A Multi-Objective Green Hub Location Problem with Multi Item-Multi Temperature Joint Distribution for Perishable Products in Cold Supply Chain

The need to keep vaccines cold in the face of high ambient temperatures and unreliable access to electricity is a challenge that limits vaccine coverage in low and middle-income countries (LMICs). Greater vaccine thermostability is generally touted as the obvious solution. Despite conventional wisdom, comprehensive analysis of the value proposition for increasing vaccine thermostability has been lacking. Further, while significant investments have been made in increasing vaccine thermostability in recent years, no vaccine products have been commercialized as a result. We analyzed the value proposition for increasing vaccine thermostability, grounding the analysis in specific vaccine use cases (e.g., use in routine immunization [RI] programs, or in campaigns) and in the broader context of cold chain technology and country level supply chain system design. The results were often surprising. For example, cold chain costs actually represent a relatively small fraction of total vaccine delivery system costs. Further, there are critical, vaccine use case-specific temporal thresholds that need to be overcome for significant benefits to be reaped from increasing vaccine thermostability. We present a number of recommendations deriving from this analysis that suggest a rational path toward unlocking the value (maximizing coverage, minimizing total system costs) of increased vaccine thermostability, including: (1) the full range of thermostability of existing vaccines should be defined and included in their labels; (2) for new vaccines, thermostability goals should be addressed up-front at the level of the target product profile; (3) improving cold chain infrastructure and supply chain system design is likely to have the largest impact on total system costs and coverage in the short term—and will influence the degree of thermostability required in the future; (4) in the long term, there remains value in monitoring the emergence of disruptive technologies that could remove the entire RI portfolio out of the cold chain.

Truck scheduling optimization at a cold-chain cross-docking terminal with product perishability considerations

PurposeCold supply chain technology is critical for extending the shelf life of perishable leafy green vegetables. This study aims to investigate the concept of managing leafy green products using cold supply chain technology and visualise the findings.Design/methodology/approachUsing expert interviews and data visualisation approaches, this study examines how organisations deal with the complexity of cold supply chain processes and networks. Thematic data analysis was conducted. Two types of software were used to accomplish the research objectives. The first software used AntConc version 3.5.8 with word frequency (N-gram) analysis, whereas the second software, VOSViewer offered co-occurrence network visualisation and cluster analysis.FindingsThe findings show that the appropriate design of cold chain technology is critical in ensuring the freshness and quality of leafy green vegetables. The primary goal of managing the complexity of the cold supply chain is to achieve product freshness and energy efficiency. Regardless of the importance of energy efficiency, cold supply chains require warehouse management solutions for transportation and storage.Practical implicationsThis study found that proper design and selection of appropriate technology in the cold supply chain have driven the companies to improve the firms’ competitive advantage while delivering the best quality of perishable leafy green food products. In addition, the freshness, quality, safety, and health of leafy green vegetables will be determined by the company’s capacity to handle long-distance transportation and select the appropriate distribution channels and storage. Warehouse management system technology was found to be secondary compared to cold chain technology, although distribution and warehousing practices are critical for supply chain performance.Originality/valueThis study has established the conceptual indicators based on best practices and outcomes for the cold supply chain. This study argued that cold supply chain management and performance should be monitored independently. Furthermore, the theory of technological adoption can be expanded to include product nature as a driver. Finally, this study has established cold chain best practices based on a perishable supply chain perspective. The findings of this study can promote healthy foods to solve zero hunger and achieve sustainable development goals. Although this study demonstrates that technology improves supply chain practises, cold storage and logistics benefit the most from technological advancements. In contrast, non-cold supply chains benefit from technology-driven improvements in performance.

Optimum policy in hybrid manufacturing/remanufacturing system

As the most promising alternative to internal combustion engines (ICEs), electric vehicles (EVs) have an excellent development outlook. The charging route scheduling of EVs can simultaneously affect traffic congestion in the transportation network (TN) and power flow distribution in the power distribution network (PDN). The research on TN and PDN coupling networks based on the static traffic flow model is relatively mature; however, it ignores that the traffic flow will spread across periods in a short scheduling period. In this paper, a semi‐dynamic traffic flow model is proposed to represent the dynamic propagation characteristics of EVs and ICEs flow. Furthermore, the cost of carbon emission and system operation are combined as the overall goal of system optimisation. Since the model has become a more complex non‐linear model, this paper proposes to combine the heuristic sequential boundary tightening and binary expansion method to linearise the model. The study compared four cases and found that a 20% penetration rate of EVs can reduce carbon emissions by 4.2% while reducing the system's total cost by 10%. Moreover, the impact of network congestion on the spatiotemporal distribution of traffic flow and power flow in the coupled network is alleviated.

<span lang="EN-US">Continuous attempts have been made to improve the flexibility and effectiveness of distributed computing systems. Extensive effort in the fields of connectivity technologies, network programs, high processing components, and storage helps to improvise results. However, concerns such as slowness in response, long execution time, and long completion time have been identified as stumbling blocks that hinder performance and require additional attention. These defects increased the total system cost and made the data allocation procedure for a geographically dispersed setup difficult. The load-based architectural model has been strengthened to improve data allocation performance. To do this, an abstract job model is employed, and a data query file containing input data is processed on a directed acyclic graph. The jobs are executed on the processing engine with the lowest execution cost, and the system's total cost is calculated. The total cost is computed by summing the costs of communication, computation, and network. The total cost of the system will be reduced using a Swarm intelligence algorithm. In heterogeneous distributed computing systems, the suggested approach attempts to reduce the system's total cost and improve data distribution. According to simulation results, the technique efficiently lowers total system cost and optimizes partitioned data allocation.</span>

BackgroundHealth system planners aim to pursue the three goals of Triple Aim: 1) reduce health care costs; 2) improve population health; and 3) improve the care experience. Moreover, they also need measures that can reliably predict future health care needs in order to manage effectively the health system performance. Yet few measures exist to assess Triple Aim and predict future needs at a health system level. The purpose of this study is to explore the novel application of a case-mix adjustment method in order to measure and help improve the Triple Aim of health system performance.MethodsWe applied a case-mix adjustment method to a population-based analysis to assess its usefulness as a measure of health system performance and Triple Aim. The study design was a retrospective, cohort study of adults from Ontario, Canada using administrative databases: individuals were assigned a predicted illness burden score using a case-mix adjustment system from diagnoses and health utilization data in 2008, and then followed forward to assess the actual health care utilization and costs in the following year (2009). We applied the Johns Hopkins Adjusted Clinical Group (ACG) Case-Mix System to categorize individuals into 60 levels of healthcare need, called ACGs. The outcomes were: 1) Number of individuals per ACG; 2) Total system costs per ACG; and 3) Mean cost per person per ACG, which together formed a health system “dashboard”.ResultsWe identified 11.4 million adults. 16.1% were aged 65 or older, 3.2 million (28%) did not use health care services that year, and 45,000 (0.4%) were in the highest acuity ACG category using 12 times more than an average adult. The sickest 1%, 5% and 15% of the population use about 10%, 30% and 50% of total health system costs respectively. The dashboard measures 2 dimensions of Triple Aim: 1) reduced costs: when total system costs per ACG or when average costs per person is reduced; and 2) improved population health: when more people move into healthier rather than sicker ACGs. It can help to achieve the third aim, improved care experience, when ACG utilization predictions are reported to providers to proactively develop care plans.ConclusionsThe dashboard, developed via case-mix methods, measures 2 of the Triple Aim goals and can help health system planners better manage their health delivery systems.

The number of e-business companies with perishable products and cold chain logistics in China has increased year by year, but there have been few comprehensive evaluations of e-business companies with perishable products. This article selects 18 e-business companies with fresh and perishable products that are still in operation as samples, and establishes 12 evaluation indexes through data capture of customer online comments, text mining, and questionnaire survey. SPSS 22.0 software is used to conduct factor and cluster analysis. The measures that are used to evaluate these companies are analyzed through factor analysis. After the factor analysis, the status and ranking of the comprehensive power status of these companies were obtained. The data is also subjected to cluster analysis and these companies are classified as three groups and they can be managed differently based on their respective groups. Through the radar diagram, we found out that the most influential factors that consumers are concerned about EBPCs are freshness, fresh quality, and logistics distribution efficiency.

Multi-objective optimization of gas-steam-power system for an integrated iron and steel mill considering carbon emission reduction and cost

Simultaneous pricing and inventory decisions for substitute and complementary items with nonlinear holding cost

An MILP Model for the Optimization of Hybrid Renewable Energy System

Technology adoption and carbon emissions with dynamic trading among heterogeneous agents

The operational research paper in the transportation model nowadays is heading to the environmental issue. One of the famous operational research models is transshipment. Transshipment is an expanded model of transportation, whether each distribution center between the start to the destination point. In this research, the transshipment model is integrated into an environmental function, the challenge is to find the right shipment of each route from the start, distribution, and destination point considering the transportation cost and carbon emission. This research proposed a transshipment model with minimizing transportation and carbon emission cost using mixed-integer linear programming for model formulation. The solution searching used branch and bound method. This research analyzed the environmental objective function and constrain effect in the transshipment model. The model is tested in a beef distribution case study in Bogor, Indonesia that has eight source points, three distribution centers, and six destination points. The model is experimented by carbon emission limitation scenarios. The optimum result in source allocation, distribution and destination are different between the two scenarios. The carbon emission limitation affects carbon emission production and total cost.
 
 Keywords: Branch and Bound, Environmental Cost, Green Transhipment, Mixed Integer Linear Programming preferably 2-scenarios are mentioned

Government-led low carbon incentive model of the online shopping supply chain considering the O2O model

Food products are a critical part of everyday life. To increase the efficiency of the food supply chain, designing a comprehensive mathematical is necessary. This study tries to optimize a protein supply chain. This supply chain is divided into livestock and perishable products. The integration of these two supply chain echelons has been applied to create an extensive model. Moreover, sustainability has been considered as a competitive advantage in the chain. Perishable products are temperature-sensitive. Hence, a cold supply chain has been considered. The model has three objective functions: maximizing the total profit, minimizing the storage cost in the cold chain, minimizing the health risk. In dealing with uncertainty, a data-driven robust optimization method has been used. Therefore, this paper used machine learning to construct the uncertainty sets from historical data. The Torabi-Hassini method has been implemented to solve the multi-objective model. Finally, to show the applicability and efficiency of the proposed approach, a real-world case study on the poultry supply chain, including abattoirs, breeding centers, slaughtering, and selling branches, has been applied. The result shows that this methodology significantly influences total profits and improves the environmental criteria in a real-world case study. Moreover, different sensitivity analyses have been prepared to help managers make a trade-off between the robustness of the model and objective function value with various weights and calculate the influence of supply chain integration on objective functions.