Food aid supply and distribution in insecure regions: world food programme operation analysis in Ethiopia

Leiras, Adriana; Gonçalves, Paulo; Chawaguta, Bervery; Brito Junior, Irineu de; Yoshizaki, Hugo Tsugunobu Yoshida

doi:10.1590/0103-6513.20210070

Acessibilidade / Reportar erro

Brasil

Español English

sumário « anterior atual seguinte »

Sumário

Research Article • Prod. 31 • 2021 • https://doi.org/10.1590/0103-6513.20210070 copy

Food aid supply and distribution in insecure regions: world food programme operation analysis in Ethiopia

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

Paper aims

This paper analyzes the food aid supply and distribution for famine relief by the World Food Programme (WFP) in Ethiopia. Food insecurity has increasingly affected people around the world. Furthermore, the characteristics of the provision of humanitarian aid in insecure regions pose several additional challenges over traditional distribution planning (e.g., corruption, losses in last-mile distribution, security escorts).

Originality

Most previous studies addressing famine relief are qualitative, with only a handful including mathematical modeling as this work. Furthermore, we validate our mathematical model with data from a real problem setting.

Research method

We propose a stochastic transshipment network flow model to ensure the efficient allocation of limited resources.

Main findings

The results show high potential for cost savings and offer managerial insights to humanitarian logisticians on the food aid supply and distribution. Our findings indicate that policies in humanitarian logistics should focus on: (a) relaxing legislation for customs clearance of humanitarian supplies; (b) strengthening local market to increase local procurement; (c) implementing tools to enhance security; and (d) monitoring distribution to mitigate the impact of corruption.

Implications for theory and practice

The results suggest that optimizing food aid distribution in Ethiopia can save millions of people vulnerable to malnutrition.

Keywords
Humanitarian logistics; Food aid supply; Food aid distribution; Stochastic optimization; Famine relief

Indices and sets
I	Nodes; (i, i1) ∈ I (Ports, Hubs and FDPs are nodes I = P ∪ J ∪ K)
P	Ports; p ∈ P ⊂ I
K	Hubs and delivery points (extended); k ∈ K ⊂ I
J	Final destination points – FDPs; j ∈ J ⊂ I
Ω	Scenarios; ω ∈ Ω
Scenario-nondependent parameters and units
CF_i	Purchase cost of food supplies to be delivered at node i; i ∈ P ∪ K in USD/t.
FC_i	Cost of corruption of food supplies to be delivered at node i; i ∈ P ∪ K in USD/t.
CP_k	Cost of prepositioning of food supplies (holding cost) at node k; k ∈ K in USD/t.
LP_k	Upper limit for purchases of food supplies in local market to deliver at node k ∈ K in t (metric-tons).
SC_k	Capacity of storage at node k; k ∈ K in t (metric-tons).
Scenario-dependent parameters and units
$D M_{i}^{ω}$	Node i demand under scenario ω; i ∈ K ∪ J in t (metric-tons).
$U D_{i}^{ω}$	Unmet demand upper limit for at node i under scenarioω; i ∈ K ∪ J in t (metric-tons).
$C T_{i, i 1}^{ω}$	Cost of transportation from origin node i to destination node i1 under scenarioω; i ∈ P ∪ K and i1 ∈ K ∪ J in USD/t.
$C T_{i, i 1}^{ω}$	$C T_{i, i 1}^{ω} = C_{i, i 1}^{ω} \times D_{i, i 1}^{ω}$ : The cost of transportation is given by the product of the transport rate $C_{n, n 1}^{ω}$ in USD/(km x t) and the distance $D_{i, i 1}^{ω}$ in km – from i to i1 under scenario ω.
$C S e_{i, i 1}^{ω}$	Security cost from origin node i to destination node i1 under scenario ω; i ∈ P ∪ K and i1 ∈ K ∪ J in USD/t.
$A_{i}^{ω}$	Binary parameter indicating 1 if node i is accessible by any route under scenario ω, 0 otherwise; i ∈ K ∪ J and A ∈ {0,1}
$P T_{i}^{ω}$	Penalty for unmet demand at node i at scenario ω, i ∈ K ∪ J in USD/t.
P^ω	Scenarios ω probabilities
First-stage decision variables and units
s_i	The quantity of food supplies to be delivered at node i; i ∈ P ∪ K in t (metric-tons).
e_k	The quantity of food supplies prepositioned at node k; k ∈ K in t (metric-tons).
Second-stage decision variables and units
$x_{i, i 1}^{ω}$	The quantity of food supplies transported from origin node i to destination node i1 under scenario ω; i ∈ P ∪ K and i1 ∈ K ∪ J in USD/t.
$y_{i}^{ω}$	The unmet demand for food supplies at node i; under scenario ω; i ∈ K ∪ J in t (metric-tons).

Parameter	Seasonality effects		Demand (define $D M_{i}^{ω}, U D_{i}^{ω} a n d P T_{i}^{ω}$ )
			Base	High	Low
			(50%)	(30%)	(20%)
Hub accessibility (define $A_{n}^{ω}$ , $T C_{n, n 1}^{ω}$ , and $S e C_{n, n 1}^{ω}$ )	Normal conditions (no flooding)	(40%)	P ^ω1 (20%)	P ^ω6 (12%)	P ^ω11 (8%)
	Region 1 without access by North	(20%)	P^ω2 (10%)	P ^ω7 (6%)	P ^ω12 (4%)
	Hubs shut down in Region 1	(10%)	P^ω3 (5%)	P ^ω8 (3%)	P ^ω13 (2%)
	Region 2 without access by North	(20%)	P^ω4 (10%)	P ^ω9 (6%)	P ^ω14 (4%)
	Hubs shut down in Region 2	(10%)	P ^ω5 (5%)	P ^ω10 (3%)	P ^ω15 (2%)

Costs	Deterministic Solution	Stochastic Solution	Actual data
Supply	291.44	329.25	360.15
Corruption	4.54	7.33	18.00
Stock	76.95	76.95	43.22
Transportation	43.33	39.35	65.16
Security	1.56	0.68	-
Penalty	-	30.00	-
Total	417.83	483.57	486.53
Savings	68.71	2.97

		Deterministic Solution		Stochastic Solution (First Stage Decisions)
Node		Supply (t)	Stock (t)	Supply (t)	Stock (t)
Addis Ababa	22,364			22,364
Awassa	5,970		500	5,970	500
Berbera	109,171			105,876
Degehabur	25,505		5,700	25,505	5,700
Dire Dawa			56,500	42,647	56,500
Djibouti	528,790			591,539
Dukem	5,440			5,440	500
Gode	25,489		5,200	25,489	5,200
Hartichek
Jijiga			16,500		16,500
Kebri Dehar	10,030		5,725	10,030	5,725
Kombolcha	1,376		19,450	1,376	19,450
Legetafo	5,011		500	5,011	500
Mekele	2,925		20,500	2,925	20,500
Nazareth	42,942		54,045	42,942	54,045
Shinile	217		500
Sudan
Total	785,230		185,120	887,115	185,120
Supply + Stock	970,350			1,072,235

Costs	Deterministic Solution	Stochastic Solution	Actual data
Primary	33.92	29.82	48.35
Secondary	9.41	9.54	16.81
Total	43.33	39.35	65.16
Savings	21.83	25.81

Instance	Total Cost (USD x 10⁶)	Total Supply (t)	Total Stock (t)	Unmet demand FDPs (t)
Original local supply limit	483.57	887,115	185,120	308,317
50% reduction	486.93	882,115	190,120	306,438
100% increase	481.06	962,160	110,075	308,317

Scenario	Probability	Demand (t)	Unmet demand (t)	Demand served
1	20%	970.35		100.00%
2	10%	970.35		100.00%
3	5%	776.42		100.00%
4	10%	970.35		100.00%
5	5%	920.00		100.00%
6	12%	1,164.42	92.18	92.08%
7	6%	1,164.42	92.18	92.08%
8	3%	931.70		100.00%
9	6%	1,164.42	92.18	92.08%
10	3%	1,104.00	31.76	97.12%
11	8%	776.28		100.00%
12	4%	776.28		100.00%
13	2%	621.13		100.00%
14	4%	776.28		100.00%
15	2%	736.00		100.00%
Fraction of demand served			98.01%

	Hubs		FDPs
Penalty (USD/t)	Unmet demand	Scenarios	Unmet demand	Scenarios
5	1,255,950	1, 2, 4, 5, 6, 7, 9, 10	1,027,352	1-15
10	1,255,950	1, 2, 4, 5, 6, 7, 9, 10	828,609	1-15
20	857,613	6, 7, 9, 10	606,111	1-2, 4-10
25	543,836	6, 7, 9, 10	368,740	6, 7, 9, 10
30	231,316	6, 7, 9	319,850	6, 7, 9, 10
31			308,317	6, 7, 9, 10
100			308,317	6, 7, 9, 10
136
145

Associação Brasileira de Engenharia de Produção Av. Prof. Almeida Prado, Travessa 2, 128 - 2º andar - Room 231, 05508-900 São Paulo - SP - São Paulo - SP - Brazil
E-mail: production@editoracubo.com.br

Acompanhe os números deste periódico no seu leitor de RSS

[1] * irineu.brito@unesp.br