An integrated MRP and finite scheduling system to derive detailed daily schedules for a manufacturing shop

Many companies rely on Material Requirements Planning (MRP) to support their Production Scheduling and Control (PS&C) functions. Since MRP does not provide a detailed shop floor schedule, these users have to implement either a third party procedure or an internally developed procedure for shop floor controls. In this thesis we consider a class of user shops which are characterized by the following features: Homogenous machines, that is all machines can produce all products. Each product requires a setup, but several products may have a common setup. MRP requirements are specified on a weekly basis while actual requirements are specified on a hourly basis. Specifically, we develop a MRP and Finite Scheduling System (MFSS) which calculates the weekly net change requirements of products, then generates the detailed daily job order schedules, and finally sequences jobs on machine queues. The objectives of the system are to maximize the utilization of the machines and to minimize setup times. The MFSS was programmed on a personal computer-based system utilizing off-the-shelf relational database software

Hybrid Genetic Bees Algorithm applied to Single Machine Scheduling with Earliness and Tardiness Penalties

This paper presents a hybrid Genetic-Bees Algorithm based optimised solution for the single machine scheduling problem. The enhancement of the Bees Algorithm (BA) is conducted using the Genetic Algorithm's (GA's) operators during the global search stage. The proposed enhancement aims to increase the global search capability of the BA gradually with new additions. Although the BA has very successful implementations on various type of optimisation problems, it has found that the algorithm suffers from weak global search ability which increases the computational complexities on NP-hard type optimisation problems e.g. combinatorial/permutational type optimisation problems. This weakness occurs due to using a simple global random search operation during the search process. To reinforce the global search process in the BA, the proposed enhancement is utilised to increase exploration capability by expanding the number of fittest solutions through the genetical variations of promising solutions. The hybridisation process is realised by including two strategies into the basic BA, named as â\u80\u9creinforced global searchâ\u80\u9d and â\u80\u9cjumping functionâ\u80\u9d strategies. The reinforced global search strategy is the first stage of the hybridisation process and contains the mutation operator of the GA. The second strategy, jumping function strategy, consists of four GA operators as single point crossover, multipoint crossover, mutation and randomisation. To demonstrate the strength of the proposed solution, several experiments were carried out on 280 well-known single machine benchmark instances, and the results are presented by comparing to other well-known heuristic algorithms. According to the experiments, the proposed enhancements provides better capability to basic BA to jump from local minima, and GBA performed better compared to BA in terms of convergence and the quality of results. The convergence time reduced about 60% with about 30% better results for highly constrained jobs

Due-date assignment and parallel-machine scheduling with deteriorating jobs

Author name used in this publication: T. C. E. ChengAuthor name used in this publication: L. Y. KangAuthor name used in this publication: C. T. Ng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Application of lean scheduling and production control in non-repetitive manufacturing systems using intelligent agent decision support

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Lean Manufacturing (LM) is widely accepted as a world-class manufacturing paradigm, its currency and superiority are manifested in numerous recent success stories. Most lean tools including Just-in-Time (JIT) were designed for repetitive serial production systems. This resulted in a substantial stream of research which dismissed a priori the suitability of LM for non-repetitive non-serial job-shops. The extension of LM into non-repetitive production systems is opposed on the basis of the sheer complexity of applying JIT pull production control in non-repetitive systems fabricating a high variety of products. However, the application of LM in job-shops is not unexplored. Studies proposing the extension of leanness into non-repetitive production systems have promoted the modification of pull control mechanisms or reconfiguration of job-shops into cellular manufacturing systems. This thesis sought to address the shortcomings of the aforementioned approaches. The contribution of this thesis to knowledge in the field of production and operations management is threefold: Firstly, a Multi-Agent System (MAS) is designed to directly apply pull production control to a good approximation of a real-life job-shop. The scale and complexity of the developed MAS prove that the application of pull production control in non-repetitive manufacturing systems is challenging, perplex and laborious. Secondly, the thesis examines three pull production control mechanisms namely, Kanban, Base Stock and Constant Work-in-Process (CONWIP) which it enhances so as to prevent system deadlocks, an issue largely unaddressed in the relevant literature. Having successfully tested the transferability of pull production control to non-repetitive manufacturing, the third contribution of this thesis is that it uses experimental and empirical data to examine the impact of pull production control on job-shop performance. The thesis identifies issues resulting from the application of pull control in job-shops which have implications for industry practice and concludes by outlining further research that can be undertaken in this direction

Exact and Heuristic Algorithms for the Job Shop Scheduling Problem with Earliness and Tardiness Over a Common Due Date

Scheduling has turned out to be a fundamental activity for both production and service organizations. As competitive markets emerge, Just-In-Time (JIT) production has obtained more importance as a way of rapidly responding to continuously changing market forces. Due to their realistic assumptions, job shop production environments have gained much research effort among scheduling researchers. This research develops exact and heuristic methods and algorithms to solve the job shop scheduling problem when the objective is to minimize both earliness and tardiness costs over a common due date. The objective function of minimizing earliness and tardiness costs captures the essence of the JIT approach in job shops. A dynamic programming procedure is developed to solve smaller instances of the problem, and a Multi-Agent Systems approach is developed and implemented to solve the problem for larger instances since this problem is known to be NP-Hard in a strong sense. A combinational auction-based approach using a Mixed-Integer Linear Programming (MILP) model to construct and evaluate the bids is proposed. The results showed that the proposed combinational auction-based algorithm is able to find optimal solutions for problems that are balanced in processing times across machines. A price discrimination process is successfully implemented to deal with unbalanced problems. The exact and heuristic procedures developed in this research are the first steps to create a structured approach to handle this problem and as a result, a set of benchmark problems will be available to the scheduling research community

Scheduling for Service Stability and Supply Chain Coordination

This dissertation studies scheduling for service stability and for supply chain coordination as well. The scheduling problems for service stability are studied from the single perspective of a firm itself, while the scheduling problems for supply chain coordination are investigated from the perspective of a supply chain. Both the studies have broad applications in real life. In the first study, several job scheduling problems are addressed, with the measure of performance being job completion time variance (CTV). CTV minimization is used to represent service stability, since it means that jobs are completed in a relative concentrated period of time. CTV minimization also conforms to the Just-in-time philosophy. Two scheduling problems are studied on multiple identical parallel machines. The one problem does not restrict the idle times of machines before their job processing, while the other does. For these two scheduling problems, desirable properties are explored and heuristic algorithms are proposed. Computational results show the excellent performances of the proposed algorithms. The third scheduling problem in the first study is considered on a single machine and from the users’ perspective rather than the system’s perspective. The performance measure is thus class-based completion time variance (CB-CTV). This problem is shown to be able to be transformed into multiple CTV problems. Therefore, the well-developed desirable properties of the CTV problem can be applied to solve the CB-CTV problem. The tradeoff between the CB-CTV problem and the CTV problem is also investigated. The second study deals with scheduling coordination in a supply chain, since supply chain coordination is increasingly critical in recent years. Usually, different standpoints prevent decision makers in a supply chain from having agreement on a certain scheduling decision. Therefore conflicts arise. In pursuit of excellent performance of the whole supply chain, coordination among decision makers is needed. In this study, the scheduling conflicts are measured and analyzed from different perspectives of decision makers, and cooperation mechanisms are proposed based on different scenarios of the relative bargaining power among decision makers. The cooperation savings are examined as well

Critical path analysis type scheduling in a finite capacity environment

In order to cope with more realistic production scenarios, scheduling theory has been increasingly considering assembly job shops. Such an effort has raised synchronization of operations and components as a major scheduling issue. Most effective priority rules designed for assembly shops have incorporated measures to improve coordination when scheduling assembly structures. However, by assuming a forward loading, the priority rules designed by these studies schedule all operations as soon as possible, which often leads to an increase of the workin- progress level. This study is based on the assumption that synchronization may be improved by sequencing rules that incorporate measures to cope with the complexity of product structures. Moreover, this study favours the idea that, in order to improve synchronization and, consequently, reduce waiting time, backward loading should be considered as well. By recognizing that assembly shop structures are intrinsically networks, this study investigates the feasibility of adopting the Critical Path Method as a sequencing rule for assembly shop. Furthermore, since a Critical Path type scheduling requires a precise determination of production capacity, this study also includes Finite Capacity as a requisite for developing feasible schedules. In order to test the above assumptions, a proven and effective sequencing rule is selected to act as a benchmark and a simulation model is developed. The simulation results from several experiments showed significant reduction on the waiting time performance measure due to the adoption of the proposed critical path type priority rule. Finally, a heuristic procedure is proposed as a guideline for designing scheduling systems which incorporate Critical Path based rules and Finite Capacity approach