la Riva-Rodríguez, Eduardo Rafael Poblano-Ojinaga, Jaime Sánchez-Leal and Karina Cecilia Arredondo-Soto

Appl. Sci. 2022, 12(3), 1681; https://doi.org/10.3390/app12031681 - 06 Feb 2022

Cited by 9 | Viewed by 2097

Abstract

Machine workshops generate high scrap rates, causing non-compliance with timely delivery and high production costs. Due to their natural characteristics of a low volume, high-mix production batches, and serial and parallel configurations, generally the causes of their failure are not well documented. Thus, [...] Read more.

Machine workshops generate high scrap rates, causing non-compliance with timely delivery and high production costs. Due to their natural characteristics of a low volume, high-mix production batches, and serial and parallel configurations, generally the causes of their failure are not well documented. Thus, to reduce the scrap rate, and evaluate and improve their reliability, their system characteristics must be considered. Based on them, our proposed methodology allows us to evaluate the system, subsystem, and component–subsystem relationship by using either the Weibull and/or the exponential distribution. The strategy to improve the system performance includes reliability tools, expert interviews, cluster analysis, and root-cause analysis. In the application case, the failure sources were found to be mechanical and human errors. The component maintenance/setup, institutional conditions/attitude, and subsystem process/operation were the machine factors that presented the lowest reliability indices. The improved activities were monitored based on the Weibull β and η parameters that affect the system reliability. Finally, by using a life–effort analysis, and the method of comparative analysis of two sequential periods, we identified the causes that generated a change in the Weibull parameters. The contribution of this methodology lies in the grouping of the tools in the proposed application context. Full article

(This article belongs to the Special Issue New Trends in Production and Operations Management)

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21 pages, 1053 KiB

Open AccessArticle

Characterization and Design for Last Mile Logistics: A Review of the State of the Art and Future Directions

by Hyeong Suk Na, Sang Jin Kweon and Kijung Park

Appl. Sci. 2022, 12(1), 118; https://doi.org/10.3390/app12010118 - 23 Dec 2021

Cited by 7 | Viewed by 4739

Abstract

One of the most challenging problems in last mile logistics (LML) has been the strategic delivery due to various market risks and opportunities. This paper provides a systematic review of LML-related studies to find current issues and future opportunities for the LML service [...] Read more.

One of the most challenging problems in last mile logistics (LML) has been the strategic delivery due to various market risks and opportunities. This paper provides a systematic review of LML-related studies to find current issues and future opportunities for the LML service industry. To that end, 169 works were selected as target studies for in-depth analysis of recent LML advances. First, text mining analysis was performed to effectively understand the underlying LML themes in the target studies. Then, the novel definition and typology of LML delivery services were suggested. Finally, this paper proposed the next generation of LML research through advanced delivery technique-based LML services, environmentally sustainable LML systems, improvement of LML operations in real industries, effective management of uncertainties in LML, and LML delivery services for decentralized manufacturing services. We believe that this systematic literature review can serve as a useful tool for LML decision makers and stakeholders. Full article

(This article belongs to the Special Issue New Trends in Production and Operations Management)

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Review

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26 pages, 695 KiB

Open AccessReview

A Taxonomy of Idea Management Tools for Supporting Front-End Innovation

by Di Zhu, Abdullah Al Mahmud and Wei Liu

Appl. Sci. 2023, 13(6), 3570; https://doi.org/10.3390/app13063570 - 10 Mar 2023

Cited by 2 | Viewed by 1541

Abstract

Idea management is a crucial pillar of corporate management. Organizations may save research expenses, influence future development, and maintain distinctive competency by controlling front-end ideas. To date, several idea management tools have been developed. However, it is unknown to what extent they support [...] Read more.

Idea management is a crucial pillar of corporate management. Organizations may save research expenses, influence future development, and maintain distinctive competency by controlling front-end ideas. To date, several idea management tools have been developed. However, it is unknown to what extent they support the idea management process. Therefore, this scoping review aims to understand the classification of idea management tools and their effectiveness through an overview of the academic literature. Electronic databases (Scopus, ACM Digital Library, Web of Science Core Index, Elsevier ScienceDirect, and SpringerLink) were searched, and a total of 38 journal papers (n = 38) from 2010 to 2020 were retrieved. We identified 30 different types of idea management tools categorized as digital tools (n = 21), guidelines (n = 5), and frameworks (n = 4), and these tools have been utilized by software designers, hardware designers, and stakeholders. The identified tools may support various stages of idea management, such as capturing, generating, implementing, monitoring, refinement, retrieving, selection, and sharing. However, most tools only support a single stage (either capture or generate), and they cannot track the life cycle of the ideas, which may lead to misunderstanding. Therefore, it is essential to develop tools for managing ideas that would allow end users, designers, and other stakeholders to minimize bias in selecting and prioritizing ideas. Full article

(This article belongs to the Special Issue New Trends in Production and Operations Management)

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