PhD Thesis Abstract: Full Text (Solomon Ratt)

Thanks to Solomon Ratt for providing the dissertation abstract – both in English and in Cree – for us to study here, and thanks to Aydin Torkabadi for the really thoughtful gesture of respect for First Languages.

Read the full story here from CBC:

Application of Adaptive Neuro-Fuzzy Inference Systems in Development of Just-In-Time Pull Product Control Policies

Abstract: Just-in-Time manufacturing processes minimize inventory levels through effective control strategies. Pull Production Control Policies (PPCPs) authorize when and where to process, minimizing inventory levels while maximizing order fulfilment. This study investigates the development of PPCPs, by designing authorization mechanisms, measuring their performance, and evaluating alternative policies.

To do so, the research applied analytical, simulation, and intelligent computing methods, and each is examined through implementation in an actual industrial case. Horizontally Integrated Pull Control Policies (HIPCPs), combining the classic Kanban and ConWIP strategies, are proposed here as PPCP alternatives. Discrete event computer simulation techniques are applied to measure the performance of alternatives in a multi-stage and multi-layer manufacturing and assembly system with uncertain parameters. Fuzzy methods are applied to address the uncertainty. The proposed HIPCPs alternatives, together with Kanban and ConWIP, are comparatively evaluated via Fuzzy Analytical Hierarchy Process. Finally, applying intelligent methods, as a novel approach in PPCP development, the evaluation procedure was realized by an Adaptive Neuro-Fuzzy-Inference System (ANFIS) model.

All the three approaches show the effectiveness of HIPCP mechanisms for minimizing the inventory level, lead time, and backlogged orders, and maximizing the responsiveness to actual demand in the studied case. The optimum surface of performance for alternatives based on criteria domain is found.

Keywords: Just-In-Time, Pull Production Control Policies, Intelligent Systems, Adaptive Neuro Fuzzy Inference Systems, Fuzzy Inference Systems, Multi Criteria Decision Making Systems

ka-astâhk itêyihcikan-yôski-mâmitonêyihcikan ita ka-osihtahk ka-nânahitâk-ocipitamihk isihcikêwina ka-tipêyihtamihk oyasiwêwina.

âcimowinis: ka-nânahitâk-ocipitamihk osihtâwina ka-âstamêhk kâ-kanawêyihtamihk kîkwaya ta-tipêyihtamihk. kâ-ocipitamihk tipêyihtamihk oyasiwêwina (PPCP) oyasiwâtamihkk ispî êkwa ita ka-osihtahk, êkosi ka-âstamêhk kâ-kanawêyihtamihk kîkway êkwa êkosi nawac mistahi ka-kîsi-sâkaskinâhtahk itisahamâtowina. ôma nitawâpênikêwin kitâpahtamihk kâ-isi-osihtahk PPCPak nistam ka-osihtahk oyasiwêwina, kâ-ispayiki, êkwa ka-kanawâpahtamihk kotaka oyasiwêwina.

ka-kaskihtâmihk ôma nitawâpênikêwin ê-âpatahk mâmitonêyihtamihk, ê-âh-oyastâhk, êkwa ê-âpacihtahk nanâtohki-masinahikêhk-âpacihcikan êkwa pah-piscihk ê-kitâpahtamihk ita kâ-âpatahki atoskêwinihk. kâ-pimicipayik-ocipicikan-tipêyihcikan wiyasiwêwin (HIPCPs). ê-pawahikêhk kayâs Kanban êkwa ConWIP itôtamowina, pakosêyimohk ôta ka-âpatahki pah-pêtos kîkway âpatahki. kîmôt ê-ispayik mâmitonêyihcikâkan itôtamowina ê-âpacihtahk ka-tipâskahikâhk kâ-isi-ispayiki kotaka âpacihcikana nanâtohk ispayiki êkwa nanâtohk-oshtâhk êkâ kâ-kihcinâhohk. yôski-itôtamowina ka-âpatahki ka-kitâpahtamihk êkâ kâ-kihcinâhohk. kâ-pakosêyimohk HIPCPs pah-pêtos itôtamowina êkwa Kanban êkwa ConWIP mâh-mêskotâpahtamohk êwako ohci yôski-itôtamowina ka-kitâpahtamihk ka-ispastêhk itôtamowin. êkwâni êkwa piyisk, ê-astahk iyinîsiwin itôtamowina, ê-oskâyiwik itôtamowin ka-osihtahk PPCP isihcikêwin, kâ-pê-isi-kitâpahtamihk kî-sâponôkwan ê-âpatahk ê-nahastêk-mâmitonêyihcikan=-yôsi-itôtamowin-isâpahtamihk âpacihckan (ANFIS).

kahkiyaw ôhi nisto kâ-âpatahki ati-kihcinâhohk ê-miyo-ispayik HIPCP pîwâpiskwa ka-nîkastêki kâ-miciminamihk, ka-nîkânastêki, êkwa, kâ-otahk-astêki nitisahamâkêwina, êkwa ê-mâwaci-naskwêwasihêhk kâ-natomihtahk ôta kâ-kitâpahtamahk. kâ-mâwaci-miyo-âpatahk kâ-isi-atoskêhk pah-pêtos kîkwaya ê-tipahamêhk ê-pê-miskamahk.

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