Optimal Cycle Service Level for Continuous Stocked Items with Limited Storage Capacity

This paper involves determining an optimal cycle service level for continuously stocked items that explicitly considers storage space capacity. Inventory management is under a continuous review policy. The total inventory management cost consisting of ordering cost, inventory holding cost, shortage cost, and over-capacity cost. Shortage items are assumed to be backlogged. A numerical example is provided to demonstrate the method. Keywords: Continuous Review; Cycle Service Level; Storage Space Capacity; Over-Capacity Cos

An Association between Pledging Policies and the Financial Performance of Cassava Product Manufacturers

This paper involves a study to investigate the association between pledging policies by the government and financial performance of cassava product manufacturers in Thailand. A polynomial regression model is constructed to describe a key financial performance measure using a set of control variables and pledging policy variables. The control variables are obtained from financial statements of 58 starch manufacturers and 8 ethanol manufacturers that solely use fresh cassava roots as raw material during 2009-2014. Result from the model suggests an appropriate agricultural policy for the cassava product industry in Thailand. Keywords : Cassava; Control Variables; Financial Performance; Pledging Policies, Polynomial Regressio

Multi-mode resource-constrained project scheduling problem with resource vacations and task splitting

The research presented in this dissertation addresses the Multi-Mode Resource-Constrained Project Scheduling Problem (MMRCPSP) in the presence of resource unavailability. This research is motivated by the scheduling of engineering design tasks in automotive product development to minimize the project completion time, but addresses a general scheduling situation that is applicable in many contexts. The current body of MMRCPSP research typically assumes that, 1) individual resource units are available at all times when assigning tasks to resources and, 2) before assigning tasks to resources, there must be enough resource availability over time to complete the task without interruption. In many situations such as assigning engineering design tasks to designers, resources are not available over the entire project-planning horizon. In the case of engineering designers and other human resources, unavailability may be due to several reasons such as vacation, training, or being scheduled to do other tasks outside the project. In addition, when tasks are scheduled they are often split to accommodate unavailable resources and are not completed in one continuous time segment. The objectives of this research are to obtain insight into the types of project scheduling situations where task splitting may result in significant makespan improvements, and to develop a fast and effective scheduling heuristic for such situations. A designed computational experiment was used to gain insight into when task splitting may provide significant makespan improvements. Problem instances were randomly generated using a modification of a standard problem generator, and optimally solved with and without task splitting using a branch and bound algorithm. In total 3,880 problem instances were solved with and without task splitting. Statistical analysis of the experimental data reveals that high resource utilization is the most important factor affecting the improvements obtained by task splitting. The analysis also shows that splitting is more helpful when resource unavailability occurs in multiple periods of short duration versus fewer periods of long duration. Another conclusion from the analysis indicates that the project precedence structure and the number (not amount) of resources used by tasks do not significantly affect the improvements due to task splitting. Using the insights from the computational testing, a new heuristic is developed that can be applied to large problems. The heuristic is an implementation of a simple priority rule-based heuristic with a new parameter used to control the number of task splits. It is desirable to obtain the majority of task splitting benefits with the smallest number of split tasks. Computational experiments are conducted to evaluate its performance against known optimal solutions for small sized problems. A deterministic version of the heuristic found optimal solutions for 33% of the problems and a stochastic version found optimal solutions for over 70%. The average percent increase in makespan compared to optimal was 7.58% for the deterministic heuristic and less than 2% for the stochastic versions demonstrating acceptable performance

Warranty Data Analysis: A Review

Warranty claims and supplementary data contain useful information about product quality and reliability. Analysing such data can therefore be of benefit to manufacturers in identifying early warnings of abnormalities in their products, providing useful information about failure modes to aid design modification, estimating product reliability for deciding on warranty policy and forecasting future warranty claims needed for preparing fiscal plans. In the last two decades, considerable research has been conducted in warranty data analysis (WDA) from several different perspectives. This article attempts to summarise and review the research and developments in WDA with emphasis on models, methods and applications. It concludes with a brief discussion on current practices and possible future trends in WDA

Impacts of Harvesting Age and Pricing Schemes on Economic Sustainability of Cassava Farmers in Thailand under Market Uncertainty

This paper involves an analysis to determine appropriate cassava harvest practices and choose a pricing scheme between farmers and factories, cassava yards, and collectors in Thailand. Harvest practices represent all activities from land preparation to harvest. A key decision that governs the amount of resources required during cassava life cycle is the cassava’s harvesting age. The harvesting age can be from eight to 18 months in two patterns: fixed age, e.g., harvest every 12 months, and variable age, e.g., harvest at an age between 10 and 14 months. After harvesting, there are two common pricing schemes to consider, which are weight-based and starch-content-based. Factors that affect the two decisions made by Thai farmers at a given time are the market price, which highly varies within a season and between seasons, and yields in terms of weight and starch content, both of which change with cassava’s age and/or harvest month. Economic sustainability measure for Thai farmers is the average monthly profit that the farmers gain over cassava harvest cycle under uncertain market price. To handle uncertainties, a simulation model is constructed to imitate cassava planting activities from cultivation to harvest. The purpose is to evaluate various harvesting ages and two pricing schemes under uncertain cassava market prices. Market prices in 15 seasons (2006–2021) are grouped using the k-mean clustering into four price scenarios. As cassava grows in the simulation, the required resources are consumed until the decisions on harvesting time and pricing scheme are made with estimated selling probability under different price scenarios and uncertainty in cassava yield. Through simulation, harvesting age and pricing scheme that are most profitable and robust-to-system-variation are determined. Finally, a guideline for Thai farmers to choose a pricing scheme is developed based on the sensitivity analysis of the simulation model

Forecasting Daily Electricity Consumption in Thailand Using Regression, Artificial Neural Network, Support Vector Machine, and Hybrid Models

This article involves forecasting daily electricity consumption in Thailand. Electricity consumption data are provided by the Electricity Generating Authority of Thailand, the leading power utility state enterprise under the Ministry of Energy. Five forecasting techniques, including multiple linear regression, artificial neural network (ANN), support vector machine, hybrid models, and ensemble models, are implemented. The article proposes a hyperparameter tuning technique, called sequential grid search, which is based on the widely used grid search, for ANN and hybrid models. Auxiliary variables and indicator variables that can improve the models’ forecasting performance are included. From the computational experiment, the hybrid model of a multiple regression model to forecast the expected daily consumption and ANNs from the sequential grid search to forecast the error term, along with additional indicator variables for some national holidays, provides the best mean absolution percentage error of 1.5664% on the test data set