Improving the Organization of Waste Management Sites: Simulation Based Analysis

Transcription

1 Improving the Organization of Waste Management Sites: Simulation Based Analysis Elad Harison Shenkar College of Engineering and Design, Israel. Arkady Cherkassky Shenkar College of Engineering and Design, Israel. Ofer Barkai Department of Industrial Engineering and Management, Shamoon College of Engineering, Israel. Abstract The paper applies discrete event simulation methods to assess the performance of different configurations of waste management sites (WMS), as means for improving their productivity, efficiency and organization. We evaluate the functioning and performance of WMS under various real-world scenarios, such as different waste arrival times, changes of the volume of waste due to population growth, changes in the shares of different recycled materials due to increasing awareness and public campaigns, among others. The results of the model can serve policy makers, environmentalists and WMS managers in utilizing their existing facilities to improve both productivity and recycling and, thereupon, to reduce waste. Key Words: Discrete Event Simulation, Waste Management, Productivity, Efficiency JEL Classification: C 63, L 90, Q 53 1

2 1. Introduction Waste management (WM) is the sustainable process of the collection, transportation, processing, recycling and monitoring of all types of waste materials that are generally produced by human activities. The various approaches and applications of WM operations are based on broad and diverse business practices and economic principles that require efficient organization, management, manpower and equipment. WM operations include a broad variety of logistics and production related operations, including collection, transportation, processing, recycling, disposal and monitoring of waste. The paper presents a methodological framework for improving the efficiency performance of WM sites and reducing their costs by modelling of a WM site and simulating its operations and performance under varying operational and environmental conditions. 2. Methodology A computer model of a WMS was developed to study the system behavior by varying its parameters that reflect changes in the environment and the population. A process flow chart developed by Harison and Barkai (2012) serves as a basis for this model. The model was implemented within the framework of Discrete Event Simulation modelling. An entity represents an individual component of waste in each serving (truck delivery) arriving in the WMS. The entity type defines the waste component type and its proportion in the waste arriving to the WMS. A specific feature of the WMS simulation model is that an entity changes its type and attributes as a result of the waste sorting. Mass attributes of these entities are then calculated according to their proportion in the total volume of newly retrieved waste. At subsequent stages of the process that represents waste management in our model, new entities are produced and older entities are removed along the waste sorting. The upper layer of the hierarchical simulation model is a mapping of the process flowchart and determines the structure of the entities streams in the WMS. The lower layers of the model are implemented in a form of submodels, each of which corresponds to a particular stage of the process in the WMS. Fig. 1 shows the generalized scheme of the WMS model in ARENA. The modules are the main elements of the models in ARENA. The first module is the WeightInCreate module. This module generates a stream of entities (trucks) in accordance with a predetermined schedule, which can be modified in each simulation run. The modules 11, 36,38 correspond to the classification steps and generate the replacement of the entity type. The submodels simulate all specific processes in the WMS. It should be noted that the Small Waste submodel is, in turn, the higher level of hierarchy with respect to the Recycle bin for metal and Collection for composting submodels. 2

3 Figure 1: A generalized scheme of the simulation model. 3. Results 3.1 The Operations of Mafat WMS The Mafat WMS, located at the center of Israel and serves four cities that are in its vicinity. The core of Mafat s operations is based upon collection, removal, and sorting of MSW. Work studies utilizing time measurement techniques were then employed to measure the duration of work processes in the WMS. Complementary data for the analyses of the model results were obtained from Mafat s information system. Analysis of the waste reveals the following distribution of waste: Shredding material 70%; Small waste/shredding material 53%; Metal/ Small waste/shredding material 61%; Small organics/no Metal/ Small waste/shredding material 27%; Landscape waste/no Shredding material 25%; Recyclable/No Landscape waste/no Shredding material 60%; Paper and Plastics/No Small waste/shredding material 36%. Trucks arrive at the site at random and independent times with average truck arrival rate of 17 per hour. Arrivals are exponentially distributed with average time between arrivals of 3.5 minutes. 3.2 Results of the Simulation Model A series of computer experiments were carried out. The processing time at each technological process was reduced to provide constraints on the equipment utilization. Each series of the computer experiments was included 10 repeats (replications) of the simulation during 24 hours. Productivity levels of the equipment that was determined on the base of the preliminary experiments on the model were: Primary Sorting = 1767 kg/min, Shredder = 1237 kg/min, Recycle bin for paper and plastics = 212 kg/min, Recycle bin for metal = 400 kg/min, Collection for composting = 69 kg/min, Processing station for compost = 132 kg/min, Recycle Station for bulky items = 238 kg/min, Landfill = 715 kg/min. These data correspond 3

4 to the basic operation mode of the model. We measured shows the average waiting time (AWT), the maximum AWT (MAWT), the average number waiting (ANW), and the maximum ANW (MANW), resulting from ten repetitions of the 24-hour simulation model. The results also shows the average utilization index (AUI) and the minimum and maximum of the AUI (maui and MAUI) for all stages of the waste management process. Based on the comparison of queue state parameters when analyzing the simulation results, we define two types of bottlenecks in the WMS the waiting time bottleneck and the number waiting bottleneck. Stages of the WMS associated with these bottlenecks are critical stages and require management decisions. From the results of the model, it follows that stage number 3 - Primary Sorting - is the critical step (bottleneck) for the basic operation mode of the model. The graphs of the queue length in 24 hours (Figure 7) give a detailed understanding of the queue dynamics of the Primary Sorting stage. Figure 2: The queue length dynamics (the Primary Sorting bottleneck) for three workdays in the basic operation mode. As can be seen from Fig. 2, the local outliers arising in Primary Sorting queue dissolve within a few hours. More detailed analysis of the simulation results shows that the dissolve time never exceeds the duration of the work shift. One of the most important issues in practical terms is how the model responds to local surges of the incoming flow of waste collection trucks. A series of computer experiments, in which the height and duration of the burst intensity of the arrival of trucks was varied, were conducted to study the behavior of the model of WMS in such situations. Their results indicate that a short-term increase within an hour in the intensity of the trucks arrival by 47% led to a more than twofold increase in the Primary Sorting waiting time and the queue length. The Primary Sorting stage came out on top of the maximum number of the waiting entities. It should be noted that the utilization index increased only by 3% for all the stages of the process. A graph of the Primary Sorting queue length is shown in Fig. 3. This figure indeed 4

5 demonstrates the strongly pronounced localization of the burst and a slight increase in its duration. Figure 3: Dynamics of the Primary Sorting queue during 24 hours The next question tested was the response of the WMS to bursts of equal power (the same number of trucks in the burst) during different time durations. In these two experiments, the arrival of 50 trucks to the WMS was simulated for one hour and for two hours. Comparison of the results of the two experiments leads to the conclusion that the system can cope with more ease with the more distributed increase in the arrival rate over time. The maximum values of the waiting time and the queue length in the Primary Sorting bottleneck are inversely proportional to duration of the intensity burst of truck arrival. The WMS relatively easy compensated the 50% intensity change in the truck arrival rate for two hours (for one work shift). However, with further increase in the arrival rate, the WMS response (return time to the steady state) dramatically increased. Another experiment in which the arrival rate increased from 17 to 35 truck arrivals per hour between 8 am and 10 am (total 70 for two hours) shows dramatic non-linear increase of the queue length in the Primary Sorting bottleneck. As a result, the WMS could not return to a steady state for two work shifts (see Fig. 4). This situation deteriorates and becomes critical with a further increase of the truck arrival rate to 100 for two hours. 5

6 Figure 4: Dynamics of the queue length in the Primary Sorting bottleneck and in the Dump bottleneck (total 70 trucks for two hours). The effect of the queue parameters on reduction of the Dump bottleneck is not sufficient in terms of resolving the critical situation. Even when the Dump productivity increased by 100%, the situation did not improve. By doubling the Dump productivity, the bottleneck returned to the Primary Sorting and the situation in the WMS did not improve (see. Fig. 5). Fig. 5: Dynamics of the queue length in the Primary Sorting bottleneck by doubling the Dump productivity (total 150 trucks for two hours). In this situation, it seems logical to increase the productivity of the Primary Sorting simultaneously with the productivity of Dump and thus to move the bottleneck into the WMS itself. This move of the bottleneck leads to the distribution of production load between the specialized equipment within the WMS and to bottleneck splitting. 6

7 A further analysis was conducted to investigate other effects on the operation of the WMS, such as changes in the mix of waste components (for example, due to education of the society, regulation, etc.). In one of the experiments we changed the percentage of Shredding materials from 70% to 60%. All the parameters of the WMS model remained unchanged as in the basic model. The results suggest that the change increased the average waiting time of the Processing Station for Compost and Recycle Station for bulky items from 1 to 82 minutes and increased the average queue length from 0.2 to 23. These results indicate a very high sensitivity of the WMS to the composition of waste materials. A similar experiment that also decreased of 20% of the process time for the Processing Station for Compost and Recycle Station for bulky items in addition to the change in the mix of waste materials demonstrates that despite the extremely high sensitivity of the WMS, it can be stabilized through adequate speed changes of the Processing Station for Compost and Recycle Station for bulky process. 4. Discussion and Conclusion The operations and the performance of WMS usually depend on the organization and the on the physical layout of their facilities. Re-organization of sites can be the key for increasing the efficiency of waste treatment processes and the recycled-materials-to-waste ratio, while reducing the environmental impact of waste. However, changes in the organization of WMS and replacement of WM equipment can be costly and may produce little benefits or even damage the output of WMS, if new layouts and plans are not thoroughly analyzed ex ante. Our model simulates the operation of a generic WMS and the treatment processes and possibilities that waste undergoes, from its collection by trucks to the various facilities that handle it. The simulation model analyzes the sequence of WM facilities and processes as production systems that should match environmental, organizational and economic goals, which are based on the materials delivered in the WMS. Though the model is generic, it can be customized to cater to the needs and to the unique conditions of every WMS and waste composites received. The case study utilizes the model to assess the performance of an Israeli WMS under different real life scenarios. The case study results indicate that minor scenarios can largely affect the performance of WMS: traffic jams that change the frequency of truck arrivals and affect the flow of waste that enters the WMS, changes in the mix of waste composites due to education, regulation etc. In particular, small to intermediate changes in the truck arrival rate can cause major interruptions and bottlenecks in the recycling and waste treatment processes that follow, and may even cause the organization s operational collapse (i.e. not returning to balance, without substantial allocation of additional resources). Thereupon, the methodology and the model presented in this paper propose a tool for managing WMS at the micro-level of their operations. WMS managers can simulate a broad 7

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