Due-date Assignment Method Research Articles

Scheduling with position-dependent weights, due-date assignment and past-sequence-dependent setup times

In this study, we consider single-machine scheduling problems with past-sequence-dependent (denoted by psd) setup times in which the setup times of jobs are proportional to the length of already processed jobs. Under common (CON) and slack (SLK) due-date assignment methods, we prove that the weighted sum of earliness, tardiness and due-date minimization remains polynomially solvable. We also give some extensions for the scheduling problems with psd setup times.

Single-machine resource allocation scheduling with due-date assignment, deterioration effect and position-dependent weights

ABSTRACTThis article considers scheduling problems with resource allocation, deterioration effect and position-dependent weights on a single machine. For two different due-date assignment methods—i.e. common (CON) due-date assignment and slack (SLK) due-date assignment—a bi-criteria analysis is provided wherein the first objective is to minimize the weighted sum of the absolute value in lateness with a common due date (a common flow-allowance), and the second objective is to minimize the weighted sum of resource consumption. Optimality properties and algorithms are proposed to solve three different versions of considering these two criteria.

Due-date assignment scheduling involving job-dependent learning effects and convex resource allocation

ABSTRACTThis article addresses single machine resource allocation scheduling problems with learning effects, where learning effects mean job-dependent position-based learning effects. For the common due-date assignment (CON) and slack due-date assignment (SLK) methods, a bi-cost analysis of the scheduling cost and the total weighted resource consumption cost is provided. The objective is to determine the optimal job sequence and the resource allocation simultaneously, such that the scheduling cost (the total weighted resource consumption cost) is minimized subject to the total weighted resource consumption cost (the scheduling cost) being limited. Solution procedures are provided for the problems under consideration.

Due date assignment and scheduling with time and positional dependent effects

This paper studies a due date assignment and single machine scheduling problem. In this model, the actual processing time of a job depended both on its start time and position in a sequence. Each job has to be assigned a due date by which it must be completed. The goal is to determine schedule the jobs and the due dates so as to minimize the the criteria that includes the cost of changing the due dates, the total cost of discarded jobs and the total earliness of the scheduled jobs. For the proposed problems, polynomial-time algorithms are presented separately for the CON and SLK due-date assignment method versions.

Dynamic due date assignment method

Purpose The purpose of this paper is to determine the interaction between dynamic due date assignment methods and scheduling decision rules in a typical dynamic job shop production system in which setup times are sequence dependent. Two due date assignment methods and six scheduling rules are considered for detailed investigation. The scheduling rules include two new rules which are modifications of the existing rules. The performance of the job shop system is evaluated using various measures related to flow time and tardiness. Design/methodology/approach A discrete-event simulation model is developed to describe the operation of the job shop. The simulation results are subjected to statistical analysis based on the method of analysis of variance. Regression-based analytical models have been developed using the simulation results. Since the due date assignment methods and the scheduling rules are qualitative in nature, they are modeled using dummy variables. The validation of the regression models involves comparing the predictions of the performance measures of the system with the results obtained through simulation. Findings The proposed scheduling rules provide better performance for the mean tardiness measure under both the due date assignment methods. The regression models yield a good prediction of the performance of the job shop. Research limitations/implications Other methods of due date assignment can also be considered. There is a need for further research to investigate the performance of due date assignment methods and scheduling rules for the experimental conditions that involve system disruptions, namely, breakdowns of machines. Practical implications The explicit consideration of sequence-dependent setup time (SDST) certainly enhances the performance of the system. With appropriate combination of due date assignment methods and scheduling rules, better performance of the system can be obtained under different shop floor conditions characterized by setup time and arrival rate of jobs. With reductions in mean flow time and mean tardiness, customers are benefitted in terms of timely delivery promises, thus leading to improved service level of the firm. Reductions in manufacturing lead time can generate numerous other benefits, including lower inventory levels, improved quality, lower costs, and lesser forecasting error. Originality/value Two modified scheduling rules for scheduling a dynamic job shop with SDST are proposed. The analysis of the dynamic due date assignment methods in a dynamic job shop with SDST is a significant contribution of the present study. The development of regression-based analytical models for a dynamic job shop operating in an SDST environment is a novelty of the present study.

Single machine scheduling to minimise resource consumption cost with a bound on scheduling plus due date assignment penalties

We study a single-machine scheduling problem in a flexible framework, where both job processing times and due dates are decision variables to be determined by the scheduler. We consider the case where each of the job processing times is a convex decreasing function of the amount of non-renewable resource that is allocated to the corresponding processing operation. Moreover, we consider two of the more common due-date assignment methods. For each of the methods, our objective is to find a solution minimising the total resource consumption cost, given an upper bound on the value of the weighted number of tardy jobs plus due date assignment costs. Since the problem is known to be -hard, we focus on designing approximation algorithms.

Multi-Objective Comparison of Due-Date Assignment Methods in a Dynamic Job Shop with Sequence Dependent setup Time

Multi-Objective Comparison of Due-Date Assignment Methods in a Dynamic Job Shop with Sequence Dependent setup Time

Simulation and Comparison Analysis of Due Date Assignment Methods Using Scheduling Rules in a Job Shop Production System

Simulation and Comparison Analysis of Due Date Assignment Methods Using Scheduling Rules in a Job Shop Production System

Single-machine scheduling CON/SLK due window assignment problems with sum-of-processed times based learning effect

This paper studies systems that can be modeled by single-machine scheduling problems with due date assignment. The actual job processing time is a function of the sum of the processing times of the jobs already processed. The due date assignment methods include the common due date (CON) and the slack due date (SLK). The problem is to determine optimal due date values that minimize objective functions which includes the cost of changing the due dates, a possible penalty for the total earliness of the scheduled jobs and the total penalty for discarding jobs. For the common due date and slack due date, we give polynomial-time dynamic programming algorithms to find the optimal jobs sequence, respectively.

Due-date assignment and single-machine scheduling with generalised position-dependent deteriorating jobs and deteriorating multi-maintenance activities

This paper addresses a single-machine scheduling problem with simultaneous consideration of due-date assignment, generalised position-dependent deteriorating jobs, and deteriorating maintenance activities. It is assumed that the actual processing time of a job is a general non-decreasing function depending on the number of maintenance activities performed before it and its position in a sequence. Moreover, the machine may be subject to several maintenance activities up to a limit over the scheduling horizon. The maintenance activities do not necessarily restore the machine fully to its original perfect state and the duration of a maintenance activity depends on its start time. The objective is to find jointly the optimal job sequence, maintenance frequency and maintenance positions to minimise an objective function that includes the cost of due-date assignment, the cost of discarding jobs that cannot be completed by their due dates and the earliness of the scheduled jobs under the popular CON and SLK due-date assignment methods. We provide polynomial-time solution algorithms for various versions of the problem.

An enhanced model for SDBR in a random reentrant flow shop environment

This study proposed an enhanced simplified drum-buffer-rope (SDBR) model to be applied in a reentrant flow shop (RFS) in which job processing times are generated from a discrete uniform distribution and machine breakdowns are subject to an exponential distribution. In this enhanced SDBR model, the due-date assignment method, order release rule and dispatching rule were improved. The due dates and release dates of orders were determined by considering the total planned load of the capacity-constrained resource (CCR) in a random RFS. The deviation rate of buffer status is used as a dispatching rule to eliminate the influence of machine breakdowns. Simulations based on a real case company are used to evaluate the effective of the proposed model. The experimental results showed that our approach yields better performance than the other methods in terms of six due-date-related indexes when the product mix is with a large proportion of multi-reentrant orders and when the utilisation of CCR increases from 60 to 90%.

Optimal due-date assignment problem with learning effect and resource-dependent processing times

In this paper, we consider a single-machine earliness-tardiness scheduling problem with due-date assignment, in which the processing time of a job is a function of its position in a sequence and its resource allocation. The due date assignment methods studied include the common due date, and the slack due date, which reflects equal waiting time allowance for the jobs. For each combination of due date assignment method and processing time function, we provide a polynomial-time algorithm to find the optimal job sequence, due date values, and resource allocations that minimize an integrated objective function, which includes earliness, tardiness, due date assignment, and total resource consumption costs.

Simulation modeling and analysis of due-date assignment methods and scheduling decision rules in a dynamic job shop production system

This paper presents the salient aspects of a simulation study conducted to investigate the interaction between due-date assignment methods and scheduling rules in a typical dynamic job shop production system. The due-date assignment methods investigated are dynamic processing plus waiting time, total work content, dynamic total work content and random work content method. Seven scheduling rules are incorporated in the simulation model for the scheduling of jobs on machines. The performance of the system is evaluated using various measures based on flow time and tardiness of jobs. Simulation experiments are carried out for the different scenarios that arise out of the combination of due-date assignment methods and scheduling rules. The simulation results are subjected to statistical analysis. It is found that dynamic due-date assignment methods provide better performance. Regression-based metamodels are developed using the simulation results. These metamodels have been found to be valid analytical models of the simulation model.

Optimal Due Date Assignment and Resource Allocation to Minimize the Weighted Number of Tardy Jobs on a Single Machine

With the increased emphasis on the effective management of operational issues in supply chains, the timely delivery of products has become even more important. Companies have to quote attainable delivery dates and then meet these, or face large tardiness penalties. We study systems that can be modeled by single-machine scheduling problems with due date assignment and controllable job-processing times, which are either linear or convex functions of the amount of a continuously divisible and nonrenewable resource that is allocated to the task. The due date assignment methods studied include the common due date, the slack due date, which reflects equal waiting time allowance for the jobs, and the most general method of unrestricted due dates, when each job may be assigned a different due date. For each combination of due date assignment method and processing-time function, we provide a polynomial-time algorithm to find the optimal job sequence, due date values, and resource allocations that minimize an integrated objective function, which includes the weighted number of tardy jobs, and due date assignment, makespan, and total resource consumption costs.

Development of a regression-based method with case-based tuning to solve the due date assignment problem

Many regression-based methods to date have been proposed for solving the due date assignment (DDA) problem. The advantages of regression-based DDA methods are that they are easy to both put into practice and comprehend. However, relatively little scheduling research has focused on improving the performances of regression-based DDA methods. The performance of a regression-based DDA method could be improved if its values of regression coefficients could provide a more accurate and precise flowtime estimation for each individual job. The difficulty in doing this stems from the dynamic and stochastic nature of production environment that precludes accurate estimation. Therefore, the aim of this study is to suggest a particular methodology for setting the regression coefficients to improve the performance of regression-based DDA method. In particular, the regression-based DDA method achieved by our suggested methodology is able to adjust the values of coefficients dynamically to best predict the job due date based on the condition of the shop at the instant of job entry. To evaluate the robustness of the methodology, an experimental design was used with four regression coefficient determining procedures, two shop models, and three dispatching rules. The results of this investigation clearly indicate that significant improvements in the performance of regression-based DDA method can occur when the suggested methodology is used.

Real-time due-date promising by build-to-order environments

A vast amount of literature exists on scheduling to meet due dates, but very little work considers how to set these due dates before scheduling the orders. A method is described for real-time promising of order due dates that is applicable to discrete build-to-order environments facing dynamic order arrivals. When computing a due date, the method considers: (1) dynamic time-phased availability of resources required for each operation of the order, (2) individual order-specific characteristics and (3) existing commitments to orders that arrived previously. Performance of the method surpasses that of due-date assignment methods previously examined in the literature and also those commonly used in practice. The median and standard deviation of absolute flow-time estimation error and of absolute lateness are chosen as the primary performance criteria because they capture both positive and negative error in flow-time estimation of each individual order. Computational results from large-scale simulation studies of realistic systems with 20 resources and up to 100 000 orders also indicate the method is highly scalable.

Using Data Mining for Due Date Assignment in a Dynamic Job Shop Environment

Due date assignment is an important task in shop floor control, affecting both timely delivery and customer satisfaction. Due date related performances are impacted by the quality of the due date assignment methods. Among the simple and easy to implement due date assignment methods, the total work content (TWK) method achieves the best performance for tardiness related performance criteria and is most widely used in practice and in study. The performance of the TWK method can be improved if the due date allowance factor k could render a more precise and accurate flowtime estimation of each individual job. In this study, in order to improve the performance of the TWK method, we have presented a model that incorporated a data mining tool – Decision Tree – for mining the knowledge of job scheduling about due date assignment in a dynamic job shop environment, which is represented by IF-THEN rules and is able to adjust an appropriate factor k according to the condition of the shop at the instant of job arrival, thereby reducing the due date prediction errors of the TWK method. Simulation results show that our proposed rule-based TWK due date assignment (RTWK) model is significantly better than its static and dynamic counterparts (i.e., TWK and Dynamic TWK methods). In addition, the RTWK model also extracted comprehensive scheduling knowledge about due date assignment, expressed in the form of IF-THEN rules, allowing production managers to easily understand the principles of due date assignment .

A bottleneck-based due-date assignment methodology

On-time delivery is one of the most important performance criteria in today's manufacturing systems. To enhance delivery performance, the appropriate assignment of an order's due-date is necessary. In recent years, many due-date setting methods based on regression analysis models have been proposed. Although some of them emphasise the use of shop floor information in due-date setting, the bottleneck scheduling information has not yet been taken into consideration by those methods. It is the objective of this paper to propose a new due-date assignment method based on bottleneck scheduling information. To assign an order's due-date, this proposed method first optimises the job sequence at the bottleneck using a Tabu search algorithm and then predicts the order's flow time after its completion of its bottleneck operation. Experimental results show that the proposed method can greatly enhance the delivery performance.

Job shop scheduling for missed due-date performance

Relatively little job shop scheduling research has focused on the missed due-date performance. In this paper, we investigate how two important decision factors, namely dispatching rules and due-date assignment methods, affect the missed due-date performance in job shop scheduling. A new dispatching rule and two new due-date setting models are developed in order to improve the performance criteria based on missed due dates. The new due-date models are capable of adjusting dynamically the flowtime estimation by using feedback information about current shop load conditions. Simulation results show that the proposed dynamic due-date models are significantly better than their static counterparts. The best missed due-date performance is observed when a combination of a dynamic due-date setting model and an appropriate due-date-dependent dispatching rule is employed. In addition, the new models are simple and easy to implement without preliminary runs for parameter estimation.

Due-date assignment and single machine scheduling with compressible processing times

In this paper we consider a due-date assignment and single-machine scheduling problem in which the jobs have compressible processing times. Two models are defined according to the due-date assignment methods used. The first model applies the common (constant) due-date assignment method to assign the due-dates, while in the second model the due-dates are assigned using the slack due-date assignment method. The objective is to determine the optimal sequence, the optimal due-dates and the optimal processing time compressions to minimize a total penalty function based on earliness, tardiness, due-dates and compressions. We solve the problem by formulating it as an assignment problem which is polynomially solvable. For the case that all the jobs have a common upper bound for compressions and an equal unit compression penalty, we present an O( n log n) algorithm to obtain the optimal solution.