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When demands exceed capacities, suppliers allocate available supply to customers based on customer importance and advance demand information. The accuracy of advance demand information interacts with the length of customer order lead times and influences overall customer service levels. In this paper, we analyse industrial contract portfolios with customer-specific terms in order to derive insights for contract portfolio management and the design of demand fulfilment processes. For this purpose, we develop a framework for analysis of contract portfolios capturing the dynamics of industrial planning processes. The framework is applied to portfolios from the semiconductor sector. Our numerical analysis shows that, in order to improve service levels, demand fulfilment processes and contract portfolio management must especially take into account the length of order lead times and the accuracy of advance demand information. Even though suppliers often prefer long order lead times, our analysis shows that demand fulfilment performance is not primarily determined by the absolute length of the order lead times but by the presence of a negative correlation with the accuracy of advance demand information in the entire contract portfolio. Consequently, these factors require increased attention in the management of contract portfolios and in the negotiation of individual contracts. [ABSTRACT FROM AUTHOR]

IntroductionShelter allocation is one of the most important measures in urban disaster prevention and mitigation planning. Meanwhile, it is essentially a comprehensive planning problem combining resource allocation and traffic routing. A reasonable allocation scheme can avoid congestion, improve evacuation efficiency, and reduce the casualty rate. Owing to the large region and large evacuation population demand, quickly solving the complex allocation problem is somewhat challenging, and thus, the optimal results are difficult to obtain with the increase of evacuation scale by traditional allocation methods.MethodsThis article aims to establish a shelter allocation model for large-scale evacuation, which employs an improved quantum genetic algorithm (IQGA) based on spreading operation and considering the total evacuation distance, the capacity constraint of evacuation sites, and the dispersion of allocation results, and compare allocation schemes of the spreading model with those of models that consider different constraints.Results and discussionResults show that the allocation model with the spreading operation has better allocation results than that without the spreading operation. For the allocation model with spreading operation, the spreading model with different spreading speeds is more reasonable than that with the same spreading speed, and the allocation results are closer to the ideal results with the increase of constraints. In addition, according to the allocation results, the evacuation route map and the evacuation heat map are drawn to intuitively understand the distribution scheme of each shelter.

Progressive popularization of plug-in electric vehicles (PEVs) calls for synergistic deployment of public charging infrastructures to serve the recharging needs in future urban systems. For this end, a new planning framework to facilitate efficient accommodation of PEVs via gridable parking lots (PLs) is proposed in this study. Different from most previous works, the presented method explicitly considers the potential uncertainties in the users’ behaviors in the PL planning, which will allow decision-makers to better assess the effect of human factors on the efficiency of their investment. Moreover, the concerned PL allocation problem has been investigated in conjunction with the optimal contract offer for PEV users to obtain the final planning scheme. The overall problem is formulated as a two-stage stochastic programming model, where the first-stage mainly deals with the planning decisions associated with PLs, including siting and sizing of PLs and contractual arrangements; while the second stage evaluates the operational performance of the suggested PL system under different scenario realizations of PEV behaviors. A combinatorial optimization algorithm is used to solve the problem. The proposed framework is demonstrated on an illustrative testing system and numerical results verify the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Matching supply with demand remains a challenging task for many companies, especially when purchasing and production must be planned with sufficient lead time, demand is uncertain, overall supply may not suffice to fulfill all of the projected demands, and customers differ in their level of importance. The particular structure of sales organizations often adds another layer of complexity: These organizations often have multi-level hierarchical structures that include multiple geographic sales regions, distribution channels, customer groups, and individual customers (e.g., key accounts). In this paper, we address the problem of "allocation planning" in such sales hierarchies when customer demand is stochastic, supply is scarce, and the company's objective is to meet individual customer groups' service-level targets. Our first objective is to determine when conventional allocation rules lead to optimal (or at least acceptable) results and to characterize their optimality gap relative to the theoretical optimum. We find that these popular rules lead to optimal results only under very restrictive conditions and that the loss in optimality is often substantial. This result leads us to pursue our second objective: to find alternative (decentral) allocation approaches that generate acceptable performance under conditions in which the conventional allocation rules lead to poor results. We develop two alternative (decentral) allocation approaches and derive conditions under which they lead to optimal allocations. Based on numerical analyses, we show that these alternative approaches outperform the conventional allocation rules, independent of the conditions under which they are used. Our results suggest that they lead to near-optimal solutions under most conditions. [ABSTRACT FROM AUTHOR]

With the prevalence of cloud computing applications, cloud data centers (CDCs) are proliferating around the world. However, in the context of hybrid energy‑carbon markets environment, the high energy costs and environmental expenses caused by CDC operation are pressing CDC owners to restructure the future development of CDC in a more economical and low-carbon manner. The potential spatio-temporal transferability and reducibility of workloads provides CDCs with significant flexibility in their operations and thus may interact with the power grid as active demand users. Nonetheless, other than private data centers, public CDCs have no direct control over the workloads submitted by terminal cloud users. As such, this paper presents a bi-level model for CDC allocation planning, so as to incorporate cloud service-demand response (CS-DR) from the perspective of a hybrid electricity-carbon market. The upper level pertains to multi-domain resource collaborative planning model, which determines the optimum siting and sizing of CDCs as well as the incentive design for CS-DR program, with the objective of maximizing the total expected benefits of CDC. The lower level models correspond to the market clearing (electricity-carbon tariff model) of Independent system operator (ISO) and the decision-making of cloud users regarding CS-DR participation. The proposed model belongs to a bi-level mixed integer nonlinear programming problem with two non-convex lower levels, which can be intractable in mathematics. To solve such difficult problem, a hybrid solution method combining multiple linearization techniques and reformulation and decomposition (R&D) strategy based on column-and-constraint generation (C&CG) algorithm is developed. The proposed model is demonstrated on a modified IEEE 30-bus test case, and the simulation results verified the effectiveness of the proposed approach. • A bi-level model to allow cost-effective and decarbonizing planning of CDC under the electricity-carbon market is proposed. • Modelling of CDC from the both physical and information aspects. • An incentive-based approach to promote cloud users in the provision of flexibility is developed. • A hybrid solution method combining multiple linearization and decomposition and reformulation techniques is developed. • Effectiveness of the proposed approach is verified via comparative analysis. [ABSTRACT FROM AUTHOR]

This paper analyses factors affecting the production of greenhouse gases from the treatment of residual municipal waste. The analysis is conducted so that the environmentally-friendly decision-making criteria may be later implemented into an optimisation task, which allocates waste treatment capacities. A simplified method of life cycle assessment is applied to describe environmental impact of the allocation. Global warming potential (GWP) is employed as a unit to quantify greenhouse gases (GHG) emissions. The objective is to identify the environmental burdens and credits measured by GWP for the three fundamental methods for treatment of residual waste unsuitable for material recovery. The three methods are waste-to-energy (WTE), landfilling and mechanicalbiological treatment (MBT) with subsequent utilization of refuse-derived fuel. The composition of the waste itself and content of fossil-derived carbon and biogenic carbon are important parameters to identify amounts of GHG. In case of WTE, subsequent use of the energy, e.g., in district heating systems in case of heat, is another important parameter to be considered. GWP function dependant on WTE capacity is introduced. The conclusion of this paper provides an assessment of the potential benefits of the results in optimisation tasks for the planning of overall strategy in waste management. [ABSTRACT FROM AUTHOR]

To match products of different quality with end market preferences under supply uncertainty, it is crucial to integrate product quality information in logistics decision making. We present a case of this integration in a meat processing company that faces uncertainty in delivered livestock quality. We develop a stochastic programming model that exploits historical product quality delivery data to produce slaughterhouse allocation plans with reduced levels of uncertainty in received livestock quality. The allocation plans generated by this model fulfil demand for multiple quality features at separate slaughterhouses under prescribed service levels while minimizing transportation costs. We test the model on real world problem instances generated from a data set provided by an industrial partner. Results show that historical farmer delivery data can be used to reduce uncertainty in quality of animals to be delivered to slaughterhouses. [ABSTRACT FROM AUTHOR]

Highlights: [•] We analyze the efficient allocation of probe devices to taxi dispatch centers. [•] Pass frequency and required sample size are modeled from actual data. [•] Efficiency is defined as the probability of satisfying the required sample size. [•] Efficiency of data collection changes according to the data collection period. [•] Efficient data collection requires the allocation of probe devices to suburban areas. [Copyright &y& Elsevier]

Traffic allocation planning is commonly required for mass evacuation management. It primarily relies on efficient coordination and appropriate utilization of roadway capacity and available traffic resources. However, traffic and evacuee information are usually difficult to be obtained and consequently of various uncertainties in data. Especially, stochastic information may often exist in evacuation management systems. In this study, a two-stage interval-stochastic evacuation management (TISEM) model was developed for supporting the evacuation planning under uncertainty, by which stochastic and interval evacuation information could be well reflected and communicated in the system. In addition, by adopting the proposed model, a case study abstracted from the City of Wuhan was introduced and solved through an interactive method. Results indicated that useful solutions for planning evacuation routes could be generated based on results of the model. As well, through the model, complex relationships between evacuation time, environmental influences and economic factors could be systematically analyzed. It demonstrated that the proposed TISP model is practical and applicable in real world, and is helpful for authorities to make decisions allocating vehicles before evacuation starts. [ABSTRACT FROM AUTHOR]