Optimal Operational Decisions Research Articles

How contraflow enhances clearance time during assisted mass evacuation – A case study exploring the Australian 2013–14 Gippsland bushfires

Evacuation during a catastrophic disaster is a crucial operation that needs to be appropriately managed and is of more importance when considering the elderly and people with disabilities. The uncertain and unpredictable nature of disasters can cause long-term repercussions, especially in traffic congestion. This study presents a mathematical model to formulate traffic balance for regular and assisted evacuation (that is, disabled and the elderly) whilst considering traffic congestion in evacuation clearance time by applying contraflow. A Branch and Price (B&P) related approach is developed to help solve the proposed model in large-size problems. The presented algorithm is applied to a case study of Australia’s 2013–14 bushfires in Gippsland, located in the eastern part of Victoria. A variation test is performed to evaluate the robustness of results generated by the developed model. Results indicate that the participation percentage of edges is different based on their location, capacity, and sustainability of blockage. The edges’ capacity influences the evacuated population most compared to route capacity and time window. The output of this approach enables authorities to improve the resilience of communities by making optimal strategic and operational decisions for enhancing an evacuation response as well as influencing appropriate policies.

The Optimal Financing Decisions of Capital-Constrained Manufacturers under Different Power Structures

This paper investigates the optimal financing decisions of capital-constrained manufacturers under different power structures. Using a Stackelberg game model, it analyzes the optimal equilibrium operational decisions of capital-constrained manufacturers at varying levels of internal capital. The study finds that, compared to a power structure dominated by eco-innovative enterprises, a power structure led by ordinary enterprises enhances the level of eco-innovation of innovative products and the overall profitability of the supply chain. When eco-innovative enterprises are well-capitalized, internal financing has lower costs but may lead to idle funds, while bank financing and mixed financing have higher costs but make full use of available capital. When eco-innovative enterprises are undercapitalized, mixed financing is the optimal choice. The research employs numerical simulations to analyze the impacts of consumer environmental awareness, innovation investment costs, and production costs on the level of eco-innovation in products, manufacturers’ profits, and the overall profitability of the supply chain, providing decision-making references for governments and enterprises.

The optimal operation decision of gas station considering charging pile installation – conflict and coordination

The optimal operation decision of gas station considering charging pile installation – conflict and coordination

Multi-Objective Optimal Operation Decision for Parallel Reservoirs Based on NSGA-II-TOPSIS-GCA Algorithm: A Case Study in the Upper Reach of Hanjiang River

The parallel reservoirs in the upper reach of the Hanjiang River are key projects for watershed management, development, and protection. The optimal operation of parallel reservoirs is a multiple-stage, multiple-objective, and multiple-decision attributes complex decision problem. Taking Jiaoyan–Shimen parallel reservoirs as an example, a method of multi-objective optimal operation decision of parallel reservoirs (MOODPR) was proposed. The multi-objective optimal operation model (MOOM) was constructed. The new algorithm coupling NSGA-II, TOPSIS, and GCA was used to solve the MOODPR problem. The method of MOODPR was formed by coupling problem identification, model construction, an optimization solution, and scheme evaluation. The results show that (1) combining the Euclidean distance with the grey correlation degree to construct a new hybrid closeness degree makes the multi-attribute decision making method more scientific and feasible. (2) The NSGA-II-TOPSIS-GCA algorithm is applied to obtain decision schemes, which provide decision support for management. (3) It can be seen from the Pareto chart that for the Jiaoyan–Shimen parallel reservoirs, the comprehensive water supply was negatively related to ecology. (4) The comprehensive water supply and ecological AAPFD value in the extraordinarily dry year was 4.212 × 108 m3 and 4.953. The number of maximum continuous water shortage periods was 4 and 6. The maximum ten-day water shortage was 4.46 × 107 m3 and 2.3 × 106 m3. The research results provide technical support and reference value to multi-objective optimal operation decisions for parallel reservoirs in the upper reach of the Hanjiang River.

Role of risk aversion in operational decisions with remanufacturing under emissions price volatility

Previous studies have extensively investigated the optimal operational decisions for firms under emission trading scheme, assuming a constant emissions price. However, the volatility of the emissions price has always been ignored, leaving the resulting operational decisions unclear for risk-averse firms. In this paper, we examine the impacts of risk aversion in the presence of emissions price volatility. Specifically, we investigate a risk-averse firm that strives to reduce associated costs through emissions abatement under emissions price volatility. The firm sells both new and remanufactured products. To illustrate the role of risk aversion, we sequentially examine risk-neutral and risk-averse scenarios using the mean–variance criterion as the risk aversion measurement. Among other results, we demonstrate that the risk-averse firm has incentives not only to remanufacture all used products, but also to increase the remanufactured quantity under certain conditions. Surprisingly, we find that risk aversion may not always hinder investments in emissions reduction. Subsequently, we discuss how risk aversion influences consumers and the environment, using the life cycle analysis-based approach to model environmental impacts. The results indicate that as the degree of risk aversion increases, consumers are worse-off, and there is a constant conflict between economic and environmental objectives. Additionally, it is interesting to find that the emission abatement investment strategy does not necessarily improve environmental benefits.

Optimal financing modes of a capital-constrained manufacturer under different sales strategies

PurposePrevious studies have rarely integrated the financing modes of a capital-constrained manufacturer with the choices of online sales strategies. To address this gap, the authors study how a manufacturer selects optimal financing modes under different sales strategies in three dual-channel supply chains.Design/methodology/approachThis paper considers three sales strategies, namely, combining a traditional retailer channel with one of the direct selling, reselling and agency selling channels, and two common financing modes, namely, bank financing and retailer financing. The authors obtain equilibrium outcomes of the manufacturer and traditional retailer and then provide the conditions for them to select optimal financing modes under three sales strategies.FindingsThe results indicate that the manufacturer’s financing decisions rely on the initial capital and interest rates, and the manufacturer selects retailer financing only if the initial capital is relatively larger. In terms of financing mode options, the retailer financing mode is more beneficial for the manufacturer under the three sales strategies. From the perspective of sales strategies, the direct selling model is more beneficial. In addition, the higher the consumer acceptance of the online channel, the more profits the manufacturer obtains.Practical implicationsThis paper provides suggestions on how the capital-constrained manufacturer chooses financing modes and sales strategies.Originality/valueThis paper integrates the financing mode and different sales strategies to investigate the manufacturer’s optimal operational decisions. These sales strategies allow us to investigate the manufacturer’s optimal financing modes in the presence of both different financing modes and sales strategies.

Applicability Assessment of GPM IMERG Satellite Heavy-Rainfall-Informed Reservoir Short-Term Inflow Forecast and Optimal Operation: A Case Study of Wan’an Reservoir in China

Satellite precipitation estimate (SPE) dedicated to reservoir inflow forecasting is very attractive as it can provide near-real-time information for reservoir monitoring. However, the potential of SPE retrievals with fine temporal resolution in supporting the high-quality pluvial flood inflow forecast and robust short-term operation of a reservoir remains unclear. In this study, the hydrological applicability of half-hourly Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM IMERG) heavy rainfall data was explored using a synthetic experiment of flood inflow forecast at sub-daily to daily lead times and resultant reservoir short-term operation. The event-based flood forecast was implemented via the rainfall–runoff model GR4H driven by the forecasted IMERG. Then, inflow forecast-informed reservoir multi-objective optimal operation was conducted via a numerical reservoir system and assessed by the risk-based robustness indices encompassing reliability, resilience, vulnerability for water supply, and flood risk ratio for flood prevention. Selecting the Wan’an reservoir located in eastern China as the test case, the results show that the flood forecast forced with IMERG exhibits slightly lower accuracy than that driven by the gauge rainfall across varying lead times. For a specific robustness index, its trends between IMERG and gauge rainfall inputs are comparable, while its magnitude depends on varying lead times and scale ratios (i.e., the reservoir scale). The pattern that the forecast errors in IMERG increase with the lead time is changed in the resultant inflow forecast series and dynamics in the robustness indices for the optimal operation decision. This indicates that the flood forecast model coupled with reservoir operation system could partly compensate the original SPE errors. Our study highlights the acceptable hydrological applicability of IMERG rainfall towards reservoir inflow forecast for robust operation, despite the intrinsic error in SPE.

Joint Optimal Production Planning in Supply Chains under Diverse Low-Carbon Policies

This paper studies the impact of diverse carbon emission regulations on optimal operation decisions by exploiting the economic production lot model of a multi-manufacturer system in a low-carbon environment. The optimal production planning for multiple enterprises in terms of cost optimization and carbon emission optimization are presented in various scenarios of the fully decentralized system under carbon taxation, cap and trade, and mandatory carbon cap. We prove a unified framework for modeling the scenarios enabled by carbon tax and cap-and-trade policies. Furthermore, the decision vector of the optimal production lot is obtained by analyzing the joint decision scenario based on shared carbon emission caps. We give a comparative analysis of two settings of fully decentralized and carbon quota sharing. The results show that the decision of joint production and stock preparation can reduce the total operational costs of the multi-enterprise system, but the realized carbon emissions may not change or only increase slightly. Our study provides a useful reference for government carbon emission regulation and enterprise operation decision optimization under carbon neutrality and a carbon peak environment.

On urban co-modality: Non-cooperative and cooperative games among freight forwarder, carrier and transit operator

This paper models a public transit system that can serve both passengers and urban freight, i.e., urban co-modality, and investigates the system-wide impacts of co-modality on existing urban freight forwarding service, freight carrier and urban transit services. In the co-modal system, we model one transit operator that serves passengers and provides co-modal services, one freight forwarder (an intermediary) that serves freight customers and uses freight transportation services provided by a freight carrier or the transit operator, and one freight carrier that provides services to the freight forwarder. We derive the analytical conditions under which co-modal operations can improve the profits of the freight forwarder, carrier and transit operator, and the consumer surpluses of freight customers and passengers compared with the status quo without co-modality. We also analytically and numerically compare the optimal operation decisions, the three operators’ profits, and the users’ welfare under different games among the operators (non-cooperative and cooperative games). Our results show that when the three operators are in a non-cooperative relationship, the freight carrier might have a profit loss due to the decreased freight units allocated to the direct road channel; whereas, both the freight forwarder and the transit operator would have profit gains from the co-modality. The numerical studies further reveal that to ensure a Pareto-improving co-modal system, the operators have to reduce the freight/transit service fare and co-modal transportation price.

A comparative study on a firm’s optimal production decision considering risk aversion and carbon policies

To cope with global warming and achieve the goal of carbon emission reduction, various carbon policies have been implemented in many countries and regions. Some studies have indicated that with the popularity of low carbon, a small proportion of consumers show a green purchasing tendency. However, for firms, it is still difficult to predict the green purchase preferences of most consumers, and firms may face greater market demand uncertainty, which will bring profit risks. We assume that a firm’s decision is subject to its behavioral considerations, that is, its risk attitude. Considering the risk preferences of decision-makers and carbon emission policies, this paper addresses decision optimization in operations management. Our purpose is to analyze the optimal production decisions of risk-averse firms under carbon cap and carbon tax policies. Adopting the CVaR (conditional value-at-risk) criterion, we construct an optimization model with the constraints of carbon policies and obtain the analytic formula of optimal solutions. Then, we compare the optimal operational decisions for risk-averse firms and risk-neutral firms and further explore the impact of risk aversion on production and the parameters of carbon policy. Finally, some numerical studies are presented to analyze the carbon policies and risk aversion on firms’ operational decisions and give some managerial insights based on the analysis results.

On the joint network equilibrium of parking and travel choices under mixed traffic of shared and private autonomous vehicles

This paper investigates the joint network equilibrium of parking and travel route choices in the future mobility paradigm with mixed traffic of private and shared autonomous vehicles. Specifically, we consider that private autonomous vehicle (PAV) travelers need to make both route and parking choices though the vehicle can drive itself to parking after dropping off the traveler. The origin–destination-based shared autonomous vehicle (OD-SAV) ride service allows multiple travelers with a common origin and destination (OD) pair to share the same vehicle, while the routing of OD-SAVs is determined by the operator. In this context, a bi-level model is developed which optimizes the OD-SAV service fare and OD-SAV flow in the upper-level, and specifies the travel demands, route and parking choices, and network traffic equilibrium in the lower-level. In particular, PAV travelers choose their route and parking location to minimize their own travel time or cost, while the routing of OD-SAVs is subject to the decision of the operator. The OD-SAVs controlled by the operator may minimize each vehicle’s travel time (user equilibrium, ‘UE’) or minimize the total travel time of all OD-SAVs operated by the operator (Cournot-Nash equilibrium, ‘CN’). The joint equilibrium of travel and parking under either UE or CN routing for OD-SAVs can be modeled as a Variational Inequalities (VI) problem. The uniqueness/non-uniqueness properties of the joint network equilibrium are investigated. Moreover, we examine the OD-SAV service operator’s optimal operation decisions subject to the lower-level network equilibrium. Solution approaches are introduced to solve the joint equilibrium and the proposed bi-level model. Numerical studies are conducted to illustrate the model and analytical results, and also to provide further understanding.

An economic analysis of a multi-modal transportation system with ride-sourcing services and multi-class users

This paper proposes a tractable bi-level model to explore the mode choice of travelers and optimal operation decisions of a public transit operator and a ride-sourcing operator under a multi-modal transportation system. In such a multi-modal system, users may travel by bus, metro, and/or ride-sourcing service. The lower-level network model is utilized to capture the equilibrium mode choices with the consideration of travelers’ heterogeneity in terms of the value of time (VOT), where deterministic user equilibrium (DUE) and stochastic user equilibrium (SUE) are formulated and compared. In the upper-level model, subject to the DUE/SUE, the two operators optimize their operation decisions in the notion that the profit is maximized or the total system cost is minimized. The existence and uniqueness/non-uniqueness of the multi-modal mode choice equilibrium are analyzed. The optimal operation decisions are also compared under different operation regimes. Our results show that SUE model provides a more conservative estimate of the ride-sourcing operator’s profit and total system travel cost compared to DUE. Besides, the total system cost can be reduced while the profit of the ride-sourcing company can be increased under appropriate operation decisions of the public transit operator and the ride-sourcing operator. Sensitivity analysis further indicates that travelers with longer total travel distance tend to use short-distance ride-sourcing services, which suggests that the ride-sourcing operator should dispatch more vehicles to suburbs located in remote areas to enhance service quality.

Application of a Distributionally Robust Optimization Approach for Single-Period Stochastic Inventory Problems with Different Carbon Policies

While single-period stochastic inventory problems have been studied in academia and industry, the impacts of both carbon emissions reduction and partial distribution information have not been investigated. In this paper, we consider carbon emission reduction in a single-period inventory problem where both product price and demand are stochastic. When only partial information on the first and second moments of the two stochastic elements is specified, we use the max–min criterion to construct two optimization models under cap-and-trade and carbon tax policies. For each model, we prove the existence of the optimal robust decisions on production quantity and cleaning equipment investment. By comparing the profit and carbon emission between these two models, we identify certain conditions that improve the economic and environmental performance of the manufacturer. Finally, we conduct a numerical study to test the robustness of the optimal operational decisions under each carbon policy.

The choice between the agency and reselling modes considering green technology with the cap-and-trade scheme

Given an online platform and a manufacturer, we research the platform mode choice where the two firms' cooperative mode is either the reselling or agency mode. With the cap-and-trade scheme, green technology is used to comply with this scheme and cater to consumers' environmental consciousness. We consider agency inefficiency and platform-enabled power, the platform's inherent characteristics in e-commerce, in our models. In addition, given the power structure, we consider three cases, namely the agency, manufacturer-led reselling, and platform-led reselling modes. Analyzing the optimal operational decisions, platform mode choice, and coordination problems in the three cases, we derive new and interesting findings. First, we uncover that increasing the cap weakens the optimal green level. However, increasing the cap first weakens and then enhances the optimal production quantity. Second, we uncover that the platform-led reselling mode is always more conducive to the production quantity and green level but the manufacturer-led reselling mode is more conducive to the manufacturer's profit. In addition, increasing agency inefficiency gradually enhances the market share, green level, and manufacturer's profit in the agency mode. Lastly, the manufacturer-led reselling mode can coordinate the two firms. Extending our models to consider the delivery time, we uncover that the agency and manufacturer-led reselling modes can coordinate the two firms.

Impact of Government Subsidies, Competition, and Blockchain on Green Supply Chain Decisions

At present, environmental and competitive pressures urge enterprises to engage in research and development (R&D) of green products, and a green supply chain has become the main trend in the sustainable development of enterprises. This study analyzes the optimal operation decisions of a green supply chain for two manufacturers under different competitive and cooperative relationships, considering factors such as government subsidies, consumer green preferences, and the impact of the green information trust. The results show that government subsidies can lead to higher social welfare when manufacturers have a cooperative relationship, but the optimal choice of subsidies (for R&D costs or product production costs) depends on the level of competition and the difficulty of R&D. For the manufacturers, the optimal choice of R&D strategy (individual or joint) and the use of blockchain technology also depends on the level of difficulty of R&D and the type of government subsidies. Overall, this study highlights the importance of considering various factors when making decisions in a green supply chain to achieve the best outcomes for all parties involved.

Business models for public emission reduction projects

This study investigates two business models for public emission reduction projects that promote low-carbon behavior, issue corresponding carbon credits, and facilitate the trade of carbon credits at the individual level. We construct two dynamic game-theoretical models to investigate the project owner's business model selection strategy, optimal operational decisions, and the effectiveness of the two business models in reducing carbon emissions.

Impact of carbon emission difference on the dual-channel fresh produce supply chain with capital constraints

The difference in carbon emissions has an important impact on the decision-making of dual-channel fresh produce supply chain financing. We set up a Stackelberg game model of a dual-channel supply chain under the financing strategy of banks and retailers to study the optimal operation decision and financing strategy selection. Our analysis results show that when the retailer’s interest rate and the carbon emission difference met certain conditions, the supplier always chooses the financing strategy of retailers.

Optimal financing strategy in a closed-loop supply chain for construction machinery remanufacturing with emissions abatement

<p style='text-indent:20px;'>Motivated by the remanufacturing practice of construction machinery, this paper examines the impacts of different cooperation financing and carbon emission reduction (CER) strategies on the operational decisions in a closed-loop supply chain (CLSC) where the original equipment manufacturer (OEM) produces only new products and the capital-constraint retailer produces remanufactured products and sells both new and remanufactured products to consumers. Based on the cooperation level between the OEM and the retailer, four different financing strategies, including the non-cooperation financing (NCF), the supply chain carbon financing (SCCF), the partial cooperation with delay-in-payment financing (PCD), and the full-cooperation financing (FC), are explored. Meanwhile, we extend the proposed models by considering the CER strategy implemented by the OEM, and then investigate its impact on the optimal operational and financing decisions of the retailer. The results show that: i) Under different financing strategies, the CER strategy has no impact on the retail price of remanufactured products, but can help increase the market share of new products and strengthen the market value effect for the OEM. ii) Compared with the scenario without CER, the SCCF strategy can not only ease funding pressure than the NCF strategy for the retailer but also increase the CLSC profit under the CER strategy. iii) No matter which financing strategy is adopted, the CER strategy always has a squeezing effect on the market size of remanufactured products, i.e., the cannibalization effect. iv) The CER strategy is not conducive to the development of the remanufacturing industry, but can improve the environmental performance in terms of reducing emissions and increasing the market sales for new products.</p>

An improved affine arithmetic‐based optimization method for district energy systems with uncertainties

AbstractIn the process of the district energy system (DES) operation and dispatching, there are various uncertainties brought by the strong coupling of equipment and the fluctuation of internal and external surroundings, and these uncertain parameters are often correlated. In this paper, an improved affine arithmetic‐based method is proposed to solve the economic dispatching problem of DES with correlated uncertainties. First, an optimal dispatching model considering economic and environmental factors is constructed. An affine algorithm is introduced and interval arithmetic is used to improve the problem of interval extension and high‐order noise element. Taking a classical DES system as an example, the optimal dispatching of the system is analyzed using deterministic method, interval method, and proposed method. The application effect of the proposed method under different uncertainties scale and amplitude is also discussed. The results show that the proposed method can effectively reduce the width of target value interval compared with the traditional interval optimization method, which can further contribute to the optimal operation decision.

Dispatching optimization of city gas station district energy systems with multiple uncertainties based on an improved affine arithmetic method

According to the energy consumption characteristics of gas stations, the district energy system (DES) based on natural gas and renewable energy is a suitable form of energy supply. In the process of DES operation and dispatching, there are various uncertainties brought by the strong coupling of equipment and the fluctuation of internal and external surroundings, and these uncertain parameters are often correlated. In this paper, an improved affine arithmetic (AA) based method is proposed to solve the economic dispatching problem of DES with correlated uncertainties. Firstly, an optimal dispatching model considering economic and environmental factors is constructed. An affine algorithm is introduced and interval arithmetic (IA) is used to improve the problem of interval extension and high-order noise element. Taking a classical DES system as an example, the optimal dispatching of the system is analyzed using deterministic method, interval method and the proposed method. The application effect of the proposed method under different uncertainties scale and amplitude is also discussed. The calculation results in different scenarios show that compared with the traditional IA method, the proposed method can effectively reduce the width of the target value interval by about 13%, which can further contribute to the optimal operation decision.