Abstract
The role of sustainability in the function of a company and more specifically a food company is pivotal for its financial performance. The environmental issues as well as the potential economic gains from the implementation of its principles ask for the use of multiple instruments that have been developed to green supply chains. Moreover, social issues also arise and involve the food companies social responsibility, as this can be realized through the supply of fresh products that meet consumption security standards. On this basis, the strategic design of these companies’ supply chains can assists them towards meeting their sustainability objectives as it may lead to the selection of transportation modes, location of entry points and distribution centers, and flows between the nodes of the networks under cost, environmental and social impact minimization criteria. Under this context the purpose of this manuscript is to develop and employ a multi-objective (namely cost, social-time and emission minimization) mixed integer linear programming decision-making model for the network design of sustainable supply chains of perishable food products. The specific model was implemented in the case of a fruits importer in the North-Eastern European region considering its geographical settings. To synopsize and according to our findings the suggested model is an easy to use decision-making tool that leads to a whole set of possible solutions incorporating trade-offs between three aspects of sustainability.
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Appendix
Appendix
Cost, emissions and time of transporting a TEU from node i to node j using transportation mode m
LP | EP | Mode | Cost (€) | CO2 (kg) | Time (days) |
|---|---|---|---|---|---|
Athens | Riga | Deep sea shipping vessel | 1780 | 1750 | 15 |
Klaipeda | Deep sea shipping vessel | 1730 | 1680 | 14 | |
Gdansk | Deep sea shipping vessel | 1940 | 1650 | 14 | |
Brest | Truck | 3100 | 2670 | 5 | |
Chornomorsk | Truck versus Ferryboat | 1820 | 1580 | 4 | |
Katowice | Truck | 2700 | 2440 | 4 |
Cost, emissions and time of transporting a TEU from node j to node d using transportation mode m
EP | DC | Mode | Cost (€) | CO2 (kg) | Time (days) |
|---|---|---|---|---|---|
Riga | Riga | Truck | 100 | 70 | 1 |
Vilnius | Truck | 410 | 350 | 1 | |
Warsaw | Truck | 950 | 820 | 2 | |
Minsk | Truck | 720 | 580 | 2 | |
Klaipeda | Riga | Truck | 430 | 370 | 1 |
Vilnius | Truck | 430 | 370 | 1 | |
Warsaw | Truck | 840 | 720 | 2 | |
Minsk | Truck | 730 | 580 | 2 | |
Gdansk | Riga | Truck | 970 | 840 | 2 |
Vilnius | Truck | 840 | 720 | 2 | |
Warsaw | Truck | 590 | 500 | 1 | |
Minsk | Truck | 1160 | 930 | 2 | |
Brest | Riga | Truck | 1000 | 800 | 2 |
Vilnius | Truck | 550 | 440 | 2 | |
Warsaw | Truck | 350 | 250 | 2 | |
Minsk | Truck | 350 | 420 | 1 | |
Chornomorsk | Riga | Train | 1710 | 560 | 5 |
Vilnius | Train | 1390 | 450 | 5 | |
Warsaw | Train | 1500 | 490 | 6 | |
Minsk | Train | 1180 | 380 | 4 | |
Katowice | Riga | Truck | 1300 | 1000 | 2 |
Vilnius | Truck | 1090 | 930 | 2 | |
Warsaw | Truck | 410 | 350 | 1 | |
Minsk | Truck | 1280 | 1020 | 2 |
Cost, emissions and time of transporting a TEU from node d to node r using transportation mode m
DC | RM | Mode | Cost (€) | CO2 (kg) | Time (days) |
|---|---|---|---|---|---|
Riga | Latvia | Truck | 130 | 100 | 1 |
Belarus | Truck | 750 | 600 | 2 | |
Lithuania | Truck | 420 | 360 | 1 | |
Poland | Truck | 980 | 840 | 2 | |
Vilnius | Latvia | Truck | 490 | 440 | 1 |
Belarus | Truck | 450 | 340 | 2 | |
Lithuania | Truck | 280 | 240 | 1 | |
Poland | Truck | 700 | 600 | 2 | |
Warsaw | Latvia | Truck | 1070 | 890 | 2 |
Belarus | Truck | 830 | 660 | 2 | |
Lithuania | Truck | 700 | 600 | 2 | |
Poland | Truck | 490 | 420 | 1 | |
Minsk | Latvia | Truck | 750 | 600 | 2 |
Belarus | Truck | 290 | 340 | 1 | |
Lithuania | Truck | 450 | 340 | 2 | |
Poland | Truck | 830 | 660 | 2 |
Demand at regional market r
Market | Latvia | Belarus | Lithuania | Poland | Total |
|---|---|---|---|---|---|
Demand | 1766 | 6325 | 2935 | 35220 | 46246 |
Numeric constant associated with the acceptable total transportation time
ω = 16 days
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Yakavenka, V., Mallidis, I., Vlachos, D. et al. Development of a multi-objective model for the design of sustainable supply chains: the case of perishable food products. Ann Oper Res 294, 593–621 (2020). https://doi.org/10.1007/s10479-019-03434-5
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DOI: https://doi.org/10.1007/s10479-019-03434-5