The business cycle model with a unique stable limit cycle

This paper provides the mathematical foundation to the long-standing academic belief that Goodwin's 1951 nonlinear business cycle model has a unique stable limit cycle. In spite of the asymmetric nonlinearity of investment function, the model has certainly a unique stable limit cycle in an economically meaningful region. Once solution paths start from any initial point in the region, they all tend to the limit cycle without escaping from the region or hitting the ceiling or floor of investment during a transition period. The structural stability of the model prevents the limit cycle from vanishing in the face of small perturbations.     

城市更新利益相关者交互式参与模式

城市更新是当前城市研究中的热门话题。从公众参与城市更新的背景出发,提出利益相关者参与更适用于城市更新,在此基础上研究了城市更新中参与的三种不同路径,并对三种路径进行了比较分析。认为交互式参与是城市更新利益相关者参与最合适的路径。然后,分析了不同的利益相关者在城市更新中的功能。继而对城市更新现行的公众参与模式进行研究,分析其存在的问题,最后构建了城市更新利益相关者交互式参与模式。     

零压等熵的磁场气体动力学系统的柯西问题

零压等熵的磁场气体动力学方程组是等熵的磁场气体动力学系统在忽略压力效应后而得到的数学模型,本文主要研究零压等熵的磁场气体动力学方程组的一类初值问题,本文研究该系统的柯西问题,借助局部解的存在唯一性定理,通过一致先验估计,在适当的初值假设条件下,证明了该柯西问题整体C1解的存在唯一性,并给出古典解的生命期估计。     

Local determinacy of prices in an overlapping generations model with continuous trading

We characterize the determinacy properties of the intertemporal equilibrium for a continuous-time, pure-exchange, overlapping generations economy with logarithmic preferences. Using recent advances in the theory of functional differential equations, we show that the equilibrium is locally unique and that prices converge to a balanced growth path and are determined.     

A bargaining set for roommate problems

Since stable matchings may not exist, we propose a weaker notion of stability based on the credibility of blocking pairs. We adopt the weak stability notion of Klijn and Massó (2003) for the marriage problem and we extend it to the roommate problem. We first show that although stable matchings may not exist, a weakly stable matching always exists in a roommate problem. Then, we adopt a solution concept based on the credibility of the deviations for the roommate problem: the bargaining set. We show that weak stability is not sufficient for a matching to be in the bargaining set. We generalize the coincidence result for marriage problems of Klijn and Massó (2003) between the bargaining set and the set of weakly stable and weakly efficient matchings to roommate problems. Finally, we prove that the bargaining set for roommate problems is always non-empty by making use of the coincidence result.     

Forecasting an accumulated series based on partial accumulation II: A new Bayesian method for short series with stable seasonal patterns

The problem of forecasting a time series with only a small amount of data is addressed within a Bayesian framework. The quantity to be predicted is the accumulated value of a positive and continuous variable for which partially accumulated data are available. These conditions appear in a natural way in many situations. A simple model is proposed to describe the relationship between the partial and total values of the variable to be forecasted assuming stable seasonality, which is specified in stochastic terms. Analytical results are obtained for both the point forecast and the entire posterior predictive distribution. The proposed technique does not involve approximations. It allows the use of non-informative priors so that implementation may be automatic. The procedure works well when standard methods cannot be applied due to the reduced number of observations. It also improves on previous results published by the authors. Some real examples are included.     

Feasibility on the integration of passenger and freight transportation in rural areas: A service mode and an optimization model

Public transport operators in rural areas have been under pressure from weak profitability and emission issues. At the same time, scattered demand for transport has been preventing logistics systems from reaching the last mile in residential areas. Multimodal transport can synergistically integrate passenger and freight demand, increase transportation network coverage, and reduce the cost of transportation, while demand-driven services improve the flexibility and reliability of operational decisions. Therefore, this paper proposes a demand-driven passenger-and-freight-integration service (DDPFIS) mode. From the perspective of routing decisions, a new mixed-integer linear programming model based on the green vehicle routing problem is formulated to assist public transport operator’s complex decision-making. In the proposed model, vehicle capacity is fully utilized through a combination of passenger and freight demand so that optimal decisions are made about vehicle routing. Numerical experiments are designed and conducted based on realistic instances with the results indicating that: the DDPFIS mode enables effective integration of different demands, leading to high-level vehicle capacity utilization and cost reduction; and compared with two conventional models of vehicle routing problems, the proposed model achieves lower fuel consumption and cost for all problem sizes. In addition, some important management insights are provided, e.g., a greater proportion of integrated service nodes is not necessarily better; and it is more suitable to provide a service for rural residents who are relatively insensitive to time.     

A discrete‐choice model for large heterogeneous panels with interactive fixed effects with an application to the determinants of corporate bond issuance

What is the effect of funding costs on the conditional probability of issuing a corporate bond? We study this question in a novel dataset covering 5610 issuances by US firms over the period from 1990 to 2014. Identification of this effect is complicated because of unobserved, common shocks such as the global financial crisis. To account for these shocks, we extend the common correlated effects estimator to settings where outcomes are discrete. Both the asymptotic properties and the small‐sample behavior of this estimator are documented. We find that for non‐financial firms yields are negatively related to bond issuance but that the effect is larger in the pre‐crisis period.     

A genetic algorithm for the partial binary constraint satisfaction problem: an application to a frequency assignment problem

We describe a genetic algorithm for the partial constraint satisfaction problem. The typical elements of a genetic algorithm, selection, mutation and cross-over, are filled in with combinatorial ideas. For instance, cross-over of two solutions is performed by taking the one or two domain elements in the solutions of each of the variables as the complete domain of the variable. Then a branch-and-bound method is used for solving this small instance. When tested on a class of frequency assignment problems this genetic algorithm produced the best known solutions for all test problems. This feeds the idea that combinatorial ideas may well be useful in genetic algorithms.     

Processes with volatility-induced stationarity: an application for interest rates

In this paper we propose a refinement of the existing definition of volatility-induced stationarity that allows us to distinguish between processes with drift and diffusion induced stationarity and processes with pure volatility-induced stationarity. We also propose a classification of stationary processes with volatility-induced stationarity according to the volatility that is needed to inject stationarity. Processes with volatility-induced stationarity are potentially applicable to interest rate time-series since, as has been acknowledged, mean-reversion effects occur mainly in periods of high volatility. As such, we provide evidence that the logarithm of the Fed funds rate can be modelled as a local martingale with volatility-induced stationarity.     

On solving bias‐corrected non‐linear estimation equations with an application to the dynamic linear model

In a seminal paper, Mak, Journal of the Royal Statistical Society B, 55, 1993, 945, derived an efficient algorithm for solving non‐linear unbiased estimation equations. In this paper, we show that when Mak's algorithm is applied to biased estimation equations, it results in the estimates that would come from solving a bias‐corrected estimation equation, making it a consistent estimator if regularity conditions hold. In addition, the properties that Mak established for his algorithm also apply in the case of biased estimation equations but for estimates from the bias‐corrected equations. The marginal likelihood estimator is obtained when the approach is applied to both maximum likelihood and least squares estimation of the covariance matrix parameters in the general linear regression model. The new approach results in two new estimators when applied to the profile and marginal likelihood functions for estimating the lagged dependent variable coefficient in the dynamic linear regression model. Monte Carlo simulation results show the new approach leads to a better estimator when applied to the standard profile likelihood. It is therefore recommended for situations in which standard estimators are known to be biased.     

Minimax optimality of CUSUM for an autoregressive model

Different change point models for AR(1) processes are reviewed. For some models, the change is in the distribution conditional on earlier observations. For others, the change is in the unconditional distribution. Some models include an observation before the first possible change time – others not. Earlier and new CUSUM type methods are given, and minimax optimality is examined. For the conditional model with an observation before the possible change, there are sharp results of optimality in the literature. The unconditional model with possible change at (or before) the first observation is of interest for applications. We examined this case and derived new variants of four earlier suggestions. By numerical methods and Monte Carlo simulations, it was demonstrated that the new variants dominate the original ones. However, none of the methods is uniformly minimax optimal.     

A logistics model for emergency supply of critical items in the aftermath of a disaster

This paper proposes a logistics model for delivery of prioritized items in disaster relief operations. It considers multi-items, multi-vehicles, multi-periods, soft time windows, and a split delivery strategy scenario, and is formulated as a multi-objective integer programming model. To effectively solve this model we limit the number of available tours. Two heuristic approaches are introduced for this purpose. The first approach is based on a genetic algorithm, while the second approach is developed by decomposing the original problem. We compare these two approaches via a computational study. The multi-objective problem is converted to a single-objective problem by the weighted sum method. A case study is presented to illustrate the potential applicability of our model. Also, presented is a comparison of our model with that proposed in a recent paper by Balcik et al. [6]. The results show that our proposed model outperforms theirs in terms of delivering prioritized items over several time periods.     

Social enterprise factory location and allocation model: Small scale manufacturing for East Africa

The manufacturing location decision for social enterprises that work in the context of sustainable development is rarely based on a quantitative, analytical process. As a result, decisions may be far from profit-maximizing. Location and allocation optimization models have the potential to improve decisions and thus enable such enterprises to scale up their business as well as their impact. We develop and explain a single-period single-factory model, and also a two-echelon location and allocation model, to provide enterprises with information about optimal factory locations, and with future demand allocations and capacity-changing decision information. We apply the models to a company that manufactures and distributes solar cookers in East Africa. Our results illustrate that quantitative location and allocation models can significantly affect social enterprises by improving profitability. The case study shows strong cost-reduction potential of local manufacturing in developing countries due to high transportation costs for small production volumes. We discuss this model-recommended decision by weighing it against associated opportunities and risks. This paper aims to enable and encourage social and sustainability-oriented manufacturing enterprises to apply operations research methods in their strategic factory location decision-making.     

A hierarchical location model for determining capacities of neonatal intensive care units in Korea

Reforming the healthcare delivery system to provide optimum care to sick newborn infants is a critical task in Korea. Motivated by the efforts of the Korean government, we study a capacity allocation model to design an optimal capacity allocation plan for neonatal intensive care units (NICUs). Our model considers the following properties: 1) the hierarchical feature of neonatal care services and 2) the congestion effect in NICU operations. We develop a mathematical model that combines a hierarchical location model with queuing theory. We subsequently apply the proposed model to the problem of allocating capacities to NICUs in Korea. We provide information that can help policymakers draw an initial plan by evaluating various capacity allocation scenarios. We further examine two policy alternatives for improving accessibility to neonatal care. One involves increasing service capacity by adaptively adding resources to NICUs, and the other includes expanding physical service coverage by introducing helicopter transport. The results show that each alternative can contribute toward improving accessibility, and we believe that these findings will have practical implications for developing a better neonatal care system.     

A multi-objective model for lot-sizing with supplier selection for an assembly system

Lot-sizing with supplier selection (LS-SS) is a fast-growing offspring of two major problem parents in logistics and supply chain management (‘lot-sizing’ and ‘supplier selection’). The model proposed in this paper is an attempt to extend it to an assembly system, by formulating a multi-objective model for an integrative problem of LS-SS for assembly items. The total costs of the system, consisting of purchasing, ordering, transportation, assembly, and holding, is considered the first objective function, while the total reliability of the finished products is considered the second objective function. The decision-maker aims to minimise the total costs while maximising the total reliability. Several constraints of the system (e.g. storage capacity, supplier production capacity) are taken into account. Given the complexity of the model, a heuristic evolutionary algorithm is proposed to solve the model. The results indicate which assembly items to order in which quantities, from which suppliers and in which time periods.     

Towards an integrated model for the transference of environmental responsibility

This research explores how environmental responsibility transfers to others. Although environmental responsibility is one of the most debated topics in environmental management literature, a framework describing how a teacher's environmental responsibility shapes the student's environmental responsibility is still lacking. Adopting emotional contagion theory as a lens, the paper addresses this issue via the mediation of emotional contagion and the moderation of a teacher's environmental concern. Based on a survey of teachers and students (N = 283) conducted in Pakistan, our findings provide evidence that a teacher's environmental responsibility can better transfer to students via emotional contagion. Additionally, our data establish that the transference of environmental responsibility from teachers to students via emotional contagion is more effective when the teacher has a high level of environmental concern than when he or she has a low level of environmental concern. Finally, the authors discuss the implications for policy in detail.     

Labour adjustment costs: Estimation of a dynamic discrete choice model using panel data for Greek manufacturing firms

In this paper we estimate a dynamic structural model of employment at firm level. Our dataset consists of a balanced panel of 2790 Greek manufacturing firms. The empirical evidence of this dataset stresses three important stylized facts: (a) there are periods in which firms decide not to change their labour input, (b) there are periods of large employment changes (lumpy nature of labour adjustment) and (c) the commonality is employment spikes to be followed by smooth and low employment growth periods. Following Cooper and Haltiwanger [Cooper, R.W. and Haltiwanger, J. “On the Nature of Capital Adjustment Costs”, Review of Economic Studies, 2006; 73(3); 611–633], we consider a dynamic discrete choice model of a general specification of adjustment costs including convex, non-convex and “disruption of production” components. We use a method of simulated moments procedure to estimate the structural parameters. Our results indicate considerable fixed costs in the Greek employment adjustment.     

A socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: An interactive possibilistic programming approach for a real case study

In uncertain circumstances like the COVID-19 pandemic, designing an efficient Blood Supply Chain Network (BSCN) is crucial. This study tries to optimally configure a multi-echelon BSCN under uncertainty of demand, capacity, and blood disposal rates. The supply chain comprises blood donors, collection facilities, blood banks, regional hospitals, and consumption points. A novel bi-objective Mixed-Integer Linear Programming (MILP) model is suggested to formulate the problem which aims to minimize network costs and maximize job opportunities while considering the adverse effects of the pandemic. Interactive possibilistic programming is then utilized to optimally treat the problem with respect to the special conditions of the pandemic. In contrast to previous studies, we incorporated socio-economic factors and COVID-19 impact into the BSCN design. To validate the developed methodology, a real case study of a Blood Supply Chain (BSC) is analyzed, along with sensitivity analyses of the main parameters. According to the obtained results, the suggested approach can simultaneously handle the bi-objectiveness and uncertainty of the model while finding the optimal number of facilities to satisfy the uncertain demand, blood flow between supply chain echelons, network cost, and the number of jobs created.