Due-date assignment and single machine scheduling with deteriorating jobs

Author name used in this publication: T. C. E. ChengAuthor name used in this publication: L. KangAuthor name used in this publication: C. T. Ng2003-2004 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Due-date assignment and parallel-machine scheduling with deteriorating jobs

Author name used in this publication: T. C. E. ChengAuthor name used in this publication: L. Y. KangAuthor name used in this publication: C. T. Ng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Solving Weighted Number of Operation Plus Processing Time Due-Date Assignment, Weighted Scheduling and Process Planning Integration Problem Using Genetic and Simulated Annealing Search Methods

Traditionally, the three important manufacturing functions, which are process planning, scheduling and due-date assignment, are performed separately and sequentially. For couple of decades, hundreds of studies are done on integrated process planning and scheduling problems and numerous researches are performed on scheduling with due date assignment problem, but unfortunately the integration of these three important functions are not adequately addressed. Here, the integration of these three important functions is studied by using genetic, random-genetic hybrid, simulated annealing, random-simulated annealing hybrid and random search techniques. As well, the importance of the integration of these three functions and the power of meta-heuristics and of hybrid heuristics are studied

Scheduling Jobs and a Variable Maintenance on a Single Machine with Common Due-Date Assignment

We investigate a common due-date assignment scheduling problem with a variable maintenance on a single machine. The goal is to minimize the total earliness, tardiness, and due-date cost. We derive some properties on an optimal solution for our problem. For a special case with identical jobs we propose an optimal polynomial time algorithm followed by a numerical example

Solving Integrated Process Planning, Dynamic Scheduling, and Due Date Assignment Using Metaheuristic Algorithms

Because the alternative process plans have significant contributions to the production efficiency of a manufacturing system, researchers have studied the integration of manufacturing functions, which can be divided into two groups, namely, integrated process planning and scheduling (IPPS) and scheduling with due date assignment (SWDDA). Although IPPS and SWDDA are well-known and solved problems in the literature, there are limited works on integration of process planning, scheduling, and due date assignment (IPPSDDA). In this study, due date assignment function was added to IPPS in a dynamic manufacturing environment. And the studied problem was introduced as dynamic integrated process planning, scheduling, and due date assignment (DIPPSDDA). The objective function of DIPPSDDA is to minimize earliness and tardiness (E/T) and determine due dates for each job. Furthermore, four different pure metaheuristic algorithms which are genetic algorithm (GA), tabu algorithm (TA), simulated annealing (SA), and their hybrid (combination) algorithms GA/SA and GA/TA have been developed to facilitate and optimize DIPPSDDA on the 8 different sized shop floors. The performance comparisons of the algorithms for each shop floor have been given to show the efficiency and effectiveness of the algorithms used. In conclusion, computational results show that the proposed combination algorithms are competitive, give better results than pure metaheuristics, and can effectively generate good solutions for DIPPSDDA problems

A State-of-the-Art Survey of Due Date Assignment and Scheduling Research: SLK, TWK and Other Due Date Assignment Models

This paper is a review of the results on the due date assignment and scheduling problems in the static deterministic case. In the problems under consideration, the objective is to find optimal values of the due dates and the related optimal schedule so that to minimize a given criterion based on the due dates and the completion times of jobs. The problems with due date determination have received considerable attention in the last ten years due to the introduction of new methods of inventory management such as Just-In-Time systems. According to the Just-In-Time concept jobs are to be completed neither too early nor too late which leads to the problems with non-regular measure of performance that includes earliness and tardiness costs. The due date assignment models where due dates depend on the jobs' processing times or on the positions of the jobs in the schedule are considered. The results on algorithms and complexity of the due date assignment and scheduling problems are summarized

Multi-Period Cell Loading and Job Sequencing in a Cellular Manufacturing System

In this paper, a multi-period cell loading problem is addressed, where the objectives are to minimise the number of tardy jobs (nT) in a multi-period planning horizon and optimise the scheduling of tardy jobs. Three cell loading and job scheduling strategies are proposed and tested with two newly developed mixed integer programming models. Additionally, three types of due dates (tight, medium and loose) and three different demand levels were considered. Finally, two tardy job assignment methods were proposed to observe the impact on nT. Case problems were solved based on minimising nT, Tmax and total tardiness (TT) objectives and cost sensitivity analysis was performed. Results indicated that, the first strategy, (early start allowance and tardy job assignment after each period) performed better in terms of nT. For the secondary objectives, tradeoffs were observed among different strategies depending on the type of due date, demand level and tardy job assignment method

A State-of-the-Art Survey of Due Date Assignment and Scheduling Research: Common Due Date

In this paper, we aim at providing a unified framework of the common due date assignment and scheduling problems in the deterministic case by surveying the literature concerning the models involving single machine and parallel machines. We focus on static production settings in which a fixed set of is available for processing as opposed to dynamic production settings where jobs continuously arrive in the system and should be scheduled on-line. The problems with due date determination have received considerable attention in the last ten years due to the introduction of new methods of inventory management such as Just-In-Time (JIT) systems. The common due date model which is also known in scheduling literature as CON model, where CON stands for constant flow allowance, corresponds, for instance, to an assembly system in which the components of the product should be ready at the same time, or to a shop where several jobs constitute a single customer's order. In the problems under consideration, the objective is to find an optimal value of the common due date and the related optimal schedule in order to optimize a given criterion based on the due date and the completion times of jobs. The results on the algorithms and complexity of the common due date assignment and scheduling problems are summarized

Common Due-Date Problem: Exact Polynomial Algorithms for a Given Job Sequence

This paper considers the problem of scheduling jobs on single and parallel machines where all the jobs possess different processing times but a common due date. There is a penalty involved with each job if it is processed earlier or later than the due date. The objective of the problem is to find the assignment of jobs to machines, the processing sequence of jobs and the time at which they are processed, which minimizes the total penalty incurred due to tardiness or earliness of the jobs. This work presents exact polynomial algorithms for optimizing a given job sequence or single and parallel machines with the run-time complexities of $O(n \log n)$ and $O(mn^2 \log n)$ respectively, where $n$ is the number of jobs and $m$ the number of machines. The algorithms take a sequence consisting of all the jobs $(J_i, i=1,2,\dots,n)$ as input and distribute the jobs to machines (for $m>1$) along with their best completion times so as to get the least possible total penalty for this sequence. We prove the optimality for the single machine case and the runtime complexities of both. Henceforth, we present the results for the benchmark instances and compare with previous work for single and parallel machine cases, up to $200$ jobs.Comment: 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computin