Off-Line and Dynamic Production Scheduling – A Comparative Case Study

Abstract

Accepted: 16 February 2016 A comprehensive case study of manufacturing scheduling solutions development is given. It includes highly generalized scheduling problem as well as a few scheduling modes, methods and problem models. The considered problem combines flexible job shop structure, lot streaming with variable sublots, transport times, setup times, and machine calendars. Tabu search metaheuristic and constraint programming methods have been used for the off-line scheduling. Two dynamic scheduling methods have also been implemented, i.e., dispatching rules for the completely reactive scheduling and a multi-agent system for the predictivereactive scheduling. In these implementations three distinct models of the problem have been used, based on: graph representation, optimal constraint satisfaction, and Petri net formalism. Each of these solutions has been verified in computational experiments. The results are compared and some findings about advantages, disadvantages, and suggestions on using the solutions are formulated.

Optimization of management information support as a basis for organizational transformations at an enterprise

“…It is also possible case, when for a particular F 1 subsystem the set of rules consists of 10 and the degree of compliance is t  1 = 0,9, while for another subsystem F 2 , the set of rules consists of 100, t  2 = 0,85. This means that subsystem S 1 is characterized by 10 rules, 9 of which are carried out by the organizational unit t, and subsystem S 2 is characterized by 100 rules, 85 of which are executed by the organizational unit t. In this case, in accordance with the rule on formula (19), the organizational unit t is recommended to refer to the subsystem S 1 . In this case, the partitioning of the system S into subsystems should be more uniform.…”

“…The most fully such requirements correspond to simulation models as Petri Nets [7,8,9]. Petri Nets are widely used for modelling socio-economic processess: Cheng et al used Petri Nets for resourse management in building design process [10], also they were used in modeling of energy consumption [11,12,13], in planning of construction projects [14], modelling of flexible manufacturing sytems [15], modeling discrete event process [16], at resource allocation [17], in assesing of information security risk [18], in dynamic programming scheduling [19] simulation for small and medium enterprises [20], production logistics and supply chain system [21], stochastic behaviour analysis of industrial system [22] accounting information system [23], control of production task flows [24], reverse-engineering cycles [25], real-time operation planning system in distributed manufacturing network [26],in supply chain finance business process [27], in integrating system dynamics [28], for integrating purchasing, production and packaging for Kanban system [29], in safety analysis of production systems [30], to verify BPMN Models [31]. D.A.…”

Management and Production Engineering Review

“…Recent research uses the latest method of heuristics method on scheduling one machine with different processing time and due date assuming the work cannot be reprocessed to mini-mize tardiness [9]. Previous research also raised the issue of scheduling in a flexible job shop [16], hybrid flow shop, and parallel flow shop [1] manufacturing systems while this study used a pure flow shop system.…”

“…Scheduling is basically assigning the tasks that need to be completed for the project to be finished in a timely manner [15]. Scheduling is closely related to complexity theory which means objectives and cases are NP-Hard problems [16] so it can be solved by the heuristic method. A previous study proved that the Palmer and Campbell Dudek Smith (CDS) heuristic methods show the minimum value of average of makespan and average utilization of machine [17].…”