Price elasticity of demand and capacity expansion features in an enhanced ABC product-mix decision model

In recent years, activity-based costing (ABC) has become a popular cost and operations management technique to improve the accuracy of firms’ product or service costs in order to help the firms stay competitive. Since the product-mix decision is an important ABC application, most studies in the ABC literature were generally focused on the effect of ABC analysis on the product-mix decision or product cost calculation. However, these studies usually ignored some important factors, such as: capacity expansions, management's degree of control over resources, purchase discount, and change of product's price. Hence, in this paper, we consider these factors to propose a more general model. This model can help managers to make a product-mix decision and identify excess resources so that managers can redeploy them to optimise resource usage. Furthermore, since previous studies did not consider the impact of price changes on product-mix decisions, this paper also examines the impact of reducing product price with different price elasticity of demand (ε ^D) on the simulated company's profit.     

Efficient optimization of process parameters in shadow mask manufacturing using NNPLS and genetic algorithm

A shadow mask, the primary component of a cathode ray tube (CRT), is used to prevent the outer edges of electron beams from hitting incorrect phosphor dots. It is fabricated by means of a photo-etching process consisting of a few hundred/thousand process parameters. A primary concern in the management of the process is to determine the optimal process parameter settings necessary to sustain the desired levels of product quality. The characteristics of the process, including a large number of process parameters and collinear observed data, make it difficult to accomplish the primary concern. To cope with the difficulties, a two-phase approach is employed that entails the identification of a few critical process parameters, followed by determination of the optimal parameter settings. The former is obtained through the operator's domain knowledge and the NNPLS-based prediction model built between process parameters and quality defects. The latter is obtained by solving an optimization problem using a genetic algorithm (GA). A comparative study shows that the proposed approach improves product quality greatly in the shadow-mask manufacturing process.     

Multi-objective sustainable process plan generation in a reconfigurable manufacturing environment: exact and adapted evolutionary approaches

Achieving competitiveness in nowadays manufacturing market goes through being cost and time-efficient as well as environmentally harmless. Reconfigurable manufacturing system (RMS) is a paradigm that is able to meet these challenges due to its scalability and integrability. In this paper, we aim to solve the multi-objective sustainable process plan generation problem in a reconfigurable environment. In addition to the total production cost and the completion time, we use the amount of greenhouse gases (GHG) emitted during the manufacturing process as a sustainability criterion. We propose an iterative multi-objective integer linear programming (I-MOILP) approach and its comparison with adapted versions of the two well-known evolutionary algorithms, respectively, the Archived Multi-Objective Simulated Annealing (AMOSA) and the Non-dominated Sorting Genetic Algorithm (NSGA-II). Moreover, we study the influence of the probabilities of genetic operators on the convergence of the adapted NSGA-II. To illustrate the applicability of the three approaches, an example is presented and obtained numerical results analysed.