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The BVRLA ICFM Professional Fleet Consultant Development Programme, developed in conjunction with the ICFM, is targeted at sales and business development executives within BVRLA member companies. The four‐day training programme equips participants with the skills and knowledge to align their sales role more closely with the demands and responsibilities of the client fleet manager. This paper aims to describe the programme.

The paper describes the tailored approach of the programme.

The first module covers the principles of fleet management, with a focus on understanding the role and responsibilities of a fleet manager; the principles of asset and risk management; and the influence of a client's stake holders, company culture and market segmentation. It also looks at wider concerns influencing fleet choice, including the environment. The second module covers the delivery of fleet solutions; the various budgetary and taxation considerations; key acquisition and disposal options; supplier management; and the relevant legal and health and safety obligations.

The new programme, launched in October 2008, has been enthusiastically welcomed by the leasing and fleet management industry, which has seen its customers grow more sophisticated in their business processes and, as a consequence, ever more demanding of their service providers. BVRLA members recognise the value in an industry‐acknowledged qualification that demonstrates both their investment in employee development and their commitment to understanding the needs of their customers. The Professional Fleet Consultant Development Programme course is externally accredited by the Institute of Leadership and Management.

The paper is the first detailed description of the origins, design, delivery of the BVRLA ICFM Professional Fleet Consultant Development Programme.

The purpose of this study is to develop a holistic optimization model for an integrated sustainable fleet planning and closed-loop supply chain (CLSC) network design problem under uncertainty.

A novel mixed-integer programming model that is able to consider interactions between vehicle fleet planning and CLSC network design problems is first developed. Uncertainties of the product demand and return fractions of the end-of-life products are handled by a chance-constrained stochastic program. Several Pareto optimal solutions are generated for the conflicting sustainability objectives via compromise and fuzzy goal programming (FGP) approaches.

The proposed model is tested on a real-life lead/acid battery recovery system. By using the proposed model, sustainable fleet plans that provide a smaller fleet size, fewer empty vehicle repositions, minimal CO₂ emissions, maximal vehicle safety ratings and minimal injury/illness incidence rate of transport accidents are generated. Furthermore, an environmentally and socially conscious CLSC network with maximal job creation in the less developed regions, minimal lost days resulting from the work's damages during manufacturing/recycling operations and maximal collection/recovery of end-of-life products is also designed.

Unlike the classical network design models, vehicle fleet planning decisions such as fleet sizing/composition, fleet assignment, vehicle inventory control, empty repositioning, etc. are also considered while designing a sustainable CLSC network. In addition to sustainability indicators in the network design, sustainability factors in fleet management are also handled. To the best of the authors' knowledge, there is no similar paper in the literature that proposes such a holistic optimization model for integrated sustainable fleet planning and CLSC network design.

This study aims to minimize the transportation-related cost in distribution while utilizing a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach. An increased cost in distribution is a major problem for many companies due to the absence of efficient planning methods to overcome operational challenges in distinct distribution networks. The problem addressed in this study is to minimize the transportation-related cost in distribution while using a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach which has not gained the adequate attention in the literature.

This study formulated the transportation problem as a vehicle routing problem with a heterogeneous fixed fleet and workload balancing, which is a combinatorial optimization problem of the NP-hard category. The model was solved using both the simulated annealing and a genetic algorithm (GA) adopting distinct local search operators. A greedy approach has been used in generating an initial solution for both algorithms. The paired t-test has been used in selecting the best algorithm. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet compositions of the heterogeneous fleet. Results were analyzed using analysis of variance (ANOVA) and Hsu’s MCB methods to identify the best scenario.

The solutions generated by both algorithms were subjected to the t-test, and the results revealed that the GA outperformed in solution quality in planning a heterogeneous fleet for distribution with load balancing. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet utilization with different compositions of the heterogeneous fleet. Results were analyzed using ANOVA and Hsu’s MCB method and found that removing the lowest capacities trucks enhances the average vehicle utilization with reduced travel distance.

The developed model has considered both planning of heterogeneous fleet and the requirement of work load balancing which are very common industry needs, however, have not been addressed adequately either individually or collectively in the literature. The adopted solution methodologies to solve the NP-hard distribution problem consist of metaheuristics, statistical analysis and scenario analysis are another significant contribution. The planning of distribution operations not only addresses operational-level decision, through a scenario analysis, but also strategic-level decision has also been considered.

The planning of distribution operations not only addresses operational-level decisions, but also strategic-level decisions conducting a scenario analysis.

The purpose of the study is to establish the relationship between public health sector performance (PHSP), fleet management system, perceived service quality (PSQ) and management style (MS).

A total of 260 managerial employees were randomly selected from 5 major public hospitals in Zimbabwe to participate in this cross-sectional survey.

Fleet management system was found to positively influence both PSQ and PHSP. The results indicated that PSQ has a positive effect on PHSP. MS was found to moderate the effect of fleet management system on both PSQ and PHSP.

The current study provides fresh insights and validates extant knowledge on PHSP, fleet management and PSQ within the public health sector departments. It extends further knowledge on the public health performance in the Sub-Saharan region, as it shows that dimensions on fleet management have a direct influence on PHSP.

Energy groups are cargo owners with large amounts of energy sources (such as coal) to transport. To achieve a satisfactory tradeoff between the reliability requirements of the sea transportation process and the need to control the investment cost, they usually set up a self-owned fleet supplemented by a chartered fleet. This paper aims to investigate the best fleet structure and to evaluate the investment scheme under volatile circumstances in the shipping market.

The authors construct a mathematical model to determine the ratio of the self-owned fleet to the total fleet to minimize fleet operating costs. The volatility of both freight rates and oil prices is taken into consideration. The CPLEX solver is used to empirically analyze real data from an energy group in China, and the ship investment plan is evaluated considering the technical and economic feasibility.

If the ratio of the self-owned fleet to the total fleet is increased to the optimal of 90.40%, the total operating cost is reduced by 33.98%. Thus, the energy group should increase its capacity with a Panamax vessel of approximately 82,000 DWT. Purchasing a 5-year-old secondhand ship and building a new ship both have good investment return indicators.

For cargo owners engaging in transporting bulk cargo domestically in China, the suggested fleet ratio can provide a reference with a universal application scale, given the boundary economic conditions (including the volatility of freight rates and oil prices in the shipping market) in the paper.

A replacement model over two cycles, with decision variables based on age at replacement of the current fleet and size of the new fleet is considered. In order to consider both age at replacement and fleet size, the concept of penalty cost for unmet demand is introduced and modelled using results from birth‐and‐death processes. Optimal values for decision variables may be found through minimization of the total discounted cost per unit time or the equivalent rent value. The role of the penalty cost and its influence on decision variables is emphasized. A numerical solution is proposed and illustrated using data on a particular fleet of medical equipment.

The purpose of this study is to investigate the best fleet for a new purchase based on multi-objective optimization on the basis of ratio (MOORA), reference point and multi-MOORA methods. This study further identifies critical parameters for fleet performance monitoring and exploring optimum range of critical parameters using Monte Carlo simulation. At the end of this study, fleet maintenance management and operations have been discussed in the perspectives of risk management.

Fleet categories and fleet performance monitoring parameters have been identified using the literature survey and Delphi method. Further, real-time data has been analyzed using MOORA, reference point and multi-MOORA methods. Taguchi and full factorial design of experiment (DOE) are used to investigate critical parameters for fleet performance monitoring.

Fleet performance monitoring is done based on fuel consumption (FC), CO₂ emission (CE), coolant temperature (CT), fleet rating, revenue generation (RG), fleet utilization, total weight and ambient temperature. MOORA, reference point and multi-MOORA methods suggested the common best alternative for a particular category of the fleet (compact, hatchback and sedan). FC and RG are the critical parameters for monitoring the fleet performance.

The geographical aspects have not been considered for this study.

A pilot run of 300 fleets shows saving of Rs. 2,611,013/- (US$36,264.065), which comprises total maintenance cost [Rs. 1,749,033/- (US$24,292.125)] and FC cost [Rs. 861,980/- (US$11,971.94)] annually.

Reduction in CE (4.83%) creates a positive impact on human health. The reduction in the breakdown maintenance of fleet improves the reliability of fleet services.

This study investigates the most useful parameters for fleet management are FC, CE, CT. Taguchi DOE and full factorial DOE have identified FC and RG as a most critical parameters for fleet health/performance monitoring.

The purpose of this paper is to develop an original model and a solution procedure for solving jointly three main strategic fleet management problems (fleet composition, replacement and make-or-buy), taking into account interdependencies between them.

The three main strategic fleet management problems were analyzed in detail to identify interdependencies between them, mathematically modeled in terms of integer nonlinear programing (INLP) and solved using evolutionary based method of a solver compatible with a spreadsheet.

There are no optimization methods combining the analyzed problems, but it is possible to mathematically model them jointly and solve together using a solver compatible with a spreadsheet obtaining a solution/fleet management strategy answering the questions: Keep currently exploited vehicles in a fleet or remove them? If keep, how often to replace them? If remove then when? How many perspective/new vehicles, of what types, brand new or used ones and when should be put into a fleet? The relatively large scale instance of problem (50 vehicles) was solved based on a real-life data. The obtained results occurred to be better/cheaper by 10% than the two reference solutions – random and do-nothing ones.

The methodology of developing optimal fleet management strategy by solving jointly three main strategic fleet management problems is proposed allowing for the reduction of the fleet exploitation costs by adjusting fleet size, types of exploited vehicles and their exploitation periods.

Sustainable transport has become a new paradigm offering efficient, equitable, and pro-environment transport services. Many intermodal freight systems, especially those for port-to-rail networks, consist of multiple routes starting from and ending at the same port in order to exploit economies of scale. It is of interest to railway operators, therefore, to improve the efficiency of the system by finding the optimal fleet size (the number of cars assigned to a route) and frequency for each route. This paper proposes a model which determines the optimal frequency of each route under the total fleet size constraint for the one-to-many distribution. Trains carry items from one port to their destinations on their predetermined routes. This paper focuses on situations in which items from one port are transported to many destinations via railroads. The tradeoffs between transportation and inventory costs determine optimal frequency under the total fleet size and capacity constraints. The optimal frequency and fleet size of each route are calculated and then updated at the end of each step of the model. The model that we have developed in this paper is validated by port-to-rail freight data from actual shipments in Korea. The results of the analysis show that the proposed model can provide a more reliable and realistic representation of the real one-to-many distribution than the other alternatives which are commonly used. This study not only forms the theoretical basis of an effective and rational freight operation, but it also contributes to the assessment of the existing and planned logistics systems.

The purpose of this paper is to develop a simple model illustrating the benefits of operating a diverse fleet of aircraft.

The paper is theoretical. It describes how real options are beneficial to the firm in both capital budgeting and risk management. It illustrates the use of real options in the airline industry, and how real options are used as a risk management tool. Then the model is developed which illustrates how a diverse fleet can provide an airline protection against fuel price risk.

The results of the model show that a diverse fleet is value enhancing to an airline during periods when fuel prices are high or uncertain. Furthermore, this paper shows that a diverse fleet provides an airline with an operational hedge to jet fuel prices. Though the paper focuses on the airline industry, the results are applicable to those industries vulnerable to volatile input costs, and prohibitive abandonment and re‐entry costs.

The paper uses real option analysis to show the benefits for an airline deriving from operating a diverse fleet of aircraft.