Search

Supply or service networks are vulnerable to hazards that can stem from both unintentional and intentional human actions, as well as natural calamities. To ensure vital infrastructure resilience in these networks, address the interdiction facility location problem. Currently, diverse groups of attackers target supply and service systems to cause maximum disruption. Collaboration among attackers improves system vulnerability detection accuracy and realism. This research examines the challenges of interdiction location with different defense systems and heterogeneous attackers. To address this challenge, a mixed-integer non-linear bi-level programming model was considered. Heuristic optimization methods including variable neighborhood search, simulated annealing, and hybrid variable neighborhood search are used to efficiently solve the suggested model. The outcomes of our investigation suggest that implementing various protective methods leads to an escalation in system damage when attackers collaborate. Furthermore, the findings illustrate the efficacy of the suggested algorithms in resolving interdiction location issues within supply or service networks. [ABSTRACT FROM AUTHOR]

The purpose of this article is to develop a competitive supply chain network (SCN) in the face of uncertainty. The objective of the leader chain is to maximize total network profits by strategically locating suppliers, manufacturers, distribution centers, and retailers. Additionally, the follower chain seeks to maximize the network's profit. Both factors, optimal flow allocation to different echelons of the SCN and product pricing, are examined in the leader chain and follower chain. The KKT conditions are used in this article to convert a bi-level model to a one-level model. Additionally, a fuzzy programming technique is used to control the problem's uncertain parameters. According to the results obtained using the fuzzy programming technique, increasing the uncertainty rate increases demand while decreasing the OBFV and average selling price of products. Finally, the problem was untangled using a novel hybrid genetic and ant-lion optimization algorithm (HGALO). The results of problem solving in larger sizes demonstrate HGALO's superior efficiency in comparison with the other algorithm. [ABSTRACT FROM AUTHOR]

Sustainability in the lean supply chain is the consideration of all economic, social and environmental aspects in an integrated system. This study aims to design a novel multi-objective sustainable lean supply chain (LSC) network under uncertainty which all economic, social and environmental aspects are considered. The designed LSC network model incorporates multiple echelons, including retailers, distribution centers, production centers, suppliers, and final customers. The purpose of this paper is to demonstrate how a robust-fuzzy optimization technique can be used to control uncertainty parameters such as transportation costs, facility capacity, and demand. The deployment of this technique in the design of the LSC network leads to increased reliability in supplying the customer demand. The model's objective functions are to minimize the total cost of designing LSC networks (economic aspect), to minimize waste in production units (environmental aspect), and to maximize the overall sustainability performance indicator (SPI) (social aspect). To achieve these objectives and to identify the Pareto front, we investigated both exact and meta-heuristic methods. The results indicated that when lean management tools are used in supply chain network conditions, the SPI increases and waste in the production units decreases. Additionally, it increases the cost of network design. Moreover, increasing the effect of uncertainty results in increased demand and decreased capacity in the supply chain network, resulting in an increase in network-related costs. However, the increase in the rate of uncertainty has resulted in an increase in waste in manufacturing units and a decline in the SPI. Finally, it was determined through numerous experiments and statistical tests that meta-heuristics algorithms are more efficient at solving the designed model in less time. As a result, the most efficient solution method was determined to be the MOGWO algorithm. [ABSTRACT FROM AUTHOR]

In many cases where emergency medical services are required, ground and air ambulances cooperate in a shared activity to transfer the injured from the accident scene to treatment centers. The cooperation becomes necessary, particularly where the air ambulance can not land at the accident scene, and it sometimes seems essential to make use of transfer points during the cooperation. Given that the demand pattern might change during time, it does not seem logical to formulate the problem statically. Hence, in this paper, we have presented a multi-period maximal coverage location model, which simultaneously locates the transfer points, ground ambulance stations, air ambulance stations, and allocates ambulances to them. Also, the available ambulances are moved among the established stations if required subject to demand changes during the planning horizon. Coverage is provided based on the times it takes the ambulance to arrive at the accident scene and to transfer from the accident scene to the nearest treatment center. In this research, four methods of providing service to the injured and transferring them to treatment centers have been considered. In the presented model, the inaccessibility of ambulances has been considered in light of the notion of backup coverage. Given the complexity of the problem and the impossibility to solve it optimally at large-scale problems, a heuristic algorithm with the greedy approach has been presented for solving it. The obtained computational results demonstrate the efficiency of the proposed algorithm in solving different problems as compared to CPLEX. [ABSTRACT FROM AUTHOR]

Hub location problems appear at the core of strategic planning for many transportation and logistics problems. This class of optimization problems has received a wide gamut of research attention during the last few decades. However, the associated mathematical formulations are mainly developed with deterministic parameters to avoid the complexities and computational difficulties. In this paper, we put forward an extension to the uncapacitated single allocation p- hub median problem under uncertainty in which demands and travel times are stochastic. A robust optimization approach is used to deal with uncertainty in parameters and design a robust hub-and-spoke network. The developed model minimizes the total expected transportation costs, while bounding the relative regret in each scenario. To efficiently solve the model, a two-stage approach is investigated to design two hybrid heuristics. We firstly apply a meta-heuristic (i.e., variable neighborhood search (VNS) or particle swarm optimization (PSO)) to find the best combination for the hubs location. Then, by fixing the hubs location, the original model easily decomposes by scenario for which an efficient tabu search (TS) is designed. Encouraging results on a wide-range of test problems are reported. [ABSTRACT FROM AUTHOR]

This paper studies the problem of determining optimal facility location and network design decisions over multiple time periods considering equity-based objective. The problem determines optimal locations of facilities and the design of the underlying network, taking into account budget constraints on opening facilities and constructing links for each time period during the planning horizon. The objective is to minimize the sum over all time periods of the maximum travel time between nodes and facilities for each time period. We propose a mixed-integer, non-linear programming model to address the problem. The non-linear terms of the model relevant to the objective function can be easily converted to linear terms. Due to the high complexity of the model, CPLEX could not solve the problem efficiently. Hence, we propose two heuristics based on linear relaxation and a hybrid algorithm based on Variable Neighborhood Search to solve the problem. We tested the performance of the proposed algorithms on two data sets with up to 60 nodes, 708 candidate links, and 20 time periods compared to each other and the CPLEX solver. [ABSTRACT FROM AUTHOR]

Location-allocation problems are a class of complicated optimization problems that determine the location of facilities and the allocation of customers to the facilities. In this paper, the uncapacitated continuous location-allocation problem is considered, and a particle swarm optimization approach, which has not previously been applied to this problem, is presented. Two algorithms including classical and hybrid particle swarm optimization algorithms are developed. Local optima of the problem are obtained by two local search heuristics that exist in the literature. These algorithms are combined with particle swarm optimization to construct an efficient hybrid approach. Many large-scale problems are used to measure the effectiveness and efficiency of the proposed algorithms. Our results are compared with the best algorithms in the literature. The experimental results show that the hybrid PSO produces good solutions, is more efficient than the classical PSO, and is competitive with the best results from the literature. Additionally, the proposed hybrid PSO found better solutions for some instances than did the best known solutions in the literature. [ABSTRACT FROM AUTHOR]

In this paper, hybrid algorithms are developed for the multisource location-allocation problem in continuous space. Three hybrid algorithms are proposed to solve this problem that combine elements of several traditional metaheuristics (genetic algorithm and variable neighborhood search) and local searches to find near-optimal solutions. Many problems from the literature have been solved with these algorithms and the obtained results confirm the robustness of the proposed hybrid algorithms. Moreover, the results show that in comparison to the best methods in literature (GA and VNS), these algorithms provide some better solutions. [ABSTRACT FROM AUTHOR]