Dynamic allocation of uncertain supply for the perishable commodity supply chain

A game theoretic approach is used to analyse an inventory problem with two products, stochastic demand, and uncertain supply. The supply chain analysed includes two competing retailers selling two substitutable products and those retailer’ suppliers. Retailers face stochastic demand and replenish the inventory from the suppliers. However, both suppliers provide an indeterminate fraction of the quantity requested, due to randomness in capacity and quality. Some customers with unmet demand will substitute that product with one sold by the other retailer. We assume that the retailers are rational players with conflicting objectives. We model the retailers’ single period expected payoffs and identify the ordering decisions using Nash strategy. We prove the existence and uniqueness of the Nash solution, and provide results for numerical examples. We analyse the combined impact of product substitution and supply uncertainty on the retailers. Results suggest that supply uncertainties do not always hurt retailers’ expected payoffs.

Through incorporating option mechanism, quantity flexible strategy and quick respond strategy into perishable products' supply chain, this paper establishes the gamble models of retailer's initial order, option purchase and manufacturer production under two production and ordering modes. If manufacturer and retailer share products demand information, manufacturer can decide first period's proper production through retailer's initial order and option purchase in order to make his own profit and the whole supply chain optimization. Comparing with two flexible production modes without option mechanism, retailer may get higher expected profit and reduce his managing risk in the condition of the whole supply chain's optimal expected profit without changed; comparing with two period production modes having option mechanism and only one ordering opportunity, retailer will purchase less option and may get higher expected profit and manufacturer may also get higher expected profit.

Current day perishable supply chains require intermediate points for manual produce quality inspection. Over the last decade international seatransport of fruit and vegetables in reefer containers has grown tremendously. Reefer containers may completely close the cold chain only if produce quality inspection is automated. The observation of this growing need for automatic produce quality monitoring has triggered this research. The aim is twofold: to reveal a correlation between fruit gas exchange rates and actual produce quality aspects (1) and to automatically monitor fruit's gas exchange rates in containers from one-point gas concentration measurements only (2). Large experimental datasets have been collected to assess the correlation between respiration, ethylene production and ripeness-related firmness for Elstar apples. Both simulations and experiments have been done to test the feasibility of monitoring (variations in) respiratory activity over time. On the one hand the results prove that both objectives are feasible, at least for Elstar apples. On the other hand some uncertainties remain: 1. Uncertainty in the observed correlation between gas exchange rates and apple firmness, 2. On-line monitoring of gas exchange rates in reefer containers may be hampered by e.g. unknown leak rates, 3. To what extend may the observed correlation between quality aspects and gas exchange rates be generalised to other perishable commodities.

The annual production scheduling of open pit mines determines an optimal sequence for annually extracting the mineralized material from the ground. The objective of the optimization process is usually to maximize the total Net Present Value (NPV) of the operation. Production scheduling is typically a Mixed Integer Programming (MIP) type problem containing uncertainty in the geologic input data and economic parameters involved. Major uncertainty affecting optimization is uncertainty in the mineralized materials (resource) available in the ground which constitutes an uncertain supply for mine production scheduling. A new optimization model is developed herein based on two-stage Stochastic Integer Programming (SIP) to integrate uncertain supply to optimization; past optimization methods assume certainty in the supply from the mineral resource. As input, the SIP model utilizes a set of multiple, stochastically simulated scenarios of the mineralized materials in the ground. This set of multiple, equally probable scenarios describes the uncertainty in the mineral resource available in the ground, and allows the proposed model to generate a single optimum production schedule. The method is applied for optimizing the annual production scheduling at a gold mine in Australia and benchmarked against a traditional scheduling method using the traditional single “average type” assessment of the mineral resource in the ground. In the case study presented herein, the schedule generated using the proposed SIP model resulted in approximately 10% higher NPV than the schedule derived from the traditional approach.

In a mining complex, the mine is a source of supply of valuable material (ore) to a number of processes that convert the raw ore to a saleable product or a metal concentrate for production of the refined metal. In this context, expected variation in metal content throughout the extent of the orebody defines the inherent uncertainty in the supply of ore, which impacts the subsequent ore and metal production targets. Traditional optimization methods for designing production phases and ultimate pit limit of an open pit mine not only ignore the uncertainty in metal content, but, in addition, commonly assume that the mine delivers ore to a single processing facility. A stochastic network flow approach is proposed that jointly integrates uncertainty in supply of ore and multiple ore destinations into the development of production phase design and ultimate pit limit. An application at a copper mine demonstrates the intricacies of the new approach. The case study shows a 14% higher discounted cash flow when compared to the traditional approach.

In this paper, we propose a grouping-based dynamic routing, core, and spectrum allocation (RCSA) method for preventing spectrum fragmentation and inter-core crosstalk in elastic optical path networks based on multi-core fiber environments. Multi-core fibers enable us to considerably enhance the transmission capacity of optical links; however, this induces inter-core crosstalk, which degrades the quality of optical signals. We should thus avoid using the same frequency bands in adjacent cores in order to ensure high-quality communications. However, this simple strategy leads to inefficient use of frequency-spectrum resources, resulting in spectrum fragmentation and a high blocking probability for lightpath establishment. The proposed method allows one to overcome this difficulty by grouping lightpath-setup requests according to their required number of frequency slots. By assigning lightpath-setup requests belonging to the same group to cores according to their priority, the proposed method aims to suppress inter-core crosstalk. Furthermore, the proposed method is designed to mitigate spectrum fragmentation by determining the prioritized frequency bandwidth for lightpath-setup requests according to their required number of frequency slots. We show that the proposed method reduces the blocking of lightpath establishment while suppressing inter-core crosstalk through simulation experiments.

At present, the fixed spectrum allocation is the accepted strategy employed by the wireless industry and regulators and a large number of spectrum resources have been wasted because of the traffic load variation in different heterogeneous wireless networks. Through the analysis of recent research about dynamic spectrum allocation (DSA), vertical handoff and multi-mode base stations, the authors have proposed the shared carrier vertical network transformation (SCVNT) algorithm, which is based on minimum total blocking probability (MTBP) or minimum redundant channel difference (MRCD). In order to evaluate the performance of SCVNT, the cellular system model and user mobility model are established, and using fixed channel allocation and dynamic channel allocation in each network, the theoretical analysis and simulation experiments are made in different user mobility conditions, different SCVNT judgment periods and different thresholds of the redundant channel difference. The results indicate that SCVNT can effectively reduce total blocking probability and enhance fairness in heterogeneous wireless networks with good compatibility and applicability.

We present an efficient distributed dynamic channel allocation algorithm for mobile communications systems. The algorithm is based upon the mutual exclusion model, where the channels are grouped by the number of cells in a cluster and each group of channels cannot be shared concurrently within the cluster. We discuss the algorithm and prove its correctness. We also show that the algorithm requires at most (worst case) O(N/sub g/*N/sub n/logN/sub n/) messages, where N/sub g/ is the number of groups and N/sub n/ is the number of neighbors. This is compared to Choy's (1995) algorithm which requires O(N/sub g//sup 2/*N/sub n/), where N/sub g/ is the number of groups. Our simulation experiments indicate that the DDRA algorithm outperforms previous schemes. A 5% reduction in the denial rate, and a 10-30% reduction in the acquisition time channel were obtained using our algorithm when compared to the previous algorithm.

Coordinated control of urban intersections is crucial for alleviating traffic congestion. Traditional multi-agent reinforcement learning methods often suffer from rigid policies that struggle to adapt to dynamic traffic situations, leading to inefficient coordination. To address this, this paper proposes a dynamic role allocation reinforcement learning framework. The framework dynamically assigns cooperative roles to autonomous agents by identifying macroscopic traffic regimes, such as free-flow and downstream congestion. Each agent learns a set of heterogeneous policies tied to these roles and executes specific tasks based on its current role. This mechanism guides agents to shift from pursuing local optima to promoting global coordination. Simulation experiments in SUMO demonstrate that the proposed framework can effectively suppress congestion propagation and significantly reduce average vehicle travel time, showcasing high-performance coordinated control.

For construction activities, timely resource allocation is crucial to avoid unnecessary waiting time of resources and delay of activities, especially under the condition of limited supply of resources. Timely resource allocation, i.e. determination of an activity that has the highest priority to obtain resources at that instant, is a dynamic decision-making process dependent on real-time information during a construction process. With the consideration of operational and stochastic characteristics of construction operations and the fuzziness of multiple-decision objectives for an appropriate allocation policy (due to imprecision or subjectivity in decision criteria), a fuzzy dynamic resource allocation (FDRA) based on the fuzzy decision-making approach is developed. In order to model the timely resource allocation decisions, the FDRA is built into a discreteevent simulation system with an activity scanning strategy. The benefit of FDRA on construction productivity is analysed through simulation experimentation by which comparisons among different allocation policies are made.

Accepted by: M. Zied BABAI In recent years, global disruptions, such as the COVID-19 pandemic, have significantly exposed vulnerabilities in supply chains, particularly in the handling of perishable goods. Addressing these vulnerabilities, this study focuses on assessing risks in perishable supply chains by employing a novel decision-making approach that integrates the outranking method with a visualization technique. This study makes a methodological contribution to scholarly work by integrating VIseKriterijumska Optimizacija I Kompromisno Resenje, which provides the best compromise ranking, with Graphical Analysis for Interactive Assistance, which enhances clarity through detailed visual representation. The integration is further reinforced by a sensitivity analysis, ensuring robust and reliable findings. From a practical perspective, this study evaluates the supply chain performance of nine perishable items based on eight critical risks. The outcomes provide insights into the risk intensity of supply chains disruptions for each perishable product, facilitating the development of effective management policies. These findings offer valuable managerial insights, equipping researchers and practitioners with essential tool to formulate effective strategies and enhance decision-making processes during undesirable circumstances.

Supply chain network design problem is increasingly showing its importance, especially the perishable supply chain. This research develops a multi-objective mathematical model to design four-echelon intermodal multi-product perishable supply chain configuration in order to ensure a balance of the three pillars of sustainable development: economy, environment, and society. The optimization objective functions of the model are, respectively, minimizing costs, delivery time, emissions, and the supply-demand mismatch in time. The model addresses particular problems in the supply chain of fresh fruits, which is more challenging compared to other types of perishable products due to its seasonal characteristics. The study proposes a new approach that combines and standardizes the above objective functions into a single weighted objective function. The solution from the model supports the decision-making process at both strategic and tactical levels. Strategically, the model supports decisions about the location, size of facilities, product flows, and workforce level. Tactically, the decision variables provide information on harvest time, delivery time, the delivery route, and mode of transport. To demonstrate its practical applicability, the model is applied to Mekong Delta region, Vietnam, where a variety of fruit types, large yields, and high distribution demand in this region make designing a shared supply chain desirable for its overall economic, environmental, and social concerns. Moreover, sensitivity analysis regarding weights of different objectives is performed to assess possible changes in supply chain configurations. Application of this model to other perishable products, the addition of modes of transport, social policy, and uncertainty parameters may be suggested for future research.

Cloud computing has received increasingly attention from network computing model research, which can realize several kinds of resource sharing and dynamic resource allocation. However, how to effectively route storage resource in cloud, reduce dynamic load and cross data center data transmission, take into account global load balancing are important problems to be solved. This paper proposes a kind of SA-RS-K means resource dynamic allocation strategy based on cloud computing, which can reduce the time overhead, get the optimal computing resources and improve efficiency of algorithm. Simulation experiments in the end of paper verify the efficiency of this algorithm. DOI: http://dx.doi.org/10.11591/telkomnika.v10i6.1400 Full Text: PDF

To increase frequency efficiency in cellular communication networks, this paper describes a cell coloring based distributed frequency allocation approach (C-DFA) for all kinds of cellular networks. C-DFA has high computational efficiency and is simpler to realize than other distributed approaches. Building on C-DFA, a distributed dynamic fractional frequency allocation (DDFFA) algorithm is designed for IEEE 802.16j supported Relay Based Cellular Networks (RBCN). It is shown that: a) C-DFA can better realize frequency efficiency and network resilience compared to centralized traditional distributed frequency allocation approach. b) DDFFA can significantly increase frequency efficiency to provide high capacity and throughput to the RBCN though at the cost of extra computation and BS-BS communication. The evaluations and analysis are based on moderate and high user congestion RBCN scenarios.

The slicing technology can meet the diversified and personalized service needs of 5G networks, schedule virtual networks dynamically and improve the operation efficiency of the network. The dynamic resource allocation algorithm among slices is the key for the application of slicing technology, which has also become a research hotspot in recent years. However, the current dynamic resource allocation algorithm of 5G network slicing has many problems such as insufficient fairness, difficulty in ensuring user satisfaction and low resource utilization rate, etc. Under the circumstances of a wide variety of 5G services, it is difficult to meet the needs of user satisfaction under differentiated service conditions only from the perspective of the network. From the perspective of Quality of Experience (QoE), a QoE evaluation system for 5G network is constructed, and a QoE-based dynamic resource allocation algorithm of 5G network slicing on the basis of this system is built in this research. The results of the simulation experiments indicate that the algorithm has significant effect in improving QoE of 5G communities, and it also has good performance in improving the resource utilization rate in the communities.KeywordsQoE5GSliceResource allocationParticle swarm optimization