Optimization of the product service supply chain under the influence of presale services

<p style='text-indent:20px;'>For some high-value and technology-intensive products, customers first ask service integrators to provide presales consulting services for products with potential demand. Improving the service level of presales service will increase service costs and reduce profits, but it can also increase the demand for products. The change in market demand under the influence of services will result in a series of chain reactions, such as changes in supply chain inventory costs and distribution costs. Thus, this paper considers the changes in the product service supply chain (PSSC) network caused by changes in presale service levels and service prices from the overall perspective of the supply chain and chooses a reasonable service level and price so that service integrators and product suppliers in PSSCs can achieve a win-win situation while meeting customer needs. First, a PSSC network optimization model is established considering the presale service level and price. Then, a double-layer nested genetic algorithm with constraint reasoning is proposed to solve this problem. Finally, by calculating the PSSC case of a building material company that produces a water mist spray system for ships, the feasibility and practicability of the algorithm was verified.</p>

Using genetic algorithm to automate the generation of an open-plan office layout

Today, the concept of open plan is more and more widely accepted that many companies have switched to open-plan offices. Their design is an issue in the scope of space layout planning. Although there are many professional architectural layout design software in the market, in the real life, office designers seldom use these tools because their license fees are usually expensive and using them to solve an open-plan office design is like using an overly powerful and expensive tool to fix a minor problem. Therefore, manual drafting through a trial and error process is most often used. This article attempts to propose a lightweight tool to automate open-plan office layout generation using a nested genetic algorithm optimization with two layers, where the inner layer algorithm is embedded in the outer one. The result is enhanced by a local search. The main objective is to maximize space utilization by maximizing the size of the open workspace. This approach is different from its precedents, in that the location search is conducted on a grid map rather than several pre-selected candidate locations. Consequently, the generated layout design presents a less rigid workstation arrangement, inviting a casual and unrestrictive work environment. The real potential of the approach is reflected in the productivity of test fits. Automating and simplifying the generation of layouts for test fits can tremendously decrease the amount of time and resources required to generate them. The experimental case study shows that the developed approach is powerful and effective, making it a totally automated process.

Non-Probabilistic Robust Equilibrium Optimization of Complex Uncertain Structures

Robust optimization of complex uncertain structures usually involves multiple conflicting and competing structural performance indices. Present approaches for achieving the final design of such an optimization problem always involve a decision-making process, which is a demanding task that requires the rich experience and expert skills of designers. To overcome the difficulty, an interval robust equilibrium optimization approach is proposed to find the optimal design of complex uncertain structure based on the robust equilibrium strategy for multiple conflicting and competing structural performance indices. Specifically, a new concept of closeness and crossing coefficient between interval boundaries (CCCIBs) is proposed at first, based on which the tri-dimensional violation vectors of all interval constraints can be calculated and the feasibility of a design vector can be assessed. Then, the robust equilibrium assessment of multiple objective and constraint performance indices is investigated, based on the results of which the feasible design vectors can be directly ranked according to the robust equilibrium strategy for all structural performance indices. Subsequently, the algorithm for the robust equilibrium optimization of complex uncertain structures is developed by integrating the Kriging technique and nested genetic algorithm. The validity, effectiveness, and practicability of the proposed approach are demonstrated by two illustrative examples.

Utility-Centric Service Provisioning in Multi-Access Edge Computing

Recently, multi-access edge computing (MEC) is a promising paradigm to offer resource-intensive and latency-sensitive services for IoT devices by pushing computing functionalities away from the core cloud to the edge of networks. Most existing research has focused on effectively improving the use of computing resources for computation offloading while neglecting non-trivial amounts of data, which need to be pre-stored to enable service execution (e.g., virtual/augmented reality, video analytics, etc.). In this paper, we, therefore, investigate service provisioning in MEC consisting of two sub-problems: (i) service placement determining services to be placed in each MEC node under its storage capacity constraint, and (ii) request scheduling determining where to schedule each request considering network delay and computation limitation of each MEC node. The main objective is proposed to ensure the quality of experience (QoE) of users, which is also yet to be studied extensively. A utility function modeling user perception of service latency is used to evaluate QoE. We formulate the problem of service provisioning in MEC as an Integer Nonlinear Programming (INLP), aiming at maximizing the total utility of all users. We then propose a Nested-Genetic Algorithm (Nested-GA) consisting of two genetic algorithms, each of whom solves a sub-problem regarding service placement or request scheduling decisions. Finally, simulation results demonstrate that our proposal outperforms conventional methods in terms of the total utility and achieves close-to-optimal solutions.