Advanced planning and scheduling with outsourcing in manufacturing supply chain

INTEGRATED PROCESS PLANNING AND SCHEDULING USING GENETIC ALGORITHMS Imran Ali Chaudhry, Muhammad UsmanOriginal scientific paper Process planning and scheduling are two of the most important functions in any manufacturing system. Traditionally process planning and scheduling are considered as two separate functions. In this paper a Genetic Algorithm (GA) for integrated process planning and scheduling is proposed where selection of the best process plan and scheduling of jobs in a job shop environment are done simultaneously. In the proposed approach a domain independent spreadsheet based approach is presented to solve this class of problems. The precedence relations among job operations are considered in the model, based on which implicit representation of a feasible process plans for each job can be done. To verify the performance and feasibility of the presented approach, the proposed algorithm has been evaluated against a number of benchmark problems that have been adapted from the previously published literature. The experimental results show that the proposed approach can efficiently achieve optimal or near-optimal solutions for the problems adopted from literature. It is also demonstrated that the proposed algorithm is of general purpose in application and could be used for the optimisation of any objective function without changing the model or the basic GA routine. Keywords: integrated process planning and scheduling (IPPS); genetic algorithms; job shop Integracija projektiranja tehnoloških procesa i planiranja primjenom genetičkih algoritamaIzvorni znanstveni članak Projektiranje tehnoloških procesa i planiranje predstavljaju dvije najvažnije funkcije svakog proizvodnog procesa. Tradicionalno se one smatraju dvjema odvojenim funkcijama. U ovom se radu predlaže Genetički Algoritam (GA) za integraciju ovih aktivnosti, gdje se simultano odvija izbor najboljeg tehnološkog procesa i vremenski plan poslova u pogonu. U radu se za rješavanje te vrste problema predstavlja pristup zasnovan na proračunskoj tablici neovisnog područja. U modelu se razmatraju odnosi prvenstva u izvođenju poslova na temelju kojih se donosi implicitno predstavljanje mogućih planova za izvršenje svakog posla. Zbog provjere izvršenja i ostvarivosti predstavljenog pristupa, predloženi se algoritam provjeravao na nizu referentnih problema prilagođenih iz ranije objavljene literature. Eksperimentalni rezultati pokazuju da se predloženim pristupom mogu učinkoviti postići optimalna ili njima blizu rješenja za probleme prilagođene iz literature. Također je pokazano da predloženi algoritam ima opću namjenu i može se primijeniti za optimizaciju bilo koje objektivne funkcije bez promjene modela ili osnovne GA rutine. Ključne riječi: integracija projektiranja tehnoloških proces i planiranja (IPPS); genetički algoritami; radna mjesta

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“…Data set for experiment 6 has been adapted from Lee et al [15]. Problem consists of 8-jobs to be processed on 6-machines.…”

Section: Methodsmentioning

confidence: 99%

“…Lee et al [15] present a heuristic approach based on GA for IPPS when each customer order can be outsourced and is restricted by a due date. The objective is to minimise makespan.…”

Section: Related Researchmentioning

confidence: 99%

Integrated process planning and scheduling using genetic algorithms

Chaudhry

Usman

2017

Teh. vjesn.

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“…In the following analysis, only the SCM and ERP systems will be considered from the perspective of the production area. Both solutions derive the MRP or APS (Advanced Planning and Scheduling) planning approach both for single enterprise and supply chain (for examples see [6,12]). For distributed (made from independent companies) supply chains, the systems provides the paperless purchase/sale functions which improves purchasing/sale processes between supplier and customer however the process is still performed outside of the shop-floor (through purchase/sales departments) and basing on forecasts and medium-term plans (MRP planning procedure).…”

Section: Are "E" Solutions Applicable For "Lean" Value Adding Chains?mentioning

confidence: 99%

Information systems for lean value adding chains

Koch

Horbal

2003

Collaborative Systems for Production Management

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The paper presents the Lean Manufacturing concept from the perspective of the two narrow issues -management of the value adding chains, and computer systems including e-business applications. The performed analysis concerns "Lean" requirements for IT solutions within a single enterprise, as well as within the whole chain of cooperating companies. The analysis is based on the future value stream map, both in micro (single enterprise) and macro (supply chain) scale. The last but one section describes proposed approach how to incorporate "lean" principles into requirements for company information system.

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“…In his paper, a mixed-integer programming model is derived to solve the problem. Lee [12] have considered an outsourcing in an advanced planning and scheduling (APS) problem. The objective of the problem is to minimize the makespan, a genetic algorithm has been suggested to solve the problem.…”

Section: Introductionmentioning

confidence: 99%

Integer Programming Approach for the Outsourcing Decision Problem in a Single Machine Scheduling Problem with Due Date Constraints

Hong¹,

Lee²

2013

Korean Management Science Review

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In this paper, we considers the outsourcing decision problem in a single machine scheduling problem. The decision problem is to determine for each job whether to be processed on an in-house manufacturing or external facilities(outsourcing). Moreover, this paper considers a situation where each job has a due date. The objective of the problem is to minimize the outsourcing cost, subject to the due date constraints. The considered problem is proved to be NP-hard. Some solution properties and valid inequalities are derived, and an effective lower bound is derived based on the LP-relaxation. The results of experimental tests are presented to evaluate the performance of the suggested lower bound.

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Advanced planning and scheduling with outsourcing in manufacturing supply chain

Cited by 186 publications

References 13 publications

Integrated process planning and scheduling using genetic algorithms

Integrated process planning and scheduling using genetic algorithms

Information systems for lean value adding chains

Integer Programming Approach for the Outsourcing Decision Problem in a Single Machine Scheduling Problem with Due Date Constraints

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