Determining the Planning Horizon and Grouping Parts Families for Flexible Cellular Manufacturing.

Proceedings of the Institution of Mechanical Engineers, Part B:

Saidi‐Mehrabad

Jabalameli

et al. 2011

This research introduces a new application of mathematical modelling for designing cellular manufacturing systems integrated with group scheduling aspects under uncertain conditions. The mixed 0–1 quadratic model is able to optimize group scheduling decisions under lower satisfaction of cell formation decisions. In this way, the required processing time of parts on machines is assumed to be uncertain and also is explained by a discrete set of scenarios. This model tries to optimize the expected scheduling cost in cells plus the subcontracting cost for exceptional operations. In addition, the objectives associated with the cell formation decisions are regarded as constraints by considering the bounded objective technique, one of the multi-objective decision-making techniques. The scheduling problem in a cellular manufacturing framework is treated as group scheduling problem, which assumes that all parts in a part family are processed in the same cell and no intercellular transfer is needed. An efficient modified simulated annealing technique is proposed to solve this complex problem under an optimization rule as a subordinate section. This integrative combination algorithm is compared with global solutions and a heuristic algorithm introduced in the literature. Finally, the performance of the algorithm is verified through some test problems.

Section: Model Linearizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing expected holding cost in cellular manufacturing systems with stochastic extensions

Proceedings of the Institution of Mechanical Engineers, Part B:

Saidi‐Mehrabad

Jabalameli

et al. 2011

“…In this group, there are two approaches employed in order to resolve uncertainty, which are stochastic and fuzzy theory. There are also some cases where demand is aggregated with tactical aspects such as production planning in stochastic form [1], layout problem [2] or dynamic and multi period conditions [3]. There are also cases where, in other studies, uncertainty in products' demand is treated using fuzzy numbers [4].…”

Section: Published By Atlantis Pressmentioning

confidence: 99%

“…In actuality, many of the previous researches have explored the problems in certain conditions. Additionally, researchers usually have considered just demand of parts to be stochastic, [2], [24], [3]. 1c) In the last group, some papers have been developed to introduce heuristic methods to solve CMS problem in uncertain situations, [13], [6], [7].…”

Section: Published By Atlantis Pressmentioning

confidence: 99%

Integrating Strategic and Tactical Decisions to Robust Designing of Cellular Manufacturing under Uncertainty: Fixed Suppliers in Supply Chain

Sadjadi

Nayeri

2011

IJCIS

Cellular manufacturing (CM) has been identified as an innovative practice for manufacturer to achieve efficiency as well as flexibility under an uncertain environment. This study addresses a new mathematical robust model for a cellular manufacturing problem integrated with tactical aspects under supply chain network characteristics in the presence of uncertain internal parameter (processing times) and external parameter (demands). The model aims to minimize total cost consisting expected value and variance of tactical cost (scheduling), strategic cost (employment of suppliers in supply chain network) to produce exceptional elements as well as the cost of resource underutilization. The model can give up a series of the solutions that are less sensitive to the changes in input data. To solve the robust model, an efficient hybrid method based on new combination of genetic algorithm (GA), simulated annealing (SA) in a parallel structure are proposed under an optimization rule. The effectiveness of the proposed model is demonstrated by numerical results and the trade-off between solution robustness and model robustness is also analyzed.

Designing integrated cellular manufacturing systems with scheduling considering stochastic processing time

Int J Adv Manuf Technol

Saidi‐Mehrabad

2009

In recent decades, many researchers have studied the cellular manufacturing system with consideration of various issues such as scheduling, production planning, layout, reliability, etc. However, limited research papers have investigated this problem in an uncertain environment. The present paper addresses a stochastic problem in cellular manufacturing systems considering simultaneous multiple routings and subcontracting. In the developed problem, each part can be simultaneously produced in multiple processing routes. It is also assumed that the unsatisfied part demands as a result of limited machine capacity or high manufacturing cost could be outsourced. A two-stage stochastic programming approach is employed to take the uncertainty into consideration and to formulate the problem. The objective function is to minimize the summation of production, subcontracting, material handling, and machine idleness costs. A sample average approximation method is applied as a solution method. Also, for further illustration of the problem, a numerical example is solved and sensitivity analyses are conducted. Finally, through some numerical examples extracted from related literature, the advantages of constructing a stochastic optimization model for the problem are demonstrated.