Trade-Off Between Maximizing Throughput Rate And Minimizing System Time In Kanban Systems

Investigates the trade-off between the average throughput rate and the average systems time using kanban discipline. Considers a multistage serial production line system with materials in the system controlled by kanban discipline. Presents simulation results to evaluate the production system performance in terms of the average throughput rate and the average system time for a fixed total number of kanbans over a given number of serial workstations. Constructs and compares efficient allocation sets for three and four workstations that are generated by kanban discipline for two processing time distributions, namely, uniform and exponential distributions. Based on the simulation results from three and four work-stations, develops a general design rule to maximize the average throughput rate and to minimize the average system time. Analyses five and six workstations using the general design rule. Tests the validity of the general design rule by considering five and six workstations with a different number of Kanbans. The results show that most of the efficient sets generated by the design rule are identical to those generated by enumerating all combinations of kanban allocations. However, using the general design rule reduces the simulation work tremendously

A Multi-Criterion Approach For Kanban Allocations

In this paper, a single-item, multi-stage, serial production system is considered. Materials in the system are controlled by Kanban discipline. A fixed total number of Kanbans over a given number of serial workstations is allocated. Three conflicting objectives, the average throughput rate (to be maximized), the average work-inprocess (to be minimized), and the average flow time (to be minimized) are considered. Stochastic system simulation is used to generate the efficient set of Kanban allocations. Then the analytic hierarchy process (AHP) is utilized to identify the most-preferred allocation. An illustrative example is given. (C) 1998 Elsevier Science Ltd. All rights reserved

Trade-Off Between Maximizing Throughput Rate And Minimizing System Time In Kanban Systems

Investigates the trade-off between the average throughput rate and the average systems time using kanban discipline. Considers a multistage serial production line system with materials in the system controlled by kanban discipline. Presents simulation results to evaluate the production system performance in terms of the average throughput rate and the average system time for a fixed total number of kanbans over a given number of serial workstations. Constructs and compares efficient allocation sets for three and four workstations that are generated by kanban discipline for two processing time distributions, namely, uniform and exponential distributions. Based on the simulation results from three and four work-stations, develops a general design rule to maximize the average throughput rate and to minimize the average system time. Analyses five and six workstations using the general design rule. Tests the validity of the general design rule by considering five and six workstations with a different number of Kanbans. The results show that most of the efficient sets generated by the design rule are identical to those generated by enumerating all combinations of kanban allocations. However, using the general design rule reduces the simulation work tremendously

Modelling And Analysis Of A Multi-Stage Order Quantity Model

A single-item, multi-stage serial order quantity (MSOQ) model with constant demand is discussed. An algorithm to determine the relationship between the lot sizes of the MSOQ model is also developed. Illustrative examples are presented

A Multi-Criterion Approach For Kanban Allocations

In this paper, a single-item, multi-stage, serial production system is considered. Materials in the system are controlled by Kanban discipline. A fixed total number of Kanbans over a given number of serial workstations is allocated. Three conflicting objectives, the average throughput rate (to be maximized), the average work-inprocess (to be minimized), and the average flow time (to be minimized) are considered. Stochastic system simulation is used to generate the efficient set of Kanban allocations. Then the analytic hierarchy process (AHP) is utilized to identify the most-preferred allocation. An illustrative example is given. (C) 1998 Elsevier Science Ltd. All rights reserved

Modelling And Analysis Of A Multi-Stage Order Quantity Model

A single-item, multi-stage serial order quantity (MSOQ) model with constant demand is discussed. An algorithm to determine the relationship between the lot sizes of the MSOQ model is also developed. Illustrative examples are presented

Multi-Item-Multi-Plant Inventory Control Of Production Systems With Shortages Backorders

A multi-item model of a production-inventory system incorporating deterioration, shortages and capacity/budge constraints is considered. An optimal control policy for the model is developed using linear quadratic (LQ) theory for the case of deterministic demands. The problem of controlling large-scale production-inventory facilities is also considered, and the interaction prediction method is used to develop optimal policies. Results of simulations show that using the developed policy, any desired inventory levels can be maintained while minimizing costs and satisfying demand without violating capacity constraints