This readme.txt file 20210209 was generated by Eduardo Guzman

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GENERAL INFORMATION
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Title of Dataset: Input Data of a novel MILP model for the production, lot-sizing and scheduling of automotive plastic components on parallel flexible injection machines

Author Information: 

	Principal Investigator: Beatriz Andres, Universitat Politè�cnica de Val�ència, Plaza Ferrandiz y Carbonell 2 Alcoy (Spain), bandres@cigip.upv.es, ORCID: 0000-0002-7920-7711.
	Associate or Co-investigator: Eduardo Guzman, Universitat Polit�ècnica de Valè�ncia, Plaza Ferrandiz y Carbonell 2 Alcoy (Spain), bguzman@cigip.upv.es, ORCID: 0000-0003-0866-2095.
	Associate or Co-investigator: Raul Poler, Universitat Politè�cnica de Val�ència, Plaza Ferrandiz y Carbonell 2 Alcoy (Spain), rpoler@cigip.upv.es, ORCID: 0000-0003-4475-6371.


Date of data collection: 20210209

Geographic location of data collection: Valencia, Comunidad Valenciana, Spain. 39.46975 -0.37739. 

Information about funding sources or sponsorship that supported the collection of the data: Universitat Polit�ècnica de Valè�ncia

General description: The data set contains the input data that was entered into the mathematical model to develop the case study. The data is synthetically generated and developed with an algorithm encoded in Python 3.7, the parameters for programming the algorithm are based on real data from a company in the automotive sector and the values have been structured according to the indices defined in the model. Input data details have been added to make the model replicable.

Keywords:  lot-sizing; scheduling; injection moulding; parallel machines; mixed integer linear programming; automotive industry.


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SHARING/ACCESS INFORMATION
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Open Access to data: Open.

Date end Embargo: -.

Licenses/restrictions placed on the data, or limitations of reuse: Creative Commons Attribution (CC-BY)

Citation for and links to publications that cite or use the data: Andres, B., Guzman, E., & Poler, R. (2021). A Novel MILP Model for the Production, Lot Sizing, and Scheduling of Automotive Plastic Components on Parallel Flexible Injection Machines with Setup Common Operators. Complexity, 2021, 1–16. https://doi.org/10.1155/2021/6667516

Links/relationships to previous or related data sets: -.

Links to other publicly accessible locations of the data: -.


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DATA & FILE OVERVIEW
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File list: Input data S1, S2, S3, S4, M1, M2, M3, M4, L1, L2, L3, L4 data set, csv, the dataset contains S1, S2, S3, S4, M1, M2, M3, M4, L1, L2, L3, L4 data set that has been entered into the mathematical model to develop the case study.

Relationship between files: The dataset contains the input data that has been entered into the mathematical model to develop the case study.

Additional related data collected that was not included in the current data package: The values have been structured according to the indices defined in the model. The indice "I" represents machines, the indice "J" represents tools, the indice "K" represents products (parts),  "T" represents time periods.

If data was derived from another source, list source: -.

Type of version of the dataset: CSV, raw data. 

Versions of the files: 20210209 last version. 

Total size: 892KB.


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METHODOLOGICAL INFORMATION
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Description of methods used for collection/generation of data: Syntetic data generated with an algorithm coded in Python 3.7.

Methods for processing the data: the raw data presented has not been processed.

Software- or Instrument-specific information needed to interpret the data, including software and hardware version numbers: Microsoft Excel 2013.

Standards and calibration information, if appropriate: -. 

Environmental/experimental conditions: -. 

Describe any quality-assurance procedures performed on the data: Syntetic data generated with an algorithm coded in Python 3.7 based on realistic data company.


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DATA-SPECIFIC INFORMATION
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Number of variables: 16.

Number of cases/rows: Input data S1, S2, S3, S4, M1, M2, M3, M4, L1, L2, L3, L4 data set. 

Variable list, defining any abbreviations, units of measure, codes or symbols used: The indice "I" represents machines, the indice "J" represents tools, the indice "K" represents products (parts),  "T" represents time periods.

Missing data codes: -. 

Specialized formats or other abbreviations used: 

aj: Total amount of tools j available for production
cbk: Backorder cost of product k
cik: Inventory cost of product k
covkt: Stock coverage defined as number of time periods for the stock minimum coverage of product k during time period t
crij: Setup cost of tool j on machine i
csj: Setup cost of preparing tool j
cstk: Coverage stockout cost of product k
dkt: Demand of product k during time period t
INVk0: Initial inventory of product k
INVMAXk: Maximum inventory units for product k during time period t
INVMINk: Minimum inventory units for product k during time period t
nct: Amount of tool changes allowed during time period t
npjk: Amount of products k no longer produced when tool j is set up
pjk: Amount of products k produced when tool j is set up
rij: 1 if tool j can be set up on machine i, 0 otherwise
tpt: Production time available during time period t