Journal of Advances in Industrial Engineering
Volume:55 Issue: 1, Winter 2021

Shortest path problem is a typical routing optimization problem that is generally involved with a multi-criteria decision-making process. Therefore, the main objective of this paper is to find the shortest path in discrete-time dynamic networks based on bi-criteria of time and reliability by considering the effect of delay times that varies according to different departure time scenarios. Firstly, the well-known single-criterion Dijkstra’s algorithm is extended to fit the conditions of a bi-criteria problem. The solutions obtained from the extended Dijkstra was then compared with a proposed ant colony optimization (ACO) algorithm via a set of multi-objective performance metrics including CPU time, error ratio, spacing and diversity metrics. The analysis was made based on three network scales ranged from small (20-100 nodes), to medium (500-1900 nodes) and large (2000-10000 nodes). The computational results obtained from the analysis suggested that the extended Dijkstra’s algorithm has a higher efficiency in medium and large scaled networks. Furthermore, the comparison of the proposed ACO versus Dijkstra’s algorithm proved the preference of ACO for networks with larger-scaled (nodes over 5000), while, for smaller and medium-scaled networks (nodes 20-2000), the extended Dijkstra’s algorithm has a dominantly better performance in CPU time as compared to proposed ACO.

Traffic congestion is one of the issues in transportation planning which imposes environmental consequences and costs. Therefore, decision-makers and policymakers should focus on appropriate transportation planning models. One of the approaches to relieve traffic congestion is imposing tolls on the users. In the present paper, attempts are made to present three transportation planning and traffic congestion management models. The first model assumes that the transportation network and the traffic flows within it are determined. Decision-maker seeks to adjust the transportation network flows so that traffic congestion can be prevented. In the second model, unlike the first one, attempts are made to design urban transportation networks via the development of routes. The third model is a mixture of the first and second models. All models proposed here are bi-objective which were addressed under uncertain conditions and disturbances. According to the results, a decision-making model was extended to rank routes. In the end, a numerical example is considered for analyzing and evaluating the proposed models. The results of the numerical example showed that the first model is the most inefficient and the third model is the most efficient. Since the proposed model can be implemented in road networks in addition to urban transportation networks, the application of the proposed models is demonstrated based on a real-world case study. The case study results showed that the efficiency of road network depends on the time interval.

Level of blood and blood products in human body is very important. Therefor, managing supply of blood is critical issue in healthcare system specially when the system faced with high demand for the product. In natural disasters, demand for blood units increase sharply because of injuries. Hence, efficiency in blood supply chain management play a significant role in this situation in supplying blood for transfusion centers, it is vital to supply in right time to prevent from casualties. Present paper proposes an optimization model for designing blood supply chain network in case of an earthquake disaster. The proposed two-stage stochastic model is Programmed based on scenarios for earthquake in a populated mega-city. The designed network has three layers; first layer is donation areas, the second layer consists distribution centers and facilities and the last layer is transfusion centers. In proposed two-stage stochastic optimization model, decisions of locating permanent collection facilities and amount of each blood type pre-inventory are made in first stage and operation decisions that have dependent on possible scenarios are made in second stage. The model also considers the possibility of blood transfusion between different blood types and its convertibility to blood derivatives regarding medical requirements. In order to solve the proposed two-stage stochastic model, L-shaped algorithm, an efficient algorithm to solve scenario based stochastic models, has been used. In addition, application of the model and the algorithm tests with real data of likely earthquake in Tehran mega city (Densest city of Iran).

Traffic congestion is one of the main reasons for the unsustainability of an urban transportation network. Changes in travel demand and streets’ capacity lead to traffic congestion in urban transportation networks, which is known as recurrent traffic congestion. This study aims to assess the performance reliability of urban transportation networks subject to recurrent traffic congestion conditions in order to help travelers to find alternative non-congested routes. A non-congested route is a route without any congested link. The network reliability is defined and modeled as two different scenarios; users’ unawareness of the network’s traffic congestion and users’ ongoing awareness of the network’s traffic congestion. In addition, a reliable network design model is provided to optimize the reliability of the network taking into account street widening policy and budget constraints. Lastly, a Quantum-Inspired Evolutionary Meta-Heuristic Algorithm is adopted; while maintaining accuracy, to reduce problem-solving time and providing the possibility of solving large-scale problems for real networks. To show the applicability of the proposed models and algorithm, they have been implemented on the Sioux Falls transportation network. The results indicate users’ awareness of traffic congestion in the network increases its reliability, and centrally located links are the first candidates for street widening.

An appropriate trade-off between total electricity costs and makespan can lead to good production planning and reduce unnecessary energy consumption. Time-of-use (TOU) electricity pricing policy has been executed in many countries which enabled industrial consumers with high energy consumption to reduce their energy costs. In this study, an unrelated parallel machines scheduling problem is considered for minimizing makespan and also energy consumption costs. Due to the importance of sequence-dependent setup times in production environments, they are considered according to the restricted duration of time periods under TOU policy. These considerations are added to the current literature. A mixed-integer bi-objective mathematical model is presented and the ε-constraint method is applied to solve small and also medium-sized instances. Because the problem is shown to be NP-hard, several large-sized instances are approximately solved using Multiple Objective Particle Swarm Optimization algorithm, and Multiple Objective Simulated Annealing algorithm. Computational experiments are conducted on randomly generated data. The results show the efficiency and appropriate performance of the proposed methods.

Many supply chains lack flexibility and adaptability in today's competitive market, resulting in customer dissatisfaction, backorders, and several extra costs for the business. Additionally, the inability to quickly meet the customer's demands and the unnecessary transportation costs is also one of the significant challenges faced by the fixed facilities' supply chain. To address these challenges, this study analyzed the mobile facilities supply chain and the production, distribution, and delivery of goods conducted by trucks based on customer preferences. This study proposes a bi-objective mixed-integer linear programming model to ensure the mobile facilities' routing and manufacturing schedules are optimized to meet the customer's needs. Furthermore, this model minimizes production and distribution costs in the shortest amount of time. An exact decomposition algorithm based on Benders decomposition is used to find high-quality solutions in a reasonable amount of time to tackle the problem efficiently. We present several acceleration strategies for increasing the convergence rate of Benders' decomposition algorithm, including Pareto optimality cut and warm-up start. The warm-up start acceleration strategy itself is a meta-heuristic based on particle swarm optimization (PSO). Using the Benders decomposition, we demonstrate the superior accuracy of our solution methodology for large-scale cases with 10 kinds of products ordered by 30 customers using 10 mobile facilities.