Pornthipa Ongkunaruk

Joint replenishment with imperfect items and price discount

A joint replenishment problem is presented to determine the ordering policy for multiple items having a certain percentage of defective units. The purpose of this paper is to study the impact of the percentage of defective units on the ordering policy. Two different scenarios are presented for joint replenishment problem: (1) without price discount and (2) with price discount. For each scenario, the total expected cost per unit time is derived and algorithms are presented to determine the family cycle length and the integer number of intervals that the replenishment quantity of each item will last. Numerical examples are presented and the results are discussed.

A joint replenishment problem considering multiple trucks with shipment and resource constraints

A joint replenishment problem (JRP) is presented to determine the optimal reordering policy for multi-items with a percentage of defective items. This JRP also has several constraints, such as shipment constraint, budget constraint, and transportation capacity constraint. At the meantime, multiple trucks, each with a fixed transportation cost, are considered and also order quantities of restricted items are not shared among the trucks during the shipment. The objective is to minimize the total expected cost per unit time. A two-dimensional genetic algorithm (GA) is provided to determine an optimal family cycle length and the reorder frequencies. A numerical example is presented and the results are discussed. Extensive computational experiments are performed to test the performance of the GA. The JRP is also solved by using an evolutionary algorithm (EA) and the results obtained from GA and EA are compared. HighlightsA joint replacement problem (JRP) is solved considering multiple trucks.JRP also includes shipment, budget, and transportation capacity constraints.The JRP is solved by a genetic algorithm (GA) and an evolutionary algorithm (EA).GA outperforms EA in terms of computational time and the total expected cost.

An ECRS-based line balancing concept: a case study of a frozen chicken producer

Purpose – The purpose of this paper is to improve the productivity of a large-sized frozen chicken manufacturer in Thailand. It analyses the production process based on work study principles and identifies the bottleneck operation. It develops three models for the chicken preparation process. Design/methodology/approach – First, analyse the current production system by collecting the cycle time of all operations in the production process based on work study principles. Then, design the production network and identify the bottleneck operation. After that, three methods – based on line balancing (LB), theory of constraints, and JIT concepts or ECRS (eliminate, combine, rearrange and simplify) – are proposed and implemented in the actual production line. Findings – With the ECRS concept, the authors implement combine by combining two stations into one station, such as handling and weighing, or weighing batter and mixing it with chicken. Then, Simplify is implemented at job E, or transporting chicken using a ...

A study of large scale food services best practices in Thailand: A case study of HORECAs

This study aimed to explore the management system of large scale food service sectors in Thailand. We studied the demand characteristics and the management practices of hotels, chain restaurants and airline caterer, and identified the best practices of each type of food service providers. Initially, the in-depth interviews with the selected chain hotels, chain restaurants and airline caterer were conducted. Next, the Integration Definition for Function Modeling (Idef0) was used to analyze the business processes which identified the input, output, control and mechanism of main activities including plan, source, make and deliver. The results showed that the demand for chain hotels and caterer were fluctuated and seasonal, but could be predicted due to the advanced orders from customers. However, it occasionally incurred an urgent order from the customer which was unpredictable. On the other hand, the demand for restaurants was highly unpredictable, fluctuated and seasonal. Therefore, the restaurants forecasted the demand based on historical data and national holidays. The best practices of the large-sized HORECA were categorized into four main activities. First, plan, they conducted a sourcing plan by bidding the suppliers annually or biweekly based on current situation. Second, source, they implemented the cold chain for raw material delivery and used a sampling plan for raw materials and food inspection. Third, make, they prepared foods under certified food standard system such as GMP, HACCP, QHS and Halal. Finally, deliver, they implemented just in time, cold chain system and global positioning system to have fast, tractable and high quality delivery. In summary, the large-sized HORECAs were the leaders of the food service sectors due to economy of scale and best practices implementation. Knowledge from these practices should be shared among other HORECAs. As a result, other organizations can improve their practices to reduce cost and increase customer satisfaction.

An integer programming for a bin packing problem with time windows: A case study of a Thai seasoning company

This study aimed to improve the transportation management of a case study company which produced seasoning powder. Recently, they had two main problems in logistics department: high transportation cost and long vehicle scheduling time. Thus, the objectives of this study were to reduce transportation cost and scheduling time. Due to the delivery in Bangkok and metropolitan area, this problem became the vehicle routing problem with time windows. Then, we proposed a three phases-heuristics which composed of clustering, vehicle allocation and vehicle routing. In the first phase, we applied mapping to visualize the locations of customers. Then, the experts determined how to cluster the customers. The benefit of this phase was to reduce the problem size. In the second phase, the customers in each zone were allocated to the truck. Due to the distribution in Bangkok and metropolitan, there was a time windows constraint for some customers. Hence, we formulated the integer programming to minimize the number of vehicles used since the company outsourced the transportation to the third party logistics (3PLs) or the logistics providers who charged the fixed cost per truck. Hence, the problem became a bin packing problem with time windows constraints. Then, we used Lingo 6.0 and solver function in Microsoft Excel to solve the problem. Finally, in the third phase, the logistics providers who had experience of the traffic and roads solved the routing problem of each truck regarding the time windows constraint of the customers. The result showed that the monthly transportation cost was reduced by 23% or 37,650 baht per month and computational time was reduced by 67%.

The harvest planning of aromatic coconut by using Monte Carlo simulation

Our research studies the harvest plan for aromatic coconut for the aromatic coconut manufacturers located in Ratchaburi province, Thailand. The supply planning is complex because there are many uncertain factors. For example, the number of coconuts in each crop is varied and depends on the season, water amount, soil fertility and pests. In addition, coconut price at farm gate is highly fluctuated. At present, the factory faces harvest planning problems due to supply shortages, price and appropriate harvest time fluctuation. Since the harvest age of the coconuts affects the quality of the product and purchasing cost. Then, we used a Monte Carlo simulation to support the harvest planning process for the company. The objective function seeks to maximize a total profit under price and supply fluctuation throughout the year with respect to a fixed period and varied period constraints. The results are compared with the scenarios which could be applied as the decision making tools for the supply planning of the company.