Zailin Guan

A very fast TS/SA algorithm for the job shop scheduling problem

The job shop scheduling problem (JSP) is one of the most notoriously intractable NP-complete optimization problems. Over the last 10-15 years, tabu search (TS) has emerged as an effective algorithmic approach for the JSP. However, the quality of solutions found by tabu search approach depends on the initial solution. To overcome this problem and provide a robust and efficient methodology for the JSP, the heuristics search approach combining simulated annealing (SA) and TS strategy is developed. The main principle of this approach is that SA is used to find the elite solutions inside big valley (BV) so that TS can re-intensify search from the promising solutions. This hybrid algorithm is tested on the standard benchmark sets and compared with the other approaches. The computational results show that the proposed algorithm could obtain the high-quality solutions within reasonable computing times. For example, 17 new upper bounds among the unsolved problems are found in a short time.

A tabu search algorithm with a new neighborhood structure for the job shop scheduling problem

Tabu search (TS) algorithms are among the most effective approaches for solving the job shop scheduling problem (JSP) which is one of the most difficult NP-complete problems. However, neighborhood structures and move evaluation strategies play the central role in the effectiveness and efficiency of the tabu search for the JSP. In this paper, a new enhanced neighborhood structure is proposed and applied to solving the job shop scheduling problem by TS approach. Using this new neighborhood structure combined with the appropriate move evaluation strategy and parameters, we tested the TS approach on a set of standard benchmark instances and found a large number of better upper bounds among the unsolved instances. The computational results show that for the rectangular problem our approach dominates all others in terms of both solution quality and performance.

A mathematical programming method for flow path design in high-mix and low-volume flow manufacturing

For the operation and control of flow manufacturing in a high-mix and low-volume production environment, a method of flow path management is introduced. It involves the dynamic formation and control of adaptable flow paths corresponding to different product families. A mathematical programming approach is presented for the design of flow paths, which takes into consideration the sharing of machines among various product families. The objective is to maximize the throughput in a predefined planning period and to minimize the number of shared machines. An illustrative example is given for demonstrating the use of the proposed method.

TOC/DBR based production planning and control in a manufacturing system with multiple system bottlenecks

Bottleneck limits the throughput of a system, and multiple bottlenecks make the system harder to plan and control. In this paper, we propose a TOC/DBR based method for production planning and control when multiple bottlenecks exist. Especially, an algorithm for bottleneck scheduling with setup time is presented, and some simulation study has been carried out in an electronics manufacturing enterprise to validate its effect.

Research on DBR Production Scheduling System Application Based on Simulation

Traditional planning methods ignore the enterprise production capacity,the paper depend on the specific case in enterprise production to build a Production Scheduling System which applies Drum-Buffer-Rope theory. This system proposes the high output, low work in process and on time delivery as the goals, considering the bottleneck resource as well as providing the master production plan with the bottleneck resource, in addition employing the simulation optimization software-Simio to optimize the bottleneck process scheduling and raise the utilization ratio of bottleneck resource.

Model of Production Planning and Control with the Integration of WLC and TOC Mechanism

In the mixed-production context of make-to-stock (MTS) and make-to-order (MTO), the manufacturing system becomes more complex for comprehensive balance of multiple objectives, including resource utilization, work in process (WIP), on-time delivery and throughput. To deal with this complexity, a dynamic production plannning and control (PPC) model with the hybrid of workload control (WLC) and theory of constraint (TOC) is presented. While the proposed three-tiered hierarchy construction model is relatively similar to WLC model, but the way in which the workloads are controled has changed dramatically. The order acceptance deals with issues of order review and delivery setting in order to balance the overall resource capacity and requirements that is necessary for on-time delivery. The order release deals with issues of release mechanisms and rules in order to control the flow of work to the production floor that is necessary for WIP and resource utilization.And the scheduling deals with issues of bottleneck identicification and task sequencing in order to protect the bottleneck capacity that is necessary for throughput.

A mathematical model for workload distribution and balancing with setup times under finite capacity constraints

Production planning is a challenging problem for enterprises especially in the high-mix and low-volume production environment. Finite capacity scheduling tries to allocate resources over time to perform a set of tasks such that precedence constraints and resource capacity constraints are not violated, and overall deadline goals are met to the best extent possible. A novel mathematical model for workload distribution and balance with setup times under finite capacity constraints is present. This model considers setup times and alternate resources, which makes it more comprehensive than others. The objective is to minimize the delay time, early time and cycle time. This model is solved by an optimization software called Xpress-MP. An example is given to illustrate its practicability.