Strategic Evacuation Network Design (SEND) under cost and time considerations

In this paper, we consider a strategic distribution network design problem for a chain enterprise, in which the decision maker needs to decide the number and location of the distribution centers (DCs) and system inventory control decisions - such as economic order quantity and safety stock decisions. In the chain enterprise, stores face random customer demand, and each store and DC, respectively, maintains a certain amount of safety stock in order to achieve a certain service level for the customers or the stores it serves. The objective is to minimize the total cost that includes location costs, inventory costs, and distribution costs in the chain enterprise. We show that this problem can be formulated as a nonlinear integer programming model, for which we propose a Lagrangian relaxation based solution algorithm. In a numerical application, we found that the benefits of having an integrated distribution design framework that includes location, inventory, and routing decisions in the same optimization model.

Extracting Event Logs from Value Stream Simulation in Production Networks for Data Farming Based Strategic Network Design

The complexity of social indicators and their subjective and often qualitative nature render their inclusion into quantitative optimization models for network design and strategic decision-making challenging. The social dimension is thus often implemented only rudimentarily, thwarting a holistic sustainability assessment and neglecting many of the social issues addressed in the sustainable development goals (SDGs). This work presents a structured process for including a comprehensive set of social aspects by selecting applicable quantitative and regionalized social indicators. This approach is applied to the case of second-generation bioethanol production in the EU. Based on inter alia the Guidelines for Social Life Cycle Assessment of Products and Organizations, the Social Hotspots Database, state-of-the-art literature, as well as previous work, we compile 9 social objective functions and 25 functions for social hotspot identification. They are evaluated alongside 1 economic and 21 environmental LCA-based objective functions in a mixed-integer linear programming (MILP) model. Key results show that social optimization either leads to large, labor-intensive or regionally focused, indicator-driven networks. Injuries and fatalities in the feedstock sectors of Central and Eastern European countries is the primary social hotspot. On the level of the overarching SDGs, SDG13 is most congruent with other goals, while SDG7 is hindered by pursuing other goals. This study's approach is novel in strategic network design and the European bioeconomy, and, by operationalizing the social dimension, enables a more holistic life cycle sustainability assessment and the consideration of the SDGs. This article met the requirements for a gold-gold JIE data openness badge described at http://jie.click/badges.

The development of efficient air freight networks is an upcoming challenge. The present book approaches this problem for cargo airlines by characterising and classifying their network structures and by developing a model for an airline's strategic network design. The book provides results which are of value for airline professionals (network efficiency analysis), policy makers (policy impact assessment) and researchers (cargo airline network design model).

PurposeThe purpose of this paper is to explore how standardized advanced planning systems (APS) can be used for solving planning problems at tactical and strategic levels, and to identify the perceived effects of using APS.Design/methodology/approachMultiple case studies involving three cases using APS software for strategic network planning and master production scheduling are conducted. Comparative analysis explores how the planning situation, the model design and use of the APS impact the perceived planning effects.FindingsFindings show how APS support cost‐optimized strategic network design in one case and how efficiency, capacity utilization and delivery service problems were decreased in two cases using APS in global master planning processes. The cases show how APS supports cross‐functional integration and supply chain commitment to a common plan. Research directions are suggested about the feasibility of APS in situations with various planning complexities, how design of the optimization model creates complexity and affects the planning process, data gathering requirements when using APS, the role and design of the planning organization, and how to achieve positive planning effects, such as finding global optimum and single plan commitment.Research limitations/implicationsThe approach is descriptive and explorative. Only three cases are studied.Practical implicationsThe findings present experiences of APS usage and identify issues to consider when using APS and potential benefits to gain from usage.Originality/valueThe practical use of APS is low and the knowledge about how it impacts supply chain planning and performance is unexplored. The findings of this paper fill some of these gaps.

To prepare evacuation plans, including an evacuation support system for vulnerable people such as the elderly, injured, pregnant, or other people requiring support during a tsunami evacuation, validating evacuation drills with respect to real-world circumstances using quantitative data is important. This study aims to clarify the importance of using evacuation drill data for evacuation planning. To this end, we measured the movements of residents during an evacuation drill conducted according to a set plan in a community and verified the observed quantitative data. We have been supporting the drills and community disaster management plan (CDMP) in the Shinyo Community of Kobe City since 2008 and hence selected this community for our study. We measured the residents’ evacuation time and speed using various transportation modes to transport vulnerable people to safety during the drill and verified the evacuation plan’s effectiveness. Quantitative data were verified in the drills and vulnerable people and their supporters, who were measured on this occasion, were evacuated from the tsunami warning area within the estimated tsunami hitting time for Hyogo prefecture of 90 min after the earthquake. Further, using four types of transportation modes—a rollator (wheeled walking aid), transport chair, wheelchair, and cart—yielded conveyance speeds (average of the time taken by a vehicle to traverse the entire evacuation route, excluding wait times at traffic signals) of 1.03, 1.42, 1.50, and 1.27 m/s, respectively. Few days later, the participants provided feedback on the drills in an evaluation meeting and deemed the evacuation plan effective. This study presents a progressive thought process that utilizes the interaction between “planning” and “training” to enable a feasible CDMP to combat disasters.

Emergency Evacuation Model and Algorithm in the Building with Several Exits

Modeling of building evacuation using ladders

We developed a two-phase network planning methodology to design a service network for DHL(HK) based on two measures of service performance: service coverage and service reliability. The methodology consists of first using an optimization model to determine a least-cost distribution network, which forms the basis of a simulation model for analyzing the network's operational characteristics. It takes into consideration such design issues as inbound and outbound shipment, equivalent shipment, cutoff time, demand fluctuation, and random behaviors. DHL decision makers can now examine trade-offs between coverage and reliability by adjusting workforce, changing cutoff time, or redesigning the service network.

Edge computing is poised to become a cornerstone of the emerging 6G landscape, where an ever-growing class of ultra-low-latency applications must be served close to the user. Despite its promise, real-world deployments remain nascent, with large-scale implementations anticipated by both Communication and Digital Service Providers (CSPs/DSPs) within the following years. Consequently, strategic edge network design is essential not only to maximize performance, but also to avoid redundant investments that can lead to an increased sum of Capital and Operational Expenditures (CAPEX/OPEX). In this work, we address a tri-fold problem: (i) the selection of deployment locations, (ii) the configuration of devices at the chosen location sites, and (iii) the assignment of the projected workload. Our objective is formulated as a weighted combination of the edge infrastructure’s establishment cost, the expected cumulative workload latency and the total expected energy consumption in the operational phase. To capture the spatial and temporal variability of demand, we solve the assignment subproblem over distinct snapshots, each representing a unique workload projection. We first present a Mixed Integer Linear Programming (MILP) formulation that yields the optimal solution; however, due to its computational intractability, we propose a novel adaptation of the Multi-Objective Evolutionary Algorithm by Decomposition (MOEA/D), with an embedded heuristic algorithm to assist in the chromosome fitness calculation. This method leverages the similarity among neighboring subproblems in a multi-objective framework to efficiently approximate the underlying Pareto frontier. In the experiments, the proposed method is contrasted with a sophisticated single-objective Rollout approach. Our results demonstrate the benefits of adopting a multi-objective algorithm in terms of performance, stability and interpretability across different scalarized subproblems. The proposed framework offers a practical intent-based decision support tool for edge infrastructure providers, weighing CAPEX against operating objectives ahead of initial deployment.

The bullwhip effect in capacitated supply chains with consideration for product life-cycle aspects

Exclusive bus lanes have demonstrated their effectiveness in increasing capacity and efficiency. However, when discussing the optimal design of exclusive bus lanes at the network level, existing studies predominantly focus on the interaction between transit and private vehicles at the links, while operational dynamics at the intersections and their impacts were insufficiently addressed. This paper contributes to developing a mathematical model for exclusive bus lane network design with operational dynamics at intersections explicitly captured. In the proposed model, lane re-configuration for transit and private vehicles at both road segments and intersections along with signal timing optimization are integrated in a unified framework. The model features a bi-level structure with the objective for reducing the total system person travel time. A genetic algorithm embedded with Emme is used for yielding solutions to the model. Results from both numerical and empirical tests demonstrate the effectiveness of the proposed model and reveal that setting of exclusive bus lanes in the network without explicit consideration of intersection operational dynamics can significantly undermine the network performance with respect to both bus passengers and private vehicles.

In recent years, different kinds of natural hazards or man-made disasters happened that were diversified and difficult to control with heavy casualties. In this paper, we focus on the rapid and systematic evacuation of large-scale densities of people after disasters to reduce loss in an effective manner. The optimal evacuation planning is a key challenge and becomes a hotspot of research and development. We design our system based on an Internet of Things (IoT) scenario that utilizes a mobile cloud computing platform in order to develop the crowd lives oriented track and help optimization system (CLOTHO). CLOTHO is an evacuation planning system for large-scale densities of people in disasters. It includes the mobile terminal (IoT side) for data collection and the cloud backend system for storage and analytics. We build our solution upon a typical IoT/fog disaster management scenario and we propose an IoT application based on an evacuation planning algorithm that uses the artificial potential field (APF), which is the core of CLOTHO. APF is conceptualized as an IoT service, and can determine the direction of evacuation automatically according to the gradient direction of the potential field, suitable for rapid evacuation of large population. Based on APF, we propose an evacuation planning algorithm names as APF with relationship attraction (APF-RA). APF-RA guides the evacuees with relationship to move to the same shelter as much as possible, to calm evacuees and realize a more humanitarian evacuation. The experimental results show that CLOTHO (using APF and APF-RA) can effectively improve convergence rate, shorten the evacuation route length and evacuation time, and make the remaining capacity of the surrounding shelters well balanced.

The contribution of tsunami evacuation analysis to evacuation planning in Chile: Applying a multi-perspective research design

An important aspect of evacuation planning is to save human population by sending the people in dangerous areas (sources) to the safe places (sinks). Optimization models for evacuation planning focus on regulating the tra_c ow in urban road networks so as to maximize the number of evacuees reaching the safe places or to minimize the evacuation time of evacuees. Recent studies show that reversing the direction of the usual tra_c ow in necessary road segments increases the tra_c ow and decreases the evacuation time signi_cantly. However, this may block the ow of some necessary support towards the source. Based on network ow models, we consider the problem of saving a path from a given node to the source, in a single-source-single-sink network, as a bilevel program. With an objective to minimize a function depending on ow rate and the path chosen, the upper level selects a path from a given node to the source, and the lower level maximizes the dynamic ow allowing arc reversals in the resulting network within a given time horizon. We discuss a solution strategy based on replacing the lower level problem by the corresponding KKT conditions. GANIT J. Bangladesh Math. Soc. 42.1 (2022) 010- 024