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Single-Level Production Planning in Petrochemical Industries Using Novel Computational Intelligence Algorithms

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Nature-Inspired Methods for Metaheuristics Optimization

Part of the book series: Modeling and Optimization in Science and Technologies ((MOST,volume 16))

Abstract

Optimal production planning requires the solution of combinatorial optimization problems that need to be efficiently modelled so as to be solved with computational intelligence (CI) techniques. In this work, we report the computational performance of five recently proposed CI techniques, namely the sanitized-teaching-learning-based optimization (s-TLBO), moth flame optimization (MFO), flower pollination optimization, water cycle optimization, and adaptive wind driven optimization on the single level production planning problem which involves complex domain-hole constraints, nonlinearities in the production and investment costs, resource and unique process constraints. In this work, the domain-hole constraints are handled using a hard-penalty approach. The performance is evaluated on the case study of the Saudi Arabia petrochemical industry to determine optimal portfolio from 54 processes for producing 24 products with three production levels. Based on 2040 (8 Cases × 51 runs × 5 algorithms) unique instances of the problem, it was observed that s-TLBO and MFO are consistently able to determine efficient solutions and that s-TLBO was able to quickly discover feasible solutions and had relatively low variance.

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Abbreviations

a sr :

amount of raw material r required in process s for producing per ton of product

k :

production capacity level, K denotes the total number of production capacity level of a process

n t :

number of active processes employed to produce product t

r :

index for raw material, r ∈ (1, .., R); R denotes the total number of raw materials

s :

index for process, s ∈ (1, .., S); S denotes the total number of processes

t :

index for product, t ∈ (1, .., T); T denotes the total number of products

x s :

total amount of product produced from process s

\( {y}_s^k \) :

binary variable to indicate the production level from process s;

\( {y}_s^k \)= 1, if \( \left({L}_s^k\le {x}_s\le {L}_s^{k+1}\right) \), else 0

z s :

binary parameter to indicate the production in domain hole; z s = 0 if (x s = 0) or (L s ≤ x s ≤ U s), else 1

B :

total available monetary resources ($)

C s :

total production cost of process s

E s :

selling price per ton of the product produced from process s

I s :

total investment cost of process s

\( {L}_s^k \) :

production capacity level of level k for process s

\( {P}_r^{rawmaterial} \) :

penalty incurred in fitness function due to insufficient raw material of type r

P Budget :

penalty incurred in fitness function due to insufficient investment cost

\( {P}_t^{uni} \) :

penalty incurred in fitness function due to violation of unique process constraint for product t

\( {Q}_s^k \) :

production cost for production capacity level k of process s

S t :

set of processes that can produce product t

U s :

maximum production from process s

\( {V}_s^k \) :

investment cost for production capacity level k of process s

λ, γ :

static penalty factor

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Authors and Affiliations

  1. Department of Chemical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    Sandeep Singh Chauhan & Prakash Kotecha

Authors
  1. Sandeep Singh Chauhan
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  2. Prakash Kotecha
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Correspondence to Prakash Kotecha .

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Editors and Affiliations

  1. Ecole Central de Nantes, Nantes cedex 3, France

    Fouad Bennis

  2. Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    Rajib Kumar Bhattacharjya

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Chauhan, S.S., Kotecha, P. (2020). Single-Level Production Planning in Petrochemical Industries Using Novel Computational Intelligence Algorithms. In: Bennis, F., Bhattacharjya, R. (eds) Nature-Inspired Methods for Metaheuristics Optimization. Modeling and Optimization in Science and Technologies, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-030-26458-1_13

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  • DOI: https://doi.org/10.1007/978-3-030-26458-1_13

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