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

Parallel-Machine Scheduling Problems with Past-Sequence-Dependent Delivery Times and Aging Maintenance

We consider parallel-machine scheduling problems with past-sequence-dependent (psd) delivery times and aging maintenance. The delivery time is proportional to the waiting time in the system. Each machine has an aging maintenance activity. We develop polynomial algorithms to three versions of the problem to minimize the total absolute deviation of job completion times, the total load, and the total completion time

Single machine scheduling with general positional deterioration and rate-modifying maintenance

We present polynomial-time algorithms for single machine problems with generalized positional deterioration effects and machine maintenance. The decisions should be taken regarding possible sequences of jobs and on the number of maintenance activities to be included into a schedule in order to minimize the overall makespan. We deal with general non-decreasing functions to represent deterioration rates of job processing times. Another novel extension of existing models is our assumption that a maintenance activity does not necessarily fully restore the machine to its original perfect state. In the resulting schedules, the jobs are split into groups, a particular group to be sequenced after a particular maintenance period, and the actual processing time of a job is affected by the group that job is placed into and its position within the group

Optimization of job shop scheduling with material handling by automated guided vehicle

Job Shop Scheduling with Material Handling has attracted increasing attention in both industry and academia, especially with the inception of Industry 4.0 and smart manufacturing. A smart manufacturing system calls for efficient and effective production planning. On a typical modern shop floor, jobs of various types follow certain processing routes through machines or work centers, and automated guided vehicles (AGVs) are utilized to handle the jobs. In this research, the optimization of a shop floor with AGV is carried out, and we also consider the planning scenario under variable processing time of jobs. The goal is to minimize the shop floor production makespan or other specific criteria correlated with makespan, by scheduling the operations of job processing and routing the AGVs. This dissertation includes three research studies that will constitute my doctoral work. In the first study, we discuss a simplified case in which the scheduling problem is reformulated into a vehicle dispatching (assignment) problem. A few AGV dispatching strategies are proposed based on the deterministic optimization of network assignment problems. The AGV dispatching strategies take future transportation requests into consideration and optimally configure transportation resources such that material handling can be more efficient than those adopting classic AGV assignment rules in which only the current request is considered. The strategies are demonstrated and validated with a case study based on a shop floor in literature and compared to classic AGV assignment rules. The results show that AGV dispatching with adoption of the proposed strategy has better performance on some specific criterions like minimizing job waiting time. In the second study, an efficient heuristic algorithm for classic Job Shop Scheduling with Material Handling is proposed. Typically, the job shop scheduling problem and material handling problem are studied separately due to the complexity of both problems. However, considering these two types of decisions in the same model offers benefits since the decisions are related to each other. In this research, we aim to study the scheduling of job operations together with the AGV routing/scheduling, and a formulation as well as solution techniques are proposed. The proposed heuristic algorithm starts from an optimal job shop scheduling solution without limiting the size of AGV fleet, and iteratively reduces the number of available vehicles until the fleet size is equal to the original requirements. The computational experiments suggest that compared to existing solution techniques in literature, the proposed algorithm can achieve comparable solution quality on makespan with much higher computational efficiency. In the third study, we take the variability of processing time into consideration in optimizing job shop scheduling with material handling. Variability caused by random effects and deterioration is discussed, and a series of models are developed to accommodate random and deteriorating processing time respectively. With random processing time, the model is formulated as a Stochastic Programming Job Shop Scheduling with Material Handling model, and with deteriorating processing time the model can be nonlinear under specific deteriorating functions. Based on a widely adopted dataset in existing literature, the stochastic programming model were solved with Pyomo, and models with deterioration were linearized and solved with CPLEX. By considering variable processing time, the JSSMH models can better adapt to real production scenarios

Due-Window Assignment and Scheduling with Multiple Rate-Modifying Activities under the Effects of Deterioration and Learning

This paper discusses due-window assignment and scheduling with multiple rate-modifying activities. Multiple types of rate-modifying activities are allowed to perform on a single machine. The learning effect and job deterioration are also integrated concurrently into the problem which makes the problem more realistic. The objective is to find jointly the optimal location to perform multiple rate-modifying activities, the optimal job sequence, and the optimal location and size of the due window to minimize the total earliness, tardiness, and due-window-related costs. We propose polynomial time algorithms for all the cases of the problem under study

Order Acceptance and Scheduling: A Taxonomy and Review

Over the past 20 years, the topic of order acceptance has attracted considerable attention from those who study scheduling and those who practice it. In a firm that strives to align its functions so that profit is maximized, the coordination of capacity with demand may require that business sometimes be turned away. In particular, there is a trade-off between the revenue brought in by a particular order, and all of its associated costs of processing. The present study focuses on the body of research that approaches this trade-off by considering two decisions: which orders to accept for processing, and how to schedule them. This paper presents a taxonomy and a review of this literature, catalogs its contributions and suggests opportunities for future research in this area

Common due date early

Ankara : The Department of Industrial Engineering and the Graduate School of Engineering and Science of Bilkent University, 2013.Thesis (Master's) -- Bilkent University, 2013.Includes bibliographical references leaves 91-96.This study considers a scheduling problem with position-dependent deteriorating jobs and a maintenance activity in a single machine. Even in the absence of maintenance activity and deterioration problem is NP-hard. A solution comprises the following: (i) positions of jobs, (ii) the position of the maintenance activity, (iii) starting time of the first job in the schedule. After the maintenance activity, machine will revert to its initial condition and deterioration will start anew. The objective is to minimize the total weighted earliness and tardiness costs. Jobs scheduled before (after) the due-date are penalized according to their earliness (tardiness) value. Polynomial (O(n log n)) time solutions are provided for some special cases. No polynomial solution exists for instances with tight due-dates. We propose a mixed integer programming model and efficient algorithms for the cases where mathematical formulation is not efficient in terms of computational time requirements. Computational results show that the proposed algorithms perform well in terms of both solution quality and computation time.Şirvan, FatmaM.S

Minimizing Total Earliness and Tardiness for Common Due Date Single-Machine Scheduling with an Unavailability Interval

This paper addresses the problem of scheduling n independent jobs on a single machine with a fixed unavailability interval, where the aim is to minimize the total earliness and tardiness (TET) about a common due date. Two exact methods are proposed for solving the problem: mixed integer linear programming formulations and a dynamic programming based method. These methods are coded and tested intensively on a large data set and the results are analytically compared. Computational experiments show that the dynamic programming method is efficient in obtaining the optimal solutions and no problems due to memory requirement