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The contemporary e-tailing marketplace insists that distribution centers are playing the roles of both wholesalers and retailers which require different storage-handling load sizes due to different product variants. To fulfill piecewise retail orders, a separate small size-fast pick area is design called “forward buffer” wherein pallets are allocated from reserve area. Due to non-uniform pallets, the static allocation policy diminishes forward space utilization and also, more than practically required buffer size has been identified as wastage. Thus, dynamic storage allocation policy is required to design for reducing storage wastage and improving throughput considering non-uniform unit load sizes. The purpose of this paper is to model such policy and develop an e-decision support system assisting enterprise practitioners with real-time decision making.

The research method is developed as a dynamic storage allocation policy and mathematical modeled as knapsack-based heuristics. The execution procedure of policy is explained as an example and tested with case-specific data. The developed model is implemented as a web-based support system and tested with rational data instances, as well as overcoming prejudices against single case findings.

The provided model considers variable size storage-handling unit loads and recommends number of pallets allocations in forward area reducing storage wastes. The algorithm searches and suggests the “just-right” amount of allocations for each product balancing existing forward capacity. It also helps to determine “lean buffer” size for forward area ensuring desired throughput. Sensitivity and buffer performance analysis is carried out for Poisson distributed data sets followed by research synthesis.

Warehouse practitioners can use this model ensuring a desired throughput level with least forward storage wastages. The model driven e-decision support system (DSS) helps for effective real-time decision making under complicated business scenarios wherein products are having different physical dimensions. It assists the researchers who would like to explore the emerging field of “lean” adoption in enterprise information and retail-distribution management.

The paper provides an inventive approach endorsing lean thinking in storage allocation policy design for a forward-reserve model. Also, the developed methodology incorporating features of e-DSS along with quantitative modeling is an inimitable research contribution justifying rational data support.

Property is a key investment asset class that offers considerable benefits in a mixed-asset portfolio. Previous studies have concluded that property allocation should be within the 10-30 per cent range. However, there seems to be wide variation in theory and practice. Historical Australian superannuation data shows that the level of allocation to property asset class in institutional portfolios has remained constant in recent decades, restricted at 10 per cent or lower. This is seen by many in the property profession as a subjective measure and needs further investigation. The purpose of this paper is to compare the performance of the AU$431 billion industry superannuation funds’ strategic balanced portfolio against ten different passive and active investment strategies.

The analysis used 20 years (1995-2015) of quarterly data covering seven benchmark asset classes, namely: Australian equities, international equities, Australian fixed income, international fixed income, property, cash and alternatives. The 11 different asset allocation models are constructed within the modern portfolio theory framework utilising Australian ten-year bonds as the risk free rate. The Sharpe ratio is used as the key risk-adjusted return performance measure.

The ten different asset allocation models perform as well as the industry fund strategic approach. The empirical results show that there is scope to increase the property allocation level from its current 10-23 per cent. Upon excluding unconstrained strategies, the recommended allocation to property for industry funds is 19 per cent (12 per cent direct and 7 per cent listed). This high allocation is backed by improved risk-adjusted return performance.

The constrained optimal, tactical and dynamic models are limited to asset weight, no short selling and turnover parameters. Other institutional constraints that can be added to the portfolio optimisation problem include transaction costs, taxation, liquidity and tracking error constraints.

The 11 different asset allocation models developed to evaluate the property allocation component in industry superannuation funds portfolio will attract fund managers to explore alternative strategies (passive and active) where risk-adjusted returns can be improved, compared to the common strategic approach with increased allocation to property assets.

The research presents a unique perspective of investigating the optimal allocation to property assets within the context of active investment strategies, such as tactical and dynamic models, whereas previous studies have focused mainly on passive investment strategies. The investigation of these models effectively contributes to the transfer of broader finance and investment market theories and practice to the property discipline.

This article aims to establish a dynamic Energy Performance Contract (EPC) risk allocation model for commercial buildings based on the theory of Incomplete Contract. The purpose is to fill the policy vacuum and allow stakeholders to manage risks in energy conservation management by EPCs to better adapt to climate change in the building sector.

The article chooses a qualitative research approach to depict the whole risk allocation picture of EPC projects and establish a dynamic EPC risk allocation model for commercial buildings in China. It starts with a comprehensive literature review on risks of EPCs. By modifying the theory of Incomplete Contract and adopting the so-called bow-tie model, a theoretical EPC risk allocation model is developed and verified by interview results. By discussing its application in the commercial building sector in China, an operational EPC three-stage risk allocation model is developed.

This study points out the contract incompleteness of the risk allocation for EPC projects and offered an operational method to guide practice. The reasonable risk allocation between building owners and Energy Service Companies can realize their bilateral targets on commercial building energy-saving benefits, which makes EPC more attractive for energy conservation.

Existing research focused mainly on static risk allocation. Less research was directed to the phased and dynamic risk allocation. This study developed a theoretical three-stage EPC risk allocation model, which provided the theoretical support for dynamic EPC risk allocation of EPC projects. By addressing the contract incompleteness of the risk allocation, an operational method is developed. This is a new approach to allocate risks for EPC projects in a dynamic and staged way.

On one side, the purpose of this paper is to numerically analyze the emergency order allocation mechanism and help managers to understand the relationship between the emergency coefficient, uncertainty and emergency cost in two‐echelon logistics service supply chain. On the other side, the purpose of this paper is to help managers understand how to deal with the problem of order allocation in the two‐echelon logistics service supply chain better in the case of emergency.

The paper presents a multi‐objective planning model for emergency order allocation and then uses numerical methods with LINGO 8.0 software to identify the model's properties. The application of the order allocation model is then presented by means of a case study.

With the augment of uncertainty, the general cost of logistics service integrator (LSI) is increasing, while the total satisfaction of all functional logistics service providers (FLSPs) is decreasing, as well as the capacity reliability; at the same time the emergency cost coefficient is closely correlative with the satisfaction and general penalty intensity of FLSPs; finally, the larger the emergency cost coefficient is, the more satisfaction of FLSPs, but the capacity reliability goes up first and down later.

Management should note that it is not better when emergency cost coefficient is bigger. The general satisfaction degree of FLSP increases with the augment of emergency cost coefficient, but there is an upper limit of the value, i.e. it will not increase indefinitely with the augment of emergency cost coefficient. This paper also has some limitations. The optional emergency cost coefficient only adopted a group of data to analyze while the trend of the reliability of logistics capacity needs to be further discussed. In addition, the algorithm of emergency order allocation model in the case of multi‐objective remains to be solved.

Under emergency conditions, LSIs can adopt this kind of model to manage their FLSPs to obtain the higher logistics performance. But LSIs should be careful selecting emergency cost coefficient. In accordance with different degrees of emergency logistics demand, LSIs can determine reasonable emergency cost coefficient, but not the bigger, the better, on the premise that LSIs acquire maximum capacity guarantee degree and overall satisfaction degree of FLSPs. FLSPs can make contract bargaining of reasonable emergency coefficient with LSIs to make both sides get the best returns and realize the benefit balance.

Many studies have emphasized the capacity allocation of manufactures, order allocation of manufacturing supply chain and scheduling model of emergency resources without monographic study of supply chain order allocation of logistics service. Because the satisfaction degree of FLSPs the cost of integrators needs to be considered in the process of order allocation, and the inventory cost of capacity does not exist, it is different from the issue of capacity allocation planning of manufacture supply chain. Meanwhile, the match of different kinds of logistics service capacity must be considered for the reason of the integrated feature of logistics service. Additionally, cost is not the most important decision objective because of the characteristics of demand uncertainty and weak economy. Accordingly, this paper considers these issues.

This paper aims to help understand how adopting risk allocation criteria impacts the delivery of public–private partnership (PPP) mass housing in Nigeria with the view of promoting the adoption of PPP housing scheme in Nigeria.

The research design adopts the census sampling approach by using well-structured questionnaires distributed to stakeholders involved in PPP-procured mass housing projects, i.e. consultants, in-house professionals, contractors and the organized private sector, registered with PPP departments in the Federal Capital Territory Development Authority, Abuja, Nigeria. Sixty-three risk factors, nine risk allocation criteria and nine project delivery indices were submitted for the respondents to rank on a Likert scale of 7. Two hypotheses were formulated to test whether the risk allocation criteria impacted PPP mass housing delivery or otherwise. The study adopts partial least square-structural equation modeling to model the effect of risk on risk allocation criteria on project delivery indices and risk severity.

The finding shows that project risk allocation criteria have less effect on project delivery indices than on risk severity. The study concludes that risk allocation principles do not directly affect the delivery of PPP-procured mass housing projects. This is evident by the path coefficient of 0.724 values, which is not statistically significant at a 5% alpha protection value. The study concludes that allocating critical risk factors influences the performance of PPP-procured mass housing projects, as the path coefficient of 0.360 is also not significantly far from 0 and at a 5% alpha protection value.

The study is one of the recent studies conducted in PPP-procured mass housing projects in Nigeria owing to the novelty of procurement option in the sector. It highlights the risk factors that can jeopardize the PPP-procured mass housing project objectives. The study is of immense value to PPP actors in the sector by providing the necessary information required to formulate risk response methods to minimize the impact of the risk factors in PPP mass housing projects.

This paper proposes a model based on minority game (MG) theory to support the decision-making regarding the efficient allocation and exploitation of resources/services among the partners of a cloud manufacturing (CMfg) system. CMfg system is a new manufacturing paradigm to share manufacturing capabilities and resources on a cloud platform. The use of a decision model to organize and manage the resources and services provided by the autonomous participants of a CMfg has crucial relevance for the system's effectiveness and efficiency.

This research proposes a noncooperation model based on MG theory. The MG is designed to make decisions on the use of resources/services among the partners of CMfg with private information. A simulation environment was developed to test the efficiency of the proposed decision model. Moreover, an ideal decision model with complete information among the partners was used as a benchmark model.

The simulation results show how the application of the proposed MG model outperforms the MG model usually proposed in the literature. In particular, the proposed decision model based on private information has an efficiency closer to the ideal model with complete information among the partners of a CMfg.

This paper advances knowledge about the application of MG in the field of CMfg system. The proposed decision-making model based on MG is a promising approach to help enterprises, and especially small and medium enterprises, to participate in CMfg initiatives and to develop their business.

Concerns the logistics system of the Taiwan power company, Taipower, and the problem it faces of coal allocation and fleet deployment of dedicated coal carriers. The coal allocation problem can be viewed as a multi‐index transportation problem requiring the formulation of a coal allocation model. Owing to the high uncertainty of shipping operations, formulates a dynamic fleet assignment model to dispatch vessels and maximize ship usage. An integrated coal allocation and fleet assignment decision support system based on these models was developed to assist the decision maker. It not only guides Taipower marine operations management in annual schedule planning, but also assists operating personnel to allocate vessels to the schedules and to make rapid adjustments. Demonstrates the potential for substantial cost improvement in Taipower coal trade.

The purpose of this paper is to propose an integrated model for allocation and leveling of human resources in IT projects.

A single case study was conducted in a large company of IT outsourcing services, which were assessed the management of 14 projects. The survey was conducted through interviews with project managers, and digital files and internal documents of the organization related to these projects.

In the analysis, it was identified that the critical path is not identified in all projects, and even when this happens, resources are not allocated in the first tasks in that path. A committee controls the allocation of resources with the assessment of skills, but there is no control of all resource constraints.

The main limiting factors for this study are: use of data of one company in the IT industry, making it difficult to generalize the model for other sectors companies; it was noted during interviews that the project managers interviewed do not always know in detail all the company’s processes for allocation and resource leveling, due to the large number of processes and different management activities of these professionals.

A model and actions for this implementation was proposed, such as training for the use of the technique of critical path; allocation and leveling done simultaneously; decisions of the management committee based on information of availability, key skills, holidays, days off of human resources; development of a software tool that integrates this information, generating graphical interfaces that are not provided by project management software with the use of an allocation factor.

The characteristics of the proposed model, as well as the use of the allocation factor, can help managers to validate their allocation models and leveling of human resources in an integrated manner.

The study explains that the granularity of analysis of resource allocation increases by decomposition of the duration of each activity in fixed time segments. It is suggested to use the mathematical concept of the allocation factor (F_a).

The purpose of this paper is to study the scope for country diversification in international portfolios of mutual funds for the “core” EMU countries. The author uses a sample of daily returns for country indices of French, German and Italian funds to investigate the quest for international diversification. The author focuses on fixed-income mutual funds during the period of the financial market turmoil since 2007.

The author compute optimal portfolio allocations from both unconstrained and constrained mean-variance frameworks that take as input the out-of-sample forecasts for the conditional mean, volatility and correlation of country-level indices for funds returns. The author also applies a portfolio allocation model based on utility maximization with learning about the time-varying conditional moments. The author compares the out-of-sample forecasting performance of 12 multivariate volatility models.

The author finds that there is a “core” EMU country also for the mutual fund industry: optimal portfolios allocate the largest portfolio weight to German funds, with Italian funds assigned a lower weight in comparison to French funds. This result is remarkably robust across competing forecasting models and optimal allocation strategies. It is also consistent with the findings from a utility-maximization model that incorporates learning about time-varying conditional moments.

This is the first study on optimal country-level diversification for a mutual fund investor focused on European countries in the fixed-income space for the turmoil period. The author uses a large array of econometric models that captures the salient features of a period characterized by large changes in volatility and correlation, and compare the performance of different optimal asset allocation models.

Prior work has focused on the impact of using alternative bases for allocating costs to products but there has been little work that evaluates the use of alternative allocation bases for allocating costs to departments. In particular, if different departments of a multi‐national firm are located in settings with different reporting requirements, exchange rate risks, and costs of capital, then the choice of cost allocation base can be important. This paper examines the economic impact of alternative service department allocation bases in a decentralised setting. A non‐linear programming (NLP) approach is used to model the problem. A review of prior literature identifies a method, based on the NLP approach, for determining the economic impact of alternative allocation bases in a multi‐product setting. The method is adapted in this paper for the multi‐divisional context. The study finds that centralised production volume decision‐making is superior to decentralised decision‐making using either revenue or volume‐based cost allocation bases. Under certain conditions, revenue‐based allocation bases are also found to be superior to volume bases. Under the assumptions of the model no distinction can be made between the centralised solution and a profit‐based allocation regime. A practical implication of this study is that designers of cost allocation systems need to consider not only the direct income‐shifting effect of different cost allocation bases but also the indirect economic effect of consequential changes in the operating decisions of the firm.