The Applications of Operations Research in Harvest Planning: A Case Study of the Sugarcane Industry in Thailand

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1 J Jpn Ind Manage Assoc 65, , 2015 Invited Paper The Applications of Operations Research in Harvest Planning: A Case Study of the Sugarcane Industry in Thailand Supachai PATHUMNAKUL 1 and Thawee NAKRACHATA-AMON 1 Abstract: In this paper, we focus on the applications of operations research (OR) for problems experienced in sugarcane harvest planning. The case of the Thai sugarcane industry is studied. Stochastic factors that affect harvest planning decisions are addressed. We also discuss the challenges involved based on the present circumstances of the sugarcane industry in Thailand. The problems we discuss in this paper include the harvester routing problem, the field merging problem, and the problem of integrating the labor force and harvester in harvesting operations. Guidelines of the OR model developed to solve the stated problems are suggested. Key words: Harvester, labor force, routing problem, sugarcane 1 INTRODUCTION Agricultural products from plants and animals are the upstream stage of various products down the value chain such as foods, apparel, medicines, and alternative energy. Due to its nature, the agricultural supply chain is difficult to manage compared to other industries because it involves several stochastic factors. In the case of the sugar cane industry, key stochastic factors are as follows: Product seasonality: As sugarcane is a seasonal crop, there can be a plentiful supply in some periods and a shortage of it in others. Production yield uncertainty: the production yield of sugarcane is affected by some uncontrollable natural factors such as changing weather or the outbreak of a disease. The yield may vary significantly from year to year or from location to location, so it is very difficult to predict the yield accurately. Variation in product quality: Sugarcane is normally priced based on the quality of its sweetness, which is measured by the sugar content of the cane (CCS). The sweetness quality varies based on many factors such as 1 Supply Chain and Logistics System Research Unit, Department of Industrial Engineering, Faculty of Engineer, Khon Kaen University, Thailand Received: November 30, 2014 the weather, the soil, the harvest timing and the skills of the growers. Product perishability: Agricultural products normally have a short shelf life. As such, sugarcane loses its quality rapidly after harvesting and perishes after a certain period. It should be crushed within 48 hours after being harvested, otherwise it becomes rotten or unusable [1]. To improve the performance of the agricultural supply chain, these factors need to be managed efficiently, which will result in products of higher quality and lower production costs. Among these stochastic factors, harvest planning is the most important task. An efficient harvesting plan can bring more profit to the entire sugarcane supply chain. The number of research studies on sugarcane harvesting problems has increased steadily. Operations research (OR) techniques are normally used in these studies [1]-[6]. Even though many OR models have been proposed in literature to accommodate sugarcane harvest planning, there are still many harvesting problems that need to be addressed and studied. For example, the harvester routing problem is the problem of designing an optimal route for a harvester to harvest a cluster of cane fields in order to share harvesting resource. In this paper, we discuss the applications of OR for sugarcane harvest planning and the challenges 328 J Jpn Ind Manage Assoc

2 involved under the present circumstances of the sugarcane industry in Thailand. The quality function, which plays an important role in the sugarcane harvest planning, is described in the next section. Harvesting models are reviewed in Section 3. New challenges of OR in harvest planning are discussed in sections 4, 5 and 6, respectively, and lastly, Section 7 is the conclusion. 2 QUALITY FUNCTION The quality and the cost of crops very much depend on the harvesting decision. Since the crop production yield stochastically varies, harvesting at the right time will result in a higher quality product and a lower production cost. In the case of sugarcane, the sweetness quality, which is measured by the CCS, is a function of harvesting time. Naturally, CCS increases in the early period after cultivating and starts to decrease after it reaches its peak. Figure 1 shows the parabolic CCS curve developed by the sugarcane quality function proposed by Jiao et. al.[4] The value of the function varies based on several factors such as the cultivating time, cane cultivar, soil type and growing environment. The ideal harvesting plan is a plan in which all sugarcane fields are harvested at the CCS peak period. However, it is difficult to ascertain the ideal plan due to many limitations such as mill capacity, harvesting resource capacity and number of delivery trucks available. In some periods, the quantity of the cane at the CCS peak may be over the capacity capable of being handled by the mill or more than the quantity that the harvesting equipment can handle. As a result, not all of the cane can be harvested. Some of it has to be harvested later after the yield has passed its peak. A good harvesting plan should be able to maximize the CCS or the yield of the total harvested cane based on fair benefits for all of the growers. 3 OR MODEL There have been various OR approaches that developed models for sugarcane harvest planning problems. The objective functions of these models mostly aim to maximize the production yield or the CCS. In Salassi et. al. [3], a mixed integer programming model was introduced for the problem to select a harvesting system (either whole stalk or combined) that maximizes the total net returns for the farms. CCS Value (%) week Fig. 1 The CCS curve based on the equation in Jiao et. al.[4]. In Yosnual and Supsomboon [7], an integer programming model was proposed for the problem of scheduling cane to be delivered to the mill using two types of trucks. An optimal number of harvesters are obtained for each harvest round [7]. In Jiao et. al.[4], the authors used linear programming to optimize the proportions of the fields that would be allocated for each harvest round. The objective is to maximize the CCS across all of the farms in the same group. In Piewthongngam et. al.[1], linear programming was applied to determine the planting dates, cultivars and harvesting period in order to maximize the overall sugar production. In Stray et. al. [6], the authors proposed a mathematical programming model and a metaheuristic algorithm to determine the harvest schedule that maximizes the total profit. Recently, Thuankaewsing et. al.[8] proposed mixed integer programming and a heuristic to maximize the sugarcane yield while concurrently leveling the benefits of the farmers who are in the same group. 4 HARVESTER ROUTING PROBLEM Recently, the use of harvesting machines (i.e., harvesters) has increased in the sugarcane industry in Thailand. Because the sugarcane growers in Thailand cannot normally afford owning an expensive harvester, most harvesters belong to a mill. In each crop year, the mill operator needs to set up a harvester Vol.65 No.4E (2015) 329

3 utilization plan. The plan includes the allocation of harvesters to the cane growing regions and the routing of each harvester throughout the harvesting season. One harvester is allocated to harvest a set of sugarcane fields in a specified region. The objective of the plan is to effectively utilize the harvesters and to obtain a high CCS with the lowest harvester moving cost. The moving cost covers the cost of the harvester moving in field (i.e., harvesting cane in the field) and the cost of the harvester traveling from one field to the next. The cost of the harvester moving in the field will be discussed later in the next section. Traveling from one field to the next can be considered as the vehicle routing problem with time window constraints (VRPTW), which has been extensively studied in OR literature. The time window constraints represent the time span of a particular field that can be harvested. A field can only be harvested within an allowed time window, in which the CCS yield may vary. The problem is depicted as shown in Fig. 2. In Fig. 2, the route of the harvester starts from the group of fields whose CCS peaks in the early period of the harvesting season, represented by E, and then it moves to the next group, whose CCS peaks in the middle period (M). Finally, the harvester moves to the next group, whose CCS peaks in later periods (L). To solve this specific problem, an OR model should be developed to optimally cluster the sugarcane fields into a set of clusters, each of which is assigned to the same number of harvesters. The objective is to determine the optimal harvesting route for each cluster based on VRPTW. The objective function (1) represents the total cost of traveling, the harvester utilization cost and the cost of CCS loss. The utilization cost is estimated by the cost of the harvester being idle. The CCS loss cost occurs if the sugarcane is not harvested at the peak CCS time. The cost component is normalized or weighted based on mill policies. h 1 h i i i (1) C r D m U q P S A where h i r m h h i index of harvester, index of sugarcane field, traveling cost per unit distance, cost per one percent of harvester being idle, q cost per one percent of CCS per unit area, C total cost of the model, D h the total distance in the route of harvester h, U h percent utilization of harvester h, P i highest CCS of the field i that could be obtained if harvested in the best period, S i CCS of field i is obtained if harvested based on the harvesting plan, growing area of field i A i 5 FIELD MERGING PROBLEM Most sugar industries, particularly in the northeast region of the country, are characterized by a large number of small independent growers [5] that are scattered over a large area surrounding the sugar mills. The size of each field is quite small. Sugarcane production is less than 300t per crop [8]. For small fields, it is not practical to use a harvester to harvest the cane. If the length of a field is too short, the harvester must make several U-turns, causing non-valueadded harvesting time and also damaging the cane at the edges of the field. Figure 3 shows an example of harvester movement in small and large fields. For practical use, the economic distance for a harvester is about 100m. That means the length of a field should be at least 100m for a harvester to be used. At the present time, most sugarcane mills try to encourage growers whose cane fields are located nearby to merge their fields in order to gain the benefits of using a harvester to harvest their cane. To employ this strategy brings problems that need to be solved, such as how to share benefits among the mergers, what incentive measures should be used to persuade growers to merge their fields, and which fields should be merged. The most important problem that should be considered is the problem of selecting which cane fields to be merged. To solve the problem, the incentive and benefit sharing among the growers needs to be calculated and evaluated. Several criteria need to be considered. The three main criteria are as follows: The CCS of the cane in the fields that are to be merged should be about the same. Otherwise, benefit sharing among the growers will be difficult. 330 J Jpn Ind Manage Assoc

4 The length of merging fields should be longer than 100m in order to gain the benefit of using the harvester. The merging fields should be prepared in the same growing pattern such as uniform land level, equal row distance and row space in order for the harvester to continuously travel across the fields. An OR model can be developed to solve this problem. The decision variables of the model cover the selected fields to be merged and the harvesting direction of the harvester along the length of the merged fields. The objective function of the model is to minimize the harvester time and also the loss of cane caused by the harvester making U-turns. The key constraints of the model are the three criteria mentioned earlier in this section. Figure 4 shows an example of merged fields. 6 LABOR AND HARVESTER INTEGRATION Even though the sugar cane harvester has been introduced in Thailand for many years ago, many farmers still harvest the sugar canes by hand. Some motives for using the labor force instead of using the harvester are as follows: The harvester is expensive. Most harvesters belong to a sugar mill. Sometimes there may not be enough number of harvesters available to harvest all the ripened cane at once. Most of the cane fields are small in size. It is not economically feasible to use a harvester if the growers do not merge their fields. Some cane fields are not rectangular in shape. The harvester may not be able to access some parts, which would then still need to be harvested by hand (see Fig. 4). To be efficiently harvested using a harvester, the field should be well prepared. The land should be uniformly level and the row distance and row spacing should be equal. If fields are not prepared for using the harvester, there will be more cane damage or loss than if the cane was harvested by hand. Some growers still believe that manually harvesting the cane is more productive than using the harvester. Fig. 2 An example of a harvester s route. Fig. 3 The movement of a harvester in small and large fields. Vol.65 No.4E (2015) 331

5 7 CONCLUSION Fig. 4 Description of field merging problem. Even though sugarcanes is still mostly harvested manually in Thailand, the future trend shows that the labor force will become scarce and the labor cost will increase steadily. The new challenge for sugar cane harvesting in Thailand is to integrate the use of the labor force and the use of the harvester in order to efficiently harvest the sugar canes. OR models can be developed to solve this problem. The objective function of the models is to either to minimize the total related cost such as the harvester moving cost, the labor cost and the CCS loss cost, or to maximize the sugar cane yield and CCS. Key constraints of the problem are the number of the harvesters, and the number of workers in the labor forces, and the mill capacity. Features of OR models for the integrating corporation of the labor force and the harvesters are as follows: Assigning fields to be harvested by the labor force or by the harvester or by both resources. Allocating labor force and harvesters to cane fields. Routing and scheduling labor force and harvesters for a set of specified fields. In the case of fields which require using both the labor force and the harvester, the harvesting operation of both resources in a field should be dependent and collaborative. Cane in a field should be harvested by the labor force and the harvester within the same week in order to gain uniformity of the cane quality and fulfill truck loads for efficient transportation. Since the speed of harvesting by the harvester is much faster than the speed when using just the labor force, the routing of both resources may be different. In this paper, the application of operations research for sugarcane harvesting problems in Thailand was discussed. Based on the available literatures, OR models have been extensively used in planning for the sugar cane harvesting operations. Most of the proposed models are related to the harvest scheduling and the sugar cane sweetness quality. Based on present circumstances, we have discussed some challenges related to in the harvest planning issue problem such as the harvester routing problem, the field merging problem, and integrating the labor force and harvester in harvesting operations. Guidelines for OR models to solve these problems were also suggested for future research. REFERENCES [1] Piewthongngam, K., Pathumnakul, S., and Setthanan, K.: Application of Crop Growth Simulation and Mathematical Modeling to Supply Chain Management in the Thai Sugar Industry, Agric. Syst., Vol. 102(1 3f), pp (2009) [2] Higgins, A.J., Muchow, R.C., Rudd, A.V., and Ford A.W.: Optimizing Harvest Date in Sugar Production: A Case Study for the Mossman Mill Region in Australia I. Development of Operations Research Model and Solution, Filed Crops Res., Vol. 57, pp (1998) [3] Salassi, M.E., Breaux, J.B., and Naquin, C.J.: Modeling Within-season Sugarcane Growth for Optimal Harvest System Selection, Agric. Syst., Vol. 73, pp (2002) [4] Jiao, Z., Higgins, A.J., and Prestwidge, D.B.: An Integrated Statistical and Optimization Approach to Increasing Sugar Production within a Mill Region, Comput. Electron. Agric., Vol. 48, pp (2005) [5] Grunow, M., Günther, H.-O., and Westinner, R.: Supply Optimization for the Production of Raw Sugar, Int. J. Prod. Econ., Vol. 110, pp (2007) [6] Stray, B.J., van Vuuren, J.H., and Bezuidenhout, C.N.: An Optimization-based Seasonal Sugar Cane Harvest Scheduling Decision Support System for Commercial Growers in South 332 J Jpn Ind Manage Assoc

6 Africa, Comput. Electron. Agric., Vol. 83, pp (2012). [7] Yosnual, J. and Supsomboon, S.: An integer programming for sugarcane factory supply allocation, Proceedings of the Fifth Asia Pacific Industrial Engineering and Management Systems Conference (2004). [8] Thuankaewsing, S., Khamjan, S., Piewthongngam, K., and Pathumnakul., S.: Harvest Scheduling Algorithm to equalize supplier benefits: a case study from the Thai sugar cane industry, Comput. Electron. Agric., Vol. 110, pp (2015) Vol.65 No.4E (2015) 333