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This study integrates the forecast capacity and superiority of dynamic simulation of back-propagation network (BPN) and system dynamics, thereby enabling the target industries to formulate coping strategies and to make adjustment in both production capabilities and manpower. The main purpose of this study is to assess and confirm what the most effective policy is for production and human resources. The forecasting result of this study indicates that Correlation and R-squared values reach 0.99852 and 0.994961 respectively after a training of BPN. Certainly, BPN serves as an effective method of forecasting target sales. Finally, the forecast value of the next three years is worked out through situational simulation of complex production models in system dynamics. The final result of this study shows that the optimum manpower should be 10 workers for the years 2011-2013, as well as the projected result will be a rise in production yield of up to 2.54%. It is the most profitable scenario for the company.

This paper proposed a mathematical model called Multi-objective Production System Design (MOPSD) model solved by Ant Colony Optimization (ACO). The MOPSD model placed a production system under the constrained factory space to achieve the minimal total material transportation flow cost. This paper discussed 2-phase pheromone layout procedure that we proposed how to transform the best design information into and the relationship and tracing technology. After modeling experiment we proved it can be put the best design information into 2-phase pheromone layout procedure and achieved the minimal total material transportation flow cost by simply tracing technology.

This paper proposes a mathematical model called Multi-objective Production System Design (MOPSD) model and a two-staged solving procedure. The MOPSD model not only considers the two-dimensional layout of a production system under the constrained factory space to achieve the minimal total material transportation flow but also determines the optimal assigned number of workstations and the optimal system cycle time concerning a simultaneous production situation. The first major objective of MOPSD model is regarded as a Quadratic Assignment Problem (QAP), but it considers the size of operation element, the multiple production lines, and the constrained two-dimensional factory space where traditional QAP studies are seldom mentioned. The second objective of the MOPSD model is to determine the optimal system cycle time as well as the optimal assigned number of workstations under the simultaneous production situation for preventing the underestimation of the system production rate. The first-stage solving procedure of the MOPSD model applies the space discrete technique to make the infinite deployable locations become finite, and then one can achieve the major objective through the system layout. In addition, the Four Position Coding Technique (FPCT) is developed to code the nodes in the deployed production system obtaining from the first stage, and therefore those FPCT codes can function as the input parameters in the second-stage solving procedure. These two solving procedures are constructed by the syntax of Lingo 9.0 extended version, and a numerical example is followed to describe the detailed solving procedure. In summary, the repeated characteristic of the MOPSD model and its solving procedure make this study a valuable decision support tool because they are constructed by the packaged software Lingo 9.0. That is, a factory can easily duplicate to solve other cases by changing its input parameters only.

This study submits a mathematical model called the optimal production lot size (OPLS) model to reach the optimal design of a network-type production system for long-term production undergoing the limitation of finite machines of each machine type. This study not only discusses the production rate, the production lot size, the production period, the machine cost, and the number of machines for each workstation into the objective, but develops an efficient step-by-step algorithm, staged algorithm, with the combinatorial applications of the maximal flow problem and the shortest route method to optimize the system profit. Additionally, the detailed procedure of searching the optimal solution is presented by a numerical example. This paper indeed contributes the applicable scheme to the design of a network-type production system for the long-term manufacturing, and provides the optimal production lot size and period for the production planners in today's manufacturing industry with profound insight

The overriding objective of this research is to develop a new technological approach by constructing a mathematical model and a heuristic algorithm to deploy an artificial reef (AR) ecosystem. This approach serves as a bridge between ecologists and engineers. The proposed model, called the layout of artificial reef communities (LARCs) model, not only applies the fractal dimension as an indicator for measuring the complexity into the objective, but considers the costs, the budget, the deploying distance of adjacent artificial reef communities (ARCs), and the number of deployed ARCs to deal with a project in designing the spatial configuration of an AR ecosystem. The main objective of the LARCs model is to achieve the maximal complexity (fractal dimension) because a higher complexity can increase the species diversity and biomass. In addition, a computerized tool written by Fortran program to conduct the design of the spatial configuration is completely proposed, and a case study is followed. This study contributes an approach and a valuable tool to ecological engineers in the deployment of an AR ecosystem.

Combinatorial resource planning for both machines and human resources in a computer numerical controlled (CNC) machining workstation has become a crucial factor for long-term manufacturing management. This paper not only mathematically constructs the combinatorial manufacturing resource planning (CMRP) model with the conception of balancing the machine productivity and the human capability, but also develops a step-by-step algorithm to reach the maximum-profit solution under the deterministic market demand. Additionally, to extend the applicability of the CMRP model, a computer program written with MATLAB is comprehensively prepared to achieve the optimum manufacturing resource planning under the forecasts of probabilistic market demand and product sales price. The versatility and adaptability of this study are exemplified through a numerical illustration from real-world industry. This paper contributes to the problem of conclusively scheduling machine productivity and human resources, as well as comprehensively optimising the production profit for the existing CNC machining industry .

The design of a multi-conveyor system in supporting machine loading and unloading has become crucial to management. However, through the mathematical model proposed in this paper, this issue becomes realistically and concretely solvable. This study not only mediates the concept of balancing the number of parallel machines, the conveyor speed for adjacent pallets, the overall relevant costs and the determination of the number of conveyors into the objective, but also develops a two-staged method to optimise the combined problem to reach a maximum profit. Additionally, the versatility of this study is exemplified through a numerical example. Moreover, the computerised sensitivity analyses are discussed in this study. This paper contributes an applicable scheme for production design in manufacturing, and provides a valuable tool to conclusively obtain the optimal profit of a given production quantity for operations research engineers in today's manufacturing with profound insight.

This research adopted the stochastic Data Envelopment Analysis (DEA) to analyse future performance efficiency through a two-staged problem solving procedure design. In Stage 1, the random production value is applied and then the future performance efficiency of firefighting branches of Tainan County is discussed using DEA. In Stage 2, efficiency improvement and resource allocation are conducted by through a future production trend analysis, and the future optimised scale of firefighting human resources allocation is determined by using the Group-Number Efficiency Scale Approach (GESA). In addition, based on the future trend evaluation, this study can provide suitable response strategies for firefighting human resources allocation for decision makers. Moreover, this study provides a referenced constructive and quantitative approach for solving the current problem of 'how to plan the future resources for optimisation'.