Available-to-promise (ATP) calculating from master production schedule (MPS) exhibit availability of finished goods that can be used to support customer order promising. This order promising mechanism is adapted in MTS (make-to-stock) production model and all orders are treated the same on first-come-first-served policy. However, increasingly mass customization results in production model gradually transfers from MTS to ATO (assembly-to-order) or MTO (make-to-order) in order to fulfill the requests from customers such as customer's preference materials or specifications for the ordered products. In ATO or MTO model, the manufacturing resource such as materials and capacity after order penetration point should be checked and allocated for order promising. Moreover, mass customization also drives the trend of customer demand to segmentation and prioritization according to product profit, sales growth potential, contracts or the relationships with customers. Therefore, this research proposes one order promising mechanism that applies mixed integer linear programming (MILP) model to prioritize allocating manufacturing resource for high profit products or important customers and to consider material and capacity constraints after order penetration point. Furthermore, this order promising mechanism takes thin film transistor liquid crystal display (TFT-LCD) manufacturing as illustration for these material and capacity constraints after order penetration point.

Adoption of innovative products such as smartphones may be due to a mixture of experienced considerations and psychological perceptions. By synthesising adoption theories through reinforcement of the role of conformity. This study explores user intention to adopt innovative smartphones in the Taiwan telecommunication market. The survey data is analysed using structural equation modelling which is based on the constructs of ease of use, compatibility, and relative advantage, analysis results indicate that informational conformity and normative conformity are positively related to the intention to use smartphones. Additionally, early adopters and early majority users have a higher likelihood of conformity driven by informational influence, while the late majority users display a higher tendency towards normative conformity. The results of this study significantly contribute to efforts of mobile communication operators to anticipate and predict a customer's intention to adopt smartphones, thus provides further insight into the development of marketing strategies for smartphones.

In many scheduling studies, researchers consider the processing times of jobs as constant numbers. This assumption sometimes is at odds with practical manufacturing process due to several sources of uncertainties arising from real-life situations. Examples are the changing working environments, machine breakdowns, tool quality variations and unavailability, and so on. In light of the phenomenon of scenario-dependent processing times existing in many applications, this paper proposes to incorporate scenario-dependent processing times into a two-machine flow-shop environment with the objective of minimizing the total completion time. The problem under consideration is never explored. To solve it, we first derive a lower bound and two optimality properties to enhance the searching efficiency of a branch-and-bound method. Then, we propose 12 simple heuristics and their corresponding counterparts improved by a pairwise interchange method. Furthermore, we set proposed 12 simple heuristics as the 12 initial seeds to design 12 variants of a cloud theory-based simulated annealing (CSA) algorithm. Finally, we conduct simulations and report the performances of the proposed branch-and-bound method, the 12 heuristics, and the 12 variants of CSA algorithm.

This paper delves into the scheduling of the two-machine flow-shop problem with step-learning, a scenario in which job processing times decrease if they commence after their learning dates. The objective is to optimize resource allocation and task sequencing to ensure efficient time utilization and timely completion of all jobs, also known as the makespan. The identified problem is established as NP-hard due to its reduction to a single machine for a common learning date. To address this complexity, this paper introduces an initial integer programming model, followed by the development of a branch-and-bound algorithm augmented with two lemmas and a lower bound to attain an exact optimal solution. Additionally, this paper proposes four straightforward heuristics inspired by the Johnson rule, along with their enhanced counterparts. Furthermore, a population-based genetic algorithm is formulated to offer approximate solutions. The performance of all proposed methods is rigorously evaluated through numerical experimental studies.

Although some uncertainty factors can occur in many practical environments, customer order scheduling problems involving two agents in such uncertain environments have not received attention in the current literature. Motivated by this observation, we address a two-agent customer order scheduling problem where various customer orders have scenario-dependent component processing times and release dates in order to find an appropriate schedule to minimize the maximum of the total completion time of the customer orders that belong to one agent and are subject to a constraint with the other agent. In order to solve this problem, a lower bound and six dominant properties are derived and used to propose a branch-and-bound algorithm to find an exact optimal solution. Afterward, three local search heuristics and two variants of a simulated annealing hyper-heuristic are proposed and empirically evaluated in order to find approximate solutions. Finally, we conclude the paper with a summary of our findings and some directions for future research.

This paper presents the comparison of the Mahalanobis-Taguchi System (MTS) and Component Search (CS) for product designs in parameter selections. Many previous works finding that the Mahalanobis-Taguchi System (MTS) algorithm is successful and effective for parameter selections. Additionally, the CS is already successfully applied in the product/system component selection problems. Based on the main aims and verifies of this work, we find that the methodologies of the MTS and the CS algorithm can easily solve product parameter selection problems. Moreover, the MTS has better performance than the CS algorithm for parameter selections in product designs.