Capacity Planning and Leadtime management

In this paper we discuss a framework for capacity planning and lead time management in manufacturing companies, with an emphasis on the machine shop. First we show how queueing models can be used to find approximations of the mean and the variance of manufacturing shop lead times. These quantities often serve as a basis to set a fixed planned lead time in an MRP-controlled environment. A major drawback of a fixed planned lead time is the ignorance of the correlation between actual work loads and the lead times that can be realized under a limited capacity flexibility. To overcome this problem, we develop a method that determines the earliest possible completion time of any arriving job, without sacrificing the delivery performance of any other job in the shop. This earliest completion time is then taken to be the delivery date and thereby determines a workload-dependent planned lead time. We compare this capacity planning procedure with a fixed planned lead time approach (as in MRP), with a procedure in which lead times are estimated based on the amount of work in the shop, and with a workload-oriented release procedure. Numerical experiments so far show an excellent performance of the capacity planning procedure

Dynamic set-up rules for hybrid flow shop scheduling with parallel batching machines

An S-stage hybrid (or flexible) flow shop, with sequence-independent uniform set-up times, parallel batching machines with compatible parallel batch families (like in casting or heat treatments in furnaces, chemical or galvanic baths, painting in autoclave, etc.) has been analysed with the purpose of reducing the number of tardy jobs (and the makespan); in Graham’s notation: FPB(m_1, m_2, … , m_S)|p-batch, STsi,b|SUM(Ui). Jobs are sorted dynamically (at each new delivery); batches are closed within sliding (or rolling) time windows and processed in parallel by multiple identical machines. Computation experiments have shown the better performance on benchmarks of the two proposed heuristics based on new formulations of the critical ratio (CRsetup) considering the ratio of allowance set-up and processing time in the scheduling horizon, which improves the weighted modified operation due date rule

Risk-Based Optimal Scheduling for the Predictive Maintenance of Railway Infrastructure

In this thesis a risk-based decision support system to schedule the predictive maintenance activities, is proposed. The model deals with the maintenance planning of a railway infrastructure in which the due-dates are defined via failure risk analysis.The novelty of the approach consists of the risk concept introduction in railway maintenance scheduling, according to ISO 55000 guidelines, thus implying that the maintenance priorities are based on asset criticality, determined taking into account the relevant failure probability, related to asset degradation conditions, and the consequent damages

Job shop scheduling under dynamic and stochastic manufacturing environment

Ankara : Department of Industrial Engineering and the Institute of Engineering and Sciences of Bilkent Univ., 1995.Thesis (Master's) -- Bilkent University, 1995.Includes bibliographical references.In practice, manufacturing systems operate under dynamic and stochastic environment where unexpected events (or interruptions) occur continuously in the shop. Most of the scheduling literature deals with the schedule generation problem which is only one aspect of the scheduling decisions. The reactive scheduling and control aspect has scarcely been addressed. This study investigates the effects of the stochastic events on the s\'stem performance and develops alternative reactive scheduling methods. In this thesis, we also study the single-pass and multi-pass scheduling heuristics in dynamic and stochastic job shop scheduling environment. We propose a simulation-based scheduling system for the multi-pass heuristics. Finally, we analyze the interactions among the operational strategies (i.e, lookahead window, scheduling period, method used for scheduling), the system conditions, and the unexpected events such as machine breakdowns and processing time variations.Kutanoğlu, ErhanM.S

An analysis of FMS scheduling problem: a beam search based algorithm and comparison of scheduling schemes

Ankara : Department of Industrial Engineering and Institute of Engineering and Sciences, Bilkent Univ., 1994.Thesis (Master's) -- Bilkent University, 1994.Includes bibliographical references leaves 77-80FMS scheduling procedures in the literature can be classified into on-line and off-line schemes according to the number of scheduling decisions made at a point in time. Online scheduling attempts to schedule operations one at a time when it is needed and off-line scheduling refers to scheduling operations of available jobs for the entire scheduling period. In the literature there is no unified argument for or against either of these scheduling schemes. This research has two main objectives: development of a new scheduling scheme called quasi on-line that makes a trade-off between on-line and off-line schemes and comparison of the proposed scheme with others under various experimental conditions. A new algorithm is proposed on which the quasi online scheme is based. The proposed algorithm is a heuristic and utilizes a beam search technique. It considers finite buffer capacity, routing and sequence flexibilities and generates machine and AGV schedules for a given scheduling period. A simulation model is also developed to implement and test scheduling schemes.Karabük, SüleymanM.S

Assembly job shop scheduling problems with component availability constraints.

Job shop scheduling has been widely studied for several decades. In generalized of the job shop scheduling problem, n jobs are to be processed on m machines under specific routings and due dates. The majority of job shop scheduling research concentrates on manufacturing environments processing string-type jobs with a linear routing where no assembly operations are involved. However, many manufacturing environments produce complex products with multi-level assembly job structures and cannot be scheduled efficiently with existing job shop scheduling techniques. Little research has been done in the area of assembly job shop scheduling, and we are not aware any of those studies consider on the availability of purchased components and the impact of component availability on the performance of assembly job shops. This research focuses on scheduling job shops that process jobs requiring multiple-levels of assembly and it also considers the availability of components that are procured from outside suppliers. By considering material constraints during production scheduling, manufacturers can increase resource utilization and improve due date performance.To represent assembly job shop scheduling problems with component availability constraints, a modified disjunctive graph formulation is developed in this research. A mixed-integer programming model with the objective of minimizing the total weighted-tardiness is also developed in this research. Several heuristic methods, described as modified shifting bottleneck procedure (MSBP), efficient shifting bottleneck procedure (ESBP) and rolling horizon procedure (RHP), are proposed to reduce the computational time required for assembly job shop scheduling problems. These methods are extended from the shifting bottleneck procedure. The performance of various flavors of the MSBP and ESBP is demonstrated on a set of test instances and compared with different dispatching rules that are widely used in practice. Results show that MSBP and ESBP outperform the dispatching rules by 18% to 16% on average.This dissertation not only studies the assembly job shop scheduling problem with component availability constraints, but also demonstrates how the decomposition methodology can reduce the complexity of NP-hard problems. Based on the relative preference of solution quality and computational time, recommendations for appropriate methods to solve assembly job shop scheduling problems with different problem sizes are given in the conclusions of this dissertation

Dynamic scheduling in a multi-product manufacturing system

To remain competitive in global marketplace, manufacturing companies need to improve their operational practices. One of the methods to increase competitiveness in manufacturing is by implementing proper scheduling system. This is important to enable job orders to be completed on time, minimize waiting time and maximize utilization of equipment and machineries. The dynamics of real manufacturing system are very complex in nature. Schedules developed based on deterministic algorithms are unable to effectively deal with uncertainties in demand and capacity. Significant differences can be found between planned schedules and actual schedule implementation. This study attempted to develop a scheduling system that is able to react quickly and reliably for accommodating changes in product demand and manufacturing capacity. A case study, 6 by 6 job shop scheduling problem was adapted with uncertainty elements added to the data sets. A simulation model was designed and implemented using ARENA simulation package to generate various job shop scheduling scenarios. Their performances were evaluated using scheduling rules, namely, first-in-first-out (FIFO), earliest due date (EDD), and shortest processing time (SPT). An artificial neural network (ANN) model was developed and trained using various scheduling scenarios generated by ARENA simulation. The experimental results suggest that the ANN scheduling model can provided moderately reliable prediction results for limited scenarios when predicting the number completed jobs, maximum flowtime, average machine utilization, and average length of queue. This study has provided better understanding on the effects of changes in demand and capacity on the job shop schedules. Areas for further study includes: (i) Fine tune the proposed ANN scheduling model (ii) Consider more variety of job shop environment (iii) Incorporate an expert system for interpretation of results. The theoretical framework proposed in this study can be used as a basis for further investigation

Parallel machine scheduling with precedence constraints and setup times

This paper presents different methods for solving parallel machine scheduling problems with precedence constraints and setup times between the jobs. Limited discrepancy search methods mixed with local search principles, dominance conditions and specific lower bounds are proposed. The proposed methods are evaluated on a set of randomly generated instances and compared with previous results from the literature and those obtained with an efficient commercial solver. We conclude that our propositions are quite competitive and our results even outperform other approaches in most cases