Enhanced Simulated Annealing for Solving Aggregate Production Planning (original) (raw)
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Application of Simulated Annealing to Solve Multi-Objectives for Aggregate Production Planning Bayda
Aggregate Production Planning, 2016
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 e ective in reducing total production costs and requires minimal time.
International Journal of Advanced Computer Science and Applications, 2019
In the planning of aggregate production, company stakeholders need a long time due to the many production variables that must be considered so that the production value can meet consumer demand with minimal production costs. The case study is the company that produces more than a type of product so there are several variables must be considered and computational time is required. Genetic Algorithm is applied as they have the advantage of searching in a solution space but are often trapped in locally optimal solutions. In this study, the authors proposed a new mathematical model in the form of a fitness function aimed at assessing the quality of the solution. To overcome this local optimum problem, the authors refined it by combining the Genetic Algorithm and Simulated Annealing so called hybrid approach. The function of Simulated Annealing is to improve every solution produced by Genetic Algorithm. The proposed hybrid method is proven to produce better solutions.
A New Optimization Method for Production Planning Problems Using Simulated Annealing
2003
Tactic planning or master production scheduling focuses on time and spatial decomposition of the aggregate planning targets and forecasts, as well as, forecast and provision of needed resources. This process becomes extremely hard and time consuming with the increase of number of products, resources and periods considered. In face of such obstacles, this work shows a study of an Artificial Intelligence technique called Simulated Annealing applied to the optimization of production planning problem, more specifically, Master Production Scheduling. This work reviews some of the fundamental theory of simulated annealing, the methodology for master production scheduling calculation, the applicability of simulating annealing to planning problems, most important results and suggestions for further studies.
PSO-based harmony search algorithm to aggregate production planning under possibilistic environment
International Journal of Services and Operations Management, 2018
This paper develops particle swarm optimisation based harmony search algorithm (PSO-HSA), a non-traditional optimisation technique to find globally optimised solutions more efficiently. PSO-HSA is developed on the structure of HSA and the concept of PSO is used to modify the improvisation process. The aim of PSO-HSA is to incorporate multiple improvisation methods as well as the complete utilisation of all the possible solution sets in improvisation steps. In addition, the complexity of predefinition for various algorithm parameters is reduced significantly. Again, this paper demonstrates possibilistic environment based multi-product, multi-period aggregate production planning with triangular possibility to handle all the imprecise parameters. The strategy is to minimise the most possible value of the imprecise total costs, maximise the possibility of obtaining lower total costs and minimise the risk of obtaining higher total costs simultaneously with weighted average aggregation. HSA and FBGA are also employed here to analyse the performance of PSO-HSA approach.
International Journal of Industrial Engineering Computations, 2013
In hierarchical production planning system, Aggregate Production Planning (APP) falls between the broad decisions of long-range planning and the highly specific and detailed short-range planning decisions. This study develops an interactive Multi-Objective Genetic Algorithm (MOGA) approach for solving the multi-product, multi-period aggregate production planning (APP) with forecasted demand, related operating costs, and capacity. The proposed approach attempts to minimize total costs with reference to inventory levels, labor levels, overtime, subcontracting and backordering levels, and labor, machine and warehouse capacity. Here several genetic algorithm parameters are considered for solving NP-hard problem (APP problem) and their relative comparisons are focused to choose the most auspicious combination for solving multiple objective problems. An industrial case demonstrates the feasibility of applying the proposed approach to real APP decision problems. Consequently, the proposed MOGA approach yields an efficient APP compromise solution for large-scale problems.
Journal of Computational and Applied Mathematics, 1995
The present paper is concerned with the grouping of book covers on offset plates in order to minimize the total production cost. The mathematical formulation of the problem involves both binary and continuous variables. As exact methods are unable to provide solutions in reasonable time, a heuristic algorithm of the simulated annealing type is proposed. At each iteration, the values of the current solution binary variables are altered in order to yield a neighboring solution. To compute the corresponding values of the continuous variables and the value of the objective function, a linear programming routine is called at each iteration. This constitutes the main originality of the present approach and is in principle applicable in mixed integer programming problems. The procedure is tested on several examples. * Corresponding author. 1 We are pleased to thank Mrs. M.P. Ladavid-Duboisse from Casterman, S.A., for submitting the problem. 0377~0427/95/$09.50 0 1995 Elsevier Science B.V. All rights reserved SSDZ 0377-0427(95)00009-7
A multi-objective production scheduling case study solved by simulated annealing
European Journal of Operational …, 2007
During several decades, research in production scheduling mainly concerns a single criterion to optimize. However, the analysis of the performance of a schedule often involves more than one aspect and therefore requires multi-objective analysis. Such situation appears in the real case study considered here.
2014
Aggregate Production planning (APP) and preventive maintenance (PM) are most important issue carried out in manufacturing environments which seeks efficient planning, scheduling and coordination of all production activities that optimizes the company's objectives. In this paper, we develop two mixed integer linear programming (MILP) models for an integrated aggregate production planning system with return products, breakdowns and preventive maintenance. The goal is to minimize production breakdowns and Preventive maintenance costs and instabilities in the work force, inventory levels and downtimes, also effect of PM on the objective function. Additionally, Taguchi method is conducted to calibrate the parameter of the meta-heuristic and select the optimal levels of the algorithm’s performance influential factors. Due to NP-hard class of APP, we implement a harmony search (HS) algorithm for solving these models. Finally, computational results show that, the objective values obtain...
Multi-objective aggregate production planning with fuzzy parameters
Advances in Engineering Software, 2010
In this paper, a direct solution method that is based on ranking methods of fuzzy numbers and tabu search is proposed to solve fuzzy multi-objective aggregate production planning problem. The parameters of the problem are defined as triangular fuzzy numbers. During problem solution four different fuzzy ranking methods are employed/tested. One of the primary objectives of this study is to show that how a multi-objective aggregate production planning problem which is stated as a fuzzy mathematical programming model can also be solved directly (without needing a transformation process) by employing fuzzy ranking methods and a metaheuristic algorithm. The results show that this can be easily achieved.