Balancing earliness and tardiness within workload control order release: an assessment by simulation

Most Workload Control literature assumes that delivery performance is determined by tardiness related performance measures only. While this may be true for companies that directly deliver to end-customers, for make-to-stock companies or firms that are part of supply chains, producing early often means large inventories in the finished goods warehouse or penalties incurred by companies downstream in the supply chain. Some earlier Workload Control studies used a so-called time limit, which constrains the set of jobs that can be considered for order release, to reduce earliness. However, recent literature largely abandoned the time limit since it negatively impacts tardiness performance. This study revisits the time limit, assessing the use of different adaptive policies that restrict its use to periods of either low or high load. By using a simulation model of a pure job shop, the study shows that an adaptive policy allows to balance the contradictory objectives of delaying the release of orders to reduce earliness and to release orders early to respond to periods of high load as quick as possible. Meanwhile, only using a time limit in periods of high load was found to be the best policy.

Green Light for Adaptive Policies on the Colorado River

The Colorado River is a critical source of water supply for 40 million people in nine states spanning two nations in western North America. Overallocated in the 20th century, its problems have been compounded by climate change in the 21st century. We review the basin’s hydrologic and water management history in order to identify opportunities for adaptive governance to respond to the challenge of reduced system flows and distill the ingredients of past successes. While significant advances have been made in the first two decades of the 21st century, these past actions have not been sufficient to halt the declines in the basin’s reservoirs. We find that the mix of federal, state, and local responsibility creates challenges for adaptation but that progress can be made through a combination of detailed policy option development followed by quick action at hydrologically driven moments of opportunity. The role of directives and deadlines from federal authorities in facilitating difficult compromises is noted. The current state of dramatically decreased overall flows has opened a window of opportunity for the adoption of water management actions that move the river system toward sustainability. Specific measures, based on the existing institutional framework and on policy proposals that have circulated within the Colorado River community, are suggested.

Stochastic Knapsack Revisited: The Service Level Perspective

A key challenge in the resource allocation problem is to find near-optimal policies to serve different customers with random demands/revenues, using a fixed pool of capacity (properly configured). Three classes of allocation policies, responsive (with perfect hindsight), adaptive (with information updates), and anticipative (with forecast information) policies, are widely used in practice. We analyze and compare the performances of these policies for both capacity minimization and revenue maximization models. In both models, the performance gaps between optimal anticipative policies and adaptive policies are shown to be bounded when the demand and revenue of each item are independently generated. In contrast, the gaps between the optimal adaptive policies and responsive policies can be arbitrarily large. More importantly, we show that the techniques developed, and the persistency values obtained from the optimal responsive policies can be used to design good adaptive and anticipative policies for the other two variants of resource allocation problems.

Lesson Learned: Reflections of Universitas Terbuka’s Non-traditional Assessment Solutions

Modern technology advancements have been reshaping and reforming education throughout the world. Furthermore, the Covid-19 outbreak is a powerful urge to establish more adaptive policies. Universitas Terbuka (UT) or the Indonesia Open University realizes that students are expecting to have a more individual and flexible learning during the Covid-19 outbreak, including the examination. UT makes uses of new technology and innovations to establish the online proctoring examination that enables students to take the examination at their own places without the present of the human proctors. Requirements to ensure the online proctoring examination meets the quality criteria were established. Previous studies show that students showed positive attitude towards the online proctoring examination. In the context of UT, due to students living in remote islands and the issue of the limited internet access might potentially challenge UT to implement the online proctoring examination. To provide solutions to the challenges, UT is demanded to develop alternative innovative solutions. Therefore, partnership with the government, private sectors, and other organizations is a necessary.

A Trade-Off Analysis of Adaptive and Non-Adaptive Future Optimized Rule Curves Based on Simulation Algorithm

This study aims to investigate the performance of Zarrineh Rud reservoir by implementing strategies for adaptation to climate change. Using sequent peak algorithm (SPA), the rule curve were simulated. Then, the optimal rule curve was procured through GA-SPA, aiming to minimize the water shortage. The future data were downscaled using SDSM based on CanEsm2 model and under RCP2.6 and RCP8.5. Finally, in view of environmental demand, reservoir performance indices were calculated for both non-adaptive and adaptive policies during all future periods (2020–2076). Results showed simulation with the static hedging rules managed to significantly reduce the average vulnerability index (by 60%) compared to no hedging, while the dynamic hedging rules outperformed static hedging rules only by 9%. Therefore, considering the insignificant improvement in reservoir performance using dynamic rules and their complexity, static hedging rules are recommended as the better option for adaptation during climate change.

Unpacking Climate Change Adaptation Strategies: An Account of Smallholder Farmer Experiences in Chimanimani District, Zimbabwe

This article reports on a study that analysed a myriad of adaptation practices adopted by smallholder farmers in Chimanimani District, Zimbabwe. Using a predominantly qualitative design, some in-depth interviews were conducted with purposefully selected key respondents. Focus group discussions with 8 to 12 smallholder farmers per group were conducted in each of the district’s 22 wards. These were corroborated by the guided observation method. The data was analysed using thematic content analysis, where broad strands of responses were synthesised and condensed into narrow themes that made them easier to interpret. Accordingly, smallholder farmers opted for drought tolerant crop and animal species, indigenous seed preservation techniques, aquaculture and conservation farming. The off-farm practices included craftwork, bee-keeping, artesian mining and trade. The sustainable livelihoods framework (SLF) was used as an analytical lens to appraise the sustainability of smallholder farmers’ choices and practices. Therefore, as farmers switched from one practice to another, many of their adaptive options reflected short-term livelihood benefits with concealed medium- to long-term environmental detriments. Strangely, some malpractices have their roots in short-sighted government policy frameworks mainstreamed to alleviate grass roots poverty. A thorough evaluation of adaptive policies is recommended so as to strengthen the adaptive capacity of smallholder farmers against the background of climate change.

Combined Engineering—Statistical Method for Assessing Solar Photovoltaic Potential on Residential Rooftops: Case of Laghouat in Central Southern Algeria

Solar energy planning becomes crucial to develop adaptive policies ensuring both energy efficiency and climate change mitigation. Cities, particularly building’s rooftops, constitute a promising infrastructure for enabling the use of locale solar resources. This study proposes a combined engineering–statistical methodology to assess the photovoltaic potential of residential rooftops. Using validated algorithms for solar simulation and geographical information system (GIS) for spatial dissemination, the proposed methodology deals with the lack of data and allows an accurate investigation of the geographical and technical potential. Applied to the municipality of Laghouat, the results reveal that suitable rooftops areas for PV installations in the examined typologies were approximately between 18 and 35%. Moreover, the deployment of distributed PV systems on residential rooftops provides significant technical potential, which could cover up to 55% of the annual electricity needs. These original findings offer a realistic assessment of the usable solar potential within municipalities, which helps decision-makers establish energy efficiency strategies by reducing energy consumption and increasing the share of renewable electricity production. Additionally, the discussion offers valuable insight into energy management and investigates eventual energy sharing among residential buildings to achieve a net-zero energy balance at the municipal level.

Improving the decision-making in DICE: self-adaptive climate policies to handle explicit uncertainty and adaptation modelling

<p>DICE (Dynamic Integrated Climate Economy) and other cost-benefit integrated assessment models are used to study the economically optimal climate policy or to evaluate economic performance of alternative policies, such as 2°C compliant emission trajectories.</p><p>Recently, DICE has been updated to provide economically optimal climate policies keeping global warming in line with the Paris Agreement. Yet, explicit uncertainty and adaptation modelling are still overlooked. Introducing these components requires a transition from the traditional perfect-foresight static decision-making framework to a dynamic one, able to change strategy in order to react to the realization of uncertainties.</p><p>In this work, starting from the updates proposed by Hansel et al. (2020), we present an updated DICE model that: i) explicitly represents adaptation in the form of temporary and long-term adaptation investment; ii) explicitly describes stochastic, parametric and structural uncertainty over the physical and socio-economic components of the model including adaptation efficiency and climate damages specification; iii) leverages self-adaptive control policies to implement a more realistic decision-making scheme that allows to adjust climate policy after that new information arises.</p><p>Results show that the self-adaptive policies allow for a reduction in the discrepancy between economically optimal climate policy and the 2°C temperature target set with the Paris Agreement, which resurfaces when introducing adaptation, also in presence of uncertainty. When using self-adaptive policies, average adaptation costs remain low and, thanks to the ability to modulate adaptation choices depending on the scenario eventually unfolding, also climate damages are maintained at a low level. As a result, more economic resources are made available for mitigation in the short-term resulting in a reduced temperature increase in 2100 for a same level of welfare.</p>