A Robust Predictive–Reactive Allocating Approach, Considering Random Design Change in Complex Product Design Processes

Abstract

In the highly dynamic complex product design process, task allocations recovered by reactive allocating decisions are usually subject to design changes. In this paper, a robust predictive-reactive allocating approach considering possible disruption times is proposed, so that it can absorb the disruption in the executing process and utilize the limited capacity of resource more effectively. Four indexes (Makespan, stability, robustness, and compression cost) are used to measure the quality of the proposed method. To illustrate the novel allocating idea, we first assign tasks to resources with the objective of a trade-off between the overall execution time and the overall design cost, which can transform the problem into a non-identical parallel environment. Then, the probability distribution sequencing (PDS) method combining with inserting idle time (IIT) is proposed to generate an original-predictive allocation. A match-up time strategy is considered to match up with the initial allocation at some point in the future. The relationship between the minimum match-up time and the compression cost is analyzed to find the optimal matchup time. Our computational results show that the proposed sequencing method is better than the shortest processing time (SPT) which is a common sequencing way mentioned in the literature. The robust predictive-reactive allocating approach is sensitive to the design change, which is helpful to reduce the reallocating cost and keep the robustness and stability.

A Method for Efficient Task Assignment Based on the Satisfaction Degree of Knowledge

“…Traditionally, the execution time of tasks is determined by project managers who rely on their subjective experience, which will inevitably bring subjective limitations. e method proposed in this paper accounts for the similarity between a previous task and the current task, and it estimates the current task's execution time using the information from similar historical tasks in knowledge base [40]. e knowledge base contains the execution time, the main needed knowledge, and the satisfaction degree of knowledge for each task.…”

Selection of Manufacturing Enterprise Innovation Design Project Based on Consumer’s Green Preferences

“…By considering the generation mechanism and the process of innovation performance in manufacturing enterprises with regard to customer preference and by referring to the existing literatures [32][33][34][35][36][37][38][39][40], in this paper we divide indicators for innovation performance in manufacturing enterprises into three categories: innovation process performance, that is, the efficiency of innovation activities; innovation output performance, that is, the effect of innovation activities; and innovation performance for customers' green preferences. In the end, the index is summed up respectively, as shown in Table 1.…”

Proactive and Reactive Multi-Project Scheduling in Uncertain Environment

“…Kolisch et al [39] proved that multi-mode resource-constrained project scheduling problem is also NP-hard. The genetic algorithm and simulated annealing algorithm have been widely used in many research fields [40]- [43], but their defects have also been found in the process of research. For example, in the early stages of the genetic algorithm, a large difference in population individuals led to premature convergence.…”