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Purpose – Land assembly can mitigate the negative environmental impacts of land fragmentation on urban areas, agriculture, and wildlife. However, the assembler faces several obstacles including transactions costs and the strategic bargaining behavior of landowners. The purpose of this chapter is to examine how the order of bargaining and the nature of contracts may impact the land assembler's problem.

Methodology – We develop theoretical predictions of subjects' behavior and compare these to behavior in a laboratory land-assembly game with monetary incentives.

Findings – Sellers bargain more aggressively when bargaining is sequential compared to simultaneous. Noncontingent contracts increase bargaining delay and the likelihood of failed agreements. Buyers and sellers act more aggressively when there are multiple bargaining periods, leading to significant bargaining delay. When a seller has an earnings advantage in the laboratory, it is the first seller to bargain in noncontingent contract treatments. In sequential bargaining treatments, most sellers preferred to be the first seller to bargain.

Research limitations – Our laboratory experiments involved only two sellers, complete information, and costless delay. Land assembly in the field may involve many sellers, incomplete information, and costly delay.

Practical implications – Some of our results contradict conventional wisdom and a common result from the land-assembly literature that it is advantageous to be the last seller to bargain, a so-called “holdout.” Our results also imply that fully overcoming the holdout problem may require subsidies or compulsory acquisition.

Originality – This chapter is one of the first to experimentally investigate the land-assembly problem, and the first to specifically examine the role of bargaining order and contract type.

This chapter presents selected multiobjective methods for multiperiod portfolio optimization problem. Portfolio models are formulated as multicriteria mixed integer programs. Reference point method together with weighting approach is proposed. The portfolio selection problem considered is based on a multiperiod model of investment, in which the investor buys and sells securities in successive investment periods. The problem objective is to allocate the wealth on different securities to optimize the portfolio expected return, the probability that the return is not less than a required level. Multiobjective methods were used to find tradeoffs between risk, return, and the number of securities in the portfolio. In computational experiments the data set of daily quotations from the Warsaw Stock Exchange were used.

This paper demonstrates that present value (PV) models can be viewed as multiperiod extensions of accrual income statements (AISs). Failure to include AIS details in PV models may lead to inaccurate estimates of earnings and rates of return on assets and equity and inconsistent rankings of mutually exclusive investments. Finally, this paper points out that rankings based on assets and equity earnings and rates of return need not be consistent, requiring financial managers to consider carefully the questions they expect PV models to answer.

AISs are used to guide the construction of PV models. Numerical examples illustrate the results. Deductions from AIS definitions demonstrate the potential conflict between asset and equity earnings and rates of return.

PV models can be viewed as multiperiod extensions of AISs. Mutually exclusive rankings based on assets and equity earnings and rates of return need not be consistent.

PV models are sometimes constructed without the details included in AISs. The result of this simplified approach to PV model construction is that earnings and rates of return may be miscalculated and rankings based as asset and equity earnings and rates of return are inconsistent. Tax adjustments for asset and equity earnings may be miscalculated in applied models.

This paper provides guidelines for properly constructing PV models consistent with AISs.

PV models are especially important for small to medium size firms that characterize much of agricultural. Providing a model consistent with AIS construction principles should help financial managers view the linkage between building financial statements and investment analysis.

This is the first paper to develop the idea that the PV model can be viewed as a multiperiod extension of an AIS.

The purpose of this paper is to set up a two‐stage stochastic integer‐programming model (TSM) for the multiperiod scheduling of multiproduct batch plants under demand uncertainty involving the constraints of material balances and inventory constraints, as well as the penalty for production shortfalls and excess.

Scheduling model is formulated as a discrete‐time State Task Network. Given a scheduling horizon consisting of several time‐periods in which product demands are placed, the objective is to select a schedule that maximizes the expected profit for a single and multiple product with a given probability level. The stochastic elements of the model are expressed with equivalent deterministic optimization models.

The TSM model not only allows for uncertain product demand correlations, but also gives different processing modes by a range of batch sizes and a task‐dependent processing time. The experimental results show that the TSM model is more appropriate than another model for multiperiod scheduling of multiproduct batch plants under correlated uncertain demand.

The choice of penalty parameter of demand uncertainty is the main limitation.

The paper provides very useful advice for multiperiod scheduling of multiproduct batch plants under demand uncertainty.

A stochastic model for the multiperiod scheduling of multiproduct batch plants under demand uncertainty was set up. A test problem involving 12 correlated uncertain product demands and two alternative models verified the availability of the TSM.

Purpose — The paper aims at an improvement of the understanding, how mobility is reported in longitudinal surveys and to develop ideas how to assess the completeness of the reported mobility.

Methodology/approach — Analyses of data quality and completeness are performed on the multiday and multiperiod data of the German Mobility Panel. Distinctions are made between differing reporting behaviours of individuals who either reported three times, two times or only once.

Findings — It can be shown that the reporting behaviours are different depending on the number of repetitions. The results illustrate that on the one hand individuals who repeat the survey in a consecutive wave tend to report with greater motivation, endurance and accuracy. On the other hand, participants who have not reported completely and accurately are more likely to drop out. These effects positively influence the quality and completeness and therefore the reliability of recorded mobility figures in multiperiod mobility surveys.

Practical implications — The analytical possibilities of combined multiday and multiperiod data in terms of the assessment of data quality will be demonstrated. Hints to identify such types of survey artefacts are presented.

This study aimed to develop the integration of the multiperiod production-distribution model in a closed-loop supply chain involving carbon emission and traceability. The developed model was for agricultural food (agri-food) products, considering the reverse flow of food waste from the disposal center (composting center) to producers.

The results indicate that integrating the production and distribution model considering food waste recycling provides low carbon emissions in lower total costs. The sensitivity analysis also found that there are trade-offs between production and distribution rate and food waste levels on carbon emission and traceability.

This study focuses on the mathematical modeling of a multiperiod production-distribution formulation for a closed-loop supply chain.

The model of the agri-food closed-loop supply chain in this study that considers food recycling and carbon emissions would help stakeholders involved in the agri-food supply chain to reduce food waste and carbon emissions.

In this paper, a maintenance strategy based on improved imperfect maintenance actions with stochastic repair times for multiperiod randomly failing equipment is developed. The main objective is to minimize the total maintenance cost by jointly finding the optimal preventive maintenance (PM) cycle and planning horizon.

A model based on the mathematical theory of reliability is developed to minimize the total maintenance cost by jointly finding the optimal couple: PM cycle T* and planning horizon H*. The proposed model aims to characterize the evolutionary impact of imperfect PM actions on the equipment failure rate and the resulting mean number of failures. The conventional threshold accepting (TA) algorithm is implemented to solve the proposed model. A numerical example for a given set of input parameters is presented in order to show the usefulness of the proposed model. A sensitivity analysis of some of the key parameters is performed to demonstrate the coherence of the developed maintenance policy.

The obtained results showed a sensitive trade-off between PM frequency and the total maintenance cost. Performing PM actions more frequently helps significantly to reduce the expected number of corrective maintenance actions and the corresponding total cost. It has also been found that improving the efficiency of the PM actions allows for maintaining the equipment less frequently by increasing the time between successive PM actions.

Given the complexity of the objective function to be minimized and the stochastic nature of the model's parameters, the authors limited this study to equally cyclic production periods over the planning horizon.

The present model aims to provide an integrated maintenance/production comprehensive framework to assist planners in establishing maintenance schedules considering multiperiod randomly failing production systems and the evolutionary impact of imperfect PM actions on the equipment failure rate.

Contrary to the majority of existing works in the literature dealing with maintenance strategies, the authors consider that repair times are stochastic to provide a more realistic framework. In addition, the developed model considers the impact of imperfect maintenance on the equipment's mean time to failure. Thus, the evolutionary impact of imperfect PM actions on the equipment failure rate and the resulting mean number of failures is characterized. Simultaneously, the production planning horizon along with the length of each PM cycle is optimized in order to minimize the total maintenance cost over the planning horizon.

Economic incentives, government regulations, and customer perspective on environmental consciousness (EC) are driving more and more companies into product recovery business, which forms the basis for a reverse supply chain. A reverse supply chain consists a series of activities that involves retrieving used products from consumers and remanufacturing (closed-loop) or recycling (open-loop) them to recover their leftover market value. Much work has been done in the areas of designing forward and reverse supply chains; however, not many models deal with the transshipment of products in multiperiods. Linear physical programming (LPP) is a newly developed method whose most significant advantage is that it allows a decision-maker to express his/her preferences for values of criteria for decision-making in terms of ranges of different degrees of desirability but not in traditional form of weights as in techniques such as analytic hierarchy process, which is criticized for its unbalanced scale of judgment and failure to precisely handle the inherent uncertainty and vagueness in carrying out pair-wise comparisons. In this chapter, two multiperiod models are proposed for a remanufacturing system, which is an element of a Reverse Supply Chain (RSC), and illustrated with numerical examples. The first model is solved using mixed integer linear programming (MILP), while the second model is solved using linear physical programming. The proposed models deliver the optimal transportation quantities of remanufactured products for N-periods within the reverse supply chain.

The new Basel Accord (known as Basel II) attempts to introduce more risk-sensitive capital requirements. We propose a multiperiod deposit insurance pricing model that incorporates specific regulatory capital requirements and the possibility of capital forbearance and moral hazard. We estimate the cost of deposit insurance under alternative regulation regimes based on the building block approach of the 1988 Basel Accord (known as Basel I) and internal model-based (IMB) capital regulation. In contrast to the building block of Basel I, Basel II's IMB capital regulation links more closely the capital requirement to a bank's actual risk. We develop a multiperiod pricing model while incorporating the effects of capital forbearance and moral hazard. The fairly-priced premium rates are computed by assuming that a bank's asset value follows a GARCH process. In contrast to previous studies based on the building block capital standard, we find that forbearance and the potential moral hazard behavior will not increase the cost of deposit insurance in the scheme of Basel II's IMB capital regulation.

The determination of the capital requirements represents the first Pillar of Solvency II. The main purpose of the new solvency regulation is to obtain more realistic modelling and assessment of the different risks insurance companies are exposed to in a balance‐sheet perspective. In this context, the Solvency Capital Requirement (SCR) standard calculation is based on a modular approach, where the overall risk is split into several modules and submodules. In Solvency II, standard formula longevity risk is explicitly considered. The purpose of this paper is to look at the backtesting approach for measuring the consistency of SCR calculations for life insurance policies.

A multiperiod approach is suggested for correctly calculating the SCR in a risk management perspective, in the sense that the amount of capital necessary to meet company future obligations year by year until the contract will be in force has to be assessed. The backtesting approach for measuring the consistency of SCR calculations for life insurance policies represents the main contribution of the research. In fact this kind of model performance is generally specified in the VaR validation analysis. In this paper, this approach is considered for testing the ex post performance of SCR calculation methodology.

The backtesting framework is able to measure, from time to time, if the insurer has allocated more or less capital to support his in‐force business, with adverse effects on free reserves and profitability or solvency.

The paper shows that the forecasting performance is an important aspect to assess the effectiveness of the model, a poor performance corresponding to a biased allocation of capital.

The backtesting approach for measuring the consistency of SCR calculations for life insurance policies represents the main contribution of the research. In fact this kind of model performance is generally specified in the VaR validation analysis. Recently, Dowd et al. have proposed it for verifying the goodness of mortality models and now, in this paper, this approach is considered for testing the ex post performance of SCR calculation methodology.