Ruedee Masuchun

An ant colony optimization method for the capacitated vehicle routing problem with stochastic demands

The ant colony optimization (ACO) method has been extensively studied recently for solving many combinatorial optimization problems. In this paper, an ant colony optimization method for solving the vehicle routing problem with stochastic demands is presented. The 2-opt local search is employed within the ACO algorithm to improve the qualities of the solutions. Simulation technique is used for the estimate of the expected cost under stochastic demands. Computational results are also reported.

Integrating m-Machine Scheduling into MRP

This paper presents an approach to integrate the m-machine production scheduling into Material Requirements Planning (MRP). In general, generating MRP considers only Bill of Materials (BOM) and ignores capacity constraints and operating sequences; therefore, a production plan is unachievable when scheduling is actually performed next on shop-floor. That is why recent research has focused on executing both MRP and scheduling simultaneously. Our approach uses an integer linear programming model to plan and schedule concurrently to look right through the capacity constraints and operating sequences. The objective function of the proposed model considers both planning and scheduling purposes that is to minimize total inventory costs and orders tardiness. All significant and inevitable concerns when separately generating MRP and schedule are incorporated with the model through several constraints. Numerical results show that this model can be used to simultaneously generate reasonable MRP as well as feasible and optimal m-machine production schedule.

Planning through information sharing across a supply chain

This paper presents a linear integer programming model to plan the production through information sharing across a supply chain. The advantage is that information sharing makes the whole supply chain globally visualized and yet decreasing in variability on the supply chain. Instead of individually plan for its own manufacturing, demand from all retailers are accumulated and then globally planned for all manufacturers. The objective function of the proposed model is to minimize the total costs involved. Numerical results verify that this model can be used to generate feasible and optimal plans across the supply chain.

Reducing schedule nervousness in production and operations under non-stationary stochastic demand: The case of an airline catering company

Schedule nervousness is a change in planned MPS that caused actual operations to be different and that will result in a disruption in the production and distribution system. Uncertainty in demand was proved to be one of its major causes. The airline catering industry is one of the most highly nervousness-sensitive industries since their future demand is not deterministic but rather considered as a random variable or referred to as non-stationary stochastic demand. This is because the exact order quantity depends on the passenger numbers which are only confirmed minutes prior to departure time. So the objectives of this paper are to compare effectiveness of the current planning policy for reducing an uncertainty in demand of the case study company with a well-known MILP for solving stochastic lot-sizing problem, Tarim and Kingsmans MILP. An obtained solution from both compare its probability of causing a negative closing inventory.

Factors affecting schedule nervousness in the production operations of airline catering industry

The airline catering industry is one of the most complex operational systems since caterers need to serve an average of one hundred thousand meals daily with the challenge of unavailability of exact order numbers right up until departure time. Typically, an optimally planned operation schedule is issued that seeks to utilize resources usage, e.g. manpower and machines. Due to the existence of schedule nervousness, operations schedules are unavoidably sub-optimally executed. The aim of this research was to identify operations-related factors that affected schedule nervousness in the production operations of an airline catering company in Thailand. Total data of 5,572 orders from two planning horizons was collected for analysis. Nervousness was quantified by the revision in initial planned schedule and tested with operation-related factors for the relationship. Results showed that forecasting inaccuracy has a positive correlation with schedule nervousness, while an occurrence of buffer stock has a negative relation.

Advanced Planning for Multi-product Across a Supply Chain

This paper presents a linear integer programming model to plan the production for multiple products. The production is planned using information shared across a supply chain. The advantage of information sharing has been proved from research that it can make the whole supply chain globally visualized and yet decreasing in variability on the supply chain. The objective function of the proposed model is to minimize the total costs involved including the production costs, the distribution costs, the setup costs of manufacturing, and the fixed costs of distributing. From previous research, one more restriction which is limitation of distribution centers has been added to make the model more realistic. Numerical results verify that this model can be used to generate feasible and optimal plans for multiple products across the supply chain.

DETERMINING BATCH SIZE IN A SUPPLY CHAIN

AbstractIn a supply chain environment, determining batch size at each stage is of importance. Using large batch size can save costs but time is wasted as trade-off. Small batch size can speed up the process but costs are increasing. To represent both aspects, this paper proposes the nonlinear integer mathematical model with the objective function of minimizing both costs and time simultaneously. This model can be used to determine (production) batch size to match up the flow of production at manufacturer with the demand at retailers and means of shipment (transfer batch size). Since the nature of this problem is NP-hard, at each stage of supply chain, the acceptable production and transfer batch sizes are obtained using the advantage of genetic algorithm approach. The numerical illustration and results show that it is not necessary to use the same batch size when manufacturing and transferring.

U-shaped line balancing model with an uncertainty time on some tasks

U-shaped Assembly Line (UAL) applies the just-in-time principle which decreases the waste in the operations. This paper presents the UAL model which bases on an uncertain task time. It consists of eight certain tasks and three uncertain tasks. The objective function of the model is the minimum number of workstations. The model formulates on a zero-one integer linear programming. The distribution of the uncertain tasks assume discrete. The procedure of the UAL model is run by INTLINPROG toolbox on MATLAB R2014a. The solution of the uncertain tasks is the earliest finish duration. The earliest finish tasks which assign along the UAL line affect the minimum workstations.