This research aimed to study the approach of the community toward the decision to grow rice and economic crops, including appropriate resource allocation for use on a farm under a large plot agricultural system. The study areas were in Phan district, Chiang Rai province, Thailand, and the data were collected from a sampling of 400 field agriculturalists. The method used was to develop a mathematical model for growing crops with multi-objectives and in multi-periods, together with an agriculturist representative and experts in multiple-criteria decision-making (MCDM). This was to prioritize the importance of alternative crops and find the appropriate allocation of the resources to achieve the targeted goal. The results showed that agriculturists prioritized most toward the criteria for growing Japanese rice with a weight of 0.179 Kg., followed by transplanted rice, transplanted glutinous rice, garlic, sown paddy rice, and sown glutinous paddy rice, respectively. The study’s results also showed that the price fluctuation of the crop products resulted in more use of land and labor in order to increase the production to compensate for the low price, and this also resulted in the higher opportunity cost of growing transplanted rice. Therefore, growing transplanted rice during in season planting was considered the most effective way, while during the off season, either garlic or Japanese rice could be grown. A collective pattern for planning for using resources together in large plot agricultural areas, together with a clear marketing target would bring about effective use of the resources and reduce the risk in revenue from the fluctuation in prices and uncertainty of yields from drought. Moreover, technology development to solve the problem of the lack of labor would be deemed an important approach toward the enhancement of the competitiveness of agriculturists in the future as well.

A vehicle routing problem with time windows (VRPTW) is an important problem with many real applications in a transportation problem. The optimum set of routes with the minimum distance and vehicles used is determined to deliver goods from a central depot, using a vehicle with capacity constraint. In the real cases, there are other objective functions that should be considered. This paper considers not only the minimum distance and the number of vehicles used as the objective function, the customer’s satisfaction with the priority of customers is also considered. Additionally, it presents a new model for a bi-objective VRPTW solved by a revised multi-choice goal programming approach, in which the decision maker determines optimistic aspiration levels for each objective function. Two meta-heuristic methods, namely simulated annealing (SA) and genetic algorithm (GA), are proposed to solve large-sized problems. Moreover, the experimental design is used to tune the parameters of the proposed algorithms. The presented model is verified by a real-world case study and a number of test problems. The computational results verify the efficiency of the proposed SA and GA.