Optimizing Reliability and Cost of system for Aggregate Production Planning in Supply Chain

Ramyar, Mohammad; Mehdizadeh, Esmaeil; Molana, Seyyed Mohammad Hadji

doi:10.24200/sci.2017.4398

Cited by 4 publications

(4 citation statements)

References 28 publications

(37 reference statements)

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“…Meta-heuristics algorithm has been proved to be an effective solution algorithm. Among the meta-heuristics, genetic algorithms (GAs) [9,11,19,[22][23][24], particle swarm optimization (PSO) [12,25,26], tabu search (TS) [9,27,28] and harmony search algorithm (HSA) [19,29,30] have been used to deal with APP models [31]. One of the widely used algorithms among meta-heuristics proposed to solve multi-objective APP problems is the Pareto-based non-dominated sorting genetic algorithm called NSGA-II (non-dominated sorting genetic algorithm-II).…”

Section: Multi-objective Function Appmentioning

confidence: 99%

Multi-objective Aggregate Production Planning for Multiple Products: A Local Search-Based Genetic Algorithm Optimization Approach

Liu

Yang

2021

Int J Comput Intell Syst

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The diversity of products and fierce competition make the stability and production cost of manufacturing industry more important. So, the purpose of this paper is to deal with the multi-product aggregate production planning (APP) problem considering stability in the workforce and total production costs, and propose an efficient algorithm. Taking into account the relationship of raw materials, inventory cost and product demand, a multi-objective programming model for multi-product APP problem is established to minimize total production costs and instability in the work force. To improve the efficiency of the algorithm, the feasible region of the planned production and the number of workers in each period are determined and a local search algorithm is used to improve the search ability. Based on the analysis of the feasible range, a genetic algorithm is designed to solve the model combined with the local search algorithm. For analyzing the effect of this algorithm, the information entropy strategy, NSGA-II strategy and multi-population strategy are compared and analyzed with examples, and the simulation results show that the model is feasible, and the NSGA-II algorithm based on the local search has a better performance in the multi-objective APP problem.

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Section: Multi-objective Function Appmentioning

confidence: 99%

Multi-objective Aggregate Production Planning for Multiple Products: A Local Search-Based Genetic Algorithm Optimization Approach

Liu

Yang

2021

Int J Comput Intell Syst

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“…In more recent papers, Ramyar et al [60] developed a multi-objective model for the APP in an SC with the objectives of minimizing the total cost and maximizing the minimum reliability of suppliers. They considered the probabilistic lead times in order to improve performance of the system and presented a Pareto-based multi-objective harmony search algorithm.…”

Section: Review Of the Literature On Production Planning And Appmentioning

confidence: 99%

“…Besides, Eqs (59). and(60) are in fact the linear state of Eq. (91) and the remaining equations are linearized in the same way.…”

mentioning

confidence: 99%

Benders Decomposition Algorithm for Robust Aggregate Production Planning Considering Pricing Decisions in Competitive Environment: A Case Study

Aazami

Saidi‐Mehrabad

2018

Scientia Iranica

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In operations research, bi-level programming is a mathematical modeling which has another optimization problem as a constraint. In the present research, regarding the current intense competition among large manufacturing companies for achieving a greater market share, a bi-level robust optimization model is developed as a leader-follower problem using Stackelberg game in the eld of Aggregate Production Planning (APP). The leader company with higher in uence intended to produce new products, which could replace the existing products. The follower companies, as rivals, were also seeking more sales, but they did not have the intention and ability to produce such new products. The price of the new products was determined by the presented elasticity relations between uncertain demand and price. After linearization, using the KKT conditions, the bi-level robust model was transformed into an ordinary uni-level model. Due to the NP-hard nature of the problem, Benders Decomposition Algorithm (BDA) was proposed for overcoming the computational complexities in large scale. Finally, using the real data of Sarvestan Sepahan Co as a leader company, the validity of the developed model as well as e ciency and convergence of the BDA was investigated. The computational results clearly showed the e ciency and e ectiveness of the proposed BDA.

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“…An Aggregate Production Planning (APP), is an activity used to determine an aggregate plan for production systems in period of 3-18 months, so that the total cost is minimized [1], and determines optimal production, labor and inventory level for each planning period in the form of product family production, based on the available sources of production and restrictions. Figure 1 indicates inputs and outputs of the APP process [2]. Given that planning is conducted at a general level and used to represent demand and production capacity from a single unit of the product family, the performance evaluation in the APP is very important for reducing the waste of common resources.…”

Section: Introduction *mentioning

confidence: 99%

Performance Evaluation in Aggregate Production Planning Using Integrated RED-SWARA Method under Uncertain Condition

Khalili

Alinezhad

2020

Scientia Iranica

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Nowadays, increasing the efficiency of production and determining the tools and methods of measuring performance are the biggest challenges faced by managers by increasing competition among companies and manufacturing centers. In the aggregate production planning, along with the performance evaluation in order to reduce the waste of resources due to the common use of resources for product family production. The present study aimed to evaluate the performance in the Aggregate Production Planning (APP). In this regard, the optimal values were determined by the multi-objective Grey Aggregate Production Planning (GAPP) model, and the weights of the input and output indicators for the performance evaluation were characterized by the Step-wise Weight Assessment Ratio Analysis (SWARA) method. Further, the efficiency of Decision-Making Units (DMUs) was determined by the Ratio Efficiency Dominance (RED) model. Then, the ranking of DMUs was conducted. In the case study of automobile parts manufacturing industry in Iran, the sensitivity analysis was performed on the proposed model and its effects were evaluated. The results indicated that the proposed model had a high degree of accuracy in evaluating performance, compared to previous models, and helps managers to make better decisions to increase the efficiency and reduce the waste of resources.

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Optimizing Reliability and Cost of system for Aggregate Production Planning in Supply Chain

Cited by 4 publications

References 28 publications

Multi-objective Aggregate Production Planning for Multiple Products: A Local Search-Based Genetic Algorithm Optimization Approach

Multi-objective Aggregate Production Planning for Multiple Products: A Local Search-Based Genetic Algorithm Optimization Approach

Benders Decomposition Algorithm for Robust Aggregate Production Planning Considering Pricing Decisions in Competitive Environment: A Case Study

Performance Evaluation in Aggregate Production Planning Using Integrated RED-SWARA Method under Uncertain Condition

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