Kharazmi University International Journal of Supply and Operations Management 23831359 8 2 2021 05 01 A Hybrid Genetic-Simulated Annealing-Auction Algorithm for a Fully Fuzzy Multi-Period Multi-Depot Vehicle Routing Problem 96 113 2837 10.22034/ijsom.2021.2.1 EN Mohsen Saffarian Birjand University of Technology, Birjand, Iran Malihe Niksirat Birjand University of Technology, Birjand, Iran Seyed Mahmood Kazemi Birjand University of Technology, Birjand, Iran Journal Article 2020 09 13 In this paper, an integer linear programming formulation is developed for a novel fuzzy multi-period multi-depot vehicle routing problem. The novelty belongs to both the model and the solution methodology. In the proposed model, vehicles are not forced to return to their starting depots. The fuzzy problem is transformed into a mixed-integer programming problem by applying credibility measure whose optimal solution is an (α,β)-credibility optimal solution to the fuzzy problem. To solve the problem, a hybrid genetic-simulated annealing-auction algorithm (HGSA), empowered by a modern simulated annealing cooling schedule function, is developed. Finally, the efficiency of the algorithm is illustrated by employing a variety of test problems and benchmark examples. The obtained results showed that the algorithm provides satisfactory results in terms of different performance criteria. Periodic routing problem Multi-Depot Hybrid algorithm auction algorithm Genetic Algorithm Simulated annealing algorithm http://www.ijsom.com/article_2837_e10519a62bba9f9383271502e6154e0c.pdf
Kharazmi University International Journal of Supply and Operations Management 23831359 8 2 2021 05 01 Economic and ecological optimization of the London urban logistics system considering infection risk during pandemic periods 114 133 2839 10.22034/ijsom.2021.2.2 EN Xuan Feng School of Strategy and Leadership, Coventry University, Coventry, UK Journal Article 2020 08 27 Urban delivery, especially the last-mile delivery, has become an increasingly important area in the global supply chain along with the boom of e-commerce. Delivery companies and merchants can introduce some innovative solutions such as the equipment of autonomous vehicles (AVs) to decrease their operating costs, environmental impact, and social risks during the delivery process. This paper mainly develops a mathematical model to get the best allocation of AVs among city logistics centers (CLCs) as a mixed delivery method. The advantage of the presented model stems from considering the equipment cost, the delivery cost, and the CO2 emission, which is measured through social carbon cost (SCC). In addition, this paper establishes a risk model considering the impact of seasonal variations to evaluate the infection risk of delivery during pandemic periods for four potential delivery scenarios: customers going to CLCs, ordering online and picking-up at CLCs, delivering by traditional vehicles (TVs), and delivering by the mixed method with the optimal allocation of AVs. The research finds the optimal allocation for a London case, reveals the relationship between the nominal service capacity (NCpa) of CLCs and the optimal number of CLCs equipped with AVs, concludes that the more CLCs are equipped with AVs, the fewer CO2 emissions and the fewer citizens will be infected, and provides some managerial insights that may help delivery companies and merchants make appropriate decisions about the allocation of AVs. Urban logistics Cost Optimization CO2 emission Infection Risk Net Present Value,Supply Chain Management http://www.ijsom.com/article_2839_c93659748150b7cfd9b8df5f61958426.pdf
Kharazmi University International Journal of Supply and Operations Management 23831359 8 2 2021 05 01 A Framework for Evaluating the Supply Chain Performance of Apparel Manufacturing Organizations 134 164 2840 10.22034/ijsom.2021.2.3 EN Naveed Khan Portsmouth Business School, University of Portsmouth, Portsmouth, UK 0000-0002-3188-8474 Alessio Ishizaka NEOMA Business School, Mont-Saint-Aignan, France Andrea Genovese Management School, University of Sheffield, Sheffield, UK Journal Article 2021 01 22 The abrogation of Multifiber Arrangement in the year 2005 pushed many developing nations into tough competition. Within the textile industry, despite having many advantages apparel manufacturing and exporting organizations (AMEOs) in developing nations are experiencing decline in their supply chain supply chain performance. Developing a comprehensive model to explore and classify factors, which affect the supply chain performance, is extremely significant. Owing to limited research in this area, an exploratory qualitative study involving a variety of organizations in apparel supply chain was carried out, in combination with a literature review, to determine the causes behind that decline. The outcome of preliminary exploratory study and literature review aided in the proposal of a conceptual framework. Employing that framework, a questionnaire survey was designed and piloted to support a quantitative study, which was conducted in the Karachi region in Pakistan. Collected data were analyzed by employing structural equation modeling. Results indicate that a number of factors have a strong influence on the supply chain performance of AMEOs. Apart from contributing to the literature, this study can also be of interest to managers and practitioners from the textile industry, as it clearly indicates areas on which AMEOs need to focus in order to improve their performance. Supply chain performance Apparel Developing Nations Manufacturing Exporting http://www.ijsom.com/article_2840_8cf94d7e9715d8563492a30ed56cab1a.pdf
Kharazmi University International Journal of Supply and Operations Management 23831359 8 2 2021 05 01 A Novel Cell Layout Problem with Reliability and Stochastic Failures 165 175 2841 10.22034/ijsom.2021.2.4 EN Amir-Mohammad Golmohammadi Department of Industrial Engineering, Arak University, Arak, Iran 0000-0001-5467-9838 Mahboobeh Honarvar Department of Industrial Engineering, Yazd University, Yazd, Iran Reza Tavakkoli_Moghaddam School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran. 0000-0002-6757-926X Hasan Hosseini-Nasab Department of Industrial Engineering, Yazd University, Yazd, Iran Journal Article 2019 08 27 The facility layout design and Cell Formation (CF) problems are the important sectors in designing a cellular manufacturing system (CMS). These problems are interrelated and simultaneous consideration of them is essential for a successful design of CMS. In this paper, a new non-linear mixed integer programming model is presented to solve the integrated cell formation and inter/intra cell layouts in continuous space. The proposed approach incorporated machine reliability with a stochastic time between failures. Some important factors such as stochastic process time, part demand, cell size, variable process routing, and both inter-cell and intra-cell layout are considered in proposed model. The objective is to minimize the total inter/intra cell transportation cost and total breakdown cost. The proposed model is then linearized to reduce computation time and an exact solver by using GAMS is proposed to tackle the computational complexity of the developed model. Results indicate the efficiency and the application of proposed model in the area of CMS conceptually Cellular manufacturing system Cell formation Cell layout Machine Reliability http://www.ijsom.com/article_2841_b23bfe7540cb79f96497ea4d835faa01.pdf
Kharazmi University International Journal of Supply and Operations Management 23831359 8 2 2021 05 01 Multi-objective Design of Balanced Sales Territories with Taboo Search: A Practical Case 176 193 2842 10.22034/ijsom.2021.2.5 EN Elias Olivares Benitez Faculty of Engineering, Universidad Panamericana, Zapopan, Mexico María Beatríz Bernábe-Loranca Faculty of Computer Science, Benemerita Universidad Autonoma de Puebla, México Santiago-Omar Caballero-Morales Popular Autonomous University of the State of Puebla, A.C., Postgraduate Department of Logistics and Supply Chain Management, Puebla, México Rafael Granillo Macias Autonomous University of Hidalgo, Campus Sahagun, Tepeapulco, Hidalgo, Mexico Journal Article 2020 05 13 Sales territory design is an important research field because salesforce allocation within territories impacts sales organization effectiveness and customer service. This work presents a novel multi-objective model for re-designing sales territories with three main objectives: sales balancing, workload balancing, and geographic balancing. To measure sales and workload balancing, the variance among territories was calculated. The metric considered for geographic balancing was the sum of the distances from every salesperson to their assigned customers. A metaheuristic algorithm based on Tabu search was developed to solve a weighted aggregate function that integrates the three objectives. The algorithm is embedded in a procedure to systematically change the weights in the aggregate objective function to produce an approximate Pareto front of solutions. The algorithm was tested with instances based on data from a company in Mexico, providing salesperson-customer assignments that can be projected in territories in geographic information systems. The algorithm converges very fast for the instances studied and produces a Pareto front efficiently. Comparing the current situation of the company to a dominating solution obtained with the algorithm in the Pareto front, a significant improvement in the balance is achieved, in the order of 42.0 - 47.1% on average in the three objective functions. Another managerial benefit achieved by the company was a better understanding for the top managers of the salesforce, the customer preferences, and the challenge of serving a large and dispersed market. Territory design Multiple criteria Metaheuristics Pareto front Salesforce Workload balance http://www.ijsom.com/article_2842_3434762012b787cb5658cc00bb519141.pdf
Kharazmi University International Journal of Supply and Operations Management 23831359 8 2 2021 05 01 Coordinating a Socially Responsible Supply Chain with Random Yield under CSR and Price Dependent Stochastic Demand 194 211 2843 10.22034/ijsom.2021.2.6 EN Joyanta Kumar Majhi Department of Mathematics, Jadavpur University, Kolkata, India Bibhas C. Giri Department of Mathematics, Jadavpur University, Kolkata, India K.S. Chaudhuri Department of Mathematics, Jadavpur University, Kolkata, India Journal Article 2020 03 06 Corporate social responsibility plays an important role in associating customers with socially responsible firms. Faithful consumers are willing to give extra money for commodities or services that incentive the firms to take corporate social responsibility (CSR). This article studies the coordination issue in a two-stage supply chain which is composed of a manufacturer and a retailer who sells a short shelf-life product in a single period. The manufacturer exhibits CSR and simultaneously determines its CSR investment and production quantity, as his production process is subject to random production yield. On the other hand, the retailer decides the selling price and order quantity simultaneously while facing price and CSR sensitive stochastic demand. We construct an agreement between the retailer and the manufacturer which comprises a revenue-sharing and a cost-sharing contract. We show that the supply chain can perfectly coordinate under this composite contract and allow arbitrary allocation of total channel profit to ensure that both the retailer and the manufacturer are benefited. We further analyze the impact of randomness in production as well as the effect of CSR investment on the performance of the entire supply chain. A numerical example is provided to explain the developed model and gain more insights. Random yield ِِِDemand uncertainty Corporate social responsibility Channel coordination Pricing http://www.ijsom.com/article_2843_46660dba01b2b8ba3b3cefd1ae13b205.pdf
Kharazmi University International Journal of Supply and Operations Management 23831359 8 2 2021 05 01 Presenting a Comprehensive Smart Model of Job Rotation as a Corporate Social Responsibility to Improve Human Capital 212 231 2844 10.22034/ijsom.2021.2.7 EN Vahid Sebt Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran 0000-0001-6402-9214 Shiva S. Ghasemi Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran 0000-0001-6565-3254 Journal Article 2021 03 14 Providing job rotation schedules among certain individuals of organizations has been the research focus in the field of job rotation. Obviously, any movement of people will cause a change in the position of others and if there are no properly defined criteria for movement, the resulting job rotation not only is not effective in the long run but also may cause serious damage to the organization. In this regard, the main purpose of this research is to find the best model developed for job rotation and solve the "job rotation scheduling problem" with respect to the factors influenced by the job. So, Health and Safety Executive (HSE) standard questionnaire was used for measuring job stress among the population of nurses in Iranian health centers (n=1221 of a 6148 population) to form databases required for the implementation of data mining. In order to make a smart model, the use of internal rules and patterns of existing data is considered and with the development of meta-heuristic models for this kind of problem, the model is solved with genetic algorithms. The current job rotation model has been developed compared to previous models because of using smart limitations resulting from the process of knowledge discovery by data mining method. In contrast with the results of the previous studies on job rotation, our results are applicable to all organizations need to have different leadership styles in order to practice corporate social responsibility(CSR) and use capabilities to identify rules that allow easy use of meta-heuristic algorithms. Job rotation Genetic Algorithm Data mining Job Stress Corporate social responsibility(CSR) Leadership style http://www.ijsom.com/article_2844_e74c69b94d5e15808905fde02683415f.pdf