Yiyo Kuo

The use of grey relational analysis in solving multiple attribute decision-making problems

There are many cases in daily life and in the workplace which pose a decision problem. Some of them involve picking the best from among multiple available alternatives. However, no single alternative works best for all performance attributes. This research proposes a multiple attribute decision making (MADM) method, grey relational analysis (GRA), for solving this kind of problem. Two cases, facility layout and dispatching rules selection problem, which have been analyzed by data envelopment analysis (DEA), were also analyzed using the GRA procedure, in order to illustrate the use of GRA. In the case of the facility layout problem, 18 alternative layouts and 6 performance attributes were considered. In the case of the problem of selecting dispatching rules, 9 alternatives dispatching rules and 7 performance attributes were considered. For the two cases examined, the results of comparisons show that GRA is efficient for solving MADM problem.

A genetic algorithms simulation approach for the multi-attribute combinatorial dispatching decision problem

This paper presents a genetic algorithms (GA) simulation approach in solving a multi-attribute combinatorial dispatching (MACD) decision problem in a flow shop with multiple processors (FSMP) environment. The simulation is capable of modeling a non-linear and stochastic problem. GA are one of the commonly used metaheuristics and are a proven tool for solving complex optimization problems. The proposed GA simulation approach addresses a complex MACD problem. Its solution quality is illustrated by a case study from a multi-layer ceramic capacitor (MLCC) manufacturing plant. Because GA search results are often sensitive to the search parameters, this research optimized the GA parameters by using regression analysis. Empirical results showed that the GA simulation approach outperformed several commonly used dispatching rules. The improvements are ranging from 33% to 61%. On the other hand, the increased shop-floor-control complexity may hinder the implementation of the system. Finally, future research directions are discussed.

The use of a grey-based Taguchi method for optimizing multi-response simulation problems

Simulation modelling is a widely accepted tool in system design and analysis, particularly when the system or environment has stochastic and nonlinear behaviour. However, it does not provide a method for optimization. In general, problems contain more than one response, which are often in conflict with each other. This article proposes a grey-based Taguchi method to solve the multi-response simulation problem. The grey-based Taguchi method is based on the optimizing procedure of the Taguchi method, and adopts grey relational analysis (GRA) to transfer multi-response problems into single-response problems. A practical case study from an integrated-circuit packaging company illustrates that differences in performance of the proposed grey-based Taguchi method and other methods found in the literature were not significant. The grey-based Taguchi method thus provides a new option when solving a multi-response simulation-optimization problem.

Tabu-search simulation optimization approach for flow-shop scheduling with multiple processors - a case study

The flow shop with multiple processors (FSMP) environment is relatively common and has a variety of applications. The majority of academic authors solve the scheduling problem of FSMP using deterministic data that ignore the stochastic nature of a real-world problem. Discrete-event simulation can model a non-linear and stochastic problem and allows examination of the likely behaviour of a proposed manufacturing system under selected conditions. However, it does not provide a method for optimization. The present paper proposes to solve the FSMP scheduling problem by using a tabu-search simulation optimization approach. It features both the stochastically modelling capability of the discrete-event simulation and the efficient local-search algorithm of tabu search. A case study from a multilayer ceramic capacitor manufacturing illustrates the proposed solution methodology. Empirical results show promise for the practical application of the proposed methodologies. Future research opportunities are then addressed.

Simulation metamodel development using uniform design and neural networks for automated material handling systems in semiconductor wafer fabrication

Abstract Simulation is very time consuming, especially for complex and large scale manufacturing systems. The process of collecting adequate sample data places limitations on any analysis. This paper proposes to overcome the problem by developing a neural network simulation metamodel that requires only a comparably small training data set. In the training data set, the configuration of all input data is generated by uniform design and the corresponding output data are the result of simulation runs. A dispatching problem for a complex simulation model of an automated material handling system (AMHS) in semiconductor manufacturing is introduced as an example. In the example, there are 23 4-levels factors, resulting in a total of 423 possible configurations. However, by using the method proposed in this paper, only 28 configurations had to be simulated in order to collect the training data. The results show that the average prediction error was 3.12%. The proposed simulation metamodel is efficient and effective in solving a practical application.

A variable neighborhood search for the multi-depot vehicle routing problem with loading cost

The purpose of this paper is to propose a variable neighbourhood search (VNS) for solving the multi-depot vehicle routing problem with loading cost (MDVRPLC). The MDVRPLC is the combination of multi-depot vehicle routing problem (MDVRP) and vehicle routing problem with loading cost (VRPLC) which are both variations of the vehicle routing problem (VRP) and occur only rarely in the literature. In fact, an extensive literature search failed to find any literature related specifically to the MDVRPLC. The proposed VNS comprises three phases. First, a stochastic method is used for initial solution generation. Second, four operators are randomly selected to search neighbourhood solutions. Third, a criterion similar to simulated annealing (SA) is used for neighbourhood solution acceptance. The proposed VNS has been test on 23 MDVRP benchmark problems. The experimental results show that the proposed method provides an average 23.77% improvement in total transportation cost over the best known results based on minimizing transportation distance. The results show that the proposed method is efficient and effective in solving problems.

Optimizing the VRP by minimizing fuel consumption

Purpose – In recent years, people have started to realize the importance of environmental protection, and in particular the problem of global warming. Consequently, many governments have started to view decreasing carbon emissions as a priority. Green transportation is one of the policies that is relevant to these efforts. This research aims to optimize the routing plan with minimizing fuel consumption.Design/methodology/approach – In this research, a model is proposed for calculating the total fuel consumption when given a routing plan. Three factors which greatly affect fuel consumption of transportation – transportation distance, transportation speed and loading weight – are taken into consideration. Then a simple Tabu Search is used to optimize the routing plan and an experimental evaluation of the proposed method is performed.Findings – It is shown that the proposed method provides substantial improvements over a method based on minimizing transportation distances.Originality/value – The experimental...

Optimizing truck sequencing and truck dock assignment in a cross docking system

Abstract Cross docking plays a very importation role in supply chain management. The efficiency of cross docking will influence the lead time, inventory level and response time to the customer. This research aims to improve the efficiency of multi-door cross docking by optimizing both inbound and outbound truck sequencing and both inbound and outbound truck dock assignment. The objective is to minimize the makespan. The problem is new in the literature and no previous formulation of the problem can be found. In order to optimize the problem, a model for calculating the makespan is proposed. When given a sequence of all inbound and outbound trucks, the calculation model can assign all inbound and outbound trucks to all inbound and outbound doors based on first come first served and then calculate the makespan. The proposed makespan calculation model is then integrated with a variable neighborhood search (VNS) which can optimize the sequence of all inbound and outbound trucks. Four simulated Annealing (SA) algorithms are adopted for comparison. The experimental results show the proposed VNS provides 8.23–40.97% improvement over the solution generated randomly. Although it does not provide the best result for all problems when compared with SA algorithms, it provides robust results within a reasonable time. Thus the proposed method is efficient and effective in solving cross docking operation problems.

A case study on the operator allocation decision for TFT‐LCD inspection and packaging process

Purpose – Thin film transistor liquid crystal display (TFT‐LCD) is the primary flat panel display (FPD) technology, which is quickly becoming pervasive in many applications including computers, mobile phones, TV monitors, and so on. The finished product of a TFT‐LCD display device is called “module”. A module is subject to a final inspection and packaging (I/P) process before it is shipped to the customer. The I/P operations are primarily manual and the present study seeks to focus on these. The I/P process is strategically important since it directly impacts on both customer service and out‐going quality levels. The operator allocation decision for I/P operations determines the through‐put of the I/P line, and is a function of demand requirement, operator availability, and product dedication.Design/methodology/approach – This research proposes to solve the I/P process operator allocation problem by mixed‐integer programming formulations. A practical case study has been adopted for the empirical illustrat...

Optimization of mixed-skill multi-line operator allocation problem

Operator allocation is one of the most important decisions that can achieve productivity gains in a labor-intensive manufacturing system. Literature focusing on the operator allocation problem does not consider the difference in operation skill requirement, which is often a constraint for practical applications. This research considered the mixed-skill multi-line operator allocation problem as a mixed integer programming formulation. A case study from Thin Film Transistor Liquid Crystal Display inspection and packaging process is adopted for empirical investigation. The results for the methodology showed promise in solving practical applications.