Omar Ghrayeb

A hybrid push/pull system in assemble-to-order manufacturing environment

A hybrid push/pull system of an assemble-to-order manufacturing environment is investigated in this paper. In this environment, raw material can be transformed into common semi-finished products at a point where next downstream operations are triggered by customer orders. The production of the earlier upstream stations is controlled by push-type production, while the production of the later downstream stations is controlled by pull-type production. The hybrid system often compromises the conflicting performance characteristics of the push and the pull environments. In the push type, high inventory cost is anticipated in the return of low delivery leadtime. On the contrary, in the pull type, high delivery leadtime is expected in the return of low inventory cost. The objective function for the presented hybrid model is to minimize the sum of inventory holding cost and delivery leadtime cost, which is the cost of the time period since customers have placed an order until it is fulfilled. The model is applied to solve the inventory and late delivery problems in an assemble-to-order manufacturer. A genetic algorithm (GA) is used. A discrete event simulation model is used to evaluate the objective function for each chromosome in the GA. The pure push and pull systems are also simulated in order to compare their performance with the hybrid system. Sensitivity analysis on the coefficient of variation (CV) of time between actual customer order arrivals and on various cost ratios of delivery leadtime and inventory are carried out. In most cases, the hybrid performs the best. Results show that the hybrid production system would save the company significantly compared to the pure push or pure pull production systems.

A bi-criteria optimization: minimizing the integral value and spread of the fuzzy makespan of job shop scheduling problems

Abstract The processing times in reality are often uncertain and this uncertainty is critical for the scheduling procedures. This article presents bi-criteria genetic algorithm approach to solve fuzzy job shop scheduling problems (JSSPs), in which the integral value and the uncertainty of the fuzzy makespan (FM), which are conflicting objectives, are minimized. In this approach, imprecise processing times are modeled as triangular fuzzy numbers (TFNs), which results in a makespan that is a triangular fuzzy number. Therefore, it is practically important to pay attention to the uncertainty of the FM. Fuzzified benchmark problems; FT 6×6, La12, La13, and La14 were used to show the effectiveness of the proposed approach compared with optimizing fuzzy JSSPs with respect to either the FM or the uncertainty separately. A sensitivity analysis is also presented that shows the effect of the vagueness (uncertainty) of the processing times on the uncertainty of the FM.

Scheduling jobs to improve weighted on-time performance of a single machine

Abstract One of the problems faced by many manufacturers is how to improve on-time delivery (OTD). Customer orders or jobs often have different levels of importance (priorities). The levels of importance are assigned by weight numbers: the higher the weight, the more important the job. One performance measure manufacturers typically use is the total weighted number of late deliveries. This paper proposes a time-efficient scheduling heuristic, the Weighted Forward Algorithm (WFA), to reduce the weighted number of late deliveries for a single machine. A comparison between the WFA heuristic and three well-known heuristics, the Earliest Due Date (EDD), the Weighted Shortest Processing Time (WSPT), and the Forward Algorithm (FA), is then performed on two data sets, representing high and low-demand production months, for a real-world manufacturing application.The results suggest that the WFA outperforms the EDD, WSPT, and FA for this objective using both data sets. At the same time, the execution time is almost instantaneous. The WFA is also benchmarked against current practice at the same manufacturer and outperforms other heuristics and current practice.

A modified rapid access heuristic for flowshop scheduling problem

This paper presents a modified rapid access (MRA) heuristic to solve the flowshop scheduling problems. This new heuristic follows the fundamental idea of the original rapid access approach by forming a two-machine subproblem, but the processing times are determined by using an exponential weighting modifier for the original linear weighting scheme without additional computational effort, then the subproblem is solved by using Johnsons two-machine algorithm. The performance of MRA is tested using instances from the literature and compared with the performance of the original rapid access (RA). Results show that the MRA outperforms the original RA in large size problems by using specific value of parameter alpha. Factorial experiment and response surface methodology are applied to determine the best alpha value which is the main factor that impacts the performance of MRA and produce the best performance of the MRA heuristic.

Simulation in retail: a case study for process improvement in the receiving area

Simulation tools allow its users to computationally model real-life systems in order to determine their best future outcome. One real-life system that can benefit from simulation is that of the retail industry. This paper describes how simulation can be an effective tool for this type of industry, especially for process improvement projects. In addition, a small case study is presented to demonstrate the use of simulation for a large retailer which needs to improve its unloading and receiving processes. Among the future ideas for research, this paper shows that less obvious methods for process improvement, such as tracking customer loyalty, can be analyzed using simulation to determine which route a retailer should take in order to please its customers. Other topics on this subject are suggested at the conclusion of this paper.

Improving the quotation process: a lean case study at a job shop

This study was conducted at a job-shop fabricating parts for the fastener industry. Due to stiff competition, the job-shop had two days to respond to all customer requests for quote. The current quotation process was repeatedly tardy. Consequently, they lost goodwill of several existing customers and failed to grow their customer base. This study thoroughly analysed the existing quotation process, recommended and implemented changes which resulted in shorter lead-times. A process flow diagram (PFD) was developed through interviews and shadowing the quote personnel. Analysing the PFD helped to identify and eliminate some of the non-valueadded tasks, resulting in shorter lead-time. Using a clustering algorithm, part families and a quote template for each family was formed. Templates facilitated standardisation of the quoting process, and helped to cross-train estimators. As a result of this study the number of lines quoted per month increased significantly; resulting in higher quote win percentage.