On Solution to ASUU Strike and Consolidated University Academic

Salary Structure II (CONUASS II) in the Nigerian Universities Using

Optimization Method

HARRISON OBIORA AMUJI1,a*, NGOZI PAULINE OLEWUEZI2,b, EVANGELINA

OZOEMENA OHAERI3,c, VIVIAN NGOZI IKEOGU4,d, JOHNSON OTTAH OKOH5,e

1,2,5Department of Statistics, 2Department of Science Laboratory Technology, Department of Logistics

and Transport Technology

1-4Federal University of Technology, Owerri

PMB 1526, Owerri Imo State

NIGERIA

Received: October 2, 2022. Revised: September 3, 2023. Accepted: October 4, 2023. Published: November 2, 2023.

1 Introduction

Academic Staff Union of the Nigerian Universities

(ASUU) is a trade union formed in 1978 as an

offshoot of Association of University Teachers

(AUT) which has earlier been in existence. The

purpose was not only to protect the interest of her

members and influence government policies but the

interest of the entire educational system in Nigeria.

It offers valuable suggestions on other issues of

national interest. ASUU and government are always

on the opposing side because of its radical nature

and intolerance to injustice from the government.

For this reason, the Nigerian government sees her as

an enemy that must be crushed as all cost. Nigerian

government has demonstrated this by disobeying the

MoUs and MoAs it willingly entered into with the

union, keeping them at a constant salary for over

fourteen years and counting, neglected their welfare,

relegated them to begging, and pay no attention to

everything ASUU stands for and these resulted to so

many strikes by the union. According to [1], ASUU

embarked on 16 strikes in 23 years, Federal

government and lecturers disagree over 13-year

MOU. The incessant strikes were not good for the

Abstract: - In this paper, we applied dynamic programming model for optimization of Consolidated University

Academic Salary Structure II (CONUASS II) for the overall interest of the academic staff and the Nigerian

University System. Our focus was on the decision policy that would help to enhance the living condition of

lecturers in the Nigerian universities thereby averting frequent strikes and disruption of academic calendars.

The frequent and incessant strikes delay students and impacts negatively to their feature; hence, anything that

could be done to stabilize the university education in Nigeria will contribute immensely to the economic growth

and stability of the country. To achieve this, we applied dynamic programming and developed an optimal

decision policy which was applied to obtain the best optimal policy needed for the highest ranking cadre in the

academic to achieve optimal remuneration of at least twice their per annum salary with subsequent adjustment

in the other cadres’ salaries accordingly. Applying the optimal decision policy, we obtained (1, 1, 1, 1, 1, 1, 2,

2, 0, 0) which optimizes the academic staff’s earning with a promotion to level 08 instead of remaining at bar

with many steps. If this policy is implemented, a professor at the bar will grow to level 08 and will therefore

earn up to at least double of his/her annual salary (N13,658,325) instead of the current stagnating salary of

(N6,020,163) per annum at the bar. This will make the lecturers to be happy and discharge their duties with

commitments and thereby addressing the perennial strikes in the Nigerian universities.

Key-Words: - ASUU strike, CONUASS II, Disruption of academic calendar, Mathematical optimization,

Dynamic programming, Optimal decision policy.

INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS,

COMPUTATIONAL SCIENCE AND SYSTEMS ENGINEERING

DOI: 10.37394/232026.2023.5.16

Harrison Obiora Amuji, Ngozi Pauline Olewuezi,

Evangelina Ozoemena Ohaeri, Vivian Ngozi Ikeogu,

Johnson Ottah Okoh

E-ISSN: 2766-9823

177

Volume 5, 2023

Nigerian education system. But the strikes have

impacted both positively and negatively to the

university system in Nigeria. The positive side of it

was the establishment of university autonomy,

TETFund, Needs Assessment Intervention Fund,

Consolidated University Academics Salary

Structure, etc. on the other hand, the negative side

include; loss of academic calendars, delay on

students’ graduation, loss of confidence in the

system, massive drift of students to foreign

universities, creation of gap in manpower

development, system decay, brain drain, etc. In

order to bring sanity into the university system and

to halt strikes, the government had agreement with

the union in the year 2009. The agreement contains

the funding of the Federal universities, separate

salary structure (Consolidated University Academic

Salary Structure II (CONUASS II)) to be reviewed

(renegotiated) every three years, Earned Academic

Allowance (EAA), University autonomy, etc. The

agreement was also adopted and applied by the State

universities, since ASUU is a national body,

whatever applies to the Federal universities trickles

down to the State owned universities.

Adequate commensurable remuneration for the

work offered by the academic staff union of the

Nigerian universities have been a problem and has

lingered over a long period of time. The

remuneration for academic staff was very poor

compared to their counterparts and what obtains in

other parts of the world. For this reason, the

Academic Staff Union of Nigerian Universities

(ASUU) was in the forefront to remedy the situation

to avert brain drain and better condition of service

for her members. ASUU is a very powerful trade

union known for struggle to better the lots of

Nigerians. In a bid to solve the problem which

resulted into several strikes, [2] came up with the

implementation of a sole salary structure for the

federal university academic staff in a circular issued

on December 8, 2009. According to the circular:

1.“The President, Commander-in-chief of the

Armed Forces of the Federal Republic of Nigeria

has approved a new salary structure for the

Academic Staff of the Federal Universities

following the collective agreement between the

Federal government of Nigeria and Academic Staff

Union of Universities on 21st October, 2009. The

new salary structure, known as Consolidated

University Academic Salary Structure II

(CONUASS II), is presented in Table1 of Appendix.

2. CONUASS II is a consolidation of the following

components:

(i) The consolidated Academic Staff Salary

Structure (CONUASS) approved by the Federal

Government of Nigeria (FGN) effective 1st January

2007 (FGN Circular No. SWC/S/04/S.302/1, dated

18th January, 2007).

(ii) The Consolidated Peculiar University Academic

Allowances (CONPUAA), exclusively for

university teaching staff and derived from

allowances not adequately reflected or not

consolidated in CONUASS.

(iii). Rent as approved by the FGN effective 1st

January 2007 (FGN Circular No.

SWC/S/04/S.302/1, dated 18th January 2007).

3. The effective date for the implementation of the

CONUASS II is 1st July, 2009.

4. All inquiries arising from this circular should be

directed to the Chairman, National Salaries,

Incomes and Wages Commission”.

CONUASS has levels 01 to 07 with 13 steps. The

level 01 is the least grade for new entrants into the

academic cadre, Graduate Assistants, with the last

steps of 6, Assistant lecturer and lecturer II have

levels 02 & 03 with step 8 as the last steps, Lecturer

I has a level of 04 with 9 steps as the last step. The

senior lecturer’s cadre, level 05, has the longest step

of 13, while professorial cadre 06 – 07 has step 10

as their last steps. The level 07 is the highest level in

the academics for full professors. At this level, a

professor can grow up to step ten and remain there

until retirement, this is called the bar level.

In this paper, we want to model the CONUASS II

data using Dynamic programming since the highest

salary for a professor is a build up from level 01 to

07, it means that the current earning of a professor is

as a result of the cumulative of salary scale from 01

to 06 plus his/her current level. This is an optimality

problem; hence, we want to determine the optimal

salary structure for academic staff to solve the

lingering and incessant strikes in the universities by

the academic staff union (ASUU). This problem is

within the domain of dynamic programming, where

the levels are the stages and the steps are the states.

The problem involves a recursive relationship and

the salary structure at each stage is dependent on the

salary structure at another stage (level) but in the

end produces an optimal salary for the academic

INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS,

COMPUTATIONAL SCIENCE AND SYSTEMS ENGINEERING

DOI: 10.37394/232026.2023.5.16

Harrison Obiora Amuji, Ngozi Pauline Olewuezi,

Evangelina Ozoemena Ohaeri, Vivian Ngozi Ikeogu,

Johnson Ottah Okoh

E-ISSN: 2766-9823

178

Volume 5, 2023

staff. So, the problem has both optimal structure and

overlapping sub-solution and therefore can be

modelled using Dynamic programming (DP).

Though the ASUU demand are numerous, ranging

from the revitalization of the Nigerian universities,

rejection of IPPIS (Integrated Payroll and Personnel

Information System) which eroded the university

autonomy that ASUU fought hard to achieve, to

static salary that made even polytechnic and

colleges of education chief lecturer’s salary placed

ahead of university professors’ salary; it becomes

worrisome to any sound mind on the condition of

university academic staff. There is urgent needed to

salvage the system. It was observed that the

government is averse to re-negotiation of the 2009

agreement which was long overdue. And the crucial

aspect of the agreement which government always

avoid is the welfare area of the agreement which has

to do with the salaries, allowance packages and their

periodic reviews, so if the packages are enhanced, it

will reverse the ugly trend in the Nigerian university

system.

But since the agreement, government has refused to

renegotiate or reviewed the agreement. The union

members are the least payed compared to their

counterparts not only in Africa but in Nigerian

higher institutions as well. Members can no longer

cope with the economic hardship inflicted on them

by this neglect. The last straw that breaks the

camel’s back was the eight (8) months strike

embarked upon by the academic staff union on

February 2022 to October 2022 where government

refused to pay them on the ground of no-work-no-

pay policy, while the government was the one who

violated the last Memorandum of Action (MoA)

reached with the union. The union lost many of her

members to death due to the union members’

inability to cater for their health and other family

needs. Many of the younger ones left the country in

search of better opportunities abroad. There was

massive brain drain that left the Nigerian

universities worse than ever. We seek to help both

the government and ASUU to devise a means of

finding a lasting solution and bring sanity into the

Nigerian Universities System. We shall apply an

optimization technique to achieve this. The

optimization technique that can deal with this

problem adequately is the dynamic programming

(DP) model.

Dynamic programming is a mathematical

optimization technique that is adapted to modelling

some complex problem that may be difficult to

model using other optimization techniques. It breaks

the entire problem into stages to arrive at optimal

solution. It makes use of optimality, that is, a

situation where the current solution is linked to the

previous events. This is achieved through recursive

relationship and backward pass to arrive at optimal

decision. Dynamic programming (DP) model

divides a set of problem into different stages with its

decision variables and has an independent decision

though the stages are not independent of one another

but link one sub-optimal stage to another sub-

optimal stage using recursive relationship. One sub-

optimal stage forms the basis for the next sub-

optimal stage and at the end, the optimal solution for

the entire problem is achieved. It relied on backward

pass approach in attaining optimality, that is, the

solution to the problem stats from the last stage and

back to the first stage. It has diverse applications,

especially in those areas where most of other non-

linear and linear optimization cannot be applies. It is

mostly used to provide solution and models for

those problems that cannot fit into known

distribution or any optimization model. Dynamic

programming has variant models, depending on the

nature of the problem to be solved, that is why it is

called “dynamic” but in general it maintains a

unique feature, which is principally anchored in

optimality, [3]. Though dynamic programming has

many advantages but one of its greatest

shortcomings is the restriction imposes on its

applications to large problems, which is the

characteristics of real life problems. This restriction

is known as the “curse of dimensionality”. The

curse of dimensionality occurs when the complexity

of the problem increases rapidly as a result of little

increasing in the number of inputs, [4]. A major

advantage of dynamic programming over other non-

linear programming techniques is that the

computing time is only linearly dependent on the

number of stage variables, but unfortunately, size

still precludes the use of dynamic programming in

the solution of realistic large-scale problems, [5].

For instance, in this current problem on the

application of DP to CONUASS, the resulting cost

matrix becomes problematic in finding it solution

because of its large dimension. But the researchers

believe that with adequate software coding, the

problem can be solved with ease.

Before a problem can qualify for DP modelling, it

must have optimal substructure and overlapping

sub-problems. Dynamic programming is used when

the sub-problem is not independent. Therefore,

Dynamic programming solves each sub-problem

just once and store the result in a table so that it can

be repeatedly retrieved and used if the need be. So,

INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS,

COMPUTATIONAL SCIENCE AND SYSTEMS ENGINEERING

DOI: 10.37394/232026.2023.5.16

Harrison Obiora Amuji, Ngozi Pauline Olewuezi,

Evangelina Ozoemena Ohaeri, Vivian Ngozi Ikeogu,

Johnson Ottah Okoh

E-ISSN: 2766-9823

179

Volume 5, 2023

if a problem does not have optimal substructure,

there is no basis for defining a recursive algorithm

to find the optimal solutions and if a problem does

not have overlapping sub-problems, we do not use

dynamic programming. A critical look into the

problem under study shows that it falls under the

purview of Dynamic programming.

1.1 Components of Dynamic programming

1. Stages: the problem can be divided into several

sub-problems (levels) which are called stages.

2. States: each stage has several states (decision

variables) associated with it.

3. Decision: at each stage, there can be multiple

choices out of which one of the best decisions

should be taken (stage decision).

4. Optimal policy: it is a rule which determines the

decision at each stage; if it is globally optimal, it is

known as Bellman’s principle of optimality.

5. Given the current state, the optimal choices for

each of the remaining states do not depend on the

previous states or decisions.

6. There exists recursive relationship that identifies

the optimal decisions for stage j, given that stage j-1

has already been solved.

7. Optimal decision at the future stage is

independent of the optimal decision at the previous

stage.

8. The final stage must be solved by itself.

1.2 Aim and Objectives

The aim of this paper is on solution to ASUU strike

and Consolidated University Academic Salary

Structure II (CONUASS II) in the Nigerian

Universities using optimization method, and the

objectives are:

1. To determine subprograms and their respective

optimal policies

2. To recursively solve the stage problems and to

obtain stage policies

3. To obtain the optimal decision policy for the

entire problem and determine the best policy that

would optimize salary structure for the overall

interest of the academic staff union members.

To organize this work in a comprehensible manner

for better understanding, we devoted section one to

introduction and background of the study, section

two to literature review, section three to materials

and methods, section four to data presentation and

analysis, section five to discussions which includes

summary, and recommendations.

2 Literature Review

In this section, we review some related works done

by other researchers in this area, though there is no

direct work done on solution to ASUU strike and

Consolidated University Academic Salary Structure

II (CONUASS II) in the Nigerian Universities using

optimization method but there are some related

works on the application of Dynamic programming.

These researchers [6], trace the development of non-

serial dynamic programming from the basic theory

underlying dynamic programming to the latest

applications of non-serial dynamic programming.

They observed that most dynamic programming

processes can be grouped into four categories: (a)

serial processes, (b) non-serial processes, (c)

Markov processes and (d) fuzzy processes. The

principle of optimality can then be applied directly

to serial multistage decision processes if the

sufficiency conditions are satisfied. On the other

hand, non-serial structure is a structure where at

least one stage in the system receives inputs from

more than one stage or sends outputs to more than

one stage. Again [7], observed that dynamic

programming is conceptually a powerful

computational technique that can solve nonlinear

stochastic control problems involving constraints in

the state and control variables. However, it has not

been generally used in solving large scale problems

because of the large high-speed memory and

excessive computational time requirements. And

these researchers [8], developed an algorithm for a

discrete discounted cost dynamic programming

problem which the author was of the opinion that it

can be obtained from the complementary slackness

theorem of linear programming. The author

observed that the policy improvement procedure for

solving such a problem coincides with the Simplex

method solution to a linear program. But [9]

observed that dynamic programming has been

proposed as an effective method of solving

INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS,

COMPUTATIONAL SCIENCE AND SYSTEMS ENGINEERING

DOI: 10.37394/232026.2023.5.16

Harrison Obiora Amuji, Ngozi Pauline Olewuezi,

Evangelina Ozoemena Ohaeri, Vivian Ngozi Ikeogu,

Johnson Ottah Okoh

E-ISSN: 2766-9823

180

Volume 5, 2023

combinatorial problems of a sequential nature. It is

considered to be computationally advantageous to

use dynamic programming since the concept can

provide convergence to an optimum solution

without a total enumeration. In the development of

dynamic programming recursion formulae, the

problem is decomposed into stages which are

evaluated independently, given a set of

environmental conditions (states). On the

complexity of a large class of problems, [10].

observed that the curse of dimensionality is the

problem caused by the exponential increase in

volume associated with adding extra dimensions to

Euclidean space. The curse of dimensionality

basically means that the error increases with the

increase in the number of features.

Also [4] observed that in Nigeria’s maritime sector,

container operators, shippers and terminal operators

are faced with challenges emanating from low

investment in container yard facilities and

multimodal transport infrastructures despite the

terminal concession reforms. Therefore, it has

become critical to employ cost effective

optimization models to reduce cargo container dwell

times and ships’ waiting times at the port. They

developed and applied Dynamic programming

model for optimal allocation of laden shipping

containers to Nigerian seaports. In another related

research, [3] opined that careful and pre-planning

before embarking on educational venture is rare, and

that is why poor academic performance is at

alarming rate in the Nigerian universities today.

Many people apply the rule of thumb in academic

planning and execution. Though we know that if a

person devotes more time to studies, there is

likelihood that the person will perform better than

he/she would perform if no such efforts are applied

but this is not always the case as assimilation and

retention capacities vary from one individual to

another. They developed and applied Dynamic

programming for Students’ Academic Planning and

Performance in the Universities. But [11] observed

that over the last three decades, policing has gone

through a period of significant change and

innovation in Nigeria. In a relatively short historical

time frame, the police have reconsidered their

fundamental mission, the nature of core strategies of

policing, and the character of their relationships

with the communities they serve. These changes and

innovations grew out of concern that policing tactics

did not produce significant impact on crime and

disorder. There is now growing consensus that the

police can control crime when they are focused on

identifiable risks, such as crime hot spots, and when

they use a range of tactics to address these ongoing

problems. They developed and applied Dynamic

programming model for optimal allocation of crime

preventing patrol team. Furthermore, these

researchers [12], demonstrated various applications

of dynamic programming in real life scenarios. Such

applications include archaeological findings, where

they observed that it may be required to arrange in

sequence a number of archaeological sites on the

basis of the various types of pottery found there.

The authors also demonstrated the application in

rehearsal scheduling, where it may be required to

order the pieces to be played at an orchestra

rehearsal so as to minimize the total man-hours

spent by the players. Also, [13] observed that many

writers on dynamic programming bemoan the lack

of practical applications of the technique. But the

increasingly powerful computing facilities now

available mean that the solution of many hitherto

intractable problems is becoming a reality. The

author worked on an aspect of the finance of British

local government. The dynamic program used in the

paper is presented as a financial control model, and

their optimization in every time period allows the

user to incorporate new information as it becomes

available. And [14] used dynamic programming

approach to design a transformer. The researcher

observed that while dynamic programming might be

an intellectually appealing way of formulating

problems, people believe that it is not useful for

solving them. The author concluded that dynamic

programming can be used in electrical engineer to

handle some of the task which appeared to be both

time-consuming and exceedingly boring. He

obtained the optimal design policy using dynamic

programming. On the other hand, [15] observed that

dynamic programming makes use of the concept of

sub-optimization and the principle of optimality in

solving a problem. An optimal policy (or a set of

decisions) has the property that whatever the initial

state and initial decision are, the remaining

decisions must constitute an optimal policy with

regard to the state resulting from the first decision.

They used dynamic programming to determine the

optimal course allocation in the Nigerian

Universities. Finally, [16] used multi-objective

dynamic programming to improve their design and

operational strategies. The researchers aimed at

adapting Dynamic programming-based

metaheuristic to solve Optimization problem and to

apply it to the multi-objective unit commitment

problem (MO-UCP). They were of the opinion that

their model overcomes the poor performance of

standard evolutionary operators on such a heavily-

constrained problem. The benefit of using such a

INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS,

COMPUTATIONAL SCIENCE AND SYSTEMS ENGINEERING

DOI: 10.37394/232026.2023.5.16

Harrison Obiora Amuji, Ngozi Pauline Olewuezi,

Evangelina Ozoemena Ohaeri, Vivian Ngozi Ikeogu,

Johnson Ottah Okoh

E-ISSN: 2766-9823

181

Volume 5, 2023

representation is that it helps the authors to design

evolutionary operators which keep most of the

constraints satisfied at all times.

3 Materials and Methods

Our focus is on solution to ASUU strike and

Consolidated University Academic Salary Structure

II (CONUASS II) in the Nigerian Universities using

optimization method. We can observe that the

present status (level) of professors depend on their

past level (status). So this relationship between the

present and past status are recursive. In other words,

the future status (level) depend on today’s status

(level).

3.1 Method of Data collection

The data for this paper is a secondary data collected

from the publication of the National Salaries,

Incomes and Wedges Commission with circular No.

SWC/S/04/S.100/II/403 of December 8, 2009. The

data was on the Consolidated University Academic

Staff Salary Structure II (CONUASS II) and the

data is still in use today since 2009.

3.2 Method of Data Analysis

We shall apply the Dynamic programming model in

equation (1),

 

)1()(*)(max),( 1nnnn

nn xSfxRxSf

 

Where

),( nn xSf

is the optimization function that

assign steps (states) to levels (cadres);

)(xRn

is the

function that assigning values to steps (states);

1n

is the optimal function from the previous

stage, it is the basis for recursive relationship that

links the previous stage to the current stage, this is

from where optimality is derived;

is the stage

variable;

is the state variables. Our dynamic

programming model will consider the long steps

without further promotion at professorship cadre

(level) as one level (CONUASS 08) and the last

level in the academic career. This was born out of

the need to accommodate the steps in the

professorial cadre.

3.3 Optimal Decision Policy

Let Si be the stage variables, i = 1, . . , n ; xi be the

decision (state) variables and ki* be the optimal

decision variable at each stage, then we can state the

optimal decision policy as follows:

S1 = n1; x1* = k1, …………, for (stage) level “01”

S2 = (n1 - k1); x2* = k2, ……, for (stage) level “02”

………………. ………………….

Sn-1 = (n-1 – kn - 1); xn-1* = kn-1 for (stage) level “n-1”

Sn = (n – kn -1); xn* = kn for (stage) level “n” (2)

Therefore, if the above salary structure is

implemented in the order (k1, k2, k3, ……, kn),

professors will earn at least double of their salaries

per annum and other academics at other levels

(cadres) will earn an enhanced salary that will

prevent incessant strikes.

4 Data Presentation and Analysis

4.1 Data Presentation

In Table1, we present the data on Consolidated

University Academic Salary Structure II

(CONUASS II), where 01, …, 07 are the levels and

1, 2, . . . ,10 are the steps. N is the unit of

measurement (amount) in naira, see Appendix.

In table2, we present the raw data from Table1 in a

Dynamic programming format and introduce levels

08, 09 and 10 to form a square dynamic

programming cost matrix, see Appendix.

4.2 Data Analysis

In this section, we analyse the problem presented in

Table2 using the Dynamic programming model

stated in equation (1) and Optimal decision policy

presented in equation (2) to arrive at the optimal

decision that will help to optimize the lecturers’

salary structure. We start with the iteration table

coded “For n = 10” down to “For n = 1” from the

iteration Tables, see Appendix.

INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS,

COMPUTATIONAL SCIENCE AND SYSTEMS ENGINEERING

DOI: 10.37394/232026.2023.5.16

Harrison Obiora Amuji, Ngozi Pauline Olewuezi,

Evangelina Ozoemena Ohaeri, Vivian Ngozi Ikeogu,

Johnson Ottah Okoh

E-ISSN: 2766-9823

182

Volume 5, 2023

Thus for the optimal solution, we apply the optimal

decision policy in equation (2) starting from the last

iteration Table “For n = 1” to the first iteration

Table “For n = 10” and arrived at:

1 ,10 1

1 xS

1 9, 110 2

2 xS

1 8, 19 3

3 xS

1 7, 18 4

4 xS

1 6, 17 5

5 xS

1 ,516 6

6 xS

2 4, 15 7

7 xS

2 2, 24 8

8 xS

0 0, 22 9

9 xS

0 0, 00 10

10  xS

Please see Appendix.

5 Discussions and Recommendations

5.1 Discussion

From the analysis, we obtained an interesting result.

In the first case, we observed the last two levels

were inadmissible, that is, step 09 and 10 cannot

apply and hence, zeros. This means that the terminal

level should be level 08. On a careful observation

again, we see that the last two levels following

levels 09 and 10 have values “2” each. These levels

should be the last promotional levels for professorial

cadres. Instead of remaining at 07 with many steps,

it is better to promote them to level 08. This new

terminal promotion will increase the annual salaries

of associate professors and professors by at least

twice of what they currently earn on this static

CONUASS II salary structure. Then the other levels

with values “1” will have their salaries adjusted with

the hope that they will enjoy salary doubling when

they attain the levels of 07 and 08. If this policy is

implemented, a professor at the bar will grow to

level 08 and will therefore earn up to at least double

of his/her annual salary (N13,658,325) instead of

the current stagnating salary of (N6,020,163) per

annum at the bar. Our discussion so far is depicted

by the optimal decision policy of: (1, 1, 1, 1, 1, 1, 2,

2, 0, 0). This measure can cushion the effect of brain

drain due to financial difficulties on the academic

staff and restore normalcy in the Nigerian

University System.

5.2 Summary

In this paper, we applied dynamic programming

model for optimization of Consolidated University

Academic Salary Structure II (CONUASS II) for the

overall interest of the academic staff and the

Nigerian University System. Our focus was on the

decision policy that will help to enhance the living

condition of lecturers in the Nigerian universities

thereby averting frequent strikes and disruption of

academic activities. The frequent and incessant

strikes delay students and impact negatively to their

feature; hence, anything that could be done to

stabilize the university education in Nigeria will

contribute immensely to the economic growth and

stability of the country. To achieve this, we applied

dynamic programming, and developed the optimal

decision policy which was applied to obtain the best

policy needed for the highest ranking cadre in the

academic to achieve optimal remuneration of at

least twice their current per annum salary with

subsequent adjustment in the other levels’ salaries

accordingly. Applying the optimal decision policy

in equation (2), we obtained (1, 1, 1, 1, 1, 1, 2, 2, 0,

0) which optimizes the academic staffs’ earning

with a promotion to level 08 instead of remaining at

bar with many steps. This is to say that even if the

government find it difficult to do salary review as

she ought to but implement this recommendation,

the lecturers will be happy and discharge their duties

with commitments and this will go a long way to

addressing the perennial strikes in the Nigerian

public universities.

5.3 Recommendations

We recommend from our findings in this paper that:

1. Federal government of Nigeria should

introduce level 08 into the CONUASS II

and make necessary adjustments in the

salary structures of other levels of

4.3 Optimal Decision Policy

INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS,

COMPUTATIONAL SCIENCE AND SYSTEMS ENGINEERING

DOI: 10.37394/232026.2023.5.16

Harrison Obiora Amuji, Ngozi Pauline Olewuezi,

Evangelina Ozoemena Ohaeri, Vivian Ngozi Ikeogu,

Johnson Ottah Okoh

E-ISSN: 2766-9823

183

Volume 5, 2023

academics. This will take care of industrial

unrest in the Nigerian public university

system thereby halting incessant ASUU

strikes.

2. More research should be carried out to find

an algorithm and codes that could solve

large class of dynamic programming

problems, as this is a problem to the

application of dynamic programming.

References:

[1] Tolu_Kolawole, D., ASUU embarks on 16

strikes in 23 years, FG, lecturers disagree over 13-

year MOU. PUNCH Newspaper, (2022).

[2] National Salaries, Income and Wages

Commission, Consolidated University Academic

Salary Structure II (CONUASS II), Federal

Government of Nigeria, (2009).

[3] Amuji, H. O., Onwuegbuchunam, D. E.,

Okoroji, L. I., Nwachi, C. C. and Mbachu, J. C.,

Development/Application of Dynamic

Programming Model for Students’ Academic

Planning and Performance in the Universities.

Greener Journal of Science, Engineering and

Technological Research, Vol.12(1), (2023), pp.20-

25.

[4] Amuji, H. O., Onwuegbuchunam, D. E.,

Aponjolosun, M. O., Okeke, K. O., Mbachu, J. C.,

& Ojutalayo, J. F., The dynamic programming

model for optimal allocation of laden containers to

Nigerian seaports. Journal of Sustainable

Development of Transport and Logistics, Vol.7(2),

(2022), pp.69-79.

[5] Esogbuo, A. O. and Marks, B. R., Non-Serial

Dynamic Programming - A Survey, Operational

Research Quarterly, Vol. 25(2), (1974), pp. 253-265

[6] Augustine O. E. and Barry R. M., Non-Serial

Dynamic Programming: A Survey. Palgrave

Macmillan Journals, Vol. 25 (2), (1974), pp. 253 –

265.

[7] Peter, J. W., A New Decomposition Procedure

for Dynamic Programming, INFORM, Vol. 18(1),

(1970), pp. 119 - 131.

[8] Doraszelski, U. and Judd, K. L., Avoiding the

Curse of Dimensionality in Dynamic Stochastic

Games, University of Pennsylvania, (2012).

[9] De Farias, D. P. and Van Roy B., The Linear

Programming Approach to Approximate Dynamic

Programming. Operations Research, Vol. 51(6),

(2003), pp. 850-865.

[10] Fernandez-Villaverde, J., Nuno, G., Sorg-

Langhans, G. and Vogler, M., Solving High-

Dimensional Dynamic Programming Problems

using Deep Learning, University of Pennsylvania,

(2020).

[11] Ogbereyivwe, O. and Ogundele, S. O., On

Optimal Allocation of Crime Preventing Patrol

Team Using Dynamic Programming, International

Journal of Mathematics and Statistics Invention,

Vol. 2(8), (2014), pp. 7 – 17.

[12] Adelson, R.M., Norman, J.M. and Laporte, G.,

A Dynamic Programming Formulation with

Diverse Applications. Journal of the Operational

Research Society, Vol. 27, (1976), pp.119-121.

[13] Peter, S., Dynamic Programming in Action,

Palgrave Macmillan Journals, Vol. 40(9), (1989),

pp. 779 - 787.

[14] Brian, M., Dynamic Programming in

Transformer Design. Palgrave Macmillan Journals,

Vol. 37(10), (1986), pp. 967 - 969.

[15] Amuji, H. O., Ugwuanyim, G. U., Ogbonna, C.

J., Iwu, H. C. and Okechukwu, B. N., The

Usefulness of Dynamic Programming in Course

Allocation in the Nigerian Universities. Open

Journal of Optimization, Vol. 6, (2017), pp.176 -

186.

[16] Sophie, J., Laetitia J. and El-Ghazali T., A

multi-objective dynamic programming-based

metaheuristic to solve a biobjective unit

commitment problem using a multi-objective

decoder, Int. J. Metaheuristics, Vol. 5(1), (2016), pp

3 -30.

Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

The authors equally contributed in the present

research, at all stages from the formulation of the

problem to the final findings and solution.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

No funding was received for conducting this study.

Conflict of Interest

The authors have no conflicts of interest to declare

that are relevant to the content of this article.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

_US

INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS,

COMPUTATIONAL SCIENCE AND SYSTEMS ENGINEERING

DOI: 10.37394/232026.2023.5.16

Harrison Obiora Amuji, Ngozi Pauline Olewuezi,

Evangelina Ozoemena Ohaeri, Vivian Ngozi Ikeogu,

Johnson Ottah Okoh

E-ISSN: 2766-9823

184

Volume 5, 2023