Case studies in industry and services
Seyed Farid Mousavi; Arash Apornak; Mohammadreza Pourhassan
Abstract
Although the importance of supply chain agility considering the necessity of speed of action, response to customers, progressive changes in the market, consumers’ needs, etc. in many industries is clear both scientifically and experimentally, today organizations have found that the benefit ...
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Although the importance of supply chain agility considering the necessity of speed of action, response to customers, progressive changes in the market, consumers’ needs, etc. in many industries is clear both scientifically and experimentally, today organizations have found that the benefit from this cooperation is greater than cases performed without collaboration with relevant organizations. Meanwhile, supply chain management refers to integration of all processes and activities in the supply chain through improving the relations and implementing the organizational processes in order to achieve competitive advantages. On the other hand, uncertainty in the supply chain results in non-optimality of decisions that are made with assumption of certainty. Accordingly, the main aim of this research is to provide a model for supply chain in an agile and flexible state based on uncertainty variables. The method of research has been based on a mathematical model, whose stages of implementation are investigated by breaking down this model step-by-step. For this purpose, in the first stage and after getting familiar with the intended modeling industry, solution and simulation were done. Eventually the results were compared indicating that through reducing the risk-taking (increasing the protection levels), the objective function which was of minimization type worsened. This study also showed that model robustification is very important in order to reduce the risk of decision-making.
Supply chain management
Arash Khosravi Rastabi; Seyed Reza Hejazi Taghanaki; Shahab Sadri; Anil Kumar; Hossein Arshad
Abstract
The objective of this study is to model a dynamic redesigning closed-loop supply chain network with capacity planning in order to minimize the costs of the network. The structure of this model consists of existing facilities including manufacturing plants, distribution and reworking centers. Any such ...
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The objective of this study is to model a dynamic redesigning closed-loop supply chain network with capacity planning in order to minimize the costs of the network. The structure of this model consists of existing facilities including manufacturing plants, distribution and reworking centers. Any such structure should change due to fluctuations in demand in order to meet customer demand. Establishing new facilities, closing the existing ones, and adding discrete capacity levels to facilities, are among the decisions which lead to necessary changes in network structure. To make the issue more realistic, it is assumed that demand and returned products are stochastic. To solve the problem, a two-stage stochastic mixed integer linear programming is modelled, followed by writing a robust counterpart of the MILP model program. An accelerated Benders decomposition algorithm is proposed to solve this model. To increase the convergence trend of this proposed algorithm, valid-inequalities and Pareto optimal cut are combined to the model. The expected performance improvement based on applying valid-inequalities and Pareto optimal cut is expressed through numerical results obtained from different samples.
Scheduling
Behnaz Zanjani; Maghsoud Amiri; Payam Hanafizadeh; Maziar Salahi
Abstract
Scheduling is an important decision-making process that aims to allocate limited resources to the jobs in a production process. Among scheduling problems, Hybrid Flow Shop (HFS) scheduling has good adaptability with most real world applications including innumerable cases of uncertainty of parameters ...
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Scheduling is an important decision-making process that aims to allocate limited resources to the jobs in a production process. Among scheduling problems, Hybrid Flow Shop (HFS) scheduling has good adaptability with most real world applications including innumerable cases of uncertainty of parameters that would influence jobs processing when the schedule is executed. Thus a suitable scheduling model should take parameters uncertainty into account. The present study develops a multi-objective Robust Mixed-Integer Linear Programming (RMILP) model to accommodate the problem with the real-world conditions in which due date and processing time are assumed uncertain. The developed model is able to assign a set of jobs to available machines in order to obtain the best trade-off between two objectives including total tardiness and makespan under uncertain parameters. Fuzzy Goal Programming (FGP) is applied to solve this multi objective problem. Finally, to study and validate the efficiency of the developed RMILP model, some instances of different size are generated and solved using CPLEX solver of GAMS software under different uncertainty levels. Experimental results show that the developed model can find a solution to show the least modifications against uncertainty in processing time and due date in an HFS problem.