Approximation algorithms for k-echelon extensions of the one warehouse multi-retailer problem View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-12

AUTHORS

Gautier Stauffer

ABSTRACT

In this paper, we consider k-echelon extensions of the deterministic one warehouse multi-retailer problem. We give constant factor approximation algorithms for some of these extensions when k is fixed. We focus first on the case without backorders and we give a (2k-1)-approximation algorithm under general assumptions on the evolution of the holding costs as products move toward the final customers. We then improve this result to a k-approximation when the holding costs are monotonically non-increasing or non-decreasing (which is a natural situation in practice). Finally we address problems with backorders: we give a 3-approximation for the one-warehouse multi-retailer problem with backlog and a k-approximation algorithm for the k-level Joint Replenishment Problem with backlog (a variant where inventory can only be kept at the final retailers). Ours results are the first constant approximation algorithms for those problems. In addition, we demonstrate the potential of our approach on a practical case. Our preliminary experiments show that the average optimality gap is around 15%. More... »

PAGES

1-29

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00186-018-0642-4

DOI

http://dx.doi.org/10.1007/s00186-018-0642-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1105086515


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0103", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Numerical and Computational Mathematics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Kedge Business School", 
          "id": "https://www.grid.ac/institutes/grid.464611.0", 
          "name": [
            "The Center of Excellence in Supply Chain (CESIT), Kedge Business School, Talence, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Stauffer", 
        "givenName": "Gautier", 
        "id": "sg:person.011322760534.33", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011322760534.33"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s10107-015-0920-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000187532", 
          "https://doi.org/10.1007/s10107-015-0920-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/nav.21488", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004155462"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0167-6377(89)90001-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005236063"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0167-6377(89)90001-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005236063"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1012120664", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-28146-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012120664", 
          "https://doi.org/10.1007/978-3-319-28146-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-28146-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012120664", 
          "https://doi.org/10.1007/978-3-319-28146-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-29210-1_64", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014678134", 
          "https://doi.org/10.1007/978-3-642-29210-1_64"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1644015.1644028", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015491763"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-36694-9_27", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022088560", 
          "https://doi.org/10.1007/978-3-642-36694-9_27"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/nav.20367", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028120006"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/nav.20367", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028120006"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-02026-1_3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036465503", 
          "https://doi.org/10.1007/978-3-642-02026-1_3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-02026-1_3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036465503", 
          "https://doi.org/10.1007/978-3-642-02026-1_3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ejor.2015.10.054", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043058284"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/(sici)1520-6750(199908)46:5<463::aid-nav2>3.0.co;2-s", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043269707"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1108/eb054814", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043287060"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ejor.2007.03.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048536234"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.orl.2012.12.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049339573"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1287/mnsc.1070.0781", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064714807"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1287/mnsc.15.9.506", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064716534"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1287/mnsc.18.5.327", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064716999"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1287/mnsc.19.5.555", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064717167"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1287/mnsc.31.11.1416", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064719969"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1287/mnsc.48.11.1446.267", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064722191"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1287/moor.1050.0178", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064722838"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1287/moor.11.4.699", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064723131"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1287/opre.17.2.262", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064727343"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1287/opre.41.2.371", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064730548"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1287/opre.41.3.549", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064730561"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1287/moor.2016.0830", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084262292"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/1.9781611973082.6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1088801382"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/1.9781611973402.4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1088801862"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-12", 
    "datePublishedReg": "2018-12-01", 
    "description": "In this paper, we consider k-echelon extensions of the deterministic one warehouse multi-retailer problem. We give constant factor approximation algorithms for some of these extensions when k is fixed. We focus first on the case without backorders and we give a (2k-1)-approximation algorithm under general assumptions on the evolution of the holding costs as products move toward the final customers. We then improve this result to a k-approximation when the holding costs are monotonically non-increasing or non-decreasing (which is a natural situation in practice). Finally we address problems with backorders: we give a 3-approximation for the one-warehouse multi-retailer problem with backlog and a k-approximation algorithm for the k-level Joint Replenishment Problem with backlog (a variant where inventory can only be kept at the final retailers). Ours results are the first constant approximation algorithms for those problems. In addition, we demonstrate the potential of our approach on a practical case. Our preliminary experiments show that the average optimality gap is around 15%.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00186-018-0642-4", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1053187", 
        "issn": [
          "1432-2994", 
          "1432-5217"
        ], 
        "name": "Mathematical Methods of Operations Research", 
        "type": "Periodical"
      }
    ], 
    "name": "Approximation algorithms for k-echelon extensions of the one warehouse multi-retailer problem", 
    "pagination": "1-29", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "a6cb3faa9667fb6c87024040544a97b6e408c98df8c4877c8315d00f4e06749a"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00186-018-0642-4"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1105086515"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00186-018-0642-4", 
      "https://app.dimensions.ai/details/publication/pub.1105086515"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T18:16", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8675_00000494.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s00186-018-0642-4"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00186-018-0642-4'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00186-018-0642-4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00186-018-0642-4'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00186-018-0642-4'


 

This table displays all metadata directly associated to this object as RDF triples.

146 TRIPLES      21 PREDICATES      54 URIs      17 LITERALS      5 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00186-018-0642-4 schema:about anzsrc-for:01
2 anzsrc-for:0103
3 schema:author N25b397272bdb4f4fb6aa3f0e30fafdc8
4 schema:citation sg:pub.10.1007/978-3-319-28146-9
5 sg:pub.10.1007/978-3-642-02026-1_3
6 sg:pub.10.1007/978-3-642-29210-1_64
7 sg:pub.10.1007/978-3-642-36694-9_27
8 sg:pub.10.1007/s10107-015-0920-3
9 https://app.dimensions.ai/details/publication/pub.1012120664
10 https://doi.org/10.1002/(sici)1520-6750(199908)46:5<463::aid-nav2>3.0.co;2-s
11 https://doi.org/10.1002/nav.20367
12 https://doi.org/10.1002/nav.21488
13 https://doi.org/10.1016/0167-6377(89)90001-1
14 https://doi.org/10.1016/j.ejor.2007.03.007
15 https://doi.org/10.1016/j.ejor.2015.10.054
16 https://doi.org/10.1016/j.orl.2012.12.004
17 https://doi.org/10.1108/eb054814
18 https://doi.org/10.1137/1.9781611973082.6
19 https://doi.org/10.1137/1.9781611973402.4
20 https://doi.org/10.1145/1644015.1644028
21 https://doi.org/10.1287/mnsc.1070.0781
22 https://doi.org/10.1287/mnsc.15.9.506
23 https://doi.org/10.1287/mnsc.18.5.327
24 https://doi.org/10.1287/mnsc.19.5.555
25 https://doi.org/10.1287/mnsc.31.11.1416
26 https://doi.org/10.1287/mnsc.48.11.1446.267
27 https://doi.org/10.1287/moor.1050.0178
28 https://doi.org/10.1287/moor.11.4.699
29 https://doi.org/10.1287/moor.2016.0830
30 https://doi.org/10.1287/opre.17.2.262
31 https://doi.org/10.1287/opre.41.2.371
32 https://doi.org/10.1287/opre.41.3.549
33 schema:datePublished 2018-12
34 schema:datePublishedReg 2018-12-01
35 schema:description In this paper, we consider k-echelon extensions of the deterministic one warehouse multi-retailer problem. We give constant factor approximation algorithms for some of these extensions when k is fixed. We focus first on the case without backorders and we give a (2k-1)-approximation algorithm under general assumptions on the evolution of the holding costs as products move toward the final customers. We then improve this result to a k-approximation when the holding costs are monotonically non-increasing or non-decreasing (which is a natural situation in practice). Finally we address problems with backorders: we give a 3-approximation for the one-warehouse multi-retailer problem with backlog and a k-approximation algorithm for the k-level Joint Replenishment Problem with backlog (a variant where inventory can only be kept at the final retailers). Ours results are the first constant approximation algorithms for those problems. In addition, we demonstrate the potential of our approach on a practical case. Our preliminary experiments show that the average optimality gap is around 15%.
36 schema:genre research_article
37 schema:inLanguage en
38 schema:isAccessibleForFree false
39 schema:isPartOf sg:journal.1053187
40 schema:name Approximation algorithms for k-echelon extensions of the one warehouse multi-retailer problem
41 schema:pagination 1-29
42 schema:productId N6855bcd88e0f40ee868fafc9960366bd
43 Ncc21b523b07e4ecfb674c9c663297846
44 Nd2c54958f9ff4935b67dc66d86d9c5f0
45 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105086515
46 https://doi.org/10.1007/s00186-018-0642-4
47 schema:sdDatePublished 2019-04-10T18:16
48 schema:sdLicense https://scigraph.springernature.com/explorer/license/
49 schema:sdPublisher N0c2f264131454a84930591f67a0f08c7
50 schema:url http://link.springer.com/10.1007/s00186-018-0642-4
51 sgo:license sg:explorer/license/
52 sgo:sdDataset articles
53 rdf:type schema:ScholarlyArticle
54 N0c2f264131454a84930591f67a0f08c7 schema:name Springer Nature - SN SciGraph project
55 rdf:type schema:Organization
56 N25b397272bdb4f4fb6aa3f0e30fafdc8 rdf:first sg:person.011322760534.33
57 rdf:rest rdf:nil
58 N6855bcd88e0f40ee868fafc9960366bd schema:name readcube_id
59 schema:value a6cb3faa9667fb6c87024040544a97b6e408c98df8c4877c8315d00f4e06749a
60 rdf:type schema:PropertyValue
61 Ncc21b523b07e4ecfb674c9c663297846 schema:name dimensions_id
62 schema:value pub.1105086515
63 rdf:type schema:PropertyValue
64 Nd2c54958f9ff4935b67dc66d86d9c5f0 schema:name doi
65 schema:value 10.1007/s00186-018-0642-4
66 rdf:type schema:PropertyValue
67 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
68 schema:name Mathematical Sciences
69 rdf:type schema:DefinedTerm
70 anzsrc-for:0103 schema:inDefinedTermSet anzsrc-for:
71 schema:name Numerical and Computational Mathematics
72 rdf:type schema:DefinedTerm
73 sg:journal.1053187 schema:issn 1432-2994
74 1432-5217
75 schema:name Mathematical Methods of Operations Research
76 rdf:type schema:Periodical
77 sg:person.011322760534.33 schema:affiliation https://www.grid.ac/institutes/grid.464611.0
78 schema:familyName Stauffer
79 schema:givenName Gautier
80 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011322760534.33
81 rdf:type schema:Person
82 sg:pub.10.1007/978-3-319-28146-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012120664
83 https://doi.org/10.1007/978-3-319-28146-9
84 rdf:type schema:CreativeWork
85 sg:pub.10.1007/978-3-642-02026-1_3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036465503
86 https://doi.org/10.1007/978-3-642-02026-1_3
87 rdf:type schema:CreativeWork
88 sg:pub.10.1007/978-3-642-29210-1_64 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014678134
89 https://doi.org/10.1007/978-3-642-29210-1_64
90 rdf:type schema:CreativeWork
91 sg:pub.10.1007/978-3-642-36694-9_27 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022088560
92 https://doi.org/10.1007/978-3-642-36694-9_27
93 rdf:type schema:CreativeWork
94 sg:pub.10.1007/s10107-015-0920-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000187532
95 https://doi.org/10.1007/s10107-015-0920-3
96 rdf:type schema:CreativeWork
97 https://app.dimensions.ai/details/publication/pub.1012120664 schema:CreativeWork
98 https://doi.org/10.1002/(sici)1520-6750(199908)46:5<463::aid-nav2>3.0.co;2-s schema:sameAs https://app.dimensions.ai/details/publication/pub.1043269707
99 rdf:type schema:CreativeWork
100 https://doi.org/10.1002/nav.20367 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028120006
101 rdf:type schema:CreativeWork
102 https://doi.org/10.1002/nav.21488 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004155462
103 rdf:type schema:CreativeWork
104 https://doi.org/10.1016/0167-6377(89)90001-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005236063
105 rdf:type schema:CreativeWork
106 https://doi.org/10.1016/j.ejor.2007.03.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048536234
107 rdf:type schema:CreativeWork
108 https://doi.org/10.1016/j.ejor.2015.10.054 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043058284
109 rdf:type schema:CreativeWork
110 https://doi.org/10.1016/j.orl.2012.12.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049339573
111 rdf:type schema:CreativeWork
112 https://doi.org/10.1108/eb054814 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043287060
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1137/1.9781611973082.6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1088801382
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1137/1.9781611973402.4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1088801862
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1145/1644015.1644028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015491763
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1287/mnsc.1070.0781 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064714807
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1287/mnsc.15.9.506 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064716534
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1287/mnsc.18.5.327 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064716999
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1287/mnsc.19.5.555 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064717167
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1287/mnsc.31.11.1416 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064719969
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1287/mnsc.48.11.1446.267 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064722191
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1287/moor.1050.0178 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064722838
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1287/moor.11.4.699 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064723131
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1287/moor.2016.0830 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084262292
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1287/opre.17.2.262 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064727343
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1287/opre.41.2.371 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064730548
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1287/opre.41.3.549 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064730561
143 rdf:type schema:CreativeWork
144 https://www.grid.ac/institutes/grid.464611.0 schema:alternateName Kedge Business School
145 schema:name The Center of Excellence in Supply Chain (CESIT), Kedge Business School, Talence, France
146 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...