Abdolreza Roshani

The reliable fixed-charge facility location problem extends the fixed-cost facility location problem by incorporating facility unreliability. This paper addresses a novel capacitated version of the reliable fixed-charge facility location problem, where the failure probability of each facility is site-dependent, differing from existing literature models. Additionally, facilities are assumed to have limited capacity for demand allocation, with the option to increase capacity to a predetermined value in case of supply chain disruptions. To solve this problem, we develop a non-linear mixed-integer programming formulation and present its linear version. Given the NP-hard nature of the problem, we propose a novel Relax-and-Fix heuristic for its solution. We evaluate the efficiency of the proposed algorithm by solving a variety of experimental instances with different network sizes. Results demonstrate that the Relax-and-Fix heuristic improves upper bounds for problem instances and achieves this within a shorter computational time. Furthermore, sensitivity analysis is conducted on capacity and failure probabilities, and relevant results are presented.

This study focuses on the combined lot-sizing and scheduling challenge within a closed-loop supply chain that prioritizes sustainable production by incorporating returned products. The proposed system facilitates both the production of new items from raw materials and the remanufacturing of recovered goods using two parallel machines with limited capacity. These machines operate independently and manage operations with sequence-dependent setup times and costs. A mixed-integer programming (MIP) model is formulated to minimize total costs, which include production, remanufacturing, setup, inventory holding, backlog, and energy consumption. The model's performance is validated using a recognized benchmark instance from existing literature, highlighting its ability to produce efficient and cost-effective schedules.

Additive Manufacturing (AM), an innovative technology, plays a pivotal role in localising supply chains, with the integration of digital technologies further enhancing production flexibility and system resilience. However, AM increases digitalisation in the supply chain, which brings a range of cybersecurity vulnerabilities. Consequently, a comprehensive risk assessment framework is necessary to assist decision-makers considering AM systems. Prior research has employed Composite Indicators (CIs) for risk evaluation, aggregating sub-indicators to assess risks. However, the approach to determining suitable weights for these sub-indicators presents notable limitations such as limited discriminant power, non-unique outcome, subjective assumption on weight preference. To solve the limitation in determining the weight, this paper introduces a multi-level (three-stage) risk assessment model based on the Multiplicative Non-Parametric Approach. This model provides a thorough evaluation of threats within AM systems, considers both individual and industrial perspectives, and determines the global weights of each sub-indicator. The empirical analysis demonstrates that the proposed multi-level model effectively identifies appropriate weights for the sub-indicators, resulting in outcomes that closely align with the original assessment results. The model's performance improves as the number of analysis stages increases, reaching its peak in the final stage of analysis. ARTICLE HISTORY

With increased globalisation supply chain (SC) disruption significantly affects people, organisations and society. Supply chain network design (SCND) reduces the effects of disruption, employing mitigation strategies such as extra capacity and flexibility to make SCs resilient. Currently, no systematic literature review classifies mitigation strategies for SCND. This paper systematically reviews the literature on SCND, analysing proposed mitigation strategies and the methods used for their integration into quantitative models. First to understand the key failure drivers SCND literature is categorised using geography, with local, regional or global disruptions linked to vulnerable sections of a SC. Second, the strategies used in mathematical models to increase SC resilience are categorized as proactive, reactive, or SC design quality capabilities. Third, the relative performance of mitigation strategies is analysed to provide a comparison, identifying the most effective strategies in given contexts. Forth, mathematical modelling techniques used in resilient SCND are reviewed, identifying how strategies are integrated into quantitative models. Finally, gaps in knowledge, key research questions and future directions for researchers are described.

Electric vehicles can be perceived as a means to achieve carbon reduction, energy efficiency, and sustainable development of the transportation industry. Electric vehicle sales and deployment are increasing rapidly over time. However, electric vehicle deployment should be conducted in a planned manner, as electric vehicles have some limitations (e.g., limited driving range, refueling capacity, carrying capacity). The electric vehicle scheduling problem should be studied in detail to overcome such limitations, as it addresses them while optimizing the paths and timetables of electric vehicles. A number of studies have been dedicated towards electric vehicle scheduling. Yet, there is a lack of survey studies that cover a structural recapitulation of the electric vehicle scheduling efforts and provide a thorough overview of the existing tendencies, operations research aspects, problem-specific properties, and future research needs. For this reason, this study offers a structured survey of the existing research studies, which assessed electric vehicle scheduling. The collected studies are grouped into three categories for a detailed review, namely general electric vehicle scheduling, electric vehicle scheduling with power grid considerations, and electric vehicle scheduling with environmental considerations. A detailed description of the relevant studies along with a summary of findings and future research needs are provided for each of the study categories. In addition, a representative mathematical model is outlined for each study category in order to guide the future research. The outcomes of this research are expected to provide interesting and important insights to different groups of professionals in the field of electric vehicles.

The target of this paper is to present a novel mathematical model to design a resilient and energy efficiency additive
manufacturing supply chain. This model minimizes the total cost of designing SC by selecting the optimal location and type of
3D printers to meet customer demand through active facilities and penalizing lost demand if necessary. To evaluate the
efficiency of the proposed algorithm, several experimental instances are solved and the results for a selected case is reported.
The results obtained for the selected case demonstrate that the proposed model can effectively reduce energy costs, with only
a slight increase in expected shipment costs while the fixed location costs and expected penalty costs remains unchanged.

In this paper, the capacitated lot-sizing and scheduling problem with sequence dependent setup times and costs in a closed loop supply chain is addressed. The system utilizes the closed-loop supply chain strategy so that the multi-class single-level products are produced through both manufacturing of raw materials and remanufacturing of returned recovered products. In this system, a single machine with a limited capacity in each time period is used to perform both the manufacturing and remanufacturing operations. The sequence-dependent setup times and costs (both between two lots of products of different classes and between two lots belonging to the same class of products produced through different methods) are considered. A large-bucket mixed integer programming formulation is proposed for the problem. This model minimizes not only the manufacturing and remanufacturing costs, the setup costs and the inventory holding and backlogging costs over the planning horizon, but also the energy costs paid for the utilization of machine and the compression of processing times. Since the problem is NP-hard, a matheuristic and a grey wolf optimization algorithm are proposed to solve it. To evaluate the efficiency of the proposed algorithm, some experimental instances are generated and solved. The obtained results show the effectiveness of the proposed algorithms.

Highway-rail grade crossing (HRGC) accidents pose a serious risk of safety to highway users, including pedestrians trying to cross HRGCs. A significant increase in the number of HRGC accidents globally calls for greater research efforts, which are not limited to the analysis of accidents at HRGCs but also understanding user perception, driver behavior, potential conflicting areas at crossings, effectiveness of countermeasures and user perception towards them. HRGC safety is one of the priority areas in the State of Florida, since the state HRGCs experienced a total of 429 injuries and 146 fatalities between 2010 and 2019 with a significant increase in HRGC accidents over the last years. The present study aims to conduct a comprehensive analysis of the HRGCs that experienced accidents in Florida over the last years. The databases maintained by the Federal Rail Administration (FRA) are used to gather the relevant information for a total of 578 crossings that experienced at least one accident from 2010 to 2019. In contrast with many of the previous efforts, this study investigates a wide range of various factors, including physical and operational characteristics of crossings, vehicle and train characteristics, spatial characteristics, temporal and environmental characteristics, driver actions and related characteristics, and other relevant information. The outcomes of this research will help better understanding the major causes behind accidents at the HRGCs in the State of Florida in a holistic way by considering a variety of relevant factors, which will assist the appropriate stakeholders with implementation of safety improvement projects across the state.

Multi-sided assembly line balancing problems usually occur in plants producing big-sized products such as buses, trucks, and helicopters. In this type of assembly line, in each workstation, it is possible to install several workplaces, in which a single operator performs his/her own set of tasks at an individual mounting position. In this way, the operators can work simultaneously on the same product without hindering each other. This paper considers for the first time the multi-sided assembly line balancing problem with the objective of minimising the cycle time, proposing a new mathematical formulation to solve small-sized instances of this problem. Besides, a metaheuristic algorithm based on variable neighbourhood search hybridised with simulated annealing is developed to solve large-sized instances. The algorithm is called adaptive because of the adopted neighbourhood selection mechanism. A novel three-string representation is introduced to encode the problem solutions and six different neighbourhood generation structures are presented. The developed approach is compared to other meta-heuristics, considering some well-known in literature test instance and a real world assembly line balancing problem arising in a car body assembly line. The experimental results validate the effectiveness of the proposed algorithm.