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Automated guide vehicles (AGVs) are driverless vehicles that perform material handling operations in both flexible and conventional facilities. We provide here a review of recent work on the design of AGV guide paths and dispatching rules, including related issues such as idle vehicle location, and location of pickup and delivery stations. Different types of guide paths and related layouts, including optimal and heuristic approaches to the path design, are reviewed here. Dispatching rules and algorithms, including zone control, are also proposed and compared with commonly‐used rules.

Discusses “recurrent approaches” to determining when to despatch a consolidated load. Unlike a “non‐recurrent approach” (which sets a target time or weight prior to accumulating orders and despatches when the target is reached), recurrent approaches re‐evaluate the shipment‐release decision several times within an order accumulation cycle. Presents two probabilistic recurrent models, one assuming private transportation and the other common carriage. Compares the performance of these models with the nonrecurrent rules of despatching the “economic shipment weight” or, in the case of common carriage, the minimum volume weight. Concludes that with both forms of transportation, the decision heuristic outperforms despatching the economic shipment weight when that weight is close to vehicle capacity. Shows that, with common carriage, the use of the more sophisticated model does not yield better cost results than the minimum volume weight despatch rule. Discusses the reasons for, and implications of, these results.

Real time control and scheduling systems determine the vehicle routing plan based on the current status of the system. The status of a system can be represented by different attributes of demand such as location, quantity, and due date. The objective of this article is to propose a real time dynamic vehicle control and scheduling system for multi‐depot physical distribution. To perform the system objectives effectively, the proposed system includes five major modules. These are: global information collection system, depot controller, route planner, vehicle scheduler, vechicle route and time table feedback system. A simulation experiment is described at the end of the article to evaluate the performance of the proposed system. The results indicate that the proposed system is promising and can be implemented in practical operations.

This chapter applies the Consortium for Advanced Management, International (CAM-I) Activity-Based Cost Management (ABC/M) tool to paratransit. The intent is to enable agencies sponsoring rides to save money through sharing rides and vehicle-time.

Several paratransit cost-allocation models from Transit Cooperative Research Program (TCRP) and other sources are reviewed and one is adapted to the ABC/M methodology, based upon the author’s previous work proportionately allocating ride time among sponsoring agencies at a consolidated human service transportation agency and the price sheets used in contracted operations to minimize financial risk.

Through application of the principles of ABC/M, paratransit providers can properly allocate costs, determine the costs of providing proposed new services, plan for future vehicle acquisitions, and motivate their customers to tailor their transportation needs in a manner that will save them money and boost efficiency.

University-based transportation studies programs may be motivated to apply these strategies to urban and rural paratransit providers that serve several customer agencies.

If agencies sponsoring paratransit rides understand that funds can purchase more rides during off-peak hours or if rides are shared with clients of other agencies, then paratransit resources can be used more efficiently and to the benefit of more individuals.

By enabling the provision of more rides, a greater number of riders will be enabled to reach necessary services and participate in community life.

This is the first application of the ABC/M methodology to paratransit (and transit) and possibly to social services.

Refugee camps can be severely struck by pandemics, like potential COVID-19 outbreaks, due to high population densities and often only base-level medical infrastructure. Fast responding medical systems can help to avoid spikes in infections and death rates as they allow the prompt isolation and treatment of patients. At the same time, the normal demand for emergency medical services has to be dealt with as well. The overall goal of this study is the design of an emergency service system that is appropriate for both types of demand.

A spatial hypercube queuing model (HQM) is developed that uses queuing-theory methods to determine locations for emergency medical vehicles (also called servers). Therefore, a general optimization approach is applied, and subsequently, virus outbreaks at various locations of the study areas are simulated to analyze and evaluate the solution proposed. The derived performance metrics offer insights into the behavior of the proposed emergency service system during pandemic outbreaks. The Za'atari refugee camp in Jordan is used as a case study.

The derived locations of the emergency medical system (EMS) can handle all non-virus-related emergency demands. If additional demand due to virus outbreaks is considered, the system becomes largely congested. The HQM shows that the actual congestion is highly dependent on the overall amount of outbreaks and the corresponding case numbers per outbreak. Multiple outbreaks are much harder to handle even if their cumulative average case number is lower than for one singular outbreak. Additional servers can mitigate the described effects and lead to enhanced resilience in the case of virus outbreaks and better values in all considered performance metrics.

Some parameters that were assumed for simplification purposes as well as the overall model should be verified in future studies with the relevant designers of EMSs in refugee camps. Moreover, from a practitioners perspective, the application of the model requires, at least some, training and knowledge in the overall field of optimization and queuing theory.

The model can be applied to different data sets, e.g. refugee camps or temporary shelters. The optimization model, as well as the subsequent simulation, can be used collectively or independently. It can support decision-makers in the general location decision as well as for the simulation of stress-tests, like virus outbreaks in the camp area.

The study addresses the research gap in an optimization-based design of emergency service systems for refugee camps. The queuing theory-based approach allows the calculation of precise (expected) performance metrics for both the optimization process and the subsequent analysis of the system. Applied to pandemic outbreaks, it allows for the simulation of the behavior of the system during stress-tests and adds a further tool for designing resilient emergency service systems.

The purpose of this paper is to conceptualize how information technology (IT) enables supply chain (SC) network capabilities, which is to capitalize on SC’s existing set of resources and, at the same time, manage new combinations of SC resources to meet future market needs. The paper also develops SCM framework associated with IT-enabled SC network capabilities.

Through a case study of a leading Taiwanese petrochemical corporation, qualitative data were gathered on the IT-related SC management practices, in terms of network resource mobilizing and adaptive co-management arrangements to enable SC network capability. This research is based primarily on the interviews of the case company, supplemented by archived documents, published books, and in-depth observations.

Based on the evidence from the case, this study inductively develops a model that includes the operating processes with IT-enabled activities to achieve ambidextrous SC network capability, and the relevant framework functions in network resources and co-management activities include information co-governance, information interoperability, community engagement strategy, cyber-physical dexterity, and control enactment, which lead the SC alliances improvements for dynamic environmental changes.

Practitioners may derive strategies and tactics from the current findings to help them implement innovative information technologies and setup SC framework, during SC network capability development, to achieve SC’s sustainable competence in a dynamic market.

Researchers and practitioners may obtain a more complete view of IT-enabled SC network capability development. The proposed model reveals that developing IT-enabled SC network capabilities is a dynamic process whereby an organization’s major SC managerial activities are divided into specific network resource mobilizing and adaptive co-management arrangements.

This study aims to minimize the expected arrival time of relief vehicles to the affected areas, considering the destruction of potential routes and disruptions due to disasters. In relief operations, required relief items in each affected area and disrupted routes are considered as uncertain parameters. Additionally, for a more realistic consideration of the situations, it is assumed that the demand of each affected area could be met by multiple vehicles and distribution centers (DCs) and vehicles have limited capacity.

The current study developed a two-stage stochastic programming model for the distribution of relief items from DCs to the affected areas. Locating the DCs was the first-stage decisions in the introduced model. The second-stage decisions consisted of routing and scheduling of the vehicles to reach the affected areas.

In this paper, 7th district of Tehran was selected as a case study to assess the applicability of the model, and related results and different sensitivity analyses were presented as well. By carrying out a simultaneous sensitivity analysis on the capacity of vehicles and the maximum number of DCs that can be opened, optimal values for these parameters were determined, that would help making optimal decisions upon the occurrence of a disaster to decrease total relief time and to maximize the exploitation of available facilities.

The contributions of this paper are as below: presenting an integrated model for the distribution of relief items among affected areas in the response phase of a disaster, using a two-stage stochastic programming approach to cope with route disruptions and uncertain demands for relief items, determining location of the DCs and routing and scheduling of vehicles to relief operations and considering a heterogeneous fleet of capacitated relief vehicles and DCs with limited capacity and fulfilling the demand of each affected area by more than one vehicle to represent more realistic situations.