Scheduling with Sequencing Flexibility *

This study examines the effects of sequencing flexibility on the performance of rules used to schedule operations in manufacturing systems. The findings show that taking advantage of even low levels of sequencing flexibility in the set of operations required to do a job results in substantial improvement in the performance of scheduling rules with respect to mean flowtime. Differences in the mean flowtime measure for various rules also diminish significantly with increasing sequencing flexibility. Performance improvements additionally result for such due-date related performance measures as mean tardiness and the proportion of jobs tardy. At high levels of sequencing flexibility, some nonparametric scheduling rules outperform the shortest processing time rule in terms of the mean flowtime criterion. Rules based on job due dates also outperform rules based on operation milestones in terms of tardiness related criteria at high levels of sequencing flexibility. The implications of these findings for the design of manufacturing systems and product design are noted.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73893/1/j.1540-5915.1993.tb00477.x.pd

Integration of the Cimosa and high-level coloured Petri net modelling techniques with application in the postal process using hierarchical dispatching rules

Enterprise processes, i.e. business and manufacturing, rely on enterprise modelling and simulation tools to assess the quality of their structure and performance in an unobtrusive and cost-effective way. Each of these processes is a collaboration of inseparable elements such as resources, information, operations, and organization. In order to provide a more complete assessment of enterprise processes, a simulation approach that allows communication and interaction among these elements needs to be provided. The simulation approach requires an analysis of the performance of each element and its influence on other elements in an object-oriented way. It also needs to have the capability to represent the structures and dynamics of the elements mentioned, and to present the performance assessment comprehensively. This will ensure a more holistic simulation modelling task. These simulation requirements have motivated the investigation of the novel integration of two popular enterprise process modelling methods: Cimosa and high-level coloured Petri net. The Cimosa framework is used to formalize the enterprise modelling procedure in the aspects of representing process elements, structure, behaviours, and relationships. The high-level coloured Petri nets method provides the mechanism to simulate the dynamics of objects and their characteristics, and also to enable communication among the objects. The approach is applied on a postal process model, which involves elements from manufacturing processes, i.e. machine processing (sorting), inventory (storage), product flow, and resource planning. Simulation studies based on the hierarchical dispatching rules show that the integrated approach is able to present vital information regarding the communication method, resource management, and the effect of interactions among these manufacturing process elements, which are not provided by the current modelling system in the postal company. The current paper has presented a novel mechanism, i.e. Cimosa—HCTSPN modelling approach, to extract information on process elements and their interactions. It has also presented the novel hierarchical dispatching rules and contributed to the extension of information that can be represented for a postal process

A Hybrid Differential Evolution Approach for Simultaneous SchedulingProblems in a Flexible Manufacturing Environment

Scheduling of machines and transportation devices like Automated Guided Vehicles (AGVs) in a Flexible Manufacturing System(FMS) is a typical N-P hard problem. Even though several algorithms were employed to solve this combinatorial optimization problem, most of the work concentrated on solving the problems of machines and material handling independently. In this paper the authors have attempted to schedule both the machines and AGVs simultaneously, with makespan minimization as objective, for which Differential Evolution (DE) is applied. Operations based coding is employed to represent the solution vector, which is further modified to suit the DE application. The authors have proposed two new strategies of DE in this paper which better suits the problem. We have developed a separate heuristic for assigning the vehicles and this is integrated with the traditional DE approach. The hybridized approach is tested on a number of benchmark problems whose results outperformed those available in the literature

A control strategy for promoting shop-floor stability

This research aimed to study real-time shop floor control problem in a manufacturing environment with dual resource (machine and labour), under impact of machine breakdowns. In this study, a multiperspective (order and resource perspectives) control strategy is proposed to improve effectiveness of dispatching procedure for promoting shop floor stability. In this control strategy, both order and resource related factors have been taken into account according to information on direct upstream and succeeding workcentres. A simulated manufacturing environment has been developed as a platform for testing and analysing performances of the proposed control strategy. A series of experiments have been carried out in a variety of system settings and conditions in the simulated manufacturing environment. The experiments have shown that the proposed control strategy outperformed the ODD (Earliest Operation Due Date) rule in hostile environments, which have been described by high level of shop load and/or high intensity of machine breakdowns. In hostile environments, the proposed control strategy has given best performance when overtime was not used, and given promising results in reduction of overtime cost when overtime was used to compensate for capacity loss. Further direction of research is also suggested

A novel throughput control algorithm for semi-heterarchical industry 4.0 architecture

Modern market scenarios are imposing a radical change in the production concept, driving companies’ attention to customer satisfaction through increased product customization and quick response strategies to maintain competitiveness. At the same time, the growing development of Industry 4.0 technologies made possible the creation of new manufacturing paradigms in which an increased level of autonomy is one of the key concepts to consider. Taking the advantage from the recent development around the semi-heterarchical architecture, this work proposes a first model for the throughput control of a production system managed by such an architecture. A cascade control algorithm is proposed considering work-in-progress (WIP) as the primary control lever for achieving a specific throughput target. It is composed of an optimal control law based on an analytical model of the considered production system, and of a secondary proportional-integral-derivative controller capable of performing an additional control action that addresses the error raised by the theoretical model’s. The proposed throughput control algorithm has been tested in different simulated scenarios, and the results showed that the combination of the control actions made it possible to have continuous adjustment of the WIP of the controlled production system, maintaining it at the minimum value required to achieve the requested throughput with nearly zero errors

Heuristic scheduling algorithms for dedicated and flexible manufacturing systems

Imperial Users onl

High-Level Object Oriented Genetic Programming in Logistic Warehouse Optimization

Disertační práce je zaměřena na optimalizaci průběhu pracovních operací v logistických skladech a distribučních centrech. Hlavním cílem je optimalizovat procesy plánování, rozvrhování a odbavování. Jelikož jde o problém patřící do třídy složitosti NP-težký, je výpočetně velmi náročné nalézt optimální řešení. Motivací pro řešení této práce je vyplnění pomyslné mezery mezi metodami zkoumanými na vědecké a akademické půdě a metodami používanými v produkčních komerčních prostředích. Jádro optimalizačního algoritmu je založeno na základě genetického programování řízeného bezkontextovou gramatikou. Hlavním přínosem této práce je a) navrhnout nový optimalizační algoritmus, který respektuje následující optimalizační podmínky: celkový čas zpracování, využití zdrojů, a zahlcení skladových uliček, které může nastat během zpracování úkolů, b) analyzovat historická data z provozu skladu a vyvinout sadu testovacích příkladů, které mohou sloužit jako referenční výsledky pro další výzkum, a dále c) pokusit se předčit stanovené referenční výsledky dosažené kvalifikovaným a trénovaným operačním manažerem jednoho z největších skladů ve střední Evropě.This work is focused on the work-flow optimization in logistic warehouses and distribution centers. The main aim is to optimize process planning, scheduling, and dispatching. The problem is quite accented in recent years. The problem is of NP hard class of problems and where is very computationally demanding to find an optimal solution. The main motivation for solving this problem is to fill the gap between the new optimization methods developed by researchers in academic world and the methods used in business world. The core of the optimization algorithm is built on the genetic programming driven by the context-free grammar. The main contribution of the thesis is a) to propose a new optimization algorithm which respects the makespan, the utilization, and the congestions of aisles which may occur, b) to analyze historical operational data from warehouse and to develop the set of benchmarks which could serve as the reference baseline results for further research, and c) to try outperform the baseline results set by the skilled and trained operational manager of the one of the biggest warehouses in the middle Europe.

The Benefits of Information Sharing in Carrier-Client Collaboration

This dissertation includes three related papers to investigate different methods that can help transport providers improve their operational efficiency. The first paper models and measures the profit improvement trucking companies can achieve by collaborating with their clients to obtain advance load information (ALI). The core research method is to formulate a comprehensive and flexible mixed integer mathematical model and implement it in a dynamic rolling horizon context. The findings illustrate that access to the second day ALI can improve the profit by an average of 22%. We also found that the impact of ALI depends on radius of service and trip length but statistically independent of load density and fleet size. The second paper investigates the following question of relevance to truckload dispatchers striving for profitable decisions in the context of dynamic pick-up and delivery problems: since not all future pick-up/delivery requests are known with certainty, how effective are alternative methods for guiding those decisions? We propose an intuitive policy and integrate it into a new two-index mixed integer programming formulation, which we implement using the rolling horizon approach. On average, in one of the practical transportation network settings, the proposed policy can, with just second-day ALI, yield an optimality ratio equal to almost 90% of profits in the static optimal solution. We enhance the proposed policy by adopting the idea of a multiple scenario approach. In comparison to other dispatching methods, our proposed policies were found to be very competitive in terms of solution quality and computational efficiency. Finally, inspired by a real-life third party logistic provider, the third paper addresses a dynamic pickup and delivery problem with full truckload (DPDFL) for local operators. The main purpose of this work is to investigate the impact of potential factors on the carriers’ operational efficiency. These factors, which are usually under managerial influence, are vehicle diversion capability, the DPDFL decision interval, and how far in advance the carrier knows of clients’ shipment requirements; i.e., ALI. Through comprehensive numerical experiments and statistical analysis, we found that the ALI and re-optimization interval significantly influence the total cost, but that diversion capability does not. A major contribution of this work is that we develop an efficient benchmark solution for the DPDFL’s static version by discretization of time windows. We observed that three-day ALI and an appropriate decision interval can reduce deviation from the benchmark solution to less than 8%

Towards System State Dispatching in High-Variety Manufacturing

This study proposes a shift towards system state dispatching in the production control literature on high-variety manufacturing. System state dispatching lets the decision on what order to produce next be driven by system-wide implications while trading of an array of control objectives. This contrasts the current literature that uses hierarchical order review and release methods that control the system at release, whilst myopic priority rules control order dispatching based on local queue information. We develop such a system state dispatching method, called FOCUS, and test it using simulation. The results show that FOCUS enables a big leap forward in production control performance. Specifically, FOCUS reduces the number of orders delivered late by a factor of one to eight and mean tardiness by a factor of two to ten compared to state-of-the-art production control methods. These results are consistent over a wide variety of conditions related to routing direction, routing length, process time variability and due date tightness