masoud rabani

One of the constant problems that people with mental health conditions are faced with now is that they cannot establish a good relationship with their therapist, or the client's disease type is not in the therapist's specialty. These clients may not receive adequate treatment and stop the therapy before feeling well. Therefore, the classification of mental patients based on their disorder types and allocating a therapist with the same expertise to them could lead to better treatment and improve the quality of the therapy sessions. This paper will compare several machine learning (ML) algorithms to classify patients with mental conditions. Moreover, benefiting from the best ML algorithm, patients will be categorized into different classes based on their disorder types. Finally, a mathematical model will be developed to determine the allocation policy of therapists to each group of patients to maximize the summation of the utilization between therapists and patients. To explore the implementation of the proposed method, we have conducted a real-life case study to assess the validation of the model.

The perishable dairy industry has to deal with multiple challenges such as demand forecasting, price fluctuations, lead time, and inflated orders along with difficulties of climatic and traffic conditions, storage areas and shipment in unfavorable circumstances. This research introduces a robust bi-level mathematical model to optimize a multi-echelon Perishable Supply Chain (PSC. To this end, integrated multi-objective Mixed Integer Linear Programming (MILP) models are developed to formulate the problem. stochastic deterioration rate is taken into account as the main factor that determines model performance due to perishability of products. In order to contribute to the literature, mainly by addressing uncertainty and perishability, a solution technique based on robust programming and -constrait approach is developed to accommodate suggested bi-level model. This technique can deal with problem uncertainty while also ensuring the robustness of the overall system. Sensitivity analysis is implemented along with three well-known quality indicators to assess the performance of the proposed solution method and quality of obtained solutions. Finally, real case study is provided using the CPLEX solver to showcase the applicability of the proposed methodology and discuss the complexity of the model. Results demonstrate the efficiency of the proposed methodology in finding optimal solutions.

Nowadays, in the competitive global market, increasing market share is the main objective of the most manufacturers, however, customization, service speed, customer satisfaction, and environmental problems are vital factors that manufacturers ought to consider to expand their market share.so, the supply chain management can be applied as a proper approach to optimize these factors in whole supply chain to benefit the supply chain members. In this way, the current paper addresses an integrated production and distribution model with combination of Stackelberg competition and Make-to-order production system in different periods. In addition, this model wants to investigate how discounts impact the chain's profits with presence of competition and Make-to-Order production system. This study uses a modified Non-Dominated Sorting Genetic Algorithm II (NSGA-II) approach to solve the medium and large cases model because of the NP-hardness feature. Additionally, the model is applied to Furniture Company to demonstrate its efficacy and validity and results are provided. According to the obtained results, the modified algorithm has better performance in solving model in medium and large-scale cases. The proposed model would be beneficial to increase network efficiency by integrating production-distribution planning.

Nowadays with regards to the special role of blood supply chain facilities, the importance of blood donation centers establishment with environmental standards compliance is considerable. With the increase in population and urban sprawl, Alborz Blood Transfusion center has been looking for a suitable location to establish blood donation centers in order to providing maximum service to donors, in addition to reduce environmental damage. In this study, to find the appropriate location for blood donation centers, environmental criteria have been considered. The purpose of this study was to determine the number and establishment of blood donation centers using combined VIKOR and mathematical programming approach in order to blood donation centers have the highest level of service. From the 12 existing regions, Only 6 candidate regions were selected to determine the appropriate location for the establishment of blood donation centers using the VIKOR method as a multi-criterion decision making approach. Also, by solving the proposed mathematical model, 4 regions were selected to locating a blood donation centers that providing maximum service to donors in the regions.

Contrary to the past that inventory decisions and pricing are taken into consideration separately, due to the influence of these decisions on each other and thus profit, researchers have investigated these two issues simultaneously. Sometimes, wholesalers offer incentive financial policies to their customers in order to increase their sale. In this paper, a different combined model of inventory control and the way of its pricing for a deteriorating item with different incentive schemes including totally advance payment and partially advance, partially delayed payment are developed. We adopt a demand function jointly time and price-dependent and a backordering rate waiting time-dependent. Also shortage of allowable inventory considered. In each case, optimum price, replenishment cycle, the time with no shortage are obtained. Sensitivity analysis is performed and represented in several figures and tables. The results show that with increasing deterioration and backordering rates, the total annual profit is reduced.

The importance of order promising process has led manufacturers to use more productive production systems. Optimizing the production system is one of the ways to increase productivity. This issue becomes even more significant when some of the raw materials needed to produce different final products are homogenous. In this paper, a decision structure for the order promising process with product homogeneity and product substitution in a Hybrid Make-To-Stock and Make-To Order environment is studied. For this purpose, a bi-objective mathematical model has been designed and solved by the Lagrangian Relaxation solution method. Despite the extensive studies that have been done in this area, there are few articles that have studied the possibility of substituting the final products by the manufacturer. In order to investigate this gap, product substitution has been studied in this article. Two different types of customers are considered in this model. A case study is also conducted to evaluate the applicability of the proposed model. The results of this article show that the possibility of products substitution will reduce rejected orders and increase system profits. Also, fulfilling orders that are more flexible in terms of product delivery time is a higher priority for the manufacturer than other orders.

The post-disaster response phase aims to reduce casualties by accessing critical areas to transfer relief aid, search and rescue operations to the injured as soon as possible. Debris from the disaster blocks roads and prevents rescue teams from reaching critical areas. It is crucial to decide which routes should be cleared for relief aid transportation to reduce the negative effects of the disaster. In this study, a model for debris removal is presented to minimize access time to critical areas such as hospitals and maximize coverage of the areas. The AUGMECON 2 method has been used to solve this problem. Also, the efficiency of this solution method in Tehran has been studied, and its results have been analyzed. The results of this study indicate the importance of considering a comprehensive plan and several sites for debris removal in the disaster response phase.

The last decade has seen numerous studies focusing on the closed-loop supply chain. Accordingly, the uncertainty conditions as well as the environmental impacts of facilities are still open issues. This research proposes a new bi-objective mixed-integer linear programming model to design a closed-loop supply chain tire remanufacturing network considering environmental issues that improve performance in conditions of uncertainty associated with the tire industry. This model seeks to maximize the total profits of the network, including customer centers, collection centers, recycling centers, manufacturing/remanufacturing plants, distribution centers, and on the other hand, is looking to minimize environmental impact all over the supply chain network. Another novelty of the proposed model is in the solution methodology. By using an exact approach, the augmented ε‑constraint method, and meta-heuristic algorithm, a well-known Grasshopper Optimization Algorithm (GOA), optimal and Pareto solutions have been obtained for medium and large size sample problems. We analyze the effectiveness of these meta-heuristics through numerical experiments. Also, sensitivity analysis has been provided for some parameters of the model. Finally, the results and suggestions for future research are presented.

In this study, supply chain network design is considered. Responsibility and profitability are the company's main features, so we proposed a model to maximize profit, reducing logistic costs, especially shortage costs, to increase responsibility. Adopting the right sales policy by a seller is an issue that needs to be addressed. In this research, we try to improve sales conditions based on each channel's capacity by considering the all unit discount policy for each channel's sale. We used a price-dependent demand function to bring the situation closer to the real world. We considered the factor of advertising and inflation on demand in this issue. A mixed-integer nonlinear programming model is introduced for the problem and formulated in GAMS software; then, sensitivity analysis examines some parameters.

Proper transportation and distribution of commodities plays a pivotal role in the expenditures of supply chains. In this paper, a clustered vehicle routing problem with pick-up and delivery is studied. A fleet of distinct vehicles is concurrently responsible for distribution of medicines and collection of their wastes. Collected wastes should be sent to a waste center. To solve the problem, a bi-objective mathematical model is presented. Fairness of travelled distances among drivers and transportation expenses are two objective functions considered in the model. Since the proposed problem is NP-hard, a three-step hybrid approach is developed to solve the problem. First, K-medoids clustering algorithm allocates customers to subsets based on their coordinates. Second, a mathematical model is used for routing vehicles within each cluster. Third, NSGA-II is used to produce final result using the outcome of step 2. Extensive numerical results indicate the superiority of the proposed approach against the NSGA-II.

Designing a biofuel supply chain plays an important role in the reduction of biomass transportation costs. This study aims to present a comprehensive decision support tool (DST) for designing of the integrated biodiesel supply chain (BSC). In addition, so far no research has been found that examined hybrid first/second generation of biodiesel with considering all economic, environmental and social costs. In achieving this goal, we developed a new optimization model using mixed integer linear programming with the objective of maximizing the total profits of BSC incorporating environmental and social costs.  To do so, practical constraints including the limit of biomass, the capacity of technologies, the land availability, and especially limited capacity of each transportation vehicles are applied to this mathematical model. The main purpose of this study is to develop a DST to evaluate the commercial feasibility of BSC with focusing on multimodal and reliable transport.  To illustrate the capability of the proposed model, Iran is considered as a real application. The findings of this study indicate that some factors such as biomass availability, transportation reliability, and biofuel price can play as a pivotal role in this supply chain design and optimization. All in all, 31% increase in amount of produced biodiesel leads a marginal increase in environmental-related costs.

This paper presents a novel multi-objective location arc-routing model in order to locate disposal facilities and to design optimal routes of residential waste taking into consideration many complicated real constraints such as a heterogeneous fleet of vehicles, time windows for customers, disposal facilities and the depot, capacities for vehicles and facilities. The first objective is the minimization of transportation costs, including service costs and fuel costs of vehicles. The second one minimizes total number of utilized vehicles. And finally, the third objective function is considered for minimizing total number of established disposal centers. Moreover, to come closer to reality the service time, amount of demands, capacities and cost parameters are considered as fuzzy ones. To solve the proposed model, a credibility-based fuzzy mathematical model and its interactive solution method with three recent approaches, are used and the results are compared with each other.

Planning the freight flow from the plants to the customer zones is one of the most challenging problems in the field of supply chain management. Because of many traffic regulations, oversize/overweight vehicles often are not permitted to enter city boundaries. Therefore, intermediate facilities (city distribution centers) play a very important role in distribution networks. Accordingly, in this paper, transportation of goods from the plants to the customers is considered an integrated process containing two phases, namely, transportation from plant to distribution centers and distribution from city distribution centers to customers using small and environmentally-friendly vehicles. The Transportation Location Routing Problem (TLRP) studied can be considered as an extension of the two-echelon location routing problem. Minimizing the operational costs, and the workload balancing of the heterogeneous fleet in the distribution phase are considered as the two objective functions. A Mixed Integer Programming (MIP) model, as well as two solution approaches, based on Multi-objective Particle Swarm Optimization Algorithm, and Non-dominated Sorting Genetic Algorithm, is presented for the problem. In order to illustrate the efficacy of the proposed methods, they have been implemented on test problems of different sizes. The results show the methods are able to produce efficient solutions in a reasonable amount of time.

This paper proposes a mixed integer programming model to solve a non-identical parallel machine (NIPM) scheduling with sequence-dependent set-up times and human resiliency engineering. The presented mathematical model is formulated to consider human factors including Learning, Teamwork and Awareness. Moreover, processing time of jobs are assumed to be non-deterministic and dependent to their start time which leads to more precision and reality. The applicability of the proposed approach is demonstrated in a real world car accessories industrial unit. A hybrid metaheuristic method based on Genetic algorithm (GA) and simulated annealing (SA) is proposed to solve the problem. Parameter tuning is applied for adjustment of metaheuristic algorithm parameters.The superiority of the proposed hybrid metaheuristic method is evaluated by comparing the obtained results to GAMS, and two other hybrid metaheuristics. Moreover, it is shown that the hybrid approach provides better solutions than other hybrid approaches.

In a supplier-retailer-buyer supply chain, the supplier frequently offers the retailer a trade credit of periods, and the retailer in turn provides a trade credit of periods to her/his buyer to stimulate sales and reduce inventory. From the seller’s perspective, granting trade credit increases sales and revenue but also increases opportunity cost (i.e., the capital opportunity loss during credit period) and default risk (i.e., the percentage that the buyer will not be able to pay off her/his debt obligations). Hence, how to determine credit period is increasingly recognized as an important strategy to increase seller’s profitability. Also, many products such as fruits, vegetables, high-tech products, pharmaceuticals, and volatile liquids not only deteriorate continuously due to evaporation, obsolescence and spoilage but also have their expiration dates. In this paper along with deterioration and expiration date, we consider shortages that are very rarely investigated by researches. Therefore, this paper proposes an economic order quantity model for the retailer where: (a) the supplier provides an up-stream trade credit and the retailer also offers a down-stream trade credit, (b) the retailer’s down-stream trade credit to the buyer not only increases sales and revenue but also opportunity cost and default risk, (c) deteriorating items not only deteriorate continuously but also have their expiration dates and (d) there is a shortage allowed in each time period. We then show that the retailer’s optimal credit period and cycle time not only exist but also are unique. Furthermore, we discuss several special cases including for non-deteriorating items. Finally, we run some numerical examples to illustrate the problem and provide managerial insights.

Annually 70000 people are being killed all around the world and around 200 million encounter natural and humanmade
disasters. Thus crisis management and especially the effective management of relief logistic activities and
evacuation of the injured from incident areas seems vital. Some important activities when a disaster occurs include
relief logistic activities and delivering these items to the injured people and moving them to medical centers. Military
forces due to their potential and intrinsic capabilities as an aiding force play a key role in disaster response activities. In
recent years the role of armed forces in response to crises has increased due to: the growth in the frequency and scale of
natural disasters, increasing interest of the armed forces to take part in crises response, increasing tendency of humanitarian
responses towards the militarization. In this paper a two-stage approach is used to optimize the transferring of
relief goods to incident areas and evacuating the injured people therefrom, considering the role of armed forces. First,
a numerical model is developed in which vehicles are accounted for by integer rather than binary variable. This model
is used to determine the number of goods and transferred people. Then by using a linear equations system, the exact
instruction for vehicles is determined. In the proposed model the degree of injury of victims and the possibility of their
survival is considered. Also in mixed integer model, the location of temporary medical centers and Local Distribution
Centers (LDCs) near the incident areas are determined. Armed forces available facilities, including personnel and
vehicles that can be effective in relief logistics, are considered. The ultimate objective function tries to maximize the
number of survivors. The proposed optimization approach is applied to East Azarbaijan earthquake in 2012, however,
it is capable of being applied to any other similar situation

This study proposes a new multi-item inventory model with hybrid cost parameters under a fuzzy-stochastic constraint and permissible delay in payment. The price and marketing expenditure dependent stochastic demand and the demand dependent the unit production cost are considered. Shortages are allowed and partially backordered. The main objective of this paper is to determine selling price, marketing expenditure, credit period, and variables of inventory control simultaneously for maximizing the total profit. To solve the problem, first some transformations are applied to convert the original problem into a multi-objective nonlinear programming problem, of which each objective has signomial terms. Then, the multi-objective nonlinear programming problem is solved by first converting it into a single objective problem and then by using global optimization of signomial geometric programming problems. At the end, several numerical examples and sensitivity analysis are done to test model and solution procedure and also obtain managerial insights.

Nurse Rostering Problem (NRP) or the Nurse Scheduling Problem (NSP) is a complex scheduling problem that affects hospital personnel on a daily basis all over the world and is known to be NP-hard.The problem is to decide which members of a team of nurses should be on duty at any time, during a rostering period of, typically, one month.It is very important to efficiently utilize time and effort, to evenly balance the workload among people and to attempt to satisfy personnel preferences.With demand ever fluctuating, designing a timetable to definea work schedule for each nurse is not an easy task.A NRP deals with a very high number of constraints.A lot of big healthcare organizations around the world still construct nurses’ duty roster manually.Many optimization algorithms have been proposedto solve NRPs such as exact algorithms and (Meta)heuristic algorithms. In this paper we propose an approach that use the graph theory concept to solve the problem. We use the graph coloring and bipartite graph concept. In our approach we first formulize the problem and solve it with exact algorithm and then by using the graph concept, the solution is improved. Finally by results obtained from the graph approaches the final timetable is available.In in order to validate the proposed approach some problems with different scales are solved. We solved the problems for 30, 40, 45 and 50 nurses. In all problems the proposed approach is efficient and for instance the relationship between the nurses are presented.

This research addresses the mixed-model assembly line (MMAL) by considering various constraints. In MMALs, several types of products which their similarity is so high are made on an assembly line. As a consequence, it is possible to assemble and make several types of products simultaneously without spending any additional time. The proposed multi-objective model considers the balancing and sequencing problems, simultaneously. Based on the assembly problem, the various tasks of models are assigned to the workstations, while in the sequencing problem, a sequence of models for production is determined. The two meta-heuristic algorithms, namely MOPSO and NSGA-II are used to solve the developed model and different comparison metrics are applied to compare these two proposed meta-heuristics. Several test problems based on empirical data is used to illustrate the performance of our proposed model. The results show that NSGA-II outperforms the MOPSO algorithm in most metrics used in this paper. Moreover, the results indicate that our proposed model is more effective and efficient to assignment of tasks and sequencing models than manual strategy. Finally, conclusion remarks and future research are provided.

The purpose of response phase after a disaster is to reduce mortality by accessing affected areas in order to transport relief items and search and rescue activities as soon as possible. Debris caused by the disaster blocks the roads and prevents emergency aid teams to access the disaster affected regions. Deciding which roads to clean in order to transport relief items is crucial to diminish the negative effect of a disaster on human health. In this paper we developed a mixed integer mathematical model for the debris clearance problem considering multi-depot and multi-vehicle. The aim of our model is to minimize the maximum walk of the vehicles. For decreasing the computational time, we developed a two-phase heuristic based on simulated annealing. Validity and applicability of the proposed model and solution algorithm is tested and analysed in a region of Tehran, Iran. The results of this experiment underlie the importance of multi-depot and considering a comprehensive plan for the debris removal problem in the response phase.

In this paper، we consider a multi-site production planning problem subject to uncertainty in demand and workforce expenses. In our new mathematical model، we presented a production planning system considering failure in rework and breakdown. We also survey human workforce allocation and its expenses which are considered uncertain due to some tradeoff between company’s benefits and workforce union’s advantages. We presented a new robust particle swarm optimization to propose a model with the ability of handling uncertainties. Firstly، we apply the presented robust optimization to handle demand uncertainty separately، and then we extended our model to regard both uncertainties simultaneously. To show the practicability of the proposed algorithm، we solved a real problem in an industrial case study. We also analyzed the most important parameters in the presented robust model to find out which level of uncertainty has less constraint violation and determine the maximum budget of uncertainties that could be considered in the proposed model to expect acceptable optimal objective. The results showed that the proposed model can prepare a promising approach to fulfill an efficient production planning in a multi-site production planning.