Kullapapruk Piewthongngam

Pig procurement plan considering pig growth and size distribution

Different pig size distributions in fattening units can prevent a pig procurement plan from achieving optimal results. Plans that fail to consider the pig size distribution and pig growth are not likely to be able to cost-effectively satisfy demand for each pig size. This paper demonstrates the use of a heuristic algorithm, pig size distribution, and pig growth to create a procurement plan. The performance of the developed procurement method is compared to the traditional practices of a company studied here. The results indicate that the company is likely to save approximately 9.52% of procurement costs by adopting the proposed method. The same problems were also investigated at an industrially relevant scale, and the computational time of the proposed heuristic was found to be reasonable. Thus, the pig industry is likely to benefit from the method developed here.

Harvest scheduling algorithm to equalize supplier benefits

Harvest scheduling for a group of sugar cane farmers is studied.The objective is to maximize yield with fair benefits for all farmers.Mathematical model and heuristic are developed to solve the problem.Heuristic is applied to an industrial case study in Thailand. In this study, the harvest scheduling problem of a group of cane growers in Thailand is addressed. Each member in a group is required to consistently supply sugar cane to a mill for the entire harvest season. However, the current scheduling does not account for the time-variant cane production of each cane field, which leads to unequal opportunities for growers to harvest. A portion of growers could have the opportunity to harvest in periods that provide higher sugar cane yields, while others in the same group do not. This inefficient harvest scheduling causes conflicts between growers and unnecessary loss of sugar cane and sugar yields. An artificial neural network is applied to estimate cane yield over a harvest season. Then, an optimization model and a heuristic algorithm with the objective of maximizing the estimated sugar cane yield under the condition of fair benefits for all of the growers in the group were developed to determine the most suitable sugar cane harvest schedule for a cluster of sugar cane fields. For the heuristic, the initial solution is first constructed based on the yield trends, and the solution is then improved by the tabu search approach. The results indicated that there are potential benefits from applying the model to cane scheduling within a group of heterogeneous yield trends. Sensitivity analysis showed that the more that the yield trends in a group differ from one another, the higher the benefit the group is likely to gain from adopting the proposed framework.

Using an artificial neural network to forecast the market share of Thai rice

In this paper, the artificial neural networks (ANN) is used to estimate the market share of Thai rice in the global market. Two models are formulated under two assumptions. First, the market share depending on exporting prices of rice of Thailand, Vietnam, India, USA, Pakistan, China. Second, only the export prices of rice from Thailand, Vietnam, USA, and China are considered. The export prices are used as input parameters, while the market share of Thais rice in the global market is the only output parameter of the models. Annual data from 1980 to 2005 are gathered from United States Department of Agriculture (USDA) and Food and Agriculture Organization of the United Nations (FAO). The study showed that the second model provide more promising results with the minimum mean absolute percent error (MAPE) of 4.69% and the average MAPE of 10.92%.

Land consolidation of small-scale farms in preparation for a cane harvester

Abstract The cane cultivation areas of certain countries are primarily composed of small-scale farms. To adopt harvesting machinery efficiently, consolidating these small plots is essential. However, the decision to plough out cane ratoon to synchronize the cultivation process in consolidated land area is complicated because the plots have different cane ages and different ownerships. To address this problem, we develop a mathematical model and a heuristic method based on the greedy algorithm to create a consolidation and plough-out plan. The solution obtained using the heuristic method differs from the optimal solution less than 1.5% for small cases of 5, 10, and 15 cane plots and requires less computational time. The proposed heuristics, when used to solve large-sized problems, suggest a plan with benefits that are approximately 49.39% higher than those of the conventional unsynchronized method. This approach is likely to facilitate consolidation planning for sugar mills and cane growers, resulting in more efficient harvester utilization.

Using an artificial neural network and a mathematical model for sugarcane harvesting scheduling

In this paper, the sugarcane harvesting scheduling problem, in the northeast region of Thailand, is addressed. Since there are many small size farmers are participated as suppliers of the sugarcane mill, a harvesting schedule, which could provide the maximum production yield for sugarcane mill and also equal opportunity for farmers to harvest at their suitable time are required. A model, which is the combination of an artificial neural network (ANN) and a mathematical model, is proposed to solve the problem. The ANN is used to forecast sugarcane yield of each plantation over harvesting season. Then, the forecasted values are used by the mathematical model to find the optimal harvesting schedule. The objective function of the proposed mathematical model is to maximize the total sugarcane yield; meanwhile the harvesting scheduling maintains the equality among farmers in the group. The application of the model is also investigated with an example problem.

How soaring agricultural prices will impact the way we do feed business

The rising price of agricultural products leads to frequent change of feed recipe, which can cause a high number of reprocessing batches, elevating the overall cost of production. In this study, we proposed an artificial neural network to predict production rate. The conversion of production rate to production cost, the tips for data collection as well as tips for implementation of new feed cost estimation are also discussed. Being able to estimate production rate enables feed mills to improve their operations. In this study, we elaborate its application to feed scheduling (although the applications can be extended to other aspects such as productivity improvement, which goes beyond the scope of this particular study).

Scheduling a single machine with concurrent jobs for the frozen food industry

The studied machine scheduling is for concurrent, multiple job processing.The problem considers different processing rates and jobs arrival and departure times.The problem addresses the air blast freezing process of the frozen food industry.A mixed integer linear programming model and a heuristic algorithm are developed.The heuristic algorithm demonstrates the high potential of computational time saving. This study examines the air blast freezing process of the frozen food industry, which processes multiple products with variable processing rates. The analysis depicts a new, single machine-scheduling problem in which the machine can process multiple jobs concurrently, within its capacity. The machine processes independent jobs arriving at various times while incurring interruption costs when allowing the jobs to enter or leave the machine. A mixed integer linear programming (MILP) model and a heuristic algorithm are developed for scheduling, the objectives of which are to minimize the costs associated with machine activities including that of waiting to load, waiting to unload and interruption time. The heuristic algorithm demonstrates the high potential of the computational time savings by obtaining the solution within one-fifth of the mathematical model computational time.

Unrelated Parallel Machine Scheduling with Sequence-Dependent Setup Times in Frozen Food Industry

In this paper, a scheduling problem in the freezing station of a frozen food industry is addressed. It is a case of the unrelated parallel machines with machine-dependent and job sequence-dependent setup times problem. In a production shift, each freezing machine is allowed to process only products in the same product family. A mathematical model is developed to solve the problem in order to minimize the production related cost including production cost, fixed production cost, setup cost, inventory holding cost, and backlog cost. The proposed model is tested with a set of various problems. The results demonstrate that the proposed mathematical model could optimally solve the small sized problems.

Order selection of processed chicken under production capacity constraints

In this paper, an order selection problem for chicken processor is addressed. When customer demand is over the company production capacity, all order could not be satisfied. A mathematical model is developed to choose type of orders should be produced. The objective function of the model is to maximize profit of processor taking into account production capacity constraints. The result demonstrates that the mathematical model could practically solve the problem of the case study company under acceptable computation time.

Feed logistics management for a vertically integrated swine company

In this paper, the logistic problem from an animal feed industry to a cluster of pig farms is studied. In the swine production, the feed demand is dynamic and uncertain. Pigs in different growth stage require different feed types. Also, the feed quantity required in each growth stage is also varied depending on the animal health and environment conditions. Likewise, a planer needs to consider other constraints such as the full truckload, the minimum ordering batch of a feed mill. Taking these factors into account, the feed logistic problem is complicated. To solve this problem, the collaborative between farms, delivery trucks and feed industry is necessary. The production lot size of feed industry, the order quantity of each farm in the cluster and the transportation plan must be integrated. In this paper, an algorithm based on the mathematical model is developed. The objective function of the model is to minimize the logistic cost which is the combination of transportation and inventory costs. The developed model has been tested with a practical sample from a large swine industry in Thailand. The result showed that the method provides a practical solution which could be applied to the industry.