支持生鲜农产品物流实时决策外文翻译中英文

支持生鲜农产品物流实时决策外文翻译中英文（节选重点翻译）英文Research directions in technology development to support real-time decisions of fresh produce logistics: A review and research agendaJ. Ren Villalobos，Wladimir Silva, etcAbstractRecent developments in consumption patterns, lowering of trade barriers, the emergence of low cost/miniature sensors and information technologies, and advanced business analytics tools are changing the playing field on which most of the agri-food supply chains operate. The intelligent use of sensing and information technologies has the potential to start a new food revolution in which limited resources such as water, capital, transportation capacity and labor could be optimally exploited so that fresh food, in particular fruits and vegetables, get to the consumer with minimal or no food waste. One of the keys for making this vision a reality is transforming the data collected as these products traverse the supply chain into effective and efficient supply chain decisions. This transformation relies on that the underlying decision systems that take advantage of this data exist or can be developed. The aim of this paper is to provide an overview of the state of the art and challenges and opportunities emerging from the integration of sensing data and information into decision support systems for supply chain of fresh fruits and vegetables.Keywords: Fresh produce supply chain, Information systems and technology, Decision making1.IntroductionThe production, transportation, storage and distribution of food are critical issues facing humanity whose importance only will grow in the future. For instance, the Food and Agriculture Organization of the United Nations (FAO) has warned that to keep up with the increase of the worlds population, food production should increase by 70% by the year 2050 (FAO, 2009). In the case of fresh fruits and vegetables (to be referred in the remainder of the paper as fresh produce), this increase could be even greater since the consumption of these products is associated with health benefits (Riboli and Norat, 2003,He et al., 2007,He et al., 2006). For Instance, greater intake of fruits and vegetables have been associated with decreased risk for some cancers, cardiovascular diseases, and type 2 diabetes (He et al., 2007,Riboli and Norat, 2003,He et al., 2006). The expanded demand of fresh fruits and vegetables is both a challenge and an opportunity. One of the main challenges is how to develop efficient fresh produce supply chains that can meet the increasing global demand of these products while dealing with their intrinsic logistics and market complexities to meet the demand at affordable prices with minimum waste. Foremost among these complexities is the perishability of these products and the associated practices and infrastructure to preserve their shelf life.In terms of waste, the FAO has reported that up to a third of the food produced in the world is wasted, causing unnecessary water use and generation of greenhouse gases (Gustavsson et al., 2011). Food wasted is equivalent to the production of 28% of the land dedicated to agricultural activities. Accordingly, the FAO identifies that fruits and vegetables represent around 40% of total food waste (Gustavsson et al., 2011).A very important portion of food waste is due to the inefficiencies in the fresh produce supply chain (Papargyropoulou et al., 2014,De Steur et al., 2016).Papargyropoulou et al. (2014)identify the lack of the appropriate cold chain facilities and procedures as major contributors to food waste. Due to the perishable nature of fresh fruits and vegetables, real-time visibility of supply chain information is a key element to make appropriate flow management decisions across the supply chain. Thus, the use of data collected through the supply chain and environment information is a key element of any decision support system that seeks to reduce food waste in the fresh produce supply chain.In this regard, there is an opportunity to use sensors and information technologies whose development has been emerging and rapidly expanding in the last few years. Accordingly, the future of the fresh produce supply chain moves toward one in which the decision making is immersed in an information rich environment that results in optimal value for its stakeholders such as the grower, the final customer, the retailer, and the environment. Real-time information from all the echelons of the supply chain must be obtained by sensors and new information technologies, which include: market information, plant growth, harvest yield and quality, logistical information such as transportation costs and status of inventory, time-based quality of the products, product-traversal information, product-presentation information, and state of scarce resources such as labor, water and land.In this paper, we aim to identify a research agenda, from a supply chain management perspective, for the information-technology (IT) enabled fresh produce supply chain. This research agenda seeks to incorporate emerging technologies such as sensors and other IT sources that provide real-time data useful for planning decisions along the supply chain. The innovative use of these data could revolutionize the fresh produce supply chain by rendering benefits that include waste minimization, higher quality, and affordability of fresh produce for consumers, as well as opportunities to increase the margin of the supply chains value captured by the growers. The underlying idea is that through the judicious use of data available, better and timelier planning and operational decisions can be made along the supply chain, which in turn, contribute to minimize food waste by fostering better demand-production coordination decisions. In this paper, we focus on the use of sensors to support planning decisions along the fresh-produce supply chain.In addition to the movement of products through the supply chain, there are other flows associated with the physical flows, such as information and price transmission. Price transmission refers to the way prices (or value) of the product increases as the product travels downstream from the farm to the consumer. Information flows refers to all the data and information that is generated as the product traverses the supply chain. This includes information related to the product such as type, variety and source; related to the environment such as temperature history; related to transportation and inventory history; and may include transactional history such as price and time of sale. An important issue that needs to be explored is how to use this information, particularly that information gathered in real time using sensors and other automated information sources, to improve the overall supply chain in terms of best matching supply and demand from at least two perspectives, namely, the reduction of the profit margin gap between the last and first echelons of the supply chain, and minimization of product waste through the fresh produce supply chain. It is important to highlight the latter perspective because of the perishability nature of fresh fruits and vegetables. This characteristic of the products under consideration set the supply chain planning and coordination strategies apart from other supply chains.Using a downstream analysis of the fresh produce supply chain, the first part of the supply chain decisions consists of the decisions made at the farm level. Once the product is harvested, it is transported to a facility where it is introduced into the cold chain; that is, the temperature-controlled portion of the supply chain necessary to maintain an adequate temperature and environment during the transport, storage, processing and distribution of the products. Once the product it is under controlled temperature, it is usually sent to a processing facility where is further classified and packed, before it is sent downstream to one or more intermediate distribution centers before it reaches the final consumer.Regarding more general supply chains such as that of traditional manufacturing products, fresh produce supply chains differ in important aspects. One has already been mentioned before, i.e., the perishability of the products flowing through them, which puts constraints in the levels of inventory that can be held along the supply chain. Another important difference is the production long lead-time, as well as the high levels of variability of the supply and demand. This imposes more challenges and call for tighter coordination at the different echelons of the supply chain. Thus, the planning methods applied to traditional supply chain may not be transfer directly to fresh produce supply chains.3.Review of the existing technologies, sensors/ITThe emergence of low-cost sensors, in particular those with wireless capabilities, along with other new information sources, are impacting the environment in which the fresh produce supply chains operate. The availability of such devices and other data sources makes possible the tracking in real time of the product throughout the fresh-produce supply chain. This enables the development of value-added services to make supply chain operations more efficient and effective from the perspective of different measures of performance such as food waste, overall supply chain cost and carbon/energy footprint. These services may include traceability of the product, location services, shelf-life conservation, inventory management, market intelligence, dynamic logistics, monitoring and control, as well as better planning and coordination among the different echelons of the supply chain.In the following sub-sections, we present a review and categorization of the contributions found in the literature that make use of different technologies, methodologies and DSS (Decision support system) to support real-time decision making at the different echelons of the fresh-produce supply chain. In the ensuing discussion we classify the existing technologies and related works, in the following four categories: (i) those works that are based mostly on RFID (Radio Frequency Identification) technologies; (ii) those that are related to WSN (Wireless sensors networks), other sensors and IoT; (iii) those that deal mainly with the development or application of DSS (Decision Support Systems), Information Systems (IS) and Big Data; and (iv) all other technologies and DSS contributions that did not fall on any of the previous categories. The last category encompasses different transversal technologies such as blockchain, quality inspection or automation technologies.3.1.Agricultural practices and harvestingAgricultural practices refer to the activities related to land cultivation and production of crops. For the purposes of this discussion we will consider the decisions made at this point of the supply chain as the entry point of the supply chain. These decisions are made before the product leaves the agricultural field. We can identify in this echelon two main decisions: precision agriculture and harvesting. Precision agriculture deal with making agronomical decisions based on data specific to a location. The basis of precision agriculture is “the spatial and temporal variability in soil and crop factors within a field” (Stafford, 2000). Typical decisions in this field include related to when and how much to irrigate a plot based on the current state of information. Data to support precision agriculture come from sensors that provide information such as level of moisture in the soil, ambient temperature, hours of sun light, etc. Precision agriculture can also be used to affect and/or estimate when the harvest will occur.Some other harvesting decisions are related to decisions related to the optimal harvesting time and the determination of the harvesting strategy. Determination of the optimum harvest window is an important issue in the fresh produce supply chain, as the quality of the fruit is highly influenced by the right harvesting time, as well as the appropriate storage conditions in the postharvest period (Skic et al., 2016).As observed in the table, we did not find any contributions to the literature based on RFID technology (category i). This is explained since this type of technology is mainly used to monitor and track products, rather than to monitor agricultural practices and harvesting. Contributions in category ii are mainly related to the irrigation and monitoring at the farm level (Kim and Evans, 2009,Kim et al., 2008, respectively). They consider the use of GPS systems for monitoring the position of the sprinklers in the field.Daz et al. (2011)propose a methodology for monitoring agricultural production process based on WSN, studying different existing real-scenarios. In terms of the use of IoT for agricultural precision,Mekala and Viswanathan (2017)propose a technology for smart agriculture based on IoT with cloud computing. Finally, in terms of harvesting decisions,Overbeck et al. (2017)propose a method based on the use of innovative sensor technology to predict the optimal harvest date using sweet cherry with different ripening parameters. Their results show that this type of technology is suitable for several cherry varieties and growing systems.In category iii,He et al. (2011)propose a DSS to support the fertilization of an agricultural land implementing a WSN to collect data.Elsheikh et al. (2013)propose a DSS for planning the crop to be planted in an area, considering the geography, the water and the soil quality.Sanchez-Cohen et al. (2015)propose a DSS to support crop decisions in lands where there is a water shortage.Navarro-Helln et al. (2016)propose a DSS for smart irrigation of agricultural crops using sensors in the field to collect weather and soil information. With this information, a forecast and an optimal irrigation plan is proposed. As a case study the citrus fruits in Spain are considered.Hong and Hsieh (2016)propose an integrated control strategy with Bluetooth for irrigating romaine lettuces in a greenhouse comparing it with a traditional irrigation strategy, concluding that is possible to save about 90% of electricity and water with the integrated control strategy.Ji et al. (2017)propose a harvest model, which uses big data as input to support harvesting decision making in the food supply chain. No references for decision support systems for data-enabled harvesting were found in the open literature.3.2.Consolidation and cold chain entry pointThe cold chain is a temperature-controlled supply chain for the preservation of key characteristics of the products that flow through it. Once the products are harvested they start to lose shelf life at different rates. Keeping the integrity of the cold chain is a key element to guarantee the quality of the perishable products during their transport, storage and distribution. Otherwise, the price and commercial conditions for the products would be affected. The reduction of the shelf life and spoilage of products can be postponed with the right cold chain (Ruiz-Garcia et al., 2010a,Ruiz-Garcia et al., 2010b), where monitoring technologies could play a fundamental role to determine the exact moment in which the quality of the product is at risk (Badia-Melis et al., 2015).In category i,Aung et al. (2011)present a study in which they integrate two types of sensors (RFID and WSN) in order to monitor the temperature conditions, traceability and the quality of the perishable product.Badia-Melis et al. (2015)propose a methodology to integrate RFID and WSN for monitoring the fruit that is kept at the cold warehouse. The proposed technology allows to capture real-time data, that in turn, allows to estimate energy consumption, the loss of water of the products kept in storage, and detect any type of condensation at the interior of the chamber.In Category ii,Hayes et al. (2005)propose a web system for temperature monitoring using multiple WSNs.Kaushik and Singh (2013)propose a food storage tracking system using WSN based on Zigbee and Bluetooth.Zhang et al. (2015)present an online platform that shows, on real-time, the conditions of cold storage of perishable products to customers.Juric et al. (2016)implement IoT for the optimization and control of the cold chain. They propose a data mining methodology to analyze the information that is obtained in real time through sensors in the cold supply chain.Bogataj and Drobne (2016)propose the use of nanosensors for controlling perishable goods in the cold logistics chain, to assess the problem of fruit and vegetable decay and rotting during transportation and storage, and in general for the post-harvest loss prevention. This contribution could be part of this echelon or also in the transport and shipping one.Mahajan et al. (2016)develop a small and flexible sensor-based respirometer for real-time determination of the respiration rate, respiratory quotient (RQ), and LOL of fresh produce.Srivastava and Sadistap (2017)develop an optimized handheld embedded odor sensing system (e-nose) to assess the ripeness of the oranges. Their proposed system is an efficient nondestructive handheld system to extract quality attributes of orange cultivars.In Category iii,Guillard et al. (2015)propose a DSS for the design of fresh food packaging including a modified atmosphere packaging simulation module.Singh et al. (2016)propose a cold chain location-allocation configuration decision model considering value deterioration in terms of the limited shelf life of the products, as well as coordination between shippers and customers.Lu and Wang (2016)present a DSS to support real-time decisions to control the cold supply chain. For this, they use artificial intelligence, cloud computing, geospatial technology, ZigBee and RFID.Herbon and Ceder (2017)propose a model to support