In this paper, a crop production schedule for a cooperative is determined considering a social objective of similar revenues for all smallholder farmer members. The problem involves multiple crops, multiple smallholder farmers, and multiple periods. The current planning is performed manually and results in haphazard amounts of revenue per farmer. A priority-based max-min heuristic (PMMH) is proposed to solve this problem. A social objective of the PMMH is to minimise the standard deviation of the revenue per greenhouse. The proposed heuristic reduces this standard deviation in the solution by reallocating crops among the greenhouses of the farmers, especially those farmers with very high or very low revenues. The PMMH is tested on various problem scenarios. The solutions obtained with the PMMH provide better objective function values than those from What’s Best solver. The results also show that the revenue per greenhouse is affected by the number of greenhouses per farmer.

This paper presents a set covering model based on route representation to solve the green ship routing and scheduling problem (GSRSP) with berth time-window constraints for multiple bulk ports. A bi-objective set covering model is constructed with features based on the minimization of the total CO₂ equivalent emissions and the total travel time subject to a limited number of berths in each port, berthing time windows, and the time window for each job. The solutions are obtained using the ε-constraint method, after which a Pareto frontier is plotted. This problem is motivated by the operations of feeder barges and terminals, where the logistics control tower is used to coordinate the routing and berthing time of its barges. We show that the proposed method outperforms the weighted sum method in terms of the number of Pareto solutions and the value of the hypervolume indicator.

This paper studies the impacts of a cross-chain collaboration center model (4CM) on bulk transportation network performance. The 4CM is based on the interaction and collaboration between shippers and carriers participating in the transportation network. The network formulation is based on collaborative bidirectional multi-period vehicle routing (CBMVRP). The performance of the 4CM is represented by three transportation performance indices, including the percentage differences in the total cost, shipper cost and carrier cost between the 4CM and single-chain collaboration model (SCCM). The impacts of the 4CM are investigated using five uncertain input parameters: fuel cost, holding cost, maximum acceptable proportion of the differences between the cost of a carrier and average cost of all carriers, demand per job and number of vehicles per carrier. An experiment was conducted according to a central composite design (CCD). The proposed methodology was applied to assess bulk transportation in Thailand. The computational results revealed that a 4CM can significantly reduce the differences in the total cost, shipper cost and carrier cost. Moreover, we organized a focus group to collect data about the criteria and the probability of changing from an SCCM to a 4CM of the case study network and generated a classification tree of the levels of possibility to change from an SCCM to a 4CM for each possible situation using the ID3 algorithm.

An accurate forecasting model for call center operations allows a company to optimize the number of staffs needed. However, when a special event is planned, the call volume and pattern during that event can be expected to be different from those reflected in the historical database, and consequently, the accuracy of current forecasting methods may degenerate. Thus, a framework that can help to provide a highly accurate forecasting model in such situations is needed. In this paper, a conceptual framework for forecasting incoming calls during mobile expo events is proposed. The framework comprises four main steps: defining data types, verifying time series forecasting methods based on the defined data types, defining the forecasting model and forecasting the incoming calls. This approach can assist in systematically selecting an appropriate forecasting model when a mobile expo event is arranged. Experimental results show that this framework helps to select the appropriate forecasting model for each mobile expo event organized over one year. For the first and third mobile expo event periods, a mixed-period model with an individual forecasting approach, provides the best result, whereas in the second period, a combined forecasting approach for nonevent and mobile expo event periods, is the most appropriate.

Workforce planning is essential in today’s business management. If an organization can find and keep enough staff who have the right values, then they can provide high-quality service. This paper presents a workforce planning framework for selecting the best forecasting model in order to provide minimum wage and computer electricity costs for a mobile call center during the Songkran festival event, and to optimize workforce planning. The framework is constructed with four main steps: a study of a separate period; the separation of models with different data types; the simulation of models under different service levels to determine the number of customers waiting in a call center; and the evaluation of the models. The results from the proposed framework presented the best forecasting method and the optimal workforce plan. It is clear that this approach can assist in systematically selecting the best forecasting model. In addition, a workforce planner can use this framework to support workforce planning and cost evaluation in other event periods.