Real Supply Research Articles

An integrated blockchain-enabled multi-channel vaccine supply chain network under hybrid uncertainties

The recent pandemic caused by COVID-19 is considered an unparalleled disaster in history. Developing a vaccine distribution network can provide valuable support to supply chain managers. Prioritizing the assigned available vaccines is crucial due to the limited supply at the final stage of the vaccine supply chain. In addition, parameter uncertainty is a common occurrence in a real supply chain, and it is essential to address this uncertainty in planning models. On the other hand, blockchain technology, being at the forefront of technological advancements, has the potential to enhance transparency within supply chains. Hence, in this study, we develop a new mathematical model for designing a COVID-19 vaccine supply chain network. In this regard, a multi-channel network model is designed to minimize total cost and maximize transparency with blockchain technology consideration. This addresses the uncertainty in supply, and a scenario-based multi-stage stochastic programming method is presented to handle the inherent uncertainty in multi-period planning horizons. In addition, fuzzy programming is used to face the uncertain price and quality of vaccines. Vaccine assignment is based on two main policies including age and population-based priority. The proposed model and method are validated and tested using a real-world case study of Iran. The optimum design of the COVID-19 vaccine supply chain is determined, and some comprehensive sensitivity analyses are conducted on the proposed model. Generally, results demonstrate that the multi-stage stochastic programming model meaningfully reduces the objective function value compared to the competitor model. Also, the results show that one of the efficient factors in increasing satisfied demand and decreasing shortage is the price of each type of vaccine and its agreement.

Multi-population genetic algorithm with crowding-based local search for fuzzy multi-objective supply chain configuration

Supply chain configuration is often fuzzy and involves multiple objectives in real-world scenarios, but existing researches lack the exploration in the fuzzy aspect. Therefore, this paper establishes a fuzzy multi-objective supply chain configuration problem model to minimize the lead time and product cost oriented towards real supply chain environments. To solve the fuzzy problem, the theories of membership and closeness degree in fuzzy mathematics are adopted, and a multi-population genetic algorithm (MPGA) with crowding-based local search method is proposed. The MPGA algorithm uses two populations for optimizing the two objectives separately and effectively, and is characterized by three main innovative aspects. Firstly, a radical-and-radial selection operator is designed to balance the convergence speed and diversity of population. In the early stage of the algorithm, two populations are both optimized towards the ideal knee point, and then are separately optimized towards the two ends of the Pareto front (PF). Secondly, an elitist crossover operator is devised to promote information exchange within two populations. Thirdly, a crowding-based local search is proposed to speed up convergence by improving the crowded solutions, and to enhance diversity by obtaining new solutions around the uncrowded ones. Comprehensive experiments are tested on a fuzzy dataset with different sizes, and the integral of the hypervolume of PF is used for the evaluation of the fuzzy PF. The results show that MPGA achieves the best performance over other comparative algorithms, especially on maximum spread metric, outperforming all others by an average of 39 % across all test instances.

Prediction of the process capability for compression rubber part forming in the automotive supply chain

The paper proposes predicting production process capability for the compression rubber part in automotive supply chain management. Delivery of parts to tier 1 and OEM on time is the most important part of supply chain management, together with the delivery of on-quality and on-cost control to maintain the competitiveness of the supply chain. There are many suppliers to produce many automotive parts for tier 1. Therefore, the simulation approach properly predicts and prevents the process from getting into trouble during the actual production time. Production process quality control is critical to ensure that the good quality of the parts purchased can be delivered on time. Rubber parts are used widely in automotive, motorcycles, trucks, and other types of vehicles, with small sizes but in huge quantities to support general OEM brands and specific parts. The rubber part manufacturing process is complex and uncertain with compression moulding and rubber curing conditions. Therefore, good conditions can predict the production process's capability to commission and deliver on schedule.A neuro-fuzzy system is adopted and developed to deal with the uncertain process capability under multi-criteria decision-making.The methodology development can be used in the actual rubber part manufacturing supply chain environment and can predict uncertain problems that might occur in the subcontractor factories.The prediction of the production process capability of the rubber part supply chain might be more effective on the real-time monitoring control system and can be tracking location part progress for further planning both success or rescheduling.The platform can be applied to audit and test the actual industrial supply chain, and problem and research questions are brought about from the real supply chain in the local country.The methodology development was originally created for the particular supply chain in rubber automotive parts that can replace the existing system to obtain a more efficient performance evaluation process.

Technological features of cultural ecosystem services assessment

The benefits that city residents receive from green spaces related to maintaining their health are a group of cultural ecosystem services (CES). To avoid subjectivity in the assessment of CES, there is a methodology based on determining the effectiveness of green spaces in providing them. Currently, there is no technological scheme for assessing the CES available to urban planners. Accordingly, the purpose of the paper is to highlight the technological features of the CES assessment. Methodologically, the work consists in creating a geodatabase (DB) on the state of green spaces and anthropogenic pressure on them. All the calculation indicators proposed in the methodology should be included in the database as attributes, and their calculation should be performed as working scenarios documented in the form of automated calculation models, etc. The technology is based on QGIS/SAGA spatial analysis tools (QGIS version 3.32.1-Lima), which contains the necessary basic geoprocessing algorithms and modules for calculating the CES. The technology for estimating CES is presented in the Model_CES. The functioning of the Model_CES depends on the amount and quality of input data. As input data, the database includes information on: urban green spaces; vegetation; landscapes; areas with multi-age urban development; local areas of urban population concentration; survey statistics, etc. Attribute information is the basis for the correct operation of the Model_CES algorithms, in particular, the calculation of parameters by which it is proposed to assess the volume of CES. Adequate to the parameters, a file database in the GeoPackage format was formed in accordance with the standards of the Open Geospatial Consortium - Model_CES, which contains the input data sets: tabular: "survey_all"; vector: "Vegetation", "BGI", "local_district", "hollow", "age_consruct". Technological procedures and algorithm for calculating CES volumes and risks of their loss in Model_CES form calculation blocks: 1. a block for determining the potential of green spaces in providing CES; 2. a block for determining the effectiveness of the green space in providing CES - the real supply of CES; 3. a block for calculating the volume of ecosystem services and the risks of their loss; 4. a block for calculating the score of alternative availability of the green space. As a result, the proposed algorithm produces the output geodataset "BGI_CES". After the implementation of the technology, this dataset contains attribute information about each green space, in particular, their ability to provide cultural ecosystem services, efficiency in providing CES, the volume of CES and the risks of their loss. Thus, the paper solves the scientific task of creating an assessment technology that meets the following requirements: it is suitable for processing information that is spatially distributed; it is focused on the availability of uncertain and often limited data; it is available for use by specialists of design and other institutions that may be limited in data. The presented technology can be used in urban planning practice as a tool for improving the quality of urban green spaces, their sustainable development and providing citizens with the maximum amount of CES.

Reprint of: Quantifying the Germany shock: Structural labor-market reforms and spillovers in a currency union

We examine the effects of unilateral structural reforms within a currency union. Focusing on the surge of German competitiveness following the introduction of the Euro, we first provide reduced-form causal evidence supporting the notion that German structural labor-market reforms in the early 2000s led to a crowding-out of manufacturing employment in other Eurozone economies. To assess the impact of this German competitiveness shock, we build a quantitative multi-sector trade model that features downward nominal wage rigidities, endogenous labor supply, unemployment-insurance benefits and international savings. The fixed nominal exchange rate can create binding nominal rigidities in response to a foreign real supply shock – like the one prompted by the German reforms – resulting in significant contraction of manufacturing sectors and increased involuntary unemployment across other Eurozone countries. We consider a number of counterfactual scenarios, such as the impact of German labor-market reforms in the absence of a fixed exchange-rate regime, the role of coordinated reforms within the Eurozone and a higher average inflation rate.

Quantifying the Germany shock: Structural labor-market reforms and spillovers in a currency union

We examine the effects of unilateral structural reforms within a currency union. Focusing on the surge of German competitiveness following the introduction of the Euro, we first provide reduced-form causal evidence supporting the notion that German structural labor-market reforms in the early 2000s led to a crowding-out of manufacturing employment in other Eurozone economies. To assess the impact of this German competitiveness shock, we build a quantitative multi-sector trade model that features downward nominal wage rigidities, endogenous labor supply, unemployment-insurance benefits and international savings. The fixed nominal exchange rate can create binding nominal rigidities in response to a foreign real supply shock – like the one prompted by the German reforms – resulting in significant contraction of manufacturing sectors and increased involuntary unemployment across other Eurozone countries. We consider a number of counterfactual scenarios, such as the impact of German labor-market reforms in the absence of a fixed exchange-rate regime, the role of coordinated reforms within the Eurozone and a higher average inflation rate.

Assessing Supply Chain Resilience During the Pandemic Using Network Analysis

Disruptions induced by the COVID-19 pandemic have wreaked havoc in supply chain networks. To gain an understanding of the dynamics that had been at play, we construct a real supply chain network (scale-free) based on a seed firm (Apple), its customers, and its first- and second-tier suppliers, yielding a network of a total of 883 firms. We then use visualization to derive insight into various network characteristics and develop an agent-based model to capture the disruption of the network over a period of 400 days from the onset of the pandemic. The disruptions experienced by firms depend on the stringency of measures taken to curb the pandemic in their respective countries and the severity of disruptions experienced by suppliers in a specific region. We specifically find that spatial complexity, degree centrality, betweenness centrality, and closeness centrality have changed significantly throughout our observation period. We thus subsequently theorize on the influence of some of these characteristics on supply chain resilience (SCRes), and through our empirical tests, we find that, at the network level, Average degree and spatial complexity significantly influence SCRes. At the firm-level, we find that powerful firms within the network influence SCRes based on their betweenness centrality and closeness Centrality. Implications for managerial practice and academic research are discussed.

Kinetic models applied to quality change and shelf-life prediction of fresh-cut pineapple in food cold chain

This study developed a predictive model of the remaining shelf-life of fresh-cut pineapple by considering microbial, chemical, and sensory indicators under storage temperatures.The effect of temperature on quality indices (Ea)was29.57–96.8 kJ/mol. The acceptable limits for vitamin C loss and weight loss for fresh-cut pineapple were 27.71 mg/100 g fresh weight and 4.32 %, respectively, in accordance with cutoffs determined on the basis of sensory changes. Key quality indicators that could predict the remaining shelf-life between 5 °C and 25 °C were the TVC and WL. Vitamin C loss was only suitable when storage temperature was above 10 °C. The remaining shelf-life in a real supply chain in terms of the TVC, vitamin C loss, and WL were 9.9, 3.5, and 4 days, respectively. Overall, the shelf-life prediction model developed using selected key indicators may help in managing temperature in the fresh-cut fruit supply chain and improving storage shelf-life.

A hybrid metaheuristic with learning for a real supply chain scheduling problem

In recent decades, research on supply chain management (SCM) has enabled companies to improve their environmental, social, and economic performance.This paper presents an industrial application of logistics that can be classified as an inventory-route problem. The problem consists of assigning orders to the available warehouses. The orders are composed of items that must be loaded within a week. The warehouses provide an inventory of the number of items available for each day of the week, so the objective is to minimize the total transportation costs and the costs of producing extra stock to satisfy the weekly demand. To solve this problem a formal mathematical model is proposed. Then a hybrid approach that involves two metaheuristics: a greedy randomized adaptive search procedure (GRASP) and a genetic algorithm (GA) is proposed. Additionally, a meta-learning tuning method is incorporated into our hybridized approach, which yields better results but with a longer computation time. Thus, the trade-off of using it is analyzed.An extensive evaluation was carried out over realistic instances provided by an industrial partner. The proposed technique was evaluated and compared with several complete and incomplete solvers from the state of the art (CP Optimizer, Yuck, OR-Tools, etc.). The results showed that our hybrid metaheuristic outperformed the behavior of these well-known solvers, mainly in large-scale instances (2000 orders per week). This hybrid algorithm provides the company with a powerful tool to solve its supply chain management problem, delivering significant economic benefits every week.

Integrating information, financial, and material flows in a chemical supply chain

We address the flows involved in a real supply chain and discuss how they can be integrated for supply chain management. A scheduling model is proposed based on a State-Task Network representation to schedule order transactions and manufacturing operations in a make-to-order chemical plant. The proposed model is compared to scheduling models that focus on either the order transactions or the manufacturing operations. The advantage of the integrated approach is found in the accuracy of the solutions attained, whereas the siloed models produce suboptimal or infeasible solutions. Each model is compared in a stochastic discrete event simulation environment. An integrated scheduling model is also presented, which includes information, financial, and material flows along a three-echelon supply chain. This integrated model serves as a starting point for developing decision-support systems that take a more comprehensive view of the complex relationships amongst the different flows involved in real supply chains.

A tri-level optimization model for facility location-protection problem considering design and redesign decisions under disruption

PurposeFortification-interdiction models provide system designers with a broader perspective to identify and protect vital components. Based on this concept, the authors examine how disruptions impact critical supply systems and propose the most effective protection strategies based on three levels of decision-makers. This paper aims to investigate location and fortification decisions at the first level. Moreover, a redesign problem is presented in the third level to locate backup facilities and reallocate undisrupted facilities following the realization of the disruptive agent decisions at the second level.Design/methodology/approachTo address this problem, the authors develop a tri-level planner-attacker-defender optimization model. The model minimizes investment and demand satisfaction costs and alleviates maximal post-disruption costs. While decisions are decentralized at different levels, the authors develop an integrated solution algorithm to solve the model using the column-and-constraint generation (CCG) method.FindingsThe model and the solution approach are tested on a real supply system consisting of several hospitals and demand areas in a region in Iran. Results indicate that incorporating redesign decisions at the third level reduces maximum disruption costs.Originality/valueThe paper makes the following contributions: presenting a novel tri-level optimization model to formulate facility location and interdiction problems simultaneously, considering corrective measures at the third level to reconfigure the system after interdiction, creating a resilient supply system that can fulfill all demands after disruptions, employing a nested CCG method to solve the model.

Assessing water urban systems to the compliance of SDGs through sustainability indicators. Implementation in the valencian community

The sustainability improving must be supported in the achievement of the sustainable development goals (SDGs). The research shows a procedure, establishing 136 indicators, which are linked to the management of water systems. The procedure classified each indicator according to its intervention in the water cycle, measuring the level of sustainability applied to water systems. This methodology assesses the water systems possible and establishes a benchmarking on sustainable aspects. The developed database and the proposed procedure enable the establishment of an evaluation of the SDGs in any water system, establishing different four levels of sustainability benchmarking. These indicators were applied to 110 worldwide case studies, which were obtained from the background developed in the research. Applying this methodology will allow companies and water system managers to assess the SDG targets and incorporate new challenges to improve sustainability. As example, the methodology was applied to six real supply systems, which are located in Spain. According to the results, the contribution to water supply compliance is 42% of sustainability targets.

Multicriteria sorting method based on global and local search for supplier segmentation

AbstractThe aim of this research is to develop a robust multicriteria method to classify suppliers into ordered categories and its validation in real contexts. The proposed technique is based on a property of net flows of the PROMETHEE method and uses global and local search concepts, which are common in the optimisation field. The results obtained are compared to those from the most cited sorting algorithm, and an empirical validation and sensitivity analysis is performed using real supplier evaluation data. Furthermore, it does not require additional information from decision‐makers as other sorting algorithms do for assigning incomparable or indifferent alternatives to groups. An extension of the silhouette concept from data mining is also contributed to measure the quality of ordered classes. Both contributions are easy to apply and integrate into decision support systems for automated decisions in the supply chain management. Finally, this practical approach is also useful to classify customers and any type of alternatives or actions into ordered categories, which have an increasing number of real applications.

An integrated approach for optimizing location-inventory and location-inventory-routing problem for perishable products

The grocery market, especially the perishable product market is under amazing developments in China, accounting for half of overall food expenditure. However, the supply chain of perishable products has encountered serious problems, including significant freight losses, high inventory and transportation costs, etc. To solve these problems, the current paper proposes an integrated approach for simultaneously optimizing the decisions involved in the supply chain, typically including location, inventory, and routing strategy. A three-echelon supply chain including suppliers, DCs and retailers is structured in this paper, with the impacts of whether allowing direct shipment from supplier to retailer being considered. Two typical problems are investigated: location-inventory problem (LIP) and location-inventory-routing problem (LIRP). Mixed integer nonlinear programming model for LIP is proposed while Mixed integer linear programming models for LIRP is proposed, with the objective of minimizing the overall costs of supply chain, which are composed of infrastructure construction costs, transportation costs, inventory costs, and freight losses. CPLEX is used for LIP model solution and a two-stage heuristic algorithm based on Simulated Annealing is designed to solve the LIRP model. The proposed mathematical models and solution method are then applied to a real supply chain network for fresh milk. The optimized results suggest that considering the direct shipment from supplier to retailer is beneficial in terms of reducing the overall supply chain costs by 31%, with 43% reductions in transportation costs, 69.4% reductions in freight losses, 99.4% increases in inventory costs. With the developments of e-retailers for perishable products, allowing the direct shipment from supplier to retailer will bring more benefits in the future.

Overcoming logistics network challenges through advanced transportation visibility

The supply chain is often discussed as one cohesive and linear entity. In reality, it is an extended and complex ecosystem. Each player in the ecosystem has a different relationship with the goods it is moving to multiple types of recipients and adds a distinct type of value to the network. In addition, there are numerous supply chain disruptions that can happen along the way. These influences and the scale and complexity of the transportation ecosystem make advance transportation visibility key to any company’s success. This paper explains what real-time visibility platforms are, the capabilities they provide and describes the value they bring to their users. It also provides insights and trends around these platforms as they grow and mature. Readers will get an understanding how to transform their supply chain through visibility while facilitating seamless execution that creates real supply chain value.

A Study on Experiential Learning Model of Tourism Management in Higher Education in China: A Case Study of Chongqing Normal University

The Coronavirus disease 2019 (COVID-19) outbreak has brought a great impact on the whole tourism industry and thus has profoundly affected the market demand for tourism talents. In order to cope with the changing market environment and to solve the problem of misalignment between the real supply and demand of tourism talents, structural reform of tourism education in China is urgently needed. Tourism management is a practical and application-oriented major, which is closely integrated with the tourism industry and society, and practical teaching is an important part of it, and cognitive experience activities are an important practical teaching part of this major. In this paper, taking the tourism management major of Chongqing Normal University as an example, through analyzing the current market demand characteristics of tourism talent and the dilemma of tourism talents cultivation in colleges and universities, experiential learning was introduced in the practical teaching and the OTCPE cognitive experience model was constructed. The model is designed to solve the problem of disconnection between tourism talent training and market demand. The practical results of this model prove that it has a very good promotion effect on students’ professional ability, practical ability, learning effect, and learning initiative.

A kinetic model for predicting shelf-life of fresh extruded rice-shaped kernels (FER)

Fresh extruded rice-shaped kernels (FER) are remoulded rice products from cereals or seed flours, which have the advantages of safety, nutrition, health and time saving. However, the finished products are easy to react with oxygen, so it is necessary to develop a fast, simple and reliable approach to monitor and predict the shelf-life of FER. A comprehensive mathematical model of FER shelf-life prediction was developed using a dynamic modelling approach based on real supply chain conditions. This predictive model was developed to determine four key indexes including acid value, iodine blue value, water uptake ratio and peroxide value. The results showed that when the peroxide value was 1.6849, the FER lost its edible value, nutritional value and commodity value. Moreover, the acid value and peroxide value of FER were used to establish a first-order kinetic model, and the iodine blue value of FER was suited for a zero-order kinetic model. The validation experiment of predicted and measured shelf life showed that the relative error was 3.12%, which was <5%. Therefore, this kinetic model could be used to predict the shelf-life of FER quickly and conveniently. The kinetic-based shelf-life prediction model proposed in this study is rapid and practical, aiming to provide theoretical basis and guidance for the establishment of quality monitoring and quality evaluation systems of FER during the production, storage, transport and marketing.

The efficient and stable planning for interrupted supply chain with dual‐sourcing strategy: a robust optimization approach considering decision maker's risk attitude

• Proposing an efficient and robust dual-sourcing model for disrupted supply chain • Considering various risk attitudes and interruption uncertain situations • Processing supply interruption uncertainty with robust optimization method • Applying the real case study to analyze validity and stability of model • Providing useful managerial implications for interrupted supply chain sourcing In the face of the complex market environment and the in-depth development of globalization, ensuring the supply chain's stability and cost leadership by developing a differentiated dual-source procurement strategy is extremely important. The present study contemplates the supply chain as the research object. It also considers disruption uncertainty and decision-makers’ risk attitudes. To minimize costs and shortages, we build a dual-objective robust optimization model for a dual-sourcing strategy under interruption risk. The authors aim to explore efficient and robust planning under supply disruption uncertainty in dual-sourcing strategies by different risk attitudes. The research employs the augmented ε-constraint method to solve the proposed model by transforming the model into a single-objective mixed-integer programming model. Additionally, our study uses enterprise examples to prove the feasibility and effectiveness of the model. Moreover, it conducts risk attitude analysis, dual objective function trade-off analysis, and superiority analysis of procurement strategies. Finally, we put forward some management insights to provide a new decision-making idea for real supply chain enterprises in the case of supply interruption risk.

Supraharmonic Emissions from DC Grid Connected Wireless Power Transfer Converters

Power converters for wireless power transfer (WPT) and, in general, for electrical vehicle charging are evolving in terms of nominal power and performance, bringing along non negligible emissions in the supraharmonic range (2 kHz to 150 kHz). The large installed power and the high concentration with a relatively short separation distance can be addressed by feeding the converters through a DC grid for better dynamic response and lower impedance. The prediction of conducted emissions in real supply conditions requires carrying out measurements with low impedance values, lower than those available in line impedance stabilization networks (LISNs) for AC grids. This work proposes an approach to extrapolate converter emissions in an ideal 0 Ω condition, that together with the input impedance curve (determined by a least mean square approach) form a Norton equivalent circuit of the converter. The interaction of the converters with the DC grid and superposition of emissions can be then thoroughly evaluated by means of a general ladder grid scheme to which the Norton equivalents are connected. Such a grid model is suitable for Monte Carlo simulation aimed at assessing the degree of compensation between sources of emissions and the overall network distortion. Results using a Simulink model are provided considering emissions aggregation and compensation under random phase conditions for the following cases: close-by and separated sources (5 m and 100 m cable separation, respectively); increased number of sources studying scenarios with 3 and 10 sources; and using different resolution bandwidth values (200 Hz and 500 Hz) against a random change of the frequency of the emission components.

Rough set based information theoretic approach for clustering uncertain categorical data

Many real applications such as businesses and health generate large categorical datasets with uncertainty. A fundamental task is to efficiently discover hidden and non-trivial patterns from such large uncertain categorical datasets. Since the exact value of an attribute is often unknown in uncertain categorical datasets, conventional clustering analysis algorithms do not provide a suitable means for dealing with categorical data, uncertainty, and stability. The ability of decision making in the presence of vagueness and uncertainty in data can be handled using Rough Set Theory. Though, recent categorical clustering techniques based on Rough Set Theory help but they suffer from low accuracy, high computational complexity, and generalizability especially on data sets where they sometimes fail or hardly select their best clustering attribute. The main objective of this research is to propose a new information theoretic based Rough Purity Approach (RPA). Another objective of this work is to handle the problems of traditional Rough Set Theory based categorical clustering techniques. Hence, the ultimate goal is to cluster uncertain categorical datasets efficiently in terms of the performance, generalizability and computational complexity. The RPA takes into consideration information-theoretic attribute purity of the categorical-valued information systems. Several extensive experiments are conducted to evaluate the efficiency of RPA using a real Supplier Base Management (SBM) and six benchmark UCI datasets. The proposed RPA is also compared with several recent categorical data clustering techniques. The experimental results show that RPA outperforms the baseline algorithms. The significant percentage improvement with respect to time (66.70%), iterations (83.13%), purity (10.53%), entropy (14%), and accuracy (12.15%) as well as Rough Accuracy of clusters show that RPA is suitable for practical usage. We conclude that as compared to other techniques, the attribute purity of categorical-valued information systems can better cluster the data. Hence, RPA technique can be recommended for large scale clustering in multiple domains and its performance can be enhanced for further research.