Due-date Scheduling Research Articles

Hybrid Evolutionary Strategy and Simulated Annealing Algorithms for Integrated Process Planning, Scheduling and Due-Date Assignment Problem

Although integrated process planning and scheduling (IPPS) problem is studied extensively as scheduling with due-date assignment problem (SWDDA) in the literature, there are only a few studies on integration of these three manufacturing functions which are process planning, scheduling, and due-date assignment. Since outputs of upper stream functions effect downstream functions and higher integration gives a better global performance, it is better to integrate these three functions. In this study integration of process planning and earliest due-date scheduling (EDD) with due-date assignment is studied using hybrid evolutionary strategies (RS/ES) and hybrid simulated annealing algorithms (RS/SA). Ordinary solutions (OS), random search (RS) solutions, evolutionary strategies (ES) solutions, and simulated annealing (SA) solutions are compared with each other and hybrid solutions of ES and SA, with RS. According to the results, higher integration found better and best results are obtained with the highest integration level. ES and SA methods gave better results compared to the RS and OS, and RS/ES and RS/SA techniques were found promising search techniques.

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.

Pooling heterogeneous products for manufacturing environments

In a stochastic environment pooling naturally leads to economies of scale, but heterogeneity can also create variability. In the article, we investigate this trade-off in the case of a manufacturing environment. Pooling for queueing systems has been widely investigated in the literature on the design of service systems; however, much less attention has been given to manufacturing systems where jobs are given a due date upon arrival. In such systems it is not the elapsed time until the actual completion of the job that counts, but rather the due date lead time that can be promised to the customer in order to guarantee a high service level. The purpose of the article is to get a deeper understanding about how pooling strategies and lead-time decisions can be implemented to attain a high due-date performance. To this end, we develop a simulation and analytical study to determine the benefits of pooling manufacturing systems with heterogeneous demand streams. Our work allows managers to identify the characteristics of production systems such that a pooling strategy would be beneficial. Our results demonstrate that when a due-date setting and scheduling mechanism is implemented, heterogeneity does generally not lead to deterioration of performance, as previously observed in service environments. Our studies also reveal that the benefits of pooling in terms of the expected sojourn time obtained by a simple analytical treatment can serve as a good prediction of the benefits of pooling on the average due date lead time in a wide range of situations.

Single-machine common due-date scheduling with batch delivery costs and resource-dependent processing times

In this paper we consider the problem of single-machine batch delivery scheduling with an assignable common due date and controllable processing times. The job processing time is either a linear or a convex function of the amount of a continuously divisible and non-renewable resource allocated to the job. Finished jobs are delivered in batches and there is no capacity limit on each delivery batch. The objective is to find a job sequence, a partition of the job sequence into batches, a common due date, and resource allocation that jointly minimise a cost function based on earliness, weighted number of tardy jobs, job holding, due-date assignment, batch delivery, makespan, and resource consumption. We provide some properties of the optimal solution, and show that the problem with the linear and convex resource consumption functions can be solved in and time, respectively. We also show that some special cases of the problem can be solved by lower-order algorithms.

Common Due-Date Assignment and Scheduling on Parallel Machines with Sequence-Dependent Setup Times

This paper considers common due-date assignment and scheduling on parallel machines. The main decisions are: (a) deter-mining the common due-date; (b) allocating jobs to machines; and (c) sequencing the jobs assigned to each machine. The objective is to minimize the sum of the penalties associated with common due-date assignment, earliness and tardiness. As an extension of the existing studies on the problem, we consider sequence-dependent setup times that depend on the type of job just completed and on the job to be processed. The sequence-dependent setups, commonly found in various manufacturing systems, make the problem much more complicated. To represent the problem more clearly, a mixed integer programming model is suggested, and due to the complexity of the problem, two heuristics, one with individual sequence-dependent setup times and the other with aggregated sequence-dependent setup times, are suggested after analyzing the characteristics of the problem. Computational experiments were done on a number of test instances and the results are reported.

Fast and meta-heuristics for common due-date assignment and scheduling on parallel machines

This study considers common due-date assignment and scheduling on parallel machines. The problem has three decision variables: assigning the common-due-date, allocating jobs to parallel machines, and sequencing the jobs assigned to each machine. The objective is to minimise the sum of due-date assignment, earliness and tardiness penalties. A mathematical programming model is presented, and then two types of heuristics are suggested after characterising the optimal solution properties. The two types of heuristics are: (a) a fast two-stage heuristic with obtaining an initial solution and improvement; and (b) two meta-heuristics, tabu search and simulated annealing, with new neighbourhood generation methods. Computational experiments were conducted on a number of test instances, and the results show that each of the heuristic types outperforms the existing one. In particular, the meta-heuristics suggested in this study are significantly better than the existing genetic algorithm.

Due-date scheduling on parallel machines with job splitting and sequence-dependent major/minor setup times

This paper addresses job scheduling problems with parallel machines. To satisfy customers better in a manufacturing company, meeting due dates has been an important performance metric. Besides the numerous other factors affecting due date satisfaction, the splitting of a job through parallel machines can contribute to the reduction of production lead time, resulting in less job tardiness against their due dates. Thus, this paper presents heuristic algorithms for minimizing total tardiness of jobs to meet their due dates in a manufacturing shop with identically functioning machines. The algorithms take into account job splitting and sequence-dependent major/minor setup times. The performance of the proposed heuristics is compared with that of past three algorithms in the literature.

A note on due-date assignment and single-machine scheduling with deteriorating jobs and learning effects

The concepts of deteriorating jobs and learning effects have been individually studied in many scheduling problems. This note considers a single-machine scheduling problem with deteriorating jobs and learning effects. All of the jobs have a common (but unknown) due date. The objective is to minimize the sum of the weighted earliness, tardiness and due-date penalties. An O(n3) algorithm is proposed to optimally solve the problem with deteriorating jobs and job-dependent learning effect. Besides, an O(n log n) algorithm is provided to solve the problem with deteriorating jobs and job-independent learning effect.

Single-Machine Scheduling with Due-Date Assignment and Aging Effect under a DeterioratingMaintenance Activity Consideration

This study investigates single-machine due-date assignment and scheduling problems with a job-dependent aging effect under a deteriorating maintenance activity consideration simul-taneously. The maintenance activity can be scheduled immediately after any one of the jobs has been completed. In order to model the problem more realistic, we assume that: (1) once the maintenance activity has been completed, the machine will revert to its initial condition and the aging effect will start anew, and (2) the machine maintenance duration depends on its starting time. The objective is to find the optimal maintenance position as well as the optimal location of the common due-date for minimizing the total of earliness, tardiness and due-date costs. We introduce a polynomial O(n^4) time solution for the problem. We also present two special cases of the problem and show that they can be optimally solved by a lower order algorithm.

A note on due-date assignment and single-machine scheduling with deteriorating jobs

In this note, we study a single-machine scheduling problem with deteriorating jobs whose processing times are an increasing function of their start times. The objective is to determine the optimal due-date and schedule simultaneously to minimize the total of due-date, earliness and tardiness penalties. We give a concise analysis of the problem and provide a polynomial time algorithm to solve the problem. Moreover, the algorithm can be easily applied to the ‘mirror’ scheduling problem in which the actual processing time of a job is a decreasing function of its starting time.

Scheduling with controllable release dates and processing times: Total completion time minimization

The single machine scheduling problem with two types of controllable parameters, job processing times and release dates, is studied. It is assumed that the cost of compressing processing times and release dates from their initial values is a linear function of the compression amounts. The objective is to minimize the sum of the total completion time of the jobs and the total compression cost. For the problem with equal release date compression costs we construct a reduction to the assignment problem. We demonstrate that if in addition the jobs have equal processing time compression costs, then it can be solved in O( n 2) time. The solution algorithm can be considered as a generalization of the algorithm that minimizes the makespan and total compression cost. The generalized version of the algorithm is also applicable to the problem with parallel machines and to a range of due-date scheduling problems with controllable processing times.

A fuzzy dispatching strategy for due-date scheduling of FMSs with information delays

Conventional dispatching strategies for FMSs with routing flexibility have typically employed simple heuristics such as work-in-next-queue (WINQ) and number-in-next-queue (NINQ). The effectiveness of these heuristics, however, deteriorates in FMSs whose operational environment must cope with information delays that are non-negligible in comparison to part processing times. Such delays could arise from planned activities, e.g., acquisition, selection, processing, and transfer of plant-wide system status information as well as from unplanned events such as ERP/IT system malfunctions, mismatch of software interfaces, and erroneous inventory master files, for example. Uncertainties from information delays make a strong case for the introduction of fuzzy controllers for making scheduling decisions. This paper introduces a novel fuzzy logic-based dispatching strategy to cope with a specific manifestation of information delays, called status review delay within FMSs. Status review information delays impact system performance adversely because of the obsolescent nature of the information used in the determination of dispatch decisions. A fuzzy dispatching strategy (FDS), designed specifically for deployment within FMSs where information delays are manifest, provides an appropriate alternative to conventional dispatching strategies such as WINQ and NINQ. In the design of an FDS, relevant system-based parameters are fuzzified and an appropriate rule base is designed. Simulation experiments demonstrate the superiority of an FDS over the conventional WINQ dispatching strategy using the mean tardiness, percent tardy, and mean flowtime performance measures.

Due-Date Scheduling: Asymptotic Optimality of Generalized Longest Queue and Generalized Largest Delay Rules

Consider the following due-date scheduling problem in a multiclass, acyclic, single-station service system: Any class k job arriving at time t must be served by its due date t+Dk. Equivalently, its delay τk must not exceed a given delay or lead-time Dk. In a stochastic system, the constraint τk≤Dk must be interpreted in a probabilistic sense. Regardless of the precise probabilistic formulation, however, the associated optimal control problem is intractable with exact analysis. This article proposes a new formulation which incorporates the constraint through a sequence of convex-increasing delay cost functions. This formulation reduces the intractable optimal scheduling problem into one for which the Generalized cμ (Gcμ) scheduling rule is known to be asymptotically optimal. The Gcμ rule simplifies here to a generalized longest queue (GLQ) or generalized largest delay (GLD) rule, which are defined as follows. Let Nk be the number of class k jobs in system, λk their arrival rate, and ak the age of their oldest job in the system. GLQ and GLD are dynamic priority rules, parameterized by θ: GLQ(θ) serves FIFO within class and prioritizes the class with highest index θkNk, whereas GLD(θ) uses index θkλkak. The argument is presented first intuitively, but is followed by a limit analysis that expresses the cost objective in terms of the maximal due-date violation probability. This proves that GLQ(θ*) and GLD(θ*), where θ*,k = 1/λkDk, asymptotically minimize the probability of maximal due-date violation in heavy traffic. Specifically, they minimize [Formula: see text] for all positive t and x, where τk(s) is the delay of the most recent class k job that arrived before time s. GLQ with appropriate parameter θα also reduces “total variability” because it asymptotically minimizes a weighted sum of αth delay moments. Properties of GLQ and GLD, including an expression for their asymptotic delay distributions, are presented.

Benchmarks for scheduling on a single machine against restrictive and unrestrictive common due dates

We consider the NP-hard problem of scheduling jobs on a single machine against common due dates with respect to earliness and tardiness penalties. The paper covers two aspects: Firstly, we develop a problem generator and solve 280 instances with two new heuristics to obtain upper bounds on the optimal objective function value. Secondly, we demonstrate computationally that our heuristics are efficient in obtaining near-optimal solutions for small problem instances. The generated problem instances in combination with the upper bounds can be used as benchmarks for future approaches in the field of common due-date scheduling. Scope and purpose In connection with just-in-time production and delivery, earliness as well as tardiness penalties are of interest. Thus scheduling against common due dates has received growing attention during the last decade. Many algorithms have been developed to solve the different variants of this problem. But whenever a new algorithm for scheduling against common due dates is proposed, its quality is assessed only on a few self-generated examples. Hence it is difficult to evaluate the various approaches, particularly in comparison with each other. Therefore the goal of this paper is to present numerous benchmark problems together with some upper bounds on the optimal objective function value.

Common Due-Date Assignment and Scheduling on Single Machine with Exponential Processing Times

This article deals with the problem of common due-date assignment and scheduling on single machine with exponential processing times. The objective is to minimize the expected total cost associated with the due-date and earliness/tardiness of jobs. For large due-date cost, we have derived a closed-form solution, which is applicable to the general processing times as well. In the other case, it is shown that an optimal sequence lies among the V-shaped sequences, and an algorithm is developed for the derivation of optimal solution.

Fuzzy due-date scheduling problem with fuzzy processing time

For most of scheduling problems, jobs’ processing times and due-dates are treated as certain values, but that is not proper to all actual situations. Processing times are not constant because of measurement errors in the data sets for deciding them and/or human actions in the manufacturing process. However, we may be permitted to exceed due-dates slightly. In this paper, we propose a model dealing with uncertain processing times and flexible due-dates in consideration of real situations. Assuming the times and due-dates to be fuzzy numbers, and defining a fuzzy tardiness for a job’s due-date, we design to minimize the number of tardy jobs in our problem formulation.

A fixed interval due-date scheduling problem with earliness and due-date costs

In this paper we consider a problem where N jobs are to be scheduled on a single machine and assigned to one of the two due-dates which are given at equal intervals. Tardy jobs are not allowed, thus the due-dates are deadlines. There is a linear due-date penalty and linear earliness penalty, and the sum of these penalties is to be minimized. This problem is shown to be NP-hard. One case of the problem (the unrestricted case) is shown to be easy to solve. For the restricted case, lower and upper bounds are developed. Computational experience is reported which suggests that the bounds are quite effective.

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.

Load-oriented manufacturing control just-in-time production for job shops

Abstract The paper describes the principles of load-oriented manufacturing control as a new solution for job shops and its successful implementation in a pump-manufacturing factory. The idea of load-oriented manufacturing control is to limit and balance work-in-process inventory on a level as low as possible in order to accomplish a high work-centre utilization as well as a rapid and in-time flow of orders. As the new system keeps actual lead times on a planned level in a self-regulating way, it allows reliable due-date scheduling. Furthermore it points out the bottlenecks and performs applicable mid-term and short-term capacity planning.

Experimental investigation of an FMS due-date scheduling problem: Evaluation of machine and AGV scheduling rules

Although extensive research has been conducted to solve design and operational problems of automated manufacturing systems, many of the problems still remain unsolved. This article investigates the scheduling problems of flexible manufacturing systems (FMSs). Specifically, the relative performances of machine and automated guided vehicle (AGV) scheduling rules are analyzed against various due-date criteria. First, the relevant literature is briefly reviewed, and then the rules are tested under different experimental conditions by using a simulation model of an FMS. The sensitivity to AGV workload, buffer capacity, and processing-time distribution is also investigated to assess the robustness of the scheduling rules.