Mean Tardiness Of Jobs Research Articles

Comparing Steady-state Performance of Dispatching Rule-pairs in Open Shops

Unlike implemented in other production systems, dispatching in an open shop not only takes into account the job priority at each machine but also the selection of the next ma- chine for any jobs leaving the current machine. The control mechanism for scheduling in an open shop can be utilized by a dispatching rule-pair that consists of a machine-selection rule and a job-dispatching rule. This study conducts a steady-state simulation comparison on the perform- ance of 39 dispatching rule-pairs in open shops. We find that using NINQ as the ma- chine-selection rule can minimize the mean flowtime of jobs while using LINQ as the ma- chine-selection rule can minimize mean tardiness of jobs for most cases. The choice of the best dispatching rule-pair depends on the selected performance criterion as well as the system's con- figurations such as utilization factor, number of machines, and processing time distribution of jobs. Finally, under similar system configurations, the best job-dispatching rule in an open shop is different from that of a job shop.

AIS-SFHM APPROACH FOR OPTIMIZATION OF MULTI OBJECTIVE JOB SHOP PROBLEMS

The n-job, m-machine Job shop scheduling (JSP) problem is one of the general production scheduling problems. The JSP problem is a scheduling problem, where a set of ‘n’ jobs must be processed or assembled on a set of ‘m’ dedicated machines. Each job consists of a specific set of operations, which have to be processed according to a given technical precedence order. Job shop scheduling problem is a NP-hard combinatorial optimization problem.  In this paper, optimization of three practical performance measures mean job flow time, mean job tardiness and makespan are considered. The hybrid approach of Sheep Flocks Heredity Model Algorithm (SFHM) is used for finding optimal makespan, mean flow time, mean tardiness. The hybrid SFHM approach is tested with multi objective job shop scheduling problems. Initial sequences are generated with Artificial Immune System (AIS) algorithm and results are refined using SFHM algorithm. The results show that the hybrid SFHM algorithm is an efficient and effective algorithm that gives better results than SFHM Algorithm, Genetic Algorithm (GA). The proposed hybrid SFHM algorithm is a good problem-solving technique for job shop scheduling problem with multi criteria.

Solving Multi Objective Job Shop Scheduling Problems Using Artificial Immune System Shifting Bottleneck Approach

Scheduling problems are usually solved using heuristics to get optimal or near optimal solutions because problems found in practical applications cannot be solved to optimality using reasonable resources in many cases. Scheduling problems vary widely according to specific production tasks but most are NP-hard problems. Optimization of three practical performance measures mean job flow time, mean job tardiness and makespan are considered in this work. The Artificial Immune System Shifting Bottleneck Approach is used for finding optimal makespan, mean flow time, mean tardiness values of two benchmark problems. In this Artificial Immune System Shifting Bottleneck Approach (AISSB), initial sequences are generated with Artificial Immune System Algorithm (AIS) and Shifting Bottleneck Algorithm (SB) is used for finding final solutions. The results show that the AISSB Approach is effective algorithm that gives better results than literature results. The proposed AISSB Approach is an efficient problem-solving technique for multi objective job shop scheduling problem.

A New GT Heuristic for Solving Multi Objective Job Shop Scheduling Problems

The n-job, m-machine Job shop scheduling (JSP) problem is one of the general production scheduling problems in manufacturing system. Scheduling problems vary widely according to specific production tasks but most are NP-hard problems. Scheduling problems are usually solved using heuristics to get optimal or near optimal solutions because problems found in practical applications cannot be solved to optimality using reasonable resources in many cases. In this paper, optimization of three practical performance measures mean job flow time, mean job tardiness and makespan are considered. New Game theory based heuristic method (GT) is used for finding optimal makespan, mean flow time, mean tardiness values of different size problems. The results show that the GT Heuristic is an efficient and effective method that gives better results than Genetic Algorithm (GA). The proposed GT Heuristic is a good problem-solving technique for job shop scheduling problem with multi criteria.

Production rescheduling for machine breakdown at a job shop

Rescheduling is a procedure to repair a production plan affected by unexpected disruptions. It is important because a production schedule released to a shop floor is subject to unexpected disruptions. This paper develops two novel heuristic rescheduling procedures, AWI-J and AWI-O, to minimise mean tardiness of jobs in a job shop. The procedures are based on an active schedule-generation procedure and Wilkerson–Irwin algorithm, which is well known for minimising mean tardiness of a schedule. The performances of the new procedures are compared with those of the Affected Operations Rescheduling (AOR) procedure, which is popular in the rescheduling literature. Efficiency measures such as mean tardiness, mean flowtime, and makespan, and a stability measure, deviation of new operation start times from the original start times, are used for comparison. Test results show that AWI-J improves efficiency over AOR, and AWI-O improves efficiency and stability as well.

An efficient priority rule for scheduling job shops to minimize mean tardiness

The ability to respond quickly to customer demands is a key factor in successfully competing in today's globally competitive markets. Thus, meeting due dates could be the most important goal of scheduling in many manufacturing and service industries. Meeting due dates can naturally be translated into minimizing job tardiness. In this paper we present a priority rule for dynamic job shop scheduling that minimizes mean job tardiness. The rule is developed based on the characteristics of existing dispatching rules. With job due dates set by the generalized total work content rule, the computational results of simulation experiments show that the proposed dispatching rule consistently, and often, significantly outperforms a number of well-known priority rules in the literature. The proposed rule is also robust being the best or near-best rule for reducing the mean flowtime, for all the shop load levels and due date tightness factors tested.

Minimising mean tardiness with alternative operations in two-machine flow-shop scheduling

This paper describes the development of a mixed binary integer programming (BIP) model for scheduling alternative operations in two-machine flow-shop problems with mean tardiness as the criterion. Although the mixed BIP model provides an optimal solution, its variables and constraints increase drastically as the number of jobs increases. Therefore, an optimal solution is not always attainable within the allowable time by solving the mixed BIP model. Consequently, two heuristics suitable for the formulated scheduling problems are proposed. Computational experiments are conducted to test the accuracy and efficiency of the heuristics in generating near optimal solutions for minimising the mean tardiness of jobs.

Modelling and heuristics of FMS scheduling with multiple objectives

The performance of a scheduling system, in practice, is not evaluated to satisfy a single objective, but to obtain a trade-off schedule regarding multiple objectives. Therefore, in this research, we make use of one of the multiple objective decision-making methods, a global criterion approach, to develop a multi-objective model for solving FMS scheduling problems with consideration of three performance measures, namely minimum mean job flow time, mean job tardiness, and minimum mean machine idle time, simultaneously. In addition, hybrid heuristics, which are a combination of two common local search methods, simulated annealing and tabu search, are also proposed for solving the addressed FMS scheduling problems. The feasibility and adaptability of the proposed heuristics are investigated through experimental results.

Dynamic scheduling of manufacturing job shops using extreme value theory

Most job shop scheduling approaches reported in the literature assume that the scheduling problem is static (i.e. job arrivals and the breakdowns of machines are neglected) and in addition, these scheduling approaches may not address multiple criteria scheduling or accommodate alternate resources to process a job operation. In this paper, a scheduling method based on extreme value theory (SEVAT) is developed and addresses all the shortcomings mentioned above. The SEVAT approach creates a statistical profile of schedules through random sampling, and predicts the quality or 'potential' of a feasible schedule. A dynamic scheduling problem was designed to reflect a real job shop scheduling environment closely. Two performance measures, viz. mean job tardiness and mean job cost, were used to demonstrate multiple criteria scheduling. Three factors were identified, and varied between two levels each, thereby spanning a varied job shop environment. The results of this extensive simulation study show that the SEVAT scheduling approach produces a better performance compared to several common dispatching rules.

Experimental investigation of an FMS due-date scheduling problem: an evaluation of due-date assignment rules

Abstract This paper investigates the performance of due-date assignment rules in a flexible manufacturing system (FMS) Although emphasis is placed on a comparison of due-date assignment rules, machine and automated guided vehicle (AGV) scheduling rules are also evaluated under various experimental conditions using an FMS simulation model. The mean job tardiness is the measure of performance by which the rules are compared. The sensitivity to AGV workload, buffer capacity, and processing time distribution is also investigated to assess the robustness of the due-date assignment rules.

Manufacturing systems with forbidden early shipment: implications for choice of scheduling rules

SUMMARY Recent progress in manufacturing systems research has identified the feature of forbidding early shipment (FES) of orders as a prevalent characteristic of real systems. This report examines the implications of this feature on the choice of scheduling rules within a job shop. Simulation results of a simplified job shop reconfirm the superiority of the modified due date approach when the criterion is mean job tardiness. However, when the criterion is system inventory, the slack time per remaining operation ratio approach performs slightly better than modified due date. The shortest operation processing time yields very poor results in job shops that include the FES assumption.

A note on workload-dependent due date assignment rules

For many production systems, delivery performance relative to promised job due dates is a critical evaluation criterion. Delivery performance is affected by the way in which work is dispatched on the shop floor, and also by the way the job due dates are assigned to begin with. This pape shows how information regarding congestion levels on the shop floor can be used to assign due dates to arriving jobs in such a way that the mean tardiness of jobs is decreased without increasing the average length of the promised delivery lead times. Baker and Bertrand suggested a modification of the Total Work (TWK) rule for assigning job due dates which adjusts the job flow allowance according to the level of congestion in the shop. Their method gives longer flow allowances to jobs which arrive when the system is congested. Although their modified TWK rule results in lower mean tardiness in many settings, it also generally results in a higher proportion of jobs tardy. This paper presents an alternative modification of the TWK rule which, in most cases, provides mean tardiness as low as or lower than Baker and Bertrand's rule and also results in a lower proportion of jobs tardy. The alternative rule suggested here still results in higher proportion of tardy jobs than the non-workload adjusted rule in most settings, but suggestions are made for how this problem might be addressed.

Parametric appraisal of the JIT system

Much has been written on the essential features and potential benefits of the Justin-Time production system. Using a simulation model, some salient parameters such as (a)scheduling rules, (6) the effects of pull demands level, (c) the effects of the production Kanban size and the minimum Kan ban level, and (d) the significance of the job mix are evaluated. The study shows that a common prevailing practice of assigning greater priorities to jobs with higher pull frequencies is not as efficient as a shortest process time based rule. Unlike the traditional ‘push’ method, raising the pull demand in a JIT system does not ensure a high process utilization level. Results also show that an increase in the production Kanban size and minimum output Kanban level improve the mean job tardiness but causes an escalation in the output Kanban inventory level. Contrary to common conjecture, an enlarged product mix would actually produce better process utilization and mean job tardiness.

Job shop scheduling with modified due dates

AbstractThis paper introduces a new dispatching rule to job shop scheduling, extending earlier results to a multi‐machine environment. This new rule, which uses modified due dates is compared to other popular dispatching methods over a range of due date tightnesses at two utilization levels. The results for mean tardiness indicate that the modified operation due date (MOD) rule compares very favorably with other prominent dispatching methods.The modified due date is a job's original due date or its early finish time, whichever is larger. For an individual operation, it is the operation's original due date or the operation's early finish time, whichever is larger. A comparison of the job‐based version with the operation‐based version indicated that the operation‐based version tended to be more effective at meeting due dates.The main performance measure was mean job tardiness, although the proportion of tardy jobs was also reported, and the two measures together imply the conditional mean tardiness. The MOD rule was compared to several well‐known tardiness‐oriented priority rules, such as minimum slack‐per‐operation (S/OPN), smallest critical ratio (SCR), and COVERT. The MOD rule tended to achieve lower levels of mean tardiness than the other rules, except under conditions where due dates were quite loose. In this situation, very little tardiness occurs for any of the rules. The MOD rule appeared to be more robust than the other rules to changes in the tightness of due dates, and similar results occurred at both high and low utilizations.

A statistical comparison of the performance of simple dispatching rules for a particular set of job shops

Abstract This research involves the development and analysis of a model of a particular set of real world job shops. The characteristics of the process include workers flexibility and job routings which may include assembly operations. An experimental design was used which allowed statistical comparisons to be made. Various control and staffing policies were considered, with mean flow time and mean job tardiness used as performance criteria. The shortest imminent operation time rule was found to provide the best performance in all but one situation. Workforce flexibility was observed to significantly improve both performance criteria for all dispatching rules considered.