A new robust possibilistic programming model for reliable supply chain network design: A case study of lead-acid battery supply chain

Nowadays, the importance of caring about tremendous undesirable economical and technological effects of disruptions has impelled many researchers to design reliable supply chain networks. Moreover, the issue of intrinsic imprecision of input parameters should be gingerly regarded in the design of supply chain networks because it could have inverse impact on the quality of long-term planning decisions. Consequently, to handle the noted problems, in this paper, a reliable closed-loop supply chain network is formulated in which a new reliability method is introduced. The proposed formulation can effectively enable the design of a reliable network under different kinds of disruptions besides seeking for minimum overall costs of network design. On the one hand, a new effectual robust possibilistic programming (RPP) model is developed to confront with business-as-usual uncertainty in input parameters. Lastly, a real industrial case study is employed to validate the utility and practicability of the rendered model as well as presenting the efficiency and felicity of the developed RPP model

A reliable closed-loop supply chain network design under uncertainty: A case study of a lead-acid battery manufacturer

Nowadays, the concern about strike of disruptions and its consequent huge losses has motivated many researchers to design reliable supply chain networks. As well, inherent uncertainty of input data is a momentous issue which should be considered carefully in the design of supply chain networks due to its adverse effect on quality of strategic, tactical and operational decisions. Therefore, this paper presents a novel model for designing a reliable closed-loop supply chain network in which a new reliability method is introduced. The propounded model not only minimizes the total cost, also efficiently finds a robust network under different kind of disruptions. To cope with imprecision of parameters, an effective possibilistic programming approach is employed. Finally, a real industrial case is used to demonstrate the effectiveness and applicability of the developed fuzzy optimization model

A robust possibilistic programming model for a responsive closed loop supply chain network design

Concerns about the outbreak of perturbations and their major losses have led a lot of researchers to consider reliability while designing supply chain networks. In addition, the inherent uncertainty of input parameters is another important issue in the design of supply chain networks due to its adverse effects on strategic, tactical, and operational decisions. This present paper proposes a new model for designing a sustainable closed-loop single-product multi-component multi-level logistics network under uncertainty conditions. The model is based on a robust possibilistic programming approach. The proposed models not only minimize the total costs but also develop an effective resistant network under disruptions strikes and control the product delivery speed at appropriate safety levels. Finally, the effectiveness and applicability of the model are displayed in a national project with the actual nominal data

A robust possibilistic programming model for water allocation problem

Over the past few years, water allocation problem has increasingly spotlighted by governments, researchers and practitioners. As water plays an important role in people’s life and business environment, the problem of water allocation should be considered carefully to properly satisfy demand of water consumers. In the real world applications, problems like water allocation are uncertain owing to long-term planning horizon of such problems. Therefore, employing efficient methods for tackling uncertainty of parameters should be regarded by field researchers. In this regard, this paper proposes a bi-objective mathematical programming model for water distribution network design. The extended model maximizes total profit of water distribution as well as maximizing priority of water transferring among water customer zones. Then, to cope effectively with uncertainty of parameters, a novel robust possibilistic programming method is applied. Then, fuzzy and robust fuzzy programming models are compared against each other and output results confirm superiority and effective performance of the robust fuzzy model in the water allocation problem. Also, output results of the extended model show its accurate performance that results in applicability of the model as a strong planning tool in real world cases