Application of simulated annealing to solve multi-objectives for aggregate production planning

Abstract

Aggregate production planning (APP) is one of the most significant and complicated problems in production planning and aim to set overall production levels for each product category to meet fluctuating or uncertain demand in future and to set decision concerning hiring, firing, overtime, subcontract, carrying inventory level. In this paper, we present a simulated annealing (SA) for multi-objective linear programming to solve APP. SA is considered to be a good tool for imprecise optimization problems. The proposed model minimizes total production and workforce costs. In this study, the proposed SA is compared with particle swarm optimization (PSO). The results show that the proposed SA is effective in reducing total production costs and requires minimal time.

Solving Tri-criteria: Total Completion Time, Total Earliness, and Maximum Tardiness Using Exact and Heuristic Methods on Single-Machine Scheduling Problems

“…This section, considered compaction results of Exact and HMs to the (𝑆 𝐶𝐸 𝑀 𝑇 )-problem and (𝑆𝑃)-problem. Because we deal with the MSP, the 𝑝 𝑗 and 𝑑 𝑗 values are randomly generated for five examples s.t., 𝑝 𝑗 ∈ [1,10] and: 𝑑 𝑗 ∈ { [1,30] 1 ≤ 𝑛 ≤ 29 [1,40] 30 ≤ 𝑛 ≤ 99 [1,50] 100 ≤ 𝑛 ≤ 999 [1,70] otherwise , subject to condition 𝑑 𝑗 ≥ 𝑝 𝑗 , for 𝑗 = 1, 2, … , 𝑛.…”

“…In this scenario, one decreases the primary criterion and selects a table with the minimum value of the secondary criterion. In the second method, a Pareto set is formed and the decision maker is the one with the optimal composite objective function [10]. Hoogveen [8] presented an algorithm that finds all effective tables for 1//(∑𝐶 𝑗 , 𝐹 𝑚𝑎𝑥 ) problem.…”

Modeling and optimization of multi-layer aggregate production planning

“…Aggregate production planning (APP) is one of the most omnipresent but fluctuating problems in both the industry and academia. The battle to meet uncertain demands for different products in future as well as to decide hiring, firing, overtime, subcontract and carrying inventory level has always existed (Atiya et al, 2016). This research work presents a suitable approach for solving multi-product, multi-level and multi-period APP decision problems, with the forecast demand, related operating costs and capacity.…”

Enhanced Support Vector Machine Methods Using Stochastic Gradient Descent and Its Application to Heart Disease Dataset

“…The total number of samples in this data collection is 462. Obesity refers to a high percentage of body fat, while obesity is defined by a high weight-to-height ratio (body mass index, BMI) [27] Excessive antagonism, aggression, and competitiveness are hallmarks of the Type A personality. We will see if we can extrapolate ldl from the available data.…”