علی حسین زاده کاشان

The order-picking problem is important as one of the warehouse's logistics activities. This problem is defined as collecting orders from different warehouse locations to respond to customers' orders quickly. This paper aims to provide a multi-objective mathematical programming model for integrating the decisions of batching, routing, and scheduling of selectors with the packaging problem in a multi-warehouse environment. The objective functions include depreciation of the delivery times and total order picking costs.

In this research, first, by reviewing the literature in the field of order picking, the research gaps of the problem have been identified. Then, taking into account the main constraints of the problem, a multi-objective mathematical model has been formulated for the multi-warehouse order-picking problem. The classic Benders decomposition algorithm and the accelerated Benders decomposition algorithm have been used to solve the problem. The data related to the warehouses of a company producing sanitary products in Iran was used as a case study to validate the applicability of the proposed model, and its results were reported in the article.

The proposed model's results indicate that CPLEX can solve these problems up to small sizes in an acceptable time. Also, the numerical results show the performance of the Benders decomposition algorithm and the accelerated Benders algorithm as suitable alternatives for solving the model in large-sized problems. The calculation results obtained from the implementation of the solution methods for the proposed model showed that in terms of the number of iterations and the calculation time, the accelerated Benders algorithm had better results than the classic Benders algorithm.

Originality/Value:

 In this research, the order-picking problem with the integrity of operational decisions has been formulated as a multi-objective mathematical model for a multi-warehouse environment for the first time. Also, in this article regarding the solution method, exact solution approaches have been used for the first time considering the structure of the problem. The computation results show that the proposed algorithms are efficient and suitable methods for problem-solving.

In this research due to the importance of the U-shaped assembly line balancing and, on the other hand, the importance of human factors and setup times, we want to develop a bi-objective mathematical model minimize the cycle time and the total cost.

Since the research problem is shown to be NP-hard, NSGA-II, which is a population-based algorithm, and also SPEA-II are used to solve the problem.

A mathematical model for the problem on hand is developed. We solve the problem using NSGA-II and SPEA-II. We use four criteria for analyzing the results of the mathematical model and evaluating the performance of the multi-objective evolutionary algorithms. The experimental results demonstrate that NSGA-II is superior to SPEA-II.

Originality/Value: 

A bi-objective mathematical model for the U-shaped assembly line balancing problem considering setup-times and workers' skill is developed, and the problem is solved using two algorithms.

In today’s competitive business, companies are continuously looking for new ways to reduce their costs and improve their products’ quality. A company needs various resources to stay in the market and it can be challenging to provide these resources for small and medium-sized companies. Therefore, companies share their capacities with each other to achieve their common goals. This sharing can be done under the title of a virtual enterprise. A virtual enterprise is an economic alliance in which several companies share their capabilities to provide specific products and services. Virtual company’ advantages over the traditional companies include flexible structure and quick response to the market. VE should provide the situation for manufacturing products and services at a lower cost and higher quality with less risk and shorter lead times. So, partner selection has become a vital task for companies. The success of a virtual company is dependent on the selection of partners.In this study, a new partner selection model is presented with a VE concept integrated with collaborative transportation. The proposed model is presented in deterministic state. A numerical example is analyzed to investigate model behavior, and the results are presented and discussed. Finally, the sensitivity of the objective function is analyzed by some parameters and the results show that by increasing two parameters, the maximum number of sub-projects carried out by a partner and the transportation capacity, the total costs either decrease or remain unchanged.

E-commerce is an emerging field that is used in online trading of stocks, bonds, online banking, electronic services, etc. Since the factors of growth and survival in businesses have changed, identifying the needs of customers is one of the main concerns of every organization. On the other hand, with the advancement of technology, users try to solve their need for technology through tools and software installed on mobile phones. This causes mobile applications to be more popular than websites among users. Internet technologies allow users to share their preferences and feelings with other people, which will have a significant impact on users' decisions. This research is basic in terms of audience, descriptive in terms of objective and cross-sectional in terms of time. The purpose of the research is to identify the components related to the satisfaction of traders in online exchanges by analyzing their feelings in the space of online businesses and ranking their criteria using the method of fuzzy cognitive maps. For this purpose, we used the TF-IDF method, in order to identify the effective criteria for selection of online exchanges and by using association rules and fuzzy recognition map, the causal relationships and weights between users' needs were determined. The finding show that out of the 45 extracted criteria, only 19 Criteria have a role.and by using the weight of the criteria extracted from the method of fuzzy cognitive maps, the effective criteria in the selection of online exchanges were ranked. Internet business developers can identify their strengths and weaknesses according to these features and be more successful in the competitive environment of their business.

The globalization of the economy and the improvement of data innovation has caused the supply-oriented market to alter to the demand-oriented market. Furthermore, organizations ought to prioritize the desires of clients to proceed and survive in the competitive market, which requires the presence of supply chain management. Considering the wide assortment of products and today's competitive market, choosing the correct strategies to provide the required services according to a few primary components such as price, quality level, and the warranty period is one of the most concerns of company managers and dealers. This study sought to consider the increase in the profit of each part of the chain along with providing appropriate services and keeping up product quality, extracting and applying suitable implementation strategies. It also tried to present pricing strategies in a two-echelon supply chain comprised of one manufacturer and two competing retailers, with warranty period, quality, and price-dependent demands.
The strategies presented and discussed in this research have several implications; in addition; their improvements are practical. The profit functions of the manufacturer and the retailers were maximized under centralized and decentralized approaches. Mathematical models were developed in four distinctive cases (i) two non-cooperative frameworks, (ii) a channel-cooperative framework, and (iii) a global-cooperative framework. The producer intended to maximize his benefit by setting a diverse wholesale price for each of the retailers and the length of the common warranty period. On the other hand, retailers maximized their profit by setting the selling price for consumers. This can be explained through the models related to general cooperation strategies, cooperation within the channel, and non-cooperation (Stackelberg of the leading producer and Stackelberg of the leader retailers), furthermore; the profit function of each case and its formula is presented.
For each case, the values of the main decision variables and the profit of each member of the supply chain were calculated and appeared in a numerical case. Moreover, the total profit was compared with each other through two solution methods i.e., the Mathematica computer program and the Championship Algorithm in sports leagues (LCA). The championship algorithm in sports leagues was presented as a population-based algorithm for worldwide search in continuous space and inspired by sports competitions within the real world. During this algorithm, distinctive solutions that can be given to a problem were compared and each one was improved based on its suitability, last; a solution close to the optimum was selected.
A research gap was recognized by studying the research conducted within the field of pricing, warranty, and product quality using the game theory approach. On the other hand, the models were developed by considering all decision variables at the same time as well as between a manufacturer and two competitive retailers deciding on the retail price to maximize their profit. In this regard, four models of Stackelberg manufacturer leader, Stackelberg retailer leader, participation within the channel, and overall cooperation were considered. In all models, the LCA algorithm resulted in better answers compared with Mathematica software. In this study by investigating four techniques, we realized that the best reply can be achieved through the cooperation strategy which is more profitable than the others. Finally, sensitivity analyses were also performed on various model parameters.

Since humans have begun to fly and construct various aircraft, the occurred accidents and incidents ‎have always resulted in irreparable injuries and damages. Therefore, today, aviators especially airlines ‎continuously trying to maintain the maximum level of safety and reduce the rate of accidents by ‎spending resources. In this research, by analyzing the available reports and information for 46 accidents ‎and serious incidents in Iran's civil aviation between 1979 and 2021, first, a statistical-analytical ‎evaluation is performed, then influential factors like failures and errors in these occurrences are analyzed ‎by using failure mode and effects analysis and multi-attribute decision making. The statistical population of this research includes a panel of experts and experts in the field of aviation and air accidents, and a questionnaire was used to collect the required information. According to findings, ‎human is the most important factor, Fokker100 is the most engaged aircraft and pilots with low ages and ‎experiences are the most vulnerable factors in mentioned occurrences; in addition, failures in landing ‎system and engines and also the pilot error in not following the structures were the most important ‎factors.‎

Emphasizing the importance of competition and cooperation in supply chains caused a resurgence of game theory ‎as a tool for the analysis of interactions in a supply chain. The development of the business and the ‎complexity of retailing requires a change in the advertising approaches. Affiliate advertising is one of the ways that ‎manufacturers and distributors can jointly participate in advertising programs. Therefore, a variable is defined as ‎the participation rate, which is a percentage of local advertising costs that the producer agrees to pay. In this ‎research, we consider cooperation in advertising during two different advertising function models along with pricing ‎decisions, warranty period and quality level, in a two-level supply chain. Therefore, demand will be affected by ‎price, advertising, warranty period and quality level. Manufacturer and retailer can have cooperative advertising. ‎The theory of non-cooperative and cooperative games is the tool used to solve this problem. Due to the complexity ‎of the model, a meta-heuristic algorithm, the optics inspired optimization, which is a population-based search ‎algorithm, is used to solve the problem. In this research, it was observed that the sum of the profits claimed by both ‎parties in the non-cooperative game is smaller than the profit of the whole system in the cooperative game, and the ‎profit of each individual is also lower in the non-cooperative state than in the cooperative state. Therefore, ‎cooperation between two players increases their profits. Also, sensitivity analysis has been done on some ‎parameters, including parameters related to product quality.‎

In bold investment, obtaining a high yield from the place of investment depends on the success of investable companies, and in addition to attracting capital and financing, helping the business flourish is another goal of bold investment. The upcoming research, with the aim of studying fintech businesses, tries to provide a correct understanding of its situation in Iran. After conducting library studies and with the benefit of 20 experts, using the fuzzy Delphi technique, the key factors affecting the value of investment in the field of fintech, with the titles of the level of innovation created, environmental assurance, technological interactions, management and Applied knowledge sharing, competitive advantage created, expert human capital, financial and insurance support, advertising and marketing skills as well as liquidity and profitability were identified. The weights of the criteria were done using the Swara fuzzy method and their ranking was done using the Cocoso fuzzy technique. The results showed that the factors of the level of innovation created, financial and insurance support, and expert human capital are ranked first to third among the factors affecting bold investment in the field of fintech, respectively. Next, by analyzing 180 questionnaires distributed among 28 active companies in the field of fintech, the effect of the confirmed variables on the bold investment variable was investigated.

This study attempted to review the articles and explore the gaps and challenges in the areas of resilience, business continuity, risk, and process safety with the aim of providing several directions for future research to understand different research directions in these areas with different perspectives. In addition, in this study, the relationship of articles in these areas with each other was examined. In this research project, related studies were reviewed and reported to identify presented frameworks, models, and methods for them. In the first phase, the articles were divided into three categories according to their similarity, namely “maximizing the value of business continuity and resilience,” “maximizing process safety and the effect of risk and resilience factors,” and “minimizing risk and effect of uncertainty.” In the second phase, the appropriate conceptual frameworks titled “research house” based on resilience, business continuity, and risk categories were created for each category. In the third phase, 22 closed codes were obtained by carefully reviewing the articles, and their co-occurrence network was investigated. The main findings of this article were categorizing the studied articles, providing conceptual frameworks resulting from article analysis, and presenting a conceptual model.

As time goes on and crises increase in societies, organizations are increasingly exposed to disruption. These crises can be of natural (such as earthquakes, floods, and fires) or human (such as terrorist attacks, infectious diseases, and intentional or inadvertent employee errors). Therefore, organizations need to be resilient to protect themselves from harmful consequences. The basic aspect of resilience involves the ability of an element to return to normal after disruption and resource allocation. Obviously, in any organization, the primary goal is to allocate the least resources to recover operations and to bring activities back to the tolerance threshold so that destructive events do not stop vital activities. In this paper, a quantitative model for resource allocation is presented, which minimizes the lack of resilience. The problem has a basic assumption, that there is a shortage of resources in at least one of the available resources due to excessive demand. After solving the model by numerical experiment, the results of the model were described and it was found that destructive events were retrieved before the tolerance threshold.

Order picking operation is one of the most well-known labor and cost intensive internal logistics processes. Withdrawal of the order in response to customer need is defined in order to collect a set of orders from storage zone in the shortest possible time. The purpose of this research is to provide a scientific and practical basis considering the constraints that enforce to achieve an acceptable level of performance in order picking systems. This is done by building a Mixed Integer Linear Programming (MILP) formulation and developing an adapted solution method suited to the structure of the problem

First, by reviewing the literature in the field of order picking systems, sufficient knowledge has been obtained at the operational level, and with emphasis on warehouse management constraints, a MILP formulation is proposed by integrating order batching and picker routing. After validating the model and solving it through GAMS software, due to the nature of the problem, which is an NP-hard type, the problem is solved with an efficient algorithm, which is a grouping version of the league championship algorithm, and the results are compared. To develop the algorithm, operators are fit to the specific structure of the problem, i.e., the assignment of orders (items) to order pickers (groups)

Developing a multi-period MILP formulation for multi-trip picker routing, assuming for the first time the possibility of product replenishment and limited access to pickers. For large-scale problem instances, the league championship algorithm is used. The results indicate the effective capability and efficiency of this algorithm for solving large test problem instances.

The issue of multi-period order picking and multi-trip routing of pickers is considered for the first time ‎in this paper. Because of the limited number of pickers, this must be taken into account in modeling. ‎The assumption of product replenishment is also considered for the first time in this article and its ‎modeling has been done. In this way, orders enter the warehouse over time, during different periods, ‎and are placed in a predetermined positions. The limited access to pickers in each period is also ‎discussed for the first time in this paper. Finally, the objective function of minimizing the total ‎tardiness, which is in line with the needs of the industry, is also introduced in this paper. Regarding the ‎solution method, a league championship metaheuristic algorithm is presented which takes into ‎account the problem structure (which corresponds to the structure of grouping problems) and ‎solution generation operators have been developed to maintain the new solution.‎

In CNC machines, changes in machining conditions such as speed and feed rate will also change the operating time. Changes in these conditions also result in changes in the production cycle time and production costs. Tool life is also sensitive to these changes. Appropriate machining time is generally determined by assuming a certain lifetime for CNC machine tools to minimize production costs. However, minimizing costs usually results in increased machining time and lower output rates.

In this research, the optimal machining time is determined using a bi-objective model including minimizing the cost and total production time of a robotic cell with a CNC machine and a material handling robot. It has assumed that identical productions are produced in this robotic cell. Using the Epsilon constraint method, the proposed model is coded in GAMS software and its results are reported.

In this research, the lifespan of the CNC machine tools can be considered as a determined or probable value. The results showed that decreasing the operation time at different speeds does not necessarily impose the same cost on the system. Therefore, it is necessary to be more careful in choosing the appropriate machining time for different tools and parts. Paying attention to the rate of suddenly tool breakdowns is also important in choosing the appropriate time for machining. Using a set of non-dominated solutions, it is possible to determine the appropriate machining time in different parts to achieve a suitable level of problem objectives.

 In this research, for the first time, the failure rate of the tool as one of the cost factors in the robotic cell has been added to the cost function of a production cycle and its effect on determining the appropriate machining time has been investigated.

Milk-run logistics is a consolidation method in which vehicles are dispatched in specified time periods to collect orders from various suppliers and deliver them to assembly lines following predefined routes. Pallets of an order can sit in different arrangements in the vehicle and hence, their loading shape can be changeable. Choosing one of these shapes for the order and arranging its pallets in the vehicle as a unified cube can be handled by decision variables. The set of possible shapes for an order varies as the vehicle type changes and this imposes complexities for the case of heterogeneous fleet. Following such an observation, we introduce the idea of shape changeable loading/packing and the required set of constraints to attain a mixed integer linear formulation with the objective of minimizing total transportation costs. Besides loading issues, other considerations such as extra half cost for reverse distribution of empty pallets, order time windows, and heterogeneous fleet are considered. Given the grouping nature of the problem, a Grouping Evolution Strategy (GES) algorithm is proposed that utilizes an efficient constructive best-fit heuristic to ensure feasibility of routing and shape changeable 2D loading of orders into vehicles. Effectiveness of our approach is tested using real-world data obtained from SAIPA Group automotive company. Extensive computations signified the worth of milk-run logistics in comparison with direct shipping strategy followed by the SAIPA's logistics division. Our simulations approve that there exists a capacity for reducing the cost of direct shipment by average amount of 25% via employing milk-run strategy. Moreover, using the more complex shape changeable loading rationale can reduce the costs by 10% compared to a more straight loading method followed by SAIPA. The joint employment of milk-run logistics and shape changeable loading can result in a 32% reduction in costs on average, compared to the current shipment strategy followed by SAIPA.

In the Milkran logistics system, vehicles are sent to collect orders from suppliers and deliver them to ‎assembly lines, according to pre-planned routes. In this way, the vehicle goes to the location of ‎several suppliers to pick up orders and then is sent to one or more destinations for delivery. In this ‎logistics system, cargoes are aggregated within the vehicle and through various nodes in the logistics ‎network. This paper introduces a mixed integer linear programming model for the Milkran logistics ‎problem that takes into account considerations such as three-dimensional loading of the order ‎pallets into vehicles, 50% higher cost for returning empty pallets, order timewindows, and ‎heterogeneous fleets. Given the nature of the problem, an algorithm based on grouping evolutionary ‎strategy is introduced that uses heuristic methods to ensure vehicles’ loading and routing feasibility. ‎The effectiveness of the introduced mathematical model and meta-heuristic algorithm is measured ‎using data collected from Saipa Automotive Group. The computational results show that Milkran ‎Logistics has the ability to reduce costs by 24.5% (on average), compared to the direct shipping ‎strategy pursued by Saipa.‎

Every year, various natural disasters affect many people around the world. After a disaster occurs, it is critical to dispatch relief resources through efficient emergency logistics distribution so as to alleviate death and suffering of victims. The velocity and efficiency of relief operations depend on logistics capabilities in procurement, receipt, transmission and distribution goods to affected areas. Decision-making at times of a disaster is usually done based on experience. Hence, it is essential that an effective tool is provided in order to manage humanitarian logistics operations. In this paper, two of the main relief logistics decisions are considered in the distribution of resources to the affected areas to include scheduling and routing decisions. For this purpose, a dynamic, multi-commodity, multi-depot, multi-trip linear mathematical programming model is developed under conditions of uncertainty that simultaneously captures many important aspects relevant to the real world. Multi-period planning provides possibilities for planners to adjust plans respect to new events and past actions as more information becomes available and improve the effectiveness of plans. Since the proposed model has its own complexities, a grouping metaheuristic algorithm is presented and this is the first time that the problem is viewed from a grouping perspective. To evaluate the validity and efficiency of the proposed model, a case study for the megacity of Tehran is presented. The findings demonstrate the applicability of the presented model to solve real problems.

Forest road networks play an important role to achieve management goals in forest ecosystems and despite of implementation of the forest logging ban, their protection and maintenance cannot be stopped. Therefore, in order to allocate budget and prioritize the maintenance planning, designing a reliable model for predicting the condition of road pavement failure has great importance. The purpose of this study is to predict the performance of road pavement for planning its maintenance management using Markov chain theory. In the present study, 185 road sections in the forest roads of West Haraz were selected and the data of pavement, surface and roadside conditions were measured in different plots during 2015-2018. Then, the functional quality of the road was evaluated by the PCI index and the developed index for forest roads (FRPCI). The pavement quality was classified into five classes: excellent, good, average, bad and broken. After assessing the current situation, the road future situation was predicted over a period of ten years using the Markov chain. The results of this study based on the Markov chain model showed that the process of road decline from the upper class to the good is faster than the process of changing it from the middle class to the poor. Given that a large amount of capital is allocated in forest management plans to road construction and maintenance, need to speed up the protection and maintenance of the road, especially on better quality roads is vital. According to the study, the Markov chain model has sufficient capability in predicting the process of road deterioration and road network maintenance programs.

The oil consortium in Iran is one of the most important approaches to implementing the huge projects of the petrochemical industry. But the selection of suitable and expert partners is one of the most common bottlenecks in such cooperation networks. The purpose of this paper is to provide a practical, yet simple, solution for the decision makers to be able to choose the right candidate from the selected candidates. For this purpose, a three-stage model has been designed. In the first stage, the criteria affecting the choice of partner from the perspective of experts and reviewing the literature were first collected and weighed with the help of SWARA method. In the next stage, the ranking of partners (6 domestic and 4 foreign companies) was based on a set of decision-making methods such as COPRAS, VIKORA, SWA, TOPSIS, ARAS, MOORA, and multi-MOORA. The final stage integrated the ranking results based on the Copeland. In the end, financial capability and debt ratio and repayment potential were introduced as the most important criteria and sub-criteria. Also, partner 3 was selected as the best candidate by Copeland. Finally, in order to measure the performance of the integration of results, the Spearman correlation coefficient was used and the results of high test affinity and integration of results were obtained. Therefore, it can be said that the approach used has performed well.

Startup valuation in venture capital investment has been a complex and important issue in negotiations between entrepreneurs and venture capitalists due to the high degree of uncertainty about their future. Considering the uncertainty in the first round of financing as well as the flexibility available to venture capitalists in the previous (abandon option) and the subsequent (expand option) stages of product commercialization in this research, we present a new approach to determining the value of startup companies. Due to the lack of an analytical solution for the developed approach, the numerical method of least squares Monte-Carlo simulation (Longstaff-Schwartz) was used to solve it. Comparing the result of the model with the results of three other scenarios confirms the role and importance of the aforementioned flexibilities in the making of a venture investment decision. Finally, there were suggestions for developing an approach under competitive interactions or developing it for other stages of investment.

One of the most important issues faced by banks and financial institutions is the issue of credit risk. The significant amount of deferred bank claims around the world indicates the importance of this issue and the need to pay attention to it. So far, many efforts have been made to provide an effective model for evaluating and classification credit applicants as accurately as possible. In this regard, the present study attempts to provide a new approach for assessing the credit risk of bank customers. The support vector machine(SVM) method is combined as the main classifier of banking customers, with a feature selection method called the Binary Ant Colony Optimization Algorithm(BACO-SVM). In order to demonstrate the effectiveness of the proposed method, we used data from 85 companies from legal recipients of facilities of an Iranian bank in a 5 year interval (1393-1893) along with 16 characteristics related to each of them. The results of the BACO-SVM method have been compared with the PSO-SVM, GA-SVM, and SVM method. The results of the research indicated that BACO-SVM model has better performance in assessing credit risk rather than other methods. As the result, using the BACO-SVM method, we classify customers into two groups of good and bad account customers. Finally, in order to increase the flexibility in decision making, we will rank our good account customers with the VIKOR method. This rating will lead to a more accurate assessment of the credit risk situation of good account applicants.

The vehicle routing has many uses in transportation networks and supply chain management. Each of models, which have been provided based on the classic vehicle routing, complements this basic issue and takes into account more real-world assumptions. The present research investigated the heterogeneous vehicle routing by consideration of several product distribution depots and prioritization of customers' time windows. The proposed issue had two

First, the minimization of transportation costs, and second, the reduction of customers' waiting time, thereby increasing the customer satisfaction with regard to their prioritized time windows. Given the NP-Hard nature of this two-purpose optimization issue for its solution in large dimensions, a meta-heuristic NSGA-II algorithm was used and its parameters were adjusted by Taguchi experimental design method. We also utilized two indices to evaluate the algorithm performance. The obtained results from solving issues in small and large dimensions indicated that each algorithm had a good efficiency in solving various issues especially with large sizes in a short time.

In this paper, the problem of scheduling jobs with non-identical sizes has been studied on a single-batch processing machine, in order to minimize the makespan. Using new lower bounds, a branch and bound algorithm has been proposed to solve the problem. In this algorithm, two new methods have been used to generate lower bounds and results have been compared with the existing lower bound in literature. In order to evaluate the performance of the proposed method, test problems have been randomly generated and branch and bound algorithm has been tested with different lower bounds on these cases. Findings indicated that when the size of the jobs is large compared to the capacity of the machine, the branch and bound algorithm with the new lower bound has the best performance. When the size of the jobs is small compared to the capacity of the machine (up to half the capacity of the machine), the algorithm with existing lower bound has better performance. In addition, when the size of the jobs is neither large nor small, the lower bounds provide the best performance.

Based on predictions, services are a key component of the growth of the global economy in future (Arnold et al. 2011). Acording to Jane and Kumar (2012), services play a critical role in a supply chain. Also, according to Wang et al. (2015), a "product" or "service" must exist in each supply chain which is produced by the upstream sectors and delivered to downstream. Recently due to increasing customer expectations, companies’ competition has been replaced by the supply chains competition and as a result, competition has been increased in the simultaneous supply of products and services. This has led to challenges in integrating companies and in coordinating the materials, information and financial flow that were previously overlooked. Accordingly, a new managerial philosophy has been developed known as Product-Service Supply Chain (PSSC) (Stanley & Wisner, 2002). This study seeks to develop a performance evaluation model for the product-service supply chain in the home appliance industry, which is finally solved using Adaptive Neuro-Fuzzy Inference System (ANFIS).

In this paper, performance evaluation constructs and criteria of service supply chain are identified by reviewing the literature and exploratory and confirmatory factor analysis and then, the performance evaluation of service supply chains in Iran's home appliance industry has been performed using these constructs, criteria and ANFIS. Findings and

Based on the findings, ten main extracted constructs can be suggested for the performance evaluation of the supply chain. They include "Operational Performance (OP)", "Strategic Performance (SP)", "Financial Performance (FP)", "Performance of Information and Communication Technology (PICT)", “Return Performance” (REP), “Risk Performance (RIP)”, “Logistic Performance (LP)”, “Market Performance (MP)”, “Internal Structure Performance (PIS)” and “Growth and Innovation Performance (PGI)”, among which, the Strategic Performance (SP) and Return Performance (REP) are the most important and the least important constructs, respectively.

Based on the findings, the following practical recommendations are suggested to the companies: Enhancing the demand forecasts performance and utilizing more appropriate methods and software to improve forecasts in demand and order management areas. Improving the return management status by increased attention and more investment in return management processes. Effective investment in service development management to enhance the R&D services performance. Utilizing risk management approaches and methods to identify and take preventive actions on the risks in the companies’ service supply chain.

The origin-destination matrix, which is usually estimated by transport experts using​​ mathematical and statistical methods as well as optimization techniques, is one of the most important elements in transport planning. The wide-range usage of electronic technologies in transportation helps authorities on tracking vehicles over the road network for many purposes, so a two-stage mathematical model has been developed to estimate origin-destination matrix for vehicle fleets. The proposed model is based on the possibility of reading license plates over the path as well as selecting the shortest path from origin to destination by drivers. Two different definitions of possibility including the number of cameras in each O-D pairs comparing to its distance and the maximum required number of existing cameras are considered as criteria for developing the mathematical model. In order to evaluate the efficiency of the proposed model, the road network of the eastern provinces of Iran including Khorasan Razavi, North Khorasan, South Khorasan, Golestan, Kerman, Sistan and Baluchestan and Semnan, where there are 310 nodes and 356 one-way edges (712 two-way edges), has been selected as case study. All O-D pairs have also been collected, using electronic documents on 1395 in lunar calendar. Using the well-known software of GAMS, results revealed that the model is capable to determine the best edges for installing license plate reading cameras to cover high portion of vehicles fleets those passing over the road network

The hub location problem (HLP) is one of the most important and widely used issues in telecommunication and transportation (freight and passenger) network design. Hub location problem deals with locating the hub facilities in the network and determine the pattern based on the non-hub nodes assignment to each hub so that a specific objective function is optimized. Hubs are intermediate facilities that perform a set of tasks such as consolidation, break-bulk, sorting, etc. In other words, the traffic flows (cargo, passengers, or data) in the network rather than being sent directly from their origins to their destinations, are routed via these intermediate facilities. Established hubs in these networks can be disrupted because of events and natural disasters or deliberate disturbances during their use and in such a case an enormous cost is imposed on the operating companies. Therefore, it is crucial to have a suitable plan for reducing destructive effects of disrupted hubs in the network. In this study, an uncapacitated single allocation hub location problem under hub disruption is considered. It is assumed that every open hub in the network can fail and become unavailable after installation, in which case, the customers originally assigned to that hub, are either reassigned to other operational hubs or they do not receive service for which a penalty must be paid. The problem has been modeled as a two-stage stochastic program in which the decisions on hub locations are made in the first phase. In second phase when disruption scenario has occurred, the allocation of non-hub nodes to hubs takes place in second phase with regard to the operational hubs. A hybrid metaheuristic algorithm based on the adaptive large neighborhood search (ALNS) and simulated annealing (SA) is proposed for solving it. Extensive computational experiments based on the CAB and TR data sets are conducted. Results show the high efficiency of the proposed solution method.