Dynamic optimisation of price,warranty length and production rate

This study investigates a marketing and production problem that uses price, warranty length and production rate as simultaneous dynamic decision variables. Furthermore, this study was conducted under a policy of free replacement of defective items; and under conditions where demand was dynamic and dependent on price, warranty and cumulative sales. A continuous profit maximisation model was formulated, which first considers the expected warranty cost per item. Then, it considers the steps for dynamic optimisation, which eventually derive the optimal price, warranty length and production rate. Discretisation was then applied to the profit maximisation model and a digital computer was used to identify the optimal control paths, obtaining a finite solution that is a set of real numbers for practical application. A model-driven Decision Support System is finally established, which provides a graphical user interface for overcoming the complexity of the analytical process. Subsequently, the proposed system was tested and the analytical solution was verified using several demand functions for additive lifetime distributions, thereby demonstrating the system's effectiveness.     

Application of particle swarm optimisation for solving deteriorating inventory model with fluctuating demand and controllable deterioration rate

In most of the inventory models in the literature, the deterioration rate of goods is viewed as an exogenous variable, which is not subject to control. In the real market, the retailer can reduce the deterioration rate of product by making effective capital investment in storehouse equipments. In this study, we formulate a deteriorating inventory model with time-varying demand by allowing preservation technology cost as a decision variable in conjunction with replacement policy. The objective is to find the optimal replenishment and preservation technology investment strategies while minimising the total cost over the planning horizon. For any given feasible replenishment scheme, we first prove that the optimal preservation technology investment strategy not only exists but is also unique. Then, a particle swarm optimisation is coded and used to solve the nonlinear programming problem by employing the properties derived from this article. Some numerical examples are used to illustrate the features of the proposed model.     

Applications of particle swarm optimisation in integrated process planning and scheduling

Integration of process planning and scheduling (IPPS) is an important research issue to achieve manufacturing planning optimisation. In both process planning and scheduling, vast search spaces and complex technical constraints are significant barriers to the effectiveness of the processes. In this paper, the IPPS problem has been developed as a combinatorial optimisation model, and a modern evolutionary algorithm, i.e., the particle swarm optimisation (PSO) algorithm, has been modified and applied to solve it effectively. Initial solutions are formed and encoded into particles of the PSO algorithm. The particles “fly” intelligently in the search space to achieve the best sequence according to the optimisation strategies of the PSO algorithm. Meanwhile, to explore the search space comprehensively and to avoid being trapped into local optima, several new operators have been developed to improve the particles’ movements to form a modified PSO algorithm. Case studies have been conducted to verify the performance and efficiency of the modified PSO algorithm. A comparison has been made between the result of the modified PSO algorithm and the previous results generated by the genetic algorithm (GA) and the simulated annealing (SA) algorithm, respectively, and the different characteristics of the three algorithms are indicated. Case studies show that the developed PSO can generate satisfactory results in both applications.