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We present an application of ABS algorithms for multiple sequence alignment (MSA). The Markov decision process (MDP) based model leads to a linear programming problem (LPP), whose solution is linked to a suggested alignment. The important features of our work include the facility of alignment of multiple sequences simultaneously and no limit for the length of the sequences. Our goal here is to avoid the excessive computing time, needed by dynamic programming based algorithms for alignment of a large number of sequences. In an attempt to demonstrate the integration of the ABS approach with complex mathematical frameworks, we apply the ABS implicit LX algorithm to elucidate the LPP, constructed with the assistance of MDP. The MDP applied for MSA is a pragmatic approach and entails a scope for future work. Programming is done in the MATLAB environment

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We  deal with the relationship termination problem in the context of individual-level customer relationship management (CRM) and use a Markov decision process to determine the most appropriate occasion for termination of the relationship with a seemingly unprofitable customer. As a particular case, the beta-geometric/beta-binomial model is considered as the basis to define customer behavior and it is explained how to compute customer lifetime value when one needs to take account of the firm’s choice as to whether to continue or terminate relationship with unprofitable customers. By numerical examples provided by simulation, it is shown how a stochastic dynamic programming approach can be adopted in order to obtain a more precise estimation of the customer lifetime value as a key criterion for resource allocation in CRM.     

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This paper proposes a robust model for optimizing collaborative reverse supply chains. The primary idea is to develop a collaborative framework that can achieve the best solutions in the uncertain environment. Firstly, we model the exact problem in the form of a mixed integer nonlinear programming. To regard uncertainty, the robust optimization is employed that searches for an optimum answer with nearly all possible deviations in mind. In order to allow the decision maker to vary the protection level, we used the "budget of uncertainty" approach. To solve the np-hard problem, we suggest a hybrid heuristic algorithm combining dynamic programming, ant colony optimization and tabu search. To confirm the performance of the algorithm, two validity tests are done firstly by comparing with the previously solved problems and next by solving a sample problem with more than 900 combinations of parameters and comparing the results with the nominal case. In conclusion, the results of different combinations and prices of robustness are compared and some directions for future researches are suggested finally.

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We present a stochastic dynamic programming approach with Markov chains for optimal control of the forest sector. The forest is managed via continuous cover forestry and the complete system is sustainable. Forest industry production, logistic solutions and harvest levels are optimized based on the sequentially revealed states of the markets. Adaptive full system optimization is necessary for consistent results. The stochastic dynamic programming problem of the complete forest industry sector is solved. The raw material stock levels and the product prices are state variables. In each state and at each stage, a quadratic programming profit maximization problem is solved, as a subproblem within the STDP algorithm.

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Due to the importance of vehicle routing for delivering a large number of orders with different restrictions in the world, various optimization methods have been studied in past researches. In this article, a number of researches of recent years have been discussed, then the proposed model is described in 3 phases with the penalty index. This model has the ability to assign orders, route vehicles and determine the number of active vehicles dynamically with the aim of minimizing the total cost of distribution. By examining valid metaheuristic models and using their strengths and weaknesses, and considering multiple limitations, a new model of "dynamic 3-phase optimization" has been designed. The main application of the proposed model is for vehicle routing problems with capacity constraints of fleet number and capacity constraints (maximum and minimum number of orders). Finally, with simulation, the outputs of the model have been analyzed in different conditions . Although the limitation of maximum and minimum capacity is added to the problem, by dynamically considering the number of vehicles and using star clustering (initiative of this research), three social, environmental and economic dimensions were improved. The time for orders to reach customers decreased by 19.3%, fuel consumption and air pollution by 14.9%, and logistics costs by 8.7%. To calculate the final value of system stability, a unique 3D fuzzy model has been used. With the sensitivity analysis, we came to the conclusion that the 3-phase dynamic optimization model has led to a 14.58% improvement in system stability.

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A look at the world production and consumption indicates that production systems resiliency and sustainability is highly regarded by businessmen and the general users for long surviving of human being race and ecological endurance. By conducting theoretical studies and reviewing the literature, and searching previous studies to identify the resilience factors important to manufacturing industries, a list of effective strategies was determined. The most important strategies of resilience considered in this study are: capacity management, multi sourcing, demand management, information sharing, additional inventory holding, contracting with backups, risk management and disaster recovery, dropping market feeding strategy, enlightenment of business flow complexity, and suppliers/facilities reinforcement. In this article, DEMATEL approach is used to demonstrate how production resilience factors can impacts on each other and what the interrelationships among these factors are. After that, a questionnaire was designed for pairwise comparisons of resilience strategies of capacity scaling, multi sourcing, contracts, inventory management, risk management, and production level. Then, a system dynamics approach is used to model the interrelations among the resilience factors by taking feedback loops into consideration managing to trace their impacts on production and inventory levels. A production system with its main processes of: production order rate, planned work, work in process (WIP), production rate, inventory level, desired shipment rate, backlogs, rejected rate, rework rate, required capacity, and capacity scaling are designed for this study. This model presents a production system with circular resilience’s strategies impacts on production scaling and hence their impacts on sustainability indicators of job creation, and salary (social pillar), profit and investment (economic pillar), and ecosystem destruction (environment pillar). System dynamics approach helped us in presenting the long trends of sustainability indicators as shown by a number of figures in the body of this article. Five scenarios are developed and the results were presented to the team of our experts presenting them by wi=0, wp=0 (case 1), wi=0, wp=0.5 (case 2), wi=1, wp=0 (case 3), wi=0, wp=1 (case 4), and wi=0.36, wp=0.47 (case 5). Experts’ opinions were gathered and then use TOPSIS approach for determining the best case the among cases discussed above. The results indicates that the data generated by Vensim computer software for five cases, case 5 with wi=0.36 and wp=0.47 is the best case among all cases.

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Abstract
Urban land allocation, planning and management are a complicate problem challenging the decision makers and policy writers all around the Word. The multi objectivity nature of the problem has engaged researchers to deal with the environmental, ecological, economical, social, recreational, commercial, and residential problems simultaneously, in any region, for better decision making. These modelers neglected to consider people’s satisfaction and wellbeing due to land allocation, planning, and development. Complex problems as such as land allocation and planning are in need of suitable integrated model building for solution and analysis. It was to this end that this author proposes a system dynamics approach for studying the impacts of the decisions made, by the policy makers in the long run, on the community’ satisfaction using computer simulation. Taking one land allocation decision into consideration, the results of our proposed dynamic modeling points to this reality that people’s level of satisfaction improves, their level of incomes enhance, and the quality of their lives increases with the passage of time.
 

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With this research, author presents an understanding of business value that would be enhanced by adopting a new technology into the system. A healthcare center is the case here and the technology considered is radio frequency identification (RFID). To present such framework for evaluation purposes, a two phase analysis is introduced. In the first phase and with the help of a multi attribute decision making in the context of hierarchical fuzzy TOPSIS, an RFID-based system among a set of proposed RFID based-systems are selected. In the second phase, with the help of system dynamics approach, the behaviors of system for goal variables are determined. To fully understand this approach, a sample case is provided and analyzed. This type of integrated decision-making approach can provide a deep understanding of the system because of providing one or more trends on key system variables based upon the optimal decision made at the present time using an MADM tool.  Due to the fact that this research combines four fields of knowledge into an interesting research problem, of highly concerned to the users, it makes a true contribution to health, system dynamics, RFID and MADM. Integration of MADM and SD approaches in healthcare system has some very important benefits for healthcare managers. It allows managers in seeing the system behaviour now under the decision made at the present time using multi attribute decision making approach.