Naeme Zarrinpoor

Bio: Naeme Zarrinpoor is an academic researcher from Shiraz University of Technology. The author has contributed to research in topics: Supply chain & Sustainability. The author has an hindex of 4, co-authored 16 publications receiving 120 citations. Previous affiliations of Naeme Zarrinpoor include Alzahra University & Yazd University.

A competitive location model to obtain a specific market share while ranking facilities by shorter travel time.

Abstract: Competitive location deals with the problem of locating new facilities to provide a service (or goods) to the customers of a given geographical area where other facilities (competitors) offering the same service are already present. The new facilities will have to compete with the existing facilities for capturing the market share. This paper proposes a new model in which a new entering firm desires to obtain a specific percentage of the market share in such a way as to minimize the total costs. The total costs consist of costs earmarked for installing the new located facilities and traveling as well as the waiting costs of the customers who receive service from the facilities. Two heuristics based on genetic algorithm and tabu search are proposed to solve the model. A number of numerical examples are designed and solved in order to assess the performance of the proposed heuristics.

Optimal design of a sustainable natural gas supply chain network under uncertainty

Abstract: This study proposes a multi-objective mathematical model to design a natural gas supply chain network with a focus on sustainable development goals. Based on the economic objective of the proposed model, an attempt is made to minimize the whole supply chain costs. The environmental objective minimizes the amount of greenhouse gas emissions, electricity consumption, gas consumption, water consumption, and wastewater generation. The social objective also considers balanced employment, balanced economic development, employee welfare, employee safety, customers cost risk, and operational environment risk. Because parameters in the real world are uncertain and can rarely be estimated precisely, the uncertainty of the model parameters is taken into account. A four-stage solution approach is used to solve the proposed model. In the first step, the best-worst method is used to weight the layers in geographic information system (GIS) software. In the second step, the suggested city gate stations are determined in GIS software. In the third step, a robust possibilistic programming method is used to deal with the uncertainty of the parameters. Finally, in the fourth step, the Torabi–Hassini method is used to transform the multi-objective model into a single-objective one. In order to evaluate the efficiency of the proposed model, a real case study is conducted on Fars Province Gas Company. Numerical results confirm the efficiency of the proposed model, revealing that the uncertainty associated with the model parameters cannot be ignored because system costs are significantly affected under uncertainty. Moreover, the capacity parameter of the suggested city gate station could have a significant effect on system costs while the largest share of supply chain costs is related to transmission fixed costs.

Joint Inventory-Location Problem Under Risk of Probabilistic Facility Disruptions

Abstract: This paper studies a reliable joint inventory-location problem that optimizes facility locations, customer allocations, and inventory management decisions when facilities are subject to disruption risks (e.g., due to natural or man-made hazards). When a facility fails, its customers may be reassigned to other operational facilities in order to avoid the high penalty costs associated with losing service. The authors propose an integer programming model that minimizes the sum of facility construction costs, expected inventory holding costs and expected customer costs under normal and failure scenarios. The authors develop a Lagrangian relaxation solution framework for this problem, including a polynomial-time exact algorithm for the relaxed nonlinear subproblems. Numerical experiment results show that this proposed model is capable of providing a near-optimum solution within a short computation time. Managerial insights on the optimal facility deployment, inventory control strategies, and the corresponding cost constitutions are drawn.

A soft-computing Pareto-based meta-heuristic algorithm for a multi-objective multi-server facility location problem

Abstract: In this paper, a novel multi-objective location model within multi-server queuing framework is proposed, in which facilities behave as M/M/m queues. In the developed model of the problem, the constraints of selecting the nearest-facility along with the service level restriction are considered to bring the model closer to reality. Three objective functions are also considered including minimizing (I) sum of the aggregate travel and waiting times, (II) maximum idle time of all facilities, and (III) the budget required to cover the costs of establishing the selected facilities plus server staffing costs. Since the developed model of the problem is of an NP-hard type and inexact solutions are more probable to be obtained, soft computing techniques, specifically evolutionary computations, are generally used to cope with the lack of precision. From different terms of evolutionary computations, this paper proposes a Pareto-based meta-heuristic algorithm called multi-objective harmony search (MOHS) to solve the problem. To validate the results obtained, two popular algorithms including non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are utilized as well. In order to demonstrate the proposed methodology and to compare the performances in terms of Pareto-based solution measures, the Taguchi approach is first utilized to tune the parameters of the proposed algorithms, where a new response metric named multi-objective coefficient of variation (MOCV) is introduced. Then, the results of implementing the algorithms on some test problems show that the proposed MOHS outperforms the other two algorithms in terms of computational time.

Examining how manufacturing corporations win orders

Abstract: This study adopted 14 criteria for order-winners and qualifiers as the attributes for evaluation. The first stage used a simultaneous importance-performance analysis to analyse the competitive market situations of a corporation and its competitors. The second stage used the decision-making trial and evaluation laboratory method to analyse the attributes' causal relationships and levels of influence; then two methods of analysis were integrated to analyse and re-formulate the competitive strategies for the winning orders. As well as serving as a novel theory-based method to examine how manufacturers win orders, the proposals in this study can be applied to practical industry experiences. Veertien kriteria vir mededinging word vir attribuut evaluering voorgehou. Ten aanvang word gelyktydige vertonings gemeet ten opsigte van mededingers. Tweedens word 'n lukraakmetode aangewend by die evaluering van attribute se kousale verhoudings en invloedspeile vir mededingingspeile om sodoende bestellings te bekom. Die resulterende voorstelle kan in die praktyk toegepas word.