Facility location decisions play a critical role in the strategic design of supply chain networks. In this paper, a literature review of facility location models in the context of supply chain ...management is given. We identify basic features that such models must capture to support decision-making involved in strategic supply chain planning. In particular, the integration of location decisions with other decisions relevant to the design of a supply chain network is discussed. Furthermore, aspects related to the structure of the supply chain network, including those specific to reverse logistics, are also addressed. Significant contributions to the current state-of-the-art are surveyed taking into account numerous factors. Supply chain performance measures and optimization techniques are also reviewed. Applications of facility location models to supply chain network design ranging across various industries are presented. Finally, a list of issues requiring further research are highlighted.
•A recycling network of Lithium-Ion batteries considering CO2 emission is proposed.•The factors affecting the cost and location optimization are analyzed.•The carbon tax can influence the optimal ...network configuration.
Driven by the global campaign against climate change, the market of electric vehicles has boomed across the world in recent years. Since Lithium-Ion batteries are commonly used to power electric vehicles, a huge amount of batteries will soon reach their end-of-life; how to recycle them to reduce environmental pollution and promoting the sustainable development of the electric vehicle market has become an urgent challenge today. Implementation of the secondary reuse of used electric vehicle batteries is a valuable recycling strategy. However, there is a lack of research investigating electric vehicle batteries recycling network design at the enterprise level, which impedes the sustainable development of electric vehicles. Driven by this, this paper developed a model considering carbon emission for simulating the recycling of electric vehicle batteries. The proposed model takes into account three potential battery handling strategies (recycling, remanufacturing, and disposal) to processing used vehicle battery cells of different quality levels at different centers. A real case study from a Chinese electric vehicle manufacturer is presented, wherein a 5.7% decrease in total cost and a 21.8% reduction in CO2 emission can be achieved. Moreover, the results of the scenario analysis show that transportation costs, carbon tax, and the number of used batteries, which can change both the configuration of the network, have been identified as three major factors affecting the optimal design of recycling networks. In addition, developing more economical recycling technology for electric vehicle manufacturers to further reduce the total cost of the recycling process is the main direction. In all, this research will provide foreign researchers with a perspective on Chinese companies in terms of electric vehicle battery recycling at the enterprise level, and promote economically and environmentally sustainable development in the electric vehicle battery industry.
A robust bi-level model of the single-product multi-period network design problem is proposed for a competitive green supply chain considering pricing and inventory decisions under uncertainty and ...disruption risks. The bi-level programming approach is used through this model to demonstrate the competition among two supply chains; the leader and the follower, respectively. After modelling the competition and applying pricing decisions by defining a price-dependent demand, disruption risks are analysed through the model. The proposed model simultaneously considers demand uncertainty and disruption risks and is capable of dealing with such uncertainties by implementing resilience strategies including, inventory decisions, and having a contract with reliable suppliers. Moreover, to consider the environmental issues, controlling CO2 emissions and managing the reverse flow were added to the model. Our approach to mitigate the problem uncertainties is to use the possibilistic programming method. The Karush-Kuhan-Tucker (K-K-T) optimality conditions are deployed to make a single-level equivalent form. Since the integrated model was bi-objective, the ϵ-constraint method is implemented to make a single objective integrated model. Finally, some managerial implications are discussed through an industrial case example.
•Robust fuzzy modeling can address the effect of uncertainty in parameters.•The priority-based solution encoding is useful in construction of meta-heuristics.•The proposed whale optimization ...algorithm provides fast high-quality solutions.•The solution quality is consistent without parameter-dependent behavior.
The closed-loop supply chain (CLSC) management as one of the most significant management issues has been increasingly spotlighted by the government, companies and customers, over the past years. The primary reasons for this growing attention mainly down to the governments-driven and environmental-related regulations which has caused the overall supply cost to reduce while enhancing the customer satisfaction. Thereby, in the present study, efforts have been made to propose a facility location/allocation model for a multi-echelon multi-product multi-period CLSC network under shortage, uncertainty, and discount on the purchase of raw materials. To design the network, a mixed-integer nonlinear programming (MINLP) model capable of reducing total costs of network is proposed. Moreover, the model is developed using a robust fuzzy programming (RFP) to investigate the effects of uncertainty parameters including customer demand, fraction of returned products, transportation costs, the price of raw materials, and shortage costs. As the developed model was NP-hard, a novel whale optimization algorithm (WOA) aimed at minimizing the network total costs with application of a modified priority-based encoding procedure is proposed. To validate the model and effectiveness of the proposed algorithm, some quantitative experiments were designed and solved by an optimization solver package and the proposed algorithm. Comparison of the outcomes provided by the proposed algorithm and exact solution is indicative of high quality performance of the applied algorithm to find a near-optimal solution within the reasonable computational time.
This paper critically reviews applications of metaheuristics for solving the Transit Route Network Design Problem (TRNDP). A structured review is offered and prominent metaheuristics for tackling the ...TRNDP are evaluated, according to a benchmark network. The review findings yield a unified implementation framework, which contains common algorithmic components and different solution representations and methods, which are considered important for obtaining solutions of good quality. The paper concludes with identified gaps in research and opportunities for future research on the application of metaheuristic algorithms for solving the TRNDP.
We present a new optimization model for the tactical design of scheduled service networks for transportation systems where several entities provide service and internal exchanges and coordination ...with neighboring systems is critical. Internal exchanges represent border crossings necessitating changes of vehicles, while the coordination with neighboring systems represents intermodal operations. For a given demand, the model determines departure times of the services such that throughput time of the demand in the system is minimized. The model is an extension of the
design-
balanced capacitated multicommodity network design model that we denote
service network design with asset management and multiple fleet coordination to emphasize the explicit modeling of different vehicle fleets. Data from a real-world problem addressing the planning of new rail freight services across borders serves to illustrate the capabilities of the formulation. We analyze how synchronization with collaborating services and removal of border-crossing operations impact the throughput time for the freight. We identify a significant potential for system performance enhancement from synchronization among collaborating services for the problem studied.
With the growing need for a robust network backbone to ensure uninterrupted connectivity in the face of large-scale natural disasters, we introduce the Risk Zone-Diversified Network Design (RZDD) ...problem. This problem requires diverse paths between source-destination pairs to be risk zone-disjoint, preventing any single disaster from disrupting overall network connectivity. Unlike previous research, we propose an innovative cost framework that considers geographically overlapping links and long-term maintenance costs, providing a comprehensive approach to cost analysis. We prove the intractability of the RZDD problem and present the Risk Zone-Diversified Network Design Algorithm (RZDD-Algorithm). In small-scale networks with a single source-destination pair, our algorithm achieves optimal outcomes. Comparative analysis shows that our method reduces costs by an average of 24% compared to an SRLG algorithm that does not consider the preference of geographically overlapping links. For multiple pairs, our approach maintains a gap ratio within 4% and 7% of optimal solutions. Furthermore, experimental evaluations on large networks demonstrate reductions of 26% and 31% compared to the SRLG baseline for single pairs. We also showcase the efficiency of our method in designing large-scale networks with multiple pairs.
Breakthroughs in Wireless Energy Transfer technologies have revitalized Wireless Rechargeable Sensor Networks. However, how to schedule mobile chargers rationally has been quite a tricky problem. ...Most of the current work does not consider the variability of scenarios and how many mobile chargers should be scheduled as the most appropriate for each dispatch. At the same time, the focus of most work on the mobile charger scheduling problem has always been on reducing the number of dead nodes, and the most critical metric of network performance, packet arrival rate, is relatively neglected. In this article, we develop a DRL-based Partial Charging algorithm. Based on the number and urgency of charging requests, we classify charging requests into four scenarios. And for each scenario, we design a corresponding request allocation algorithm. Then, a Deep Reinforcement Learning algorithm is employed to train a decision model using environmental information to select which request allocation algorithm is optimal for the current scenario. After the allocation of charging requests is confirmed, to improve the Quality of Service, i.e., the packet arrival rate of the entire network, a partial charging scheduling algorithm is designed to maximize the total charging duration of nodes in the ideal state while ensuring that all charging requests are completed. In addition, we analyze the traffic information of the nodes and use the Analytic Hierarchy Process to determine the importance of the nodes to compensate for the inaccurate estimation of the node’s remaining lifetime in realistic scenarios. Simulation results show that our proposed algorithm outperforms the existing algorithms regarding the number of alive nodes and packet arrival rate.
•A green intermodal service design problem with travel time uncertainty is introduced.•A stochastic mathematical formulation using sample average approximation is developed.•A real-life case study ...along with extensive computational study is presented.
In a more and more competitive and global world, freight transports have to overcome increasingly long distances while at the same time becoming more reliable. In addition, a raising awareness of the need for environmentally friendly solutions increases the importance of transportation modes other than road. Intermodal transportation, in that regard, allows for the combination of different modes in order to exploit their individual advantages. Intermodal transportation networks offer flexible, robust and environmentally friendly alternatives to transport high volumes of goods over long distances. In order to reflect these advantages, it is the challenge to develop models which both represent multiple modes and their characteristics (e.g., fixed-time schedules and routes) as well as the transhipment between these transportation modes. In this paper, we introduce a Green Intermodal Service Network Design Problem with Travel Time Uncertainty (GISND-TTU) for combined offline intermodal routing decisions of multiple commodities. The proposed stochastic approach allows for the generation of robust transportation plans according to different objectives (i.e., cost, time and greenhouse gas (GHG) emissions) by considering uncertainties in travel times as well as demands with the help of the sample average approximation method. The proposed methodology is applied to a real-world network, which shows the advantages of stochasticity in achieving robust transportation plans.
Consolidation carriers transport shipments that are small relative to trailer capacity. To be cost effective, the carrier must consolidate shipments, which requires coordinating their paths in both ...space and time; i.e., the carrier must solve a
service network design
problem. Most service network design models rely on discretization of time—i.e., instead of determining the exact time at which a dispatch should occur, the model determines a time interval during which a dispatch should occur. While the use of time discretization is widespread in service network design models, a fundamental question related to its use has never been answered:
Is it possible to produce an optimal continuous-time solution without explicitly modeling each point in time
? We answer this question in the affirmative. We develop an iterative refinement algorithm using partially time-expanded networks that solves continuous-time service network design problems. An extensive computational study demonstrates that the algorithm not only is of theoretical interest but also performs well in practice.
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