Delivery of Perishable Export Products in Smart Cities: A Case Study in Bogotá (Colombia)

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Delivery of Perishable Export Products in Smart Cities: A Case Delivery of Perishable Export Products in Smart Cities: A Case Study in Bogotá (Colombia) Study in Bogotá (Colombia)

Gonzalo Mejía*, William Guerrero, Alfonso Sarmiento, Nathalia Serrano, Margarita Gonzalo Mejía*, William Guerrero, Sarmiento, Nathalia Serrano, Margarita Sarmiento,Alfonso and Camila Sánchez Sarmiento, and Camila Sánchez Universidad de La Sabana, Faculty of Engineering. Campus del Puente del Común Km. 7 Autopista Norte. Chía, Colombia Universidad de La Sabana, Faculty of Engineering. Campus del Puente del Común Km. 7 Autopista Norte. Chía, Colombia

Abstract Abstract Perishable export products are usually shipped to their international destinations by air. This export process is complex and involves many agents suchproducts as airlines, companies and manufacturers. of the major thatprocess transport companies face every Perishable export aretruck usually shipped to their internationalTwo destinations by air.problems This export is complex and involves day are (1) traffic and (2) congestion of trucks at the unloading platforms at the airports. In general, the lack of coordination between many agents such as airlines, truck companies and manufacturers. Two of the major problems that transport companies face every such agents amplify problems.ofWe introduce a simulationoptimization approach for the coordination of truck companies day are (1) traffic andthese (2) congestion trucks at the unloading platforms at the airports. In general, the lack of coordination between that to international destinations in the context of smart approach cities. Thefor proposed method was testedcompanies with realsuchdeliver agents export amplifyflowers these problems. We introduce a simulationoptimization the coordination of truck time traffic export and status of the airport cargo destinations areas and show improvements in comparison with the current situation. The that deliver flowers to international in thedrastic context of smart cities. The proposed method was tested with realproposed method can be embedded in smartphone applications and can use existing technologies and can be adapted to current time traffic and status of the airport cargo areas and show drastic improvements in comparison with the current situation. The practices. proposed method can be embedded in smartphone applications and can use existing technologies and can be adapted to current practices. © 2019 The Authors. Published by Elsevier B.V. © 2019 The Authors. by Elsevier Ltd. This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2019 The Authors. Published by Elsevier B.V. This is an and open access article under theresponsibility CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection review under of ICPR25 International Scientific & Advisory and Organizing committee This is an openpeer access article underthe the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the ICPR25 International Scientific & Advisory and Organizing members Selection and peer review under the responsibility of ICPR25 International Scientific & Advisory and Organizing committee committee members members Keywords: Smart City; Scheduling; Logistics; Export flowers. Keywords: Smart City; Scheduling; Logistics; Export flowers.

* Corresponding author. Tel.: 571-8615555. address:author. [email protected] * E-mail Corresponding Tel.: 571-8615555.

E-mail address: [email protected] 2351-9789 © 2019 The Authors. Published by Elsevier B.V. This is an open access under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 2351-9789 © 2019 Thearticle Authors. Published by Elsevier B.V. Selection review under the the responsibility of ICPR25 International Scientific & Advisory and Organizing committee members This is an and openpeer access article under CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer review under the responsibility of ICPR25 International Scientific & Advisory and Organizing committee members 2351-9789 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the ICPR25 International Scientific & Advisory and Organizing committee members 10.1016/j.promfg.2020.01.237

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1. Introduction Smart cities can have a significant impact on the performance of urban freights. The term ‘smart cities’ is synonymous of virtual cities, cyber cities, digital cities, networked cities, intelligent cities, knowledge cities and realtime cities; Important for these cities is the use of information technologies to facilitate relevant urbanization objectives [1]. Among these objectives, improving the performance of urban freights and reducing traffic congestion are of major importance. In this paper, we study the problem of improving the delivery of export flowers in Colombia, using information technology, smartphone applications and machine learning optimization algorithms in the context of a smart city. The Colombian flowers are among the best in the world for their variety and quality [2]. The conditions for export of flowers in Colombia are excellent due to its geographical location which provides stable weather all year long, low labor costs, and good growing and harvesting technology and practices [3]. New flower exporters such as China and some African countries have increased competition pressure to the Colombian flower industry [4]. For these reasons, the Colombian flower industry needs to be more efficient in both their production and logistics processes. Valentine's Day and Mother's Day are the most important seasons for the floriculture sector. On this date approximately 35,000 tons of flowers [5] are shipped to their international destinations, mainly though airport terminals. During such periods of high demand, congestion is heavy at such airport terminals, especially at El Dorado cargo airport in Bogotá [6]. This 110.000 square meter facility can load/unload up to 25 aircraft simultaneously and in 2015, 21 cargo airlines carried out 1.589 air operations [7]. Despite its size, long lines of trucks await at the parking lots of El Dorado cargo terminal. Not only such trucks cause congestion in the terminal and in the neighboring areas; normally truck drivers turn off the air conditioning in order to save fuel and therefore break the cold chain. Consequently, the quality of the flowers suffers as they must be kept properly refrigerated. The problem is not limited to unloading capacity: Most trucks arrive within a short time window. According to previous studies [6], the cargo terminal remains virtually empty for most of the day; only in the afternoon and during the early night hours, congestion arises. The reason why trucks arrive within such a short time window is because they cannot leave from their farms earlier. This is because flowers are rarely packed and ready before noon. Most flower growers hold no stock and rely on the same day production. This production process starts in the early hours of the day and finishes in the early afternoon. Moreover, about 90% of the trucks travel half-full [6]. Last but not least, trucks must deal with heavy afternoon traffic. Despite the fact that most farms are located within 50 Kms. (31 miles) from the airport, trucks spend sometimes over two hours from departure to arrival at the terminal. As mentioned above, poor logistics practices create heavy congestion at the airport terminal, thus affecting flower quality and increasing costs and pollution. Logistics strategies in the context of smart cities can be a possible answer to this problem. The research question can be stated as follows: Can smart city strategies and technologies reduce the truck’s lead time? The lead time here is the sum of travel, queue and unloading times. In this paper, we propose a simulation model with different departure and service policies aimed at reducing lead times, especially travel and queue times. Reducing unloading times is out of the scope of this paper. The result of this simulation study can be the basis for a smart city application that takes as inputs the travel times, unloading rates, truck size, farm region, etc., “learns” from such data and proposes a schedule for each truck that minimizes all lead times. This paper is organized as follows: Section 2 examines the flower sector in Colombia and the smart cities in the context of freights. Section 3 illustrates the simulation optimization approach and the results. Section 4 concludes and provides insights for future work. 2. Background 2.1. The Colombian Flower Sector The export flower industry is the fourth largest source of currency in Colombia [8]. As of today, Colombia is the second largest exporter of fresh flowers in the world after the Netherlands [9]. As of today, the country has 75,000 hectares dedicated to production of flowers, of which 70% are located in the Sabana (highplain) de Bogotá, 27% in

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the Río Negro northwest region and the remaining 3% in the coffee belt region [10]. According with the association of export flower growers (Asocolflores) Colombia exported 246,118 tons of flowers in 2017, and obtained revenues of $ 1.4 billion from its main markets in United States, United Kingdom, Japan, Canada and Russia [11]. As said above, in the export flower business, the on-time delivery is critical for the quality of the flower and one of the factors that affect the most the delivery is traffic and congestion at the airport terminals. 2.2. Freight Logistics Urban and peri-urban freight logistics are topics that have gained significant attention in the literature. Freight logistics are the connection between suppliers and customers, and it is carried out in goods transported between places of production to places of consumption. The impact of the inefficiencies in freights leads to increasing costs, congestion, pollution and so on [12]. Whereas geographical factors and infrastructure are the most significant variables affecting efficiency of freights, other factors such as the chaos in traffic and poor logistics practices can also affect [13]. 2.3. Background of freight in smart cities The need to find solutions to improve the human life quality in a multisectoral ways has produced concepts such as "Smart Cities". As said above, there are different definitions that essentially combine two concepts: Sustainability and massive use of information technology [14]. Several authors (e.g. [15]) evaluate smart city logistics solutions aimed at reacting quickly to the changing citizens' needs and demands. Many of the studies on this topic have focused on the environmental pollution in the cities and on reducing congestion to generate impacts on mobility. For example, Li and Yu, 2017) focused on smartphone freight application services (Apps) and CO2 emission reductions in road freight transport and to identify the core problems for improvements. Freight apps provide a mechanism that auto-match the demand and the carrier’s supply based on mobile Internet [17]. Further, Álvarez et al., [18] studied the evolution of the smart city logistics, focusing on traffic congestion costs and on driver behavior. Comi et al.,[19] presented an integrating delivery bay planning with transportation demand in a limited traffic zone. The performance of freight logistics in urban areas is closely related to avoiding traffic, quickly finding parking spots and meeting regulations, among other things [20], [21]. The use of smart technologies combined with optimization algorithms can be a valuable tool. Examples of these technologies are abundant: For example, Waze ™ and Google Maps ™ are widely used smartphone apps. These apps are intended to provide real-time traffic status and route optimization for a single user. However, such applications fall short when optimizing system performance. Clearly, this is a difficult task as it is very difficult to optimize a system with as many objectives as agents (car drivers, traffic authorities, etc.). However, in a subsystem such as the export of flowers, this can be attained if, for example, rewards/penalties are given to truck drivers. If trucks arrive outside a pre-assigned window, they will have to wait in a FIFO queue. Similar systems exist in container terminals [22]. 3. A simulation-optimization model In this section, we show that with a simulation-optimization model, it is possible to improve the system performance through the use of smart city technologies. Essentially, we mimic the actual system behavior and provide the basis for a smart city application. The smart city application would automatically collect data from truck rides, traffic and from the airport terminal and will suggest a departure time from each truck. By doing so, the time spent in traffic and queuing at the airport can be drastically reduced as we will see in the remainder of this document. In this simulation optimization model, we compare two scenarios: The current situation and an optimized situation. In the current situation, all trucks leave their farms at random times within a predefined time window set by production constraints (the flower cutting, bunching and packing) and logistics constraints (i.e. flowers must be at the airport

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before the aircraft departure). Trucks arrive at the airport terminal and are serviced with a FIFO police. No cooperation among flower exporters is expected. In the second situation, a Local Search algorithm computes the “ideal” departure date for each farm within the predefined time window aiming at reducing total lead time. This algorithm should be constantly fed with traffic, congestion and other data as in the widely known case of taxi ride predictions in New York City [23]. Data to feed the model can include weather patterns, historical data of loading and unloading rates collected at the farms, driver behavior, time of the day, day of the week and so forth. At the time of writing we have not implemented such an algorithm, but a Neural Network is the most likely option. In this paper, we use probability distributions for the sake of illustration. 3.1. Data Collection Farms are located in four geographical zones that correspond to geopolitical divisions (Bogotá metropolitan area, Central Sabana, Northern Sabana, and Western Sabana). We used a sample of 80 farms and calculated their distances to the airport with the Google Maps™ application. The means of the travel times were calculated with average speed at the different hours of the day for each geographical zone. Since travel times are stochastic, we assumed a normal distribution with the mean equal to the average speed and standard deviation equal to the 20% of the mean. Obviously, this can be argued, but for the purpose of this experiment, we believe this is reasonable. The unloading rates were calculated according to the probability distributions estimated in [6]. These unloading rates depend on the truck size and number of boxes. Since, detailed information of number of flower boxes shipped by each farm were not available, we generated such number with the uniform distributions. In this paper, we consider a typical airline with six unloading platforms. Figure 1 illustrates the location of the sampled farms.

Fig. 1. Location of farms in the vicinity of Bogotá (Colombia)

3.2. Experimental Runs We ran 30 replicates of each situation (current vs optimized). The following figures illustrate the results. Figure 2 shows the values of the objective function which is the total weighted lead time expressed in boxes-minutes. It can be

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seen that an intelligent system can provide savings of up to 12.3% on average in comparison with the current situation (57000 vs. 65000 boxes-minutes). This figure indicates an average savings of 30 minutes per truck per day in the high season. Most importantly, there is no need of additional infrastructure; only hardware and software and of course, the driver’s willingness to cooperate.

Objective function

67500 65000

62500 60000

57500 55000

FIFO

METHOD

Local Search

Fig. 2. Objective function comparing the current and the improved system.

Figures 3 and 4 show the departure, travel times and queue times in terms of the geographical zone. These values were already optimized with the aforementioned Local Search heuristic algorithm. 225 200

Travel time [min]

Dep. time [hh:mm]

5:00 p.m. 4:00 p.m. 3:00 p.m. 2:00 p.m.

175 150 125 100 75 50

1:00 p.m. BOGOTA

CENTRAL

NORTH

ZONE

WEST

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Fig. 3. Departure and Travel Times vs. Geographical Zone.

NORTH

ZONE

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Queue Time [min]

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7:00 p.m. 6:00 p.m. 5:00 p.m. 4:00 p.m. 3:00 p.m. 2:00 p.m.

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500 400 300 200 100 0

BOGOTA

CENTRAL

NORTH

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BOGOTA

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Fig. 4. Arrival and Queue Times vs. Geographical Zone

The figures indicate that there is no distinguishable pattern for the departure times. However, the arrival times should be coordinated so the vehicles of the central and northern zones arrive later in the day in comparisons with the Bogotá and the western zones. Farms located at the latter zones are nearest to the airport terminal. In terms of travel times, these are mostly proportional to distance and less influenced by time of the day. Thus, trucks leaving from distant farms spend more time traveling. The last figure show the queue times. These show that on average, queue times are independent of the geographical zone. These figures are still high (on average 120 minutes) but much lower in comparison with the actual figures reported in previous studies [6] which were around 150 minutes (2.5 hours). 4. Conclusions and Future Work In this paper, we explored the potential of improving the delivery of export flowers in the context of smart cities. In this exploratory study, we investigated the use of an easily implementable simple Local Search algorithm and we showed how with a smart strategy, the total lead time can be drastically reduced. We also analyzed how the algorithm could be improved by feeding it with up-to-date information collected automatically with existing systems (e.g. GPS and georeferencing). The algorithm should also be able to reschedule the vehicles in the event of cancellations, additions, or unforeseen situations. In future work, we will explore the use of mathematical programming algorithms and more sophisticated heuristics. Also, we will design more sophisticated methods for traffic time prediction and carbon footprint. References [1] [2] [3] [4] [5]

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