3 research outputs found
The networked handling of rush orders in customer services
Rush orders are characterised by time constraints and organisational priority. They are handled by the supplier with the aim of meeting customer requirements in as limited a timeframe as possible. Rather than focusing on rush orders as a deterministic planning problem, this paper takes an inter-organisational perspective that highlights the complex networked interactions between the supplier and the customers. In this single case study of an advanced sanitary product supplier, rush orders involve process prioritisation concerning both: (i) supplies of in-stock parts that are delivered with pre-set time objectives; and (ii) parts not in stock that must be quickly fabricated. This supply process is highly emergent, in that unexpected events or properties occur. This study considers the difficulties of determining and dealing with root causes, unexpected effects, and interventive solutions for rush orders. This operational level of analysis provides a foundation for advocating the application of complex systems thinking to solve or at least significantly mitigate the problem of rush orders. It also contributes to and advances further research on this subject
A consistent neuro-fuzzy inference system
ΠΠ΅Π»ΠΈΠΊΠΈ Π±ΡΠΎΡ Π°ΡΡΠΎΡΠ° ΡΠΌΠ°ΡΡΠ° Π΄Π° Π²Π΅Π»ΠΈΠΊΠ΅ ΠΌΠΎΠ³ΡΡΠ½ΠΎΡΡΠΈ Π΅ΠΊΡΠΏΠ΅ΡΡΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌΠ° Π»Π΅ΠΆΠ΅ Ρ Ρ
ΠΈΠ±ΡΠΈΠ΄Π½ΠΈΠΌ ΠΌΠΎΠ΄Π΅Π»ΠΈΠΌΠ°, ΡΡΠΎ ΡΡ ΠΎΠ²ΠΈ ΡΠΈΡΡΠ΅ΠΌΠΈ ΠΈ Π΄ΠΎΠΊΠ°Π·Π°Π»ΠΈ Ρ ΠΏΡΠ°ΠΊΡΠΈ. ΠΠΎΡΠΈΠ²ΠΈΡΠ°Π½ ΡΠΈΠΌΠ΅, ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈ ΠΌΠΎΠ΄Π΅Π» ΡΠΈΡΡΠ΅ΠΌΠ° Ρ ΠΎΡΠ½ΠΎΠ²ΠΈ ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΡΠ° ΠΈΠ½ΡΠ΅Π³ΡΠ°ΡΠΈΡΡ Π½Π΅ΡΡΠΎΠ½ΡΠΊΠΈΡ
ΠΌΡΠ΅ΠΆΠ° ΠΈ ΡΠ°Π·ΠΈ ΡΠΈΡΡΠ΅ΠΌΠ°, ΡΠΈΠΌΠ΅ ΡΠ΅ Π±ΠΎΡΠ΅ ΠΊΠΎΡΠΈΡΡΠ΅ Π΄ΠΎΠ±ΡΠ΅ ΡΡΡΠ°Π½Π΅ ΠΎΠ±Π° ΠΏΡΠΈΡΡΡΠΏΠ°.
ΠΠΎΠ»Π°Π·Π½Π° ΠΎΡΠ½ΠΎΠ²Π° ΠΎΠ²ΠΎΠ³ ΡΠ°Π΄Π° ΡΠ΅ Π΄Π° ΠΏΠΎΠ½Π°ΡΠ°ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΠ°, ΠΊΡΠΎΠ· ΡΠΊΡΠΏ Π»ΠΈΠ½Π³Π²ΠΈΡΡΠΈΡΠΊΠΈΡ
ΠΏΡΠ°Π²ΠΈΠ»Π°, ΡΡΠ΅Π±Π° Π΄Π° ΠΎΠΏΠΈΡΡΡΡ ΡΠΏΡΠ°Π²ΠΎ ΠΎΠ½ΠΈ ΠΊΠΎΡΠΈ ΡΠΈΡΡΠ΅ΠΌ Π½Π°ΡΠ²ΠΈΡΠ΅ ΠΏΠΎΠ·Π½Π°ΡΡ ΠΈ ΡΠ°Π·ΡΠΌΠ΅ΡΡ (Π½Π°ΡΡΠΏΡΠΎΡ Π°ΡΡΠΎΠΌΠ°ΡΡΠΊΠΈ Π³Π΅Π½Π΅ΡΠΈΡΠ°Π½ΠΈΠΌ ΠΏΡΠ°Π²ΠΈΠ»ΠΈΠΌΠ° ΠΊΠΎΡΠ° ΡΡ Π½Π°ΡΡΠ΅ΡΡΠ΅ ΡΠΎΠ³ΠΎΠ±Π°ΡΠ½Π° ΠΈ Π½Π΅ΡΠ°Π·ΡΠΌΡΠΈΠ²Π°). ΠΠ½Π°ΡΠ΅ Π΅ΠΊΡΠΏΠ΅ΡΠ°ΡΠ° ΠΈΠ· Π±ΠΈΠ»ΠΎ ΠΊΠΎΡΠ΅ ΠΎΠ±Π»Π°ΡΡΠΈ Π»Π°ΠΊΠΎ ΡΠ΅ ΠΌΠΎΠΆΠ΅ ΡΠΎΡΠΌΡΠ»ΠΈΡΠ°ΡΠΈ Π²Π΅ΡΠ±Π°Π»Π½ΠΈΠΌ ΠΈΡΠΊΠ°Π·ΠΈΠΌΠ°, Π° ΡΠ΅ΠΎΡΠΈΡΠ° ΡΠ°Π·ΠΈ ΡΠΊΡΠΏΠΎΠ²Π° ΠΈ ΡΠ°Π·ΠΈ Π»ΠΎΠ³ΠΈΠΊΠ΅ ΠΎΠΌΠΎΠ³ΡΡΠ°Π²Π° ΠΏΡΠ΅Π²ΠΎΡΠ΅ΡΠ΅ ΠΎΠ²Π°ΠΊΠ²ΠΈΡ
ΠΈΡΠΊΠ°Π·Π° Ρ ΠΎΠ΄Π³ΠΎΠ²Π°ΡaΡΡΡΠ΅ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠΊΠ΅ ΠΈΠ·ΡΠ°Π·Π΅.
ΠΠ»Π°ΡΠΈΡΠ½Π° ΡΠ΅ΠΎΡΠΈΡΠ° ΡΠ°Π·ΠΈ ΡΠΊΡΠΏΠΎΠ²Π° Π½Π΅ Π·Π°Π΄ΠΎΠ²ΠΎΡΠ°Π²Π° ΡΠ²Π΅ ΠΡΠ»ΠΎΠ²Π΅ Π°ΠΊΡΠΈΠΎΠΌΠ΅. ΠΠ· ΠΎΠ²ΠΎΠ³ ΡΠ°Π·Π»ΠΎΠ³Π° Ρ ΡΠ°Π΄Ρ ΡΠ΅ ΠΏΡΠΈΠΌΠ΅ΡΠ΅Π½Π° ΠΊΠΎΠ½Π·ΠΈΡΡΠ΅Π½ΡΠ½Π° ΡΠ΅Π°Π»Π½ΠΎ-Π²ΡΠ΅Π΄Π½ΠΎΡΠ½Π° [0,1] Π»ΠΎΠ³ΠΈΠΊΠ°, ΠΊΠΎΡΠ° ΡΠ΅ Π·Π°ΡΠ½ΠΈΠ²Π° Π½Π° ΠΈΠ½ΡΠ΅ΡΠΏΠΎΠ»Π°ΡΠΈΠ²Π½ΠΎΡ ΠΡΠ»ΠΎΠ²ΠΎΡ Π°Π»Π³Π΅Π±ΡΠΈ (ΠΠΠ). Π‘Π²Π°ΠΊΠ° Π»ΠΎΠ³ΠΈΡΠΊΠ° ΡΡΠ½ΠΊΡΠΈΡΠ° ΠΌΠΎΠΆΠ΅ ΡΠ΅ ΡΠ΅Π΄Π½ΠΎΠ·Π½Π°ΡΠ½ΠΎ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠΈΡΠ°ΡΠΈ Ρ ΠΎΠ΄Π³ΠΎΠ²Π°ΡΠ°ΡΡΡΠΈ Π³Π΅Π½Π΅ΡΠ°Π»ΠΈΠ·ΠΎΠ²Π°Π½ΠΈ ΠΡΠ»ΠΎΠ² ΠΏΠΎΠ»ΠΈΠ½ΠΎΠΌ (ΠΠΠ) ΠΊΠΎΡΠΈΡΡΠ΅ΡΠ΅ΠΌ ΠΠΠ ΠΏΡΠΈ ΡΠ΅ΠΌΡ ΡΠ΅ ΡΡΠ²Π°ΡΡ ΡΠ²ΠΈ ΠΡΠ»ΠΎΠ²ΠΈ Π·Π°ΠΊΠΎΠ½ΠΈ.
ΠΠΏΡΠ°Π²Π΄Π°Π½ΠΎΡΡ ΠΊΠΎΡΠΈΡΡΠ΅ΡΠ° ΠΊΠΎΠ½Π·ΠΈΡΡΠ΅Π½ΡΠ½ΠΎΠ³ ΠΏΡΠΈΡΡΡΠΏΠ° Π½Π°ΡΠΏΡΠ΅ ΡΠ΅ ΠΈΠ»ΡΡΡΡΠΎΠ²Π°Π½Π° Π½Π° ΠΏΡΠΈΠΌΠ΅ΡΡ ΠΊΠΎΠ½Π·ΠΈΡΡΠ΅Π½ΡΠ½ΠΎΠ³ ΡΠ°Π·ΠΈ ΡΠΈΡΡΠ΅ΠΌΠ° Π·Π°ΠΊΡΡΡΠΈΠ²Π°ΡΠ° (ΠΠ€ΠΠ‘). Π‘Π²ΡΡ
Π° ΠΏΡΠΈΠΊΠ°Π·Π°Π½ΠΎΠ³ ΠΠ€ΠΠ‘-Π° ΡΠ΅ Π΄Π° ΠΏΡΠΎΡΠ΅Π½ΠΈ ΠΌΠΎΠ³ΡΡΠ½ΠΎΡΡ Π΄Π° ΡΠ΅ ΠΏΠ°ΡΠΈΡΠ΅Π½Ρ Π½Π° Π΄ΠΈΡΠ°Π»ΠΈΠ·ΠΈ ΡΡΠ±ΡΡΠ½Π΅ ΠΌΠ°ΡΠ°ΠΌΠΈΡΠ΅ (Π»Π°Ρ. peritoneum) ΠΎΠ±ΠΎΠ»Π΅ΠΎ ΠΎΠ΄ ΠΏΠ΅ΡΠΈΡΠΎΠ½ΠΈΡΠΈΡΠ°. ΠΠΎΠ±ΠΈΡΠ΅Π½ΠΈ ΡΠ΅Π·ΡΠ»ΡΠ°ΡΠΈ ΡΠΊΠ°Π·ΡΡΡ Π½Π° ΡΠΈΡΠ΅Π½ΠΈΡΡ Π΄Π° ΠΊΠ»Π°ΡΠΈΡΠ°Π½ Π€ΠΠ‘ ΠΈ ΠΊΠΎΠ½Π·ΠΈΡΡΠ΅Π½ΡΠ°Π½ ΠΏΡΠΈΡΡΡΠΏ Π½Π΅ Π²ΠΎΠ΄Π΅ ΡΠ²Π΅ΠΊ ΠΊΠ° ΠΈΡΡΠΈΠΌ ΡΠ΅Π·ΡΠ»ΡΠ°ΡΠΈΠΌΠ°, Π° ΡΠ°Π·Π»ΠΈΠΊΠ° ΡΠ΅ Π½Π°ΡΡΠΎΡΡΠΈΠ²ΠΈΡΠ° ΠΊΠ°Π΄Π° ΠΏΡΠ°Π²ΠΈΠ»Π° ΡΠΊΡΡΡΡΡΡ Π½Π΅Π³Π°ΡΠΈΡΡ.
ΠΠ°ΠΊΠΎ Π±ΠΈ ΡΠ΅ ΠΠ€ΠΠ‘ Π΄Π°ΡΠ΅ ΡΠ½Π°ΠΏΡΠ΅Π΄ΠΈΠΎ, ΠΊΠΎΡΠΈΡΡΠ΅Π½Π° ΡΠ΅ Π½Π΅ΡΡΠΎΠ½ΡΠΊΠ° ΠΌΡΠ΅ΠΆΠ°, ΡΡ. ΡΠ΅Π½ Π°Π»Π³ΠΎΡΠΈΡΠ°ΠΌ ΡΡΠ΅ΡΠ°, ΠΊΠΎΡΠΈ, Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ ΡΠΊΡΠΏΠ° ΡΠ»Π°Π·Π½ΠΎ-ΠΈΠ·Π»Π°Π·Π½ΠΈΡ
ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ°, ΠΏΠΎΠ΄Π΅ΡΠ°Π²Π° ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ΅ ΡΠ°ΠΊΠΎ Π΄Π° Π²ΠΈΡΠ΅ ΠΎΠ΄Π³ΠΎΠ²Π°ΡΠ°ΡΡ ΡΠ΅Π°Π»Π½ΠΎΠΌ ΡΠΈΡΡΠ΅ΠΌΡ. ΠΠ° ΡΠ°Ρ Π½Π°ΡΠΈΠ½, ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈ
ΠΊΠΎΠ½Π·ΠΈΡΡΠ΅Π½ΡΠ°Π½ Π½Π΅ΡΡΠΎ-ΡΠ°Π·ΠΈ ΡΠΈΡΡΠ΅ΠΌ (ΠΠΠ€ΠΠ‘) ΠΊΠΎΡΠΈΡΡΠΈ Π·Π½Π°ΡΠ΅ ΡΠ°Π΄ΡΠΆΠ°Π½ΠΎ Ρ ΠΏΠΎΠ΄Π°ΡΠΈΠΌΠ° ΠΈ ΡΠ½Π°ΠΏΡΠ΅ΡΡΡΠ΅ Π·Π°ΠΊΡΡΡΠΈΠ²Π°ΡΠ΅. Π’Π°ΠΊΠΎΡΠ΅, Π΅Π»ΠΈΠΌΠΈΠ½ΠΈΡΠ΅ ΡΠ΅ ΡΡΠ±ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡ ΠΊΠΎΡΡ Π΅ΠΊΡΠΏΠ΅ΡΡΠΈ Ρ Π½Π΅ΠΊΠΎΡ ΠΌΠ΅ΡΠΈ ΠΈΠ·ΡΠ°ΠΆΠ°Π²Π°ΡΡ ΠΏΡΠΈΠ»ΠΈΠΊΠΎΠΌ Π΄Π΅ΡΠΈΠ½ΠΈΡΠ°ΡΠ° ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΠ°ΡΠ° ΡΠΈΡΡΠ΅ΠΌΠ°...A number of authors find that the greatest potential of expert systems lies in hybrid models, and such models have proven this viewpoint in practice.Therein lies the motivation for introducing a new system model, integrating neural networks and fuzzy systems, thus building on the best features of each of these approaches.
The main premise of this thesis is that the behavior of a system should be described, through a set of linguistic rules, by those who know and understand the system the best (as opposed to the automatic generation of rules that are often cumbersome and incomprehensible). Expert knowledge in any domain can be easily expressed in the form of verbal statements, and fuzzy set theory and fuzzy logic enable the transformation of such verbal statements into mathematical expressions.
Conventional fuzzy set theory does not satisfy all Boolean axioms. For this reason, the consistent real-valued [0,1] logic, based on the Interpolative realization of Boolean algebra (IBA), is applied in this thesis. Any logical function can be uniquely transformed into a corresponding generalized Boolean polynomial (GBP) using IBA thereby preserving all Boolean laws.
The justification for using a consistent approach is first illustrated on an example of a consistent fuzzy inference system (CFIS). The purpose of the described CFIS is to estimate the likelihood that a patient undergoing peritoneal dialysis, has peritonitis. The obtained results demonstrate that conventional FIS and the Boolean consistent approach do not always lead to the same results, and this discrepancy is most pronounced when the established rules include negations.
In order to further enhance CFIS a neural network, or, more precisely, its learning algorithm, is used to fine-tune the parameters, in accordance with a set of input-output data, so that the parameters better suit the real system. Consequently, the proposed
consistent neuro-fuzzy system (CNFIS) uses the knowledge contained in the data to improve the inference process. In addition, it eliminates the subjectivity incorporated into the system by experts when defining the parameters of the system..