Multi-time Period Research Articles

International container intermodal competitiveness: An empirical study from Chinese hub ports

The global energy crisis and environmental degradation have prompted ports to strive for green transformation. Intermodal transport, as a green and efficient form of organization in the transport sector, currently has a lot of room for development. How can the competitiveness of intermodal transport be enhanced while realizing the green sustainability of ports? This study objectively evaluates the competitiveness of international container intermodal competitiveness of hub ports and analyzes the characteristics of 16 Chinese ports from 2011 to 2020. The obstacle degree model (ODM) and the panel Tobit model considering random effects are utilized to explore the complex multidimensional elements affecting the competitiveness of intermodal transport in ports. In addressing the imbalanced characteristics of intermodal transport in ports, a variety of augmentation pathways are devised using the dynamic multi-time period fuzzy-sets qualitative comparative analysis (fsQCA) method. The results found that: (1) The competitiveness of intermodal transport in ports shows a “three echelons” structure, requiring the implementation of appropriate augmentation strategies. (2) Elements affecting the competitiveness of intermodal transport in ports are multidimensional, underscoring the significance of holistically considering internal and external elements. (3) The key to enhancing the competitiveness of intermodal transport in ports lies in striking a balance between economic development and prudent attention to multiple dimensions to ensure sustainability. This study enriches the empirical research on intermodal transport in ports. It provides valuable references for ports seeking new avenues of growth and for governments with effective management practices.

Two-echelon multi-commodity multimodal vehicle routing problem considering user heterogeneity in city logistics

The considerable increase in parcel deliveries has negatively impacted the accessibility and livability of cities. One solution strategy is to decouple short-distance from long-distance shipping so that last-mile transport can be performed with low-footprint vehicles. Such solutions are referred to in the literature as multi-echelon distribution systems. This study introduced a new variant of the two-echelon vehicle routing problem that considers multiple alternative transport modes as well as multiple commodities over a multi-period time horizon, where customers can obtain their commodities from any store. We referred to this problem as the two-echelon multi-commodity multimodal vehicle routing problem (MCM-2E-VRP). The objective of service providers is to minimize total generalized costs while satisfying customer requirements. We formulated this as a mathematical model based on a space–time network and introduced a random utility discrete choice model to capture variations in performance and preferences. We developed an adaptive large-neighborhood search (ALNS) algorithm to provide solutions for newly generated MCM-2E-VRP instances based on the Beijing Yizhuang transportation network. Extensive numerical experiments were conducted to verify the effectiveness of the proposed model and algorithm. A sensitivity analysis revealed some policy-relevant findings regarding the effects of store distribution and vehicle capacity.

Multi-period time window assignment for attended home delivery

We study a multi-period stochastic variant of the Time Window Assignment Vehicle Routing Problem, where customers’ demands, locations, and service times are uncertain. Customers are partitioned into geographical zones, each of which has to be visited a predetermined number of times over a planning period of several days. Whenever a zone is visited, a time window is assigned. Time windows are decided before knowing customers and their demands. A fleet of homogeneous vehicles is available to serve customers each day. At a tactical level, the problem looks for a static time window assignment that minimizes the expected traveling costs plus the expected penalty costs for unserved customers. We propose a two-stage formulation and a solution approach, which relies on the Sample Average Approximation Method, while encompassing a perturbation method to assign time windows in the first stage and an Adaptive Large Neighborhood Search to optimize routes in the second stage. We experimentally evaluate three instance sets, including real ones from a Canadian company, comparing our results to lower bounds from the exact solution of a deterministic equivalent formulation over a finite number of scenarios. Our method outperforms the manual approach used by the company.

Multi-time Optimal Dispatch of AC-DC Hybrid Microgrid with Spatiotemporal Discrepancy

AC-DC hybrid microgrid with high permeability of renewable energy and its special system structure poses a challenge to the rational dispatch of AC-DC hybrid microgrid. To solve the problem, a multi-time period stochastic optimization of AC-DC hybrid microgrid scheduling strategy is proposed in this paper, which considers spatiotemporal differences. In the day-ahead optimal stage, the comprehensive consideration of the operating costs and characteristics of each distributional unit and bidirectional AC/DC power converter in the microgrid, a backup plan is developed to cope with the uncertainties of wind and AC/DC loads. In the intra-day scheduling stage, multi-scenario analysis technology is used to simulate the uncertainty of wind and AC and DC loads, and the intra-day rolling optimization dispatch model is constructed to ensure the validity of the day-ahead plan and fully suppress the power fluctuations caused by the day-ahead forecasting errors. Finally, by an example, the results show that the method can effectively deal with the influence of multi-uncertainties of the AC-DC hybrid microgrid.

Uncertainty around Transgenerational Control: Implications for Innovation Prior to Succession

The transgenerational entrepreneurship perspective suggests senior-generation leaders with transgenerational control intentions (TCI) innovate to position the next generation for success, but many family firms fail to do so. We introduce transgenerational control uncertainty (TCU) as a theoretical mechanism explaining when TCI enhances innovation behaviors pre-succession. A multi-respondent, multi-time period survey of private German family firms shows that while TCI helps unleash innovation prior to succession, these effects also depend on lowering TCU as reflected in progress through the succession process. Our study suggests TCU might be a useful new construct for explaining other important differences among family firms.

Optimising Electric Vehicle Charging Station Placement Using Advanced Discrete Choice Models

We present a new model for finding the optimal placement of electric vehicle charging stations across a multiperiod time frame so as to maximise electric vehicle adoption. Via the use of stochastic discrete choice models and user classes, this work allows for a granular modelling of user attributes and their preferences in regard to charging station characteristics. We adopt a simulation approach and precompute error terms for each option available to users for a given number of scenarios. This results in a bilevel optimisation model that is, however, intractable for all but the simplest instances. Our major contribution is a reformulation into a maximum covering model, which uses the precomputed error terms to calculate the users covered by each charging station. This allows solutions to be found more efficiently than for the bilevel formulation. The maximum covering formulation remains intractable in some instances, so we propose rolling horizon, greedy, and greedy randomised adaptive search procedure heuristics to obtain good-quality solutions more efficiently. Extensive computational results are provided, and they compare the maximum covering formulation with the current state of the art for both exact solutions and the heuristic methods. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This work was supported by Hydro-Québec and the Natural Sciences and Engineering Research Council of Canada [Discovery Grant 2017-06054; Collaborative Research and Development Grant CRDPJ 536757–19]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0185 .

The cyclic production routing problem

This paper introduces the Cyclic Production Routing Problem (CPRP). The CPRP is an extension of the well-known NP-hard Production Routing Problem (PRP), which is a hard-to-solve combinatorial optimisation problem with numerous practical applications in the field of freight transportation, logistics and supply chain management. Under the PRP setting, a manufacturer is responsible for determining production decisions, as well as the timing and quantity of replenishment services offered to a set of geographically dispersed customers over a multi-period time horizon. The problem calls for jointly optimising the production, inventory, distribution and routing decisions. In this paper, the basic PRP model is modified to generate repeatable cyclic production and delivery schedules. A two-commodity flow formulation is proposed along with valid inequalities. Extensive comparisons between the basic PRP and the proposed cyclic variant on well-known benchmark instances are provided. The new variant is significantly harder to solve, especially when the vehicle fleet is limited. From a managerial perspective, the generation of cyclic production-routing schedules significantly increases all costs, whereas the number of vehicle routes required to implement a cyclic schedule is higher.

Reduction method for multi-period time series scenarios of wind power

In the planning and operation of power systems containing wind power, it is of great significance to use a small number of representative wind power time series scenarios to accurately portray the stochastic characteristics of wind power. With the increase of the number of scenarios, how to form representative typical scenarios to balance computational efficiency and accuracy is an urgent problem to be solved. In this study, a bi-directional optimization method is proposed to generate daily wind power time series scenarios based on a single-period optimal scenario generation strategy and a multi-period scenario reduction strategy. First, the Wasserstein probability distance index is used to form a scenario model that is best approximated to the probability distribution of wind power, and the optimal scenarios of each period are generated. Secondly, a wind power scenario reduction strategy that integrates spatial distance and stochastic features is proposed. The improved taboo search method is used to selectively connect the representative scenarios of each period to form the representative daily wind power time series scenarios. Finally, the effectiveness and practicality of the proposed wind power time series scenario generation method are verified by simulations.

An Optimization Method of Active Distribution Network Considering Time Variations in Load and Renewable Distributed Generation

Network optimization is one of an effective ways to enhance the performance of an active distribution network (ADN). Aiming to improve the operation and power quality of the ADN considering time variations in load and renewable distributed generation (RDG) power, a multi-time period optimization model and its dynamic solution method are proposed. Considering the real time load demand and power generation variation of RDG versus input parameters like wind speed and solar irradiance, the time variation models of load and RDG power output are developed. The minimum power loss and maximum absorption of RDG power are served as the optimization indexes to construct the dynamic muti-time period optimization model. A hybrid particle swarm optimization (HPSO) algorithm is presented based on integer coding and random coding technique, which can find the most satisfactory solutions for the proposed dynamic model. Considering the time variation of load and RDG power of ADN, the optimal network structure and RDG allocation scheme at any time interval are determined by analyzing the obtained solutions. Additionally, two ADNs with time variation in load and RDG are tested to verify the effectiveness and superiority of the proposed dynamic optimization model and HPSO algorithm. The simulation results show that the proposed method can improve the operation performance and RDG optimal utilization of the ADNs through muti-time period dynamic optimization.

Pengaruh Pembangunan Jalan Tol Trans Sumatra (JTTS) Terhadap Penerimaan Pajak Pusat

Infrastructure is very important in accelerating economic growth. The Trans Sumatra Toll Road (JTTS) is one of the national strategic infrastructure projects that aims to increase regional development on the island of Sumatra which is expected to support national economic growth. The JTTS consists of 24 segments with a length of 2,704 km that connects the northernmost point of Sumatra Island in Banda Aceh with the southernmost point in Bakauheni, Lampung Province. The presence of JTTS is expected to have a fiscal impact on the government in the form of an increase in tax revenue. This study aims to analyze the impact of JTTS development on central tax revenues. This study uses data on central tax revenues at the Tax Service Office (KPP) on the island of Sumatra in the period 2014 - 2020. This study uses the Difference in Difference with multi time periods method. The results of the study indicate that there is a positive relationship between the development of JTTS and central tax revenues.

Multi-Time Period Optimal Dispatch Strategy for Integrated Energy System Considering Renewable Energy Generation Accommodation

With the target of carbon peaking and carbon neutrality, renewable energy generation (REG) develops rapidly. The increasing penetration of REG brings along the problems of fluctuation in power flow and the possible abandonment of wind and photovoltaics (PV) generation. In this context, the so-called integrated energy system (IES) becomes a promising solution to the accommodation of REG thanks to energy storage systems and coupling devices inside. In this paper, the optimal operation model of an IES is first presented, with the schemes of green certificate trading and carbon emission right trading included to provide economic incentives for accommodating REG. Next, in order to address the problem of uncertainty in REG, the devices in the IES are divided into three types based on regulation flexibility, and a multi-time period optimal dispatching scheme is proposed, including day-ahead optimal scheduling, rolling optimal dispatching, and real-time control strategy. Finally, it is demonstrated by simulation results of a numerical example that the proposed method not only promotes the accommodation capability for REG but can also cope well with contingencies.

Global multi-period performance evaluation – New model and productivity index

In this paper, we introduce a novel multi-period data envelopment analysis (MDEA) model that attempts to circumvent the limitations of the existing MDEA models. The proposed global MDEA model is essentially based on major modifications of fundamental DEA axioms to enable a decision making unit (DMU), defined with inputs and outputs of period t, to be evaluated within the production possibility set (PPS) of another period l, t ≠ l. Building on the properties of the global MDEA model, we also introduce a global productivity index, identified as Global Progress and Regress index (GPRI), that render possible the evaluation of a DMU’s extent of progress or regress over multi-period time horizons under variable returns to scale (VRS) production technologies. This lifts the restrictions to two successive periods and constant returns to scale (CRS) of existing productivity indices. The most salient features of the new MDEA model as well as the GPRI are highlighted using an application that involves a real-life sample of 25 bank branches considered over 4 years.

Dynamic DEA Model using Window Analysis Approach with Weight Modeling

This paper study dynamic DEA model and proposes weight modeling in window analysis for multi-time period performance evaluation. It uses the weights of inputs and outputs generated as a result of static temporal efficiency evaluation to restrict weight flexibility in dynamic DEA model and observe the efficiencies, thus obtained, over multi-time period. First the optimal range of weights are determined and then it is being applied in different windows to observe the stability and consistency of efficiency evaluations. We also define consistent efficiency index and give numerical illustration to observe the working of proposed model.

Transmission Grid Resiliency Investment Optimization Model With SOCP Recovery Planning

In the face of increasing natural disasters and an aging grid, utilities need to optimally choose investments to the existing infrastructure to promote resiliency. This paper presents a new investment decision optimization model to minimize unserved load over the recovery time and improve grid resilience to extreme weather event scenarios. Our optimization model includes a network power flow model which decides generator status and generator dispatch, optimal transmission switching (OTS) during the multi-time period recovery process, and an investment decision model subject to a given budget. Investment decisions include the hardening of transmission lines, generators, and substations. Our model uses a second order cone programming (SOCP) relaxation of the AC power flow model and is compared to the classic DC power flow approximation. A case study is provided on the 73-bus RTS-GMLC test system for various investment budgets and multiple hurricane scenarios to highlight the difference in optimal investment decisions between the SOCP model and the DC model, and demonstrate the advantages of OTS in resiliency settings. Results indicate that the network models yield different optimal investments, unit commitment, and OTS decisions, and an AC feasibility study indicates our SOCP resiliency model is more accurate than the DC model.

Real-time schedule of integrated heat and power system: A multi-dimensional stochastic approximate dynamic programming approach

This paper proposes a multi-dimensional approximate dynamic programming (ADP) algorithm for the real-time schedule of integrated heat and power system (IHPS) with battery and heat storage tank (HST). The multi-time period optimization problem is reformulated under the Markov Decision Process. The high dimensional state variables are aggregated into the state of charge (SOC) of battery and overall available heat (OAH) of HST to reduce the computation of value function approximation (VFA) while ensuring approximate accuracy. Under sufficient training by uncertainty scenarios of wind power, electricity price, electrical and heat load, the approximate value function (AVF) can derive empirical knowledge and help IHPS make decisions to cope with uncertainties. The proposed ADP algorithm can efficiently take advantage of multi-energy integration and provide a near-optimal operation strategy to ensure the economy of IHPS by recursively solving the Bellman’s equation. Simulation results compared with existing methods validate the superiority of the proposed algorithm.

A Tale of Two Quotes: When Power “Corrupts” and When It Triggers “Great Responsibility”

Two seemingly contradictory quotes can be cited in the power literature. The first – and most traditionally held assumption by early power scholars – is attributed to the 19th century British politician Lord Acton: “Power tends to corrupt, and absolute power corrupts absolutely.” The other has more ambiguous roots (French Revolution, Voltaire, Churchill, Roosevelt, Uncle Ben Parker, etc.): “With great power comes great responsibility.” Though the notion that power corrupts seems to be the status quo, there are instances when individuals use their power for good. This begs the questions, why and when does power corrupt and why and when does it trigger great responsibility? To answer these questions, I draw from theorizing on cognitive morality (e.g., Kohlberg, 1976) and attribution theorizing (e.g., Heider, 1958) to argue that peoples’ reactions to having power – specifically self-/other-focused behaviors – depends on power attribution. Furthermore, I suggest that peoples’ reactions to power can be explained by where they direct their sense of responsibility – towards themselves (resulting in self-interested behaviors) or towards others (resulting in more other-interested behaviors). Moreover, behaviors directed towards others can be further understood by distinguishing the target (e.g., prosocial behaviors towards a specific individual or individuals vs. prosocial behavior towards the community or society at large). Using a multi-time period study design and structural equation modeling, I demonstrate that when powerholders attribute their power internally, they tend to participate in more self-interested work behaviors, but when they attribute their power externally, they tend to participate in more global prosocial behaviors.

An infeasible space exploring matheuristic for the Production Routing Problem

This paper proposes a novel matheuristic algorithm for solving the Production Routing Problem (PRP). The PRP is a hard-to-solve combinatorial optimization problem with numerous practical applications in the field of freight transportation, logistics and supply chain management. A manufacturer is responsible for determining production decisions, as well as the timing and the quantity of replenishment services offered to a set of geographically dispersed customers over a multi-period time horizon. The problem calls for jointly optimizing the production, inventory, distribution and routing decisions. The paper provides a novel two-commodity flow formulation and proposes a two-phase infeasible space matheuristic algorithm for solving the examined problem. The first phase deals with a relaxation of the problem to construct production-distribution plans. In the second phase, these are completed with routing information and optimized via a local search framework which oscillates between the feasible and the infeasible solution space. The framework is equipped with mixed integer programming components for restoring infeasibility and for diversifying the conducted search. Computational experiments demonstrate that the infeasibility space exploration significantly contributes to the quality of the final solutions. The results obtained by the proposed matheuristic out-match the results of state-of-the-art approaches. More specifically, 594 and 55 new best solutions out of 1440 and 90 instances of two well-established benchmark data sets of small-medium and large instances are reported, respectively.

A Large-Scale Optimization Model for Replicating Portfolios in the Life Insurance Industry

Replicating Portfolios for Economic Capital Calculations

A Robust Neutrosophic Modeling and Optimization Approach for Integrated Energy-Food-Water Security Nexus Management under Uncertainty

Natural resources are a boon for human beings, and their conservation for future uses is indispensable. Most importantly, energy-food-water security (EFWS) nexus management is the utmost need of our time. An effective managerial policy for the current distribution and conservation to meet future demand is necessary and challenging. Thus, this paper investigates an interconnected and dynamic EFWS nexus optimization model by considering the socio-economic and environmental objectives with the optimal energy supply, electricity conversion, food production, water resources allocation, and CO2 emissions control in the multi-period time horizons. Due to real-life complexity, various parameters are taken as intuitionistic fuzzy numbers. A novel method called interactive neutrosophic programming approach (INPA) is suggested to solve the EFWS nexus model. To verify and validate the proposed EFWS model, a synthetic computational study is performed. The obtained solution results are compared with other optimization approaches, and the outcomes are also evaluated with significant practical implications. The study reveals that the food production processes require more water resources than electricity production, although recycled water has not been used for food production purposes. The use of a coal-fired plant is not a prominent electricity conversion source. However, natural gas power plants’ service is also optimally executed with a marginal rate of production. Finally, conclusions and future research are addressed. This current study emphasizes how the proposed EFWS nexus model would be reliable and beneficial in real-world applications and help policy-makers identify, modify, and implement the optimal EFWS nexus policy and strategies for the future conservation of these resources.

The Nonstationary Newsvendor: Data-Driven Nonparametric Learning

We study a newsvendor problem with unknown demand distribution in a nonstationary demand environment over a multi-period time horizon. The demand in each period consists of a time-varying demand level and an additive random shock. Neither the demand level nor the random shock is separately observable. The amount of change in the demand level over the time horizon is measured by a cumulative variation metric. The problem has widespread applications, such as perishable inventory planning, staffing, and medical resource capacity planning in the wake of COVID-19. We design a nonparametric dynamic ordering policy, termed the moving window ordering policy, that tracks the shifts in the unknown demand level while accounting for the unobservable random demand shocks. To compute the order quantity in each period, this policy only needs the past demand observations, without any access to the underlying demand distribution. For a finite variation budget, we prove that our ordering policy is first-order optimal in the sense that its regret grows at the smallest possible rate. We also extend our analysis to the case of asymptotically large variation budgets. Through case studies based on real-life data, we show that our policy can save 20-80% of overage and underage costs, relative to policies widely used for perishable inventory replenishment and nurse staffing.