بانکداری یکی از مولفه های اصلی هر نظام و حکومت محسوب می شود و مدیریت صحیح و ارتقای درست آن یکی از عوامل اساسی در رشد اقتصادی کشور می باشد. بانک ها در معرض قرارگیری ریسک های متعدد و همچنین عدم کنترل هزینه های بانکی می باشند؛ در همین راستا می بایست راهکارهای مناسبی جهت بهبود عملکرد بانکها در این راستا اتخاذ نمود. یکی از این روشها، انتخاب شرکا جهت تقسیم و کاهش ریسک و به اشتراک گذاری هزینه ها می باشد؛ به طوری که بتواند ریسک درماندگی بانک را کاهش داده و میزان تسهیم بانک در کنترل هزینه ها را کاهش و منجر به رشد بانک در جهت تامین مالی و انجام امور بانکداری و در نهایت رشد اقتصادی کشور شود. در این پژوهش یک مدل چندهدفه برای انتخاب شرکا در حوزه بانکداری ارایه و در ادامه بهینه سازی آن با استفاده از الگوریتم ژنتیک چندهدفه انجام شده است. Manuscript profile

Stochastic flexible flow shop scheduling problem (SFFSSP) is one the main focus of researchers due to the complexity arises from inherent uncertainties and also the difficulty of solving such NP-hard problems. Conventionally, in such problems each machine’s job process time may encounter uncertainty due to their relevant random behaviour. In order to examine such problems more realistically, fixed interval preventive maintenance (PM) and budget constraint are considered.PM activity is a crucial task to reduce the production efficiency. In the current research we focused on a scheduling problem which a job is processed at the upstream stage and all the downstream machines get busy or alternatively PM cost is significant, consequently the job waits inside the buffers and increases the associated holding cost. This paper proposes a new more realistic mathematical model which considers both the PM and holding cost of jobs inside the buffers in the stochastic flexible flow shop scheduling problem. The holding cost is controlled in the model via the budget constraint. In order to solve the proposedmodel, three hybrid metaheuristic algorithms are introduced. They include a couple of well-known metaheuristic algorithms which have efficient quality solutions in the literature. The two algorithms of them constructed byincorporationof the particle swarm optimization algorithm (PSO) and parallel simulated annealing (PSA) methods under different random generation policies. The third one enriched based on genetic algorithm (GA) with PSA. To evaluate the performance of the proposed algorithms, different numerical examples are presented. Computational experiments revealed that the proposed algorithms embedboth desirable accuracy and CPU time. Among them, the PSO-PSAП outperforms than other algorithms in terms of makespan and CPU time especially for large size problems. Manuscript profile

A new mixed-integer multi-objective mathematical model is developed to optimize sustainable inventory-routing decisions in products agri-chain. The first aim is to optimize the network total revenue besides noticing logistics decisions related to the distribution and collection of perishable products. Also, the economical, social and environmental factors have been integrated in the proposed model. The first objective function considers some traditional terms and novel issues (e.g., postharvest biological behavior of agricultural products), which is related to deviation from ideal quality (customer's dissatisfaction) and the costs of expired products. Because old products have significant environmental impacts and require recycling, the reverse logistics framework is used to collect and bring products back to recycling. A function is applied to compute the level of deviation from suitable maturity and customer's dissatisfaction costs. A numerical example is analyzed to indicate the model's applicability by applying the ε-constraint methodology to show the opposite pattern between the two objectives. Results show that a lower level of accidents leads to lower revenue or higher costs of the supply chain. Remarking the NP-hardness of the presented model, two multi-objective meta-heuristic algorithms, namely the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Dragonfly Algorithm (MODA) are used to explore near-optimal solutions for medium and large-sized problems. Results show a better performance of the NSGA-II. Furthermore, the sensitivity analysis is presented and explained in four parts to show the trend of the proposed model by fluctuations on important parameters. Manuscript profile

Logistics in upstream oil industry is a critical task as rigs need consistent support for ongoing production. In this paper, a multi-period, multi-product and multi-hub routing and scheduling model is presented for offshore logistics problem. As rigs can be served in specific time intervals, time windows constraints are considered in the proposed model. Despite classic VRP models, vessels are not forced to return hubs at the end of duty days. Also, a vessel may leave and return back to hubs several times during the planning horizon. Moreover, the model determines which vessels are applied in each day. In other words, a vessel may be applied in some days and be inactive in other days of planning horizon. To develop a compromise model, fueling issue is considered in the model. As a rig can be supplied by different vessels in real world cases, the proposed model is split delivery. Based on these challenges and contributions, this research deploys an integrated optimization of routing and scheduling of vessels for offshore logistics. This paper deals with a combinatorial optimization model which is NP-hard. Hence, Genetic Algorithm is applied as the solution approach. The average gap between objective functions of GAMS and GA is only 1.18 percent while saving CPU time in GA is much more than GAMS (about 78.16 percent on average). The results confirm the applicability and efficiency of the GA. Manuscript profile