A multi-product FPR model with rework and an improved delivery policy

A multi-item finite production rate (FPR) model with rework and an improved delivery policy is examined in this paper. Unlike the classic FPR model whose purpose is to derive the most economic lot size for a single-product production system with perfect quality and a continuous issuing policy, this paper considers a production of multiple products on a single machine, rework of all nonconforming items produced, and a cost-reduction, multi-delivery policy. We extend the work of Chiu et al. [1] by incorporating an improved n+1 shipment policy into their model. According to such a policy, one extra delivery of finished items is made during vendor’s production uptime to satisfy product demands during the period of vendor’s uptime and rework time. When the rest of the production lot is quality assured and the rework has been finished as well, n fixed-quantity installments of finished items are delivered to customers. The objectives are to determine an optimal, common-production cycle time that minimizes the long-run average system cost per time unit, study the effects of rework and the improved delivery policy on the optimal production. Mathematical modelling and analysis is used to derive a closed-form, optimal, common-cycle time. Finally, practical usages of the obtained results are demonstrated by a numerical example

Differential evolution to solve the lot size problem.

An Advanced Resource Planning model is presented to support optimal lot size decisions for performance improvement of a production system in terms of either delivery time or setup related costs. Based on a queueing network, a model is developed for a mix of multiple products following their own specific sequence of operations on one or more resources, while taking into account various sources of uncertainty, both in demand as well as in production characteristics. In addition, the model includes the impact of parallel servers and different time schedules in a multi-period planning setting. The corrupting influence of variabilities from rework and breakdown is explicitly modeled. As a major result, the differential evolution algorithm is able to find the optimal lead time as a function of the lot size. In this way, we add a conclusion on the debate on the convexity between lot size and lead time in a complex production environment. We show that differential evolution outperforms a steepest descent method in the search for the global optimal lot size. For problems of realistic size, we propose appropriate control parameters for the differential evolution in order to make its search process more efficient.Production planning; Lot sizing; Queueing networks; Differential evolution;

Rapid assembly lines model building based on template approach and classification of problems using the cladistics technique

Competition in the global economic scenario has led to the use of simulation in many areas such as manufacturing, health systems, military systems and transportation. With the importance of simulation in supporting decision making and operations, model building has been recognised as one of the crucial steps in simulation studies. However, model building is not as easy as it may seem. It can be time-consuming and expensive, and requires special training, skills and experience. This research, therefore, aims to investigate a new method to rapidly build a simulation model based on the classification of problems in assembly lines using a cladistics technique and template approach. Three objectives were established in order to achieve the aim and a four-stage research programme was developed according to these objectives. The first stage starts by developing a thorough understanding of and collecting typical problems in assembly lines. The next stage formulates the classification of problems and the main deliverable is a cladogram, a tree structure that can be used to represent the evolution of problems and their characteristics. The third stage focuses on the development of a proof-of-concept prototype based on an established classification and template approach. The prototype helps users to develop a model by providing the physical elements and specific elements required for the performance measures analysis. The prototype is then tested and validated in the final stage. The results show that the prototype developed can help to rapidly build a simulation model and reduce model development time.EThOS - Electronic Theses Online ServiceGBUnited Kingdo