Order Allocation Strategy Research Articles

Optimal Agent-Based Pickup and Delivery with Time Windows and Electric Vehicles

The traditional methods of transporting goods and people in urban areas using vehicles powered by internal combustion engines are major contributors to pollution. As a result, an increasing number of logistics companies are transitioning to electric vehicles (EVs) for daily operations, replacing traditional engines. This shift opens research avenues regarding the integration of EVs into delivery workflows and how this can contribute to greener cities. This study tackles the EV routing problem, focusing on balancing battery constraints and optimizing routes. We formulated the problem as a pickup and delivery with time windows, incorporating electric energy consumption constraints, and utilized consensus mechanisms in an agent-based simulation context. Our evaluation used 15 scenarios, capturing variations in vehicle configurations, order generation rates, and battery and freight capacities. We compared two order allocation strategies: “Closest Allocation” and “Negotiation” consensus-based allocation. The results confirmed that the consensus-based strategy outperformed the “Closest Allocation” in metrics such as remaining orders, orders not handled in time, total distance traveled, total recharging cost, and total number of recharges. These findings have significant implications for urban planners, logistic companies, and policymakers, demonstrating that an agent-based simulation context for electric vehicles using consensus-based strategies can enhance delivery efficiency and promote sustainability.

Dynamic selection and order allocation of resilient suppliers based on improved fuzzy multi-criteria decision method

ABSTRACT A complex supply chain can improve the economic benefits of an enterprise to a certain extent, whereas its vulnerability also occurs. How to select resilient suppliers to improve enterprise risk resistance has become hot issue in supply chain management. Furthermore, due to the complexity of the supply chain and the instability of the market, the supplier selection in different periods and customer demands is dynamic. We use the best-worst approach to model the initial selection of suppliers taking into account risk factors. Based on this result, our study applies the fuzzy multi-criteria decision method to establish a multi-objective model that aims to solve the problem of dynamic supplier selection considering resilient criteria. We apply the improved wolf pack algorithm to measure the optimal order allocation strategy between profit and pollution. The supplier selection and order allocation of new energy vehicle manufacturing companies are used as a case study for verification. The results show the multi-objective model of supplier selection considering risk and resilience factors is feasible, and the dual-objective model of supplier selection and order allocation considering different demands in different periods are effective. Compared with the original algorithm, the improved Wolf pack algorithm has better comprehensive performance.

COVID-19 automotive supply chain risks: A manufacturer-supplier development approach

Epidemic outbreaks pose a significant risk to supply chain and logistics, particularly in the global automotive industry. These risks are characterized by long-term disruptions that can have ripple effects, making them challenging to predict. Contingency plans must be developed to address these risks effectively, with simulation tools being a crucial component. These tools should be used as data integration tools from multiple sources to improve risk analysis quality, and preemptive measures should be taken when possible instead of only reacting to disruptions as they occur. This paper focuses on epidemic breakouts as a distinct category of disruption risk in automotive supply chains (ASCs) and logistics. It formulates a multi-objective nonlinear model to determine the optimal selection strategy for functional supply chains and logistics providers under the impact of epidemic outbreaks. The model uses a realistic nonlinear investment return model for supplier development and order allocation strategy, considering COVID-19′s impact. Nonlinear return models for both single and multiple manufacturers and suppliers are developed. A series of sensitivity tests for various scenarios illustrates the behavior of the model and its decision-making value. Numerical experiments and results show that supplier selection and order allocation strategies are mutually influential. Facility closures and reopenings' timing can have more significant impacts on the ASC performance than upstream disruptions or epidemic propagation speed. Lead-time, epidemic spread speed, and interruption durations are also vital factors to consider in optimizing overall performance and allocating investment funds across multiple suppliers to avoid risks and maximize returns. Decision-makers should consider all relevant factors simultaneously when designing such plans to ensure optimal performance and minimize risk.

Dynamic order allocation in a duopoly hybrid workforce of competition: A machine learning approach

We develop a continuous-time stochastic differential game model that aims to capture market demand and stochastic cross-network effects, and we seek to find equilibrium order allocation strategies between the firm and the platform. By solving the Hamilton–Jacobi–Bellman (HJB) partial differential equation system, we obtain the feedback equilibrium. For a simple scenario, we derive the analytical solution which indicates that the equilibrium expenditures depend only on the marginal market thickness, and that market thickness is consistently advantageous for the value function. For the complex scenario, we propose a machine learning approach based on the Deep Galerkin Method to solve high-dimensional nonlinear HJB systems, and we demonstrate its good convergence properties. Based on reliable parameter values, our simulation results show that: (1) For higher market thickness, value functions exhibit greater sensitivity to changes in cross-network effects. (2) For a given cross-network effect, the equilibrium acquisition and retention expenditures display significant sensitivity to market thickness and time, respectively. To show the interaction between the two platforms, we present the following two results. One is that for the platform adopting a high pricing strategy, its acquisition expenditures exceed those of its competitor, while retention expenditures are the opposite. The other is that as the cross-network effect of the platform increases, its maximum profit initially rises and then declines, and the time of occurrence of the maximum profit monotonically decreases. In contrast, the competitor’s maximum profit initially declines and then rises, and the time of occurrence of the maximum profit monotonically increases.

Order allocation strategy for online car-hailing platform in the context of multi-party interests

As an emerging internet service, online car-hailing allows users to do the car-hailing via their mobile phone instead of calling cars on the road. Though the convenient service provided by online car-hailing platforms, the following problems encountered in the process of real-time order-matching still cannot be well addressed: (1) the imbalance of the accessible cars in different areas, and (2) the conflict of interests among the drivers, platforms, and passengers. To address those problems, this paper proposes a set of order allocation strategy that fully consider the changing regional characteristics and the interests of multiple parties. Firstly, a dynamic region division method is proposed to identify the corresponding area types for real-time car-hailing. Then, a novel order allocation strategy with the regional multi-party subsidy mechanism is developed for different types of areas. Lastly, to help obtain a better performance for multiple parties, a parameter optimization method for the multi-party subsidy mechanism is introduced. The proposed order allocation strategy is evaluated on the real-world dataset sourced from an online car-hailing company. Our experiments verify the feasibility and effectiveness of the proposed method, without reducing drivers’ benefits, the order response time and order response rate are greatly improved, and the cost in all is acceptable for passengers and the platform. It is illustrated that a suitable regional multi-party subsidy mechanism could help online car-hailing platforms obtain higher passenger satisfaction than that without a regional multi-party subsidy mechanism.

Emergency order allocation of e-medical supplies due to the disruptive events of the healthcare crisis

The availability of electronic (e-medical) homecare essentials, such as thermometers, oximeters, and oxygen concentrators during the peaks of the pandemic coronavirus disease (COVID-19), has been witnessed as critical in saving the lives of people across the world. This paper presents a supply order allocation strategy of e-medical homecare essentials (HCEs) in a multi-supplier environment by a distributor while ensuring sufficient and timely availability for emergency consumption during pandemic peaks. The results, based on the actual demand data of HCEs obtained from a regional HCE distributor during the pandemic peak of the second wave in India, i.e. April-May 2021, suggest that a minimum (maximum) average of 94% (98%) availability of e-medical HCEs respectively at pharmacies could be achieved during the peak demand period using the proposed emergency order allocation algorithm in this study. Conclusively, the analysis of this study could generate insightful implications for emergency operations decisions in the HCEs supply-distribution channel.

Integrated Optimization of Order Allocation and Last-Mile Multi-Temperature Joint Distribution for Fresh Agriproduct Community Retail

Community retail is an important research issue in the field of fresh agriproduct e-commerce. This paper focuses on the problem of last-mile multi-temperature joint distribution (MTJD), which combines time coupling, order allocation, and vehicle scheduling. Firstly, according to the temperature of a refrigerated truck in multi-temperature zones, a split-order packing decision is proposed to integrate the different types of fresh agriproduct. Then, the order allocation strategy is incorporated into a comprehensive picking and distribution schedule, while taking into account the time-coupling of picking, distribution, and delivery time limit. To improve consumer satisfaction and reduce order fulfillment costs, an optimization model combining multi-item order allocation and vehicle scheduling is established, to determine the optimal order allocation scheme and distribution route. Finally, taking fresh agriproduct community retail in the Gulou District of Nanjing as an example, the effectiveness and feasibility of the model are illustrated. The numerical results of medium- to large-scale examples show that, compared with the variable neighborhood search algorithm (VNS) and genetic algorithm (GA), the mixed genetic algorithm (MGA) can save 29% of CPU time and 65% of iterations. This study considers the integrated optimization of multiple links, to provide scientific decision support for fresh agriproduct e-commerce enterprises.

Meal delivery routing optimization with order allocation strategy based on transfer stations for instant logistics services

The rapid development of instant logistics services has brought not only convenience to people's life but also a great challenge to traffic management. Due to the limited delivery range of instant delivery systems, customers are usually recommended the meals nearby or pay much higher delivery fees for long-distance delivery. This study proposes a novel order splitting strategy based on transfer stations for long-distance meal orders to satisfy the diverse customer demands. The meal delivery routing problem is addressed with an order allocation strategy based on transfer stations through developing a three-stage modelling framework consisting of order combination, splitting and delivery routing for the online-to-offline instant logistics services. Normal meal orders are combined by the DBSCAN algorithm, and the cross-regional long-distance orders are split by transfer stations. Based on order combination and splitting, a mixed integer programming model is constructed for the meal delivery routing problem and solved by the adaptive large neighbourhood search algorithm. The proposed algorithm converges quickly for the tested instances constructed based on real platform data. The proposed order allocation strategy can expand the delivery scopes of couriers effectively, stimulate more potential orders and guarantee the timeliness of meal delivery.

Multiobjective decisions for provider selection and order allocation considering the position of the CODP in a logistics service supply chain

This paper formulates a nonlinear mixed-integer multiobjective optimization model for service provider selection and order allocation in different procedures under the mass-customization logistics service mode. The optimal provider selection strategy, order allocation strategy and optimal CODP location are determined simultaneously by a quantitative method. We transform the multiobjective programming model into a single-objective model by means of different methods and design an improved genetic algorithm based on multilayer coding technology to solve the model. Numerical experiments show that the algorithm designed in this paper can effectively solve the model.

G-NETWORKS AND THE OPTIMIZATION OF SUPPLY CHAINS

Supply chains are fundamental to the economy of the world and many supply chains focus on perishable items, such as food, or even clothing that is subject to a limited shelf life due to fashion and seasonable effects. G-networks have not been previously applied to this important area. Thus in this paper, we apply G-networks to supply chain systems and investigate an optimal order allocation problem for a N-node supply chain with perishable products that share the same order source of fresh products. The objective is to find an optimal order allocation strategy to minimize the purchase price per object from the perspective of the customers. An analytical solution based on G-networks with batch removal, together with optimization methods are shown to produce the desired results. The results are illustrated by a numerical example with realistic parameters.

Joint decision model of supplier selection and order allocation for the mass customization of logistics services

This paper formulates a multi-objective mixed-integer nonlinear programming model to simultaneously determine the optimal selection strategy of functional logistics service providers and the order allocation strategy under the mass customization of logistics services. An improved genetic algorithm based on multi-layer coding is designed to solve the model. Numerical experiments show that the supplier selection strategy and the order allocation strategy are mutually influential, therefore, decision makers need to consider both simultaneously to optimize the overall performance of the supply chain. The influence of some important parameters on the model results was observed through a sensitivity analysis.

Dynamic assessment of control system designs of information shared supply chain network experiencing supplier disruption

The importance of acquiring and sharing real-time disruption information in the supply chain for proper deployment of disruption mitigation strategies is well-known in the literature. However, studies in this direction are limited in the domain of supply chain dynamics. In this paper, we investigate the effect of sharing real-time disruption and inventory information to mitigate supplier disruption through proper order allocation between the suppliers. We consider a three-echelon manufacturing supply chain network where a manufacturer and first-tier suppliers adopt dual sourcing. At the first-tier supplier level, the supply chain network is subjected to random disruption. Using control engineering modeling and simulation, we first evaluate the value of information sharing in disruption mitigation efforts, and further, we examine the effect of various control system design configurations of the manufacturer to maximize its dynamic performance in the information shared supply chain settings. The results show that, in the case of upstream supplier disruption, information transparency on the vulnerabilities among supply chain members improves the performance. Further, it is observed that for a given control structure, the selection of decision parameters affect the dynamic performance of the supply chain with proper order allocation strategy during the disruption. The findings of this research can provide the basis for managers to make informed decisions about using mitigation strategies with their supply chain partners.

Dual Sourcing Under Random Supply Capacities: The Role of the Slow Supplier

Sourcing from multiple suppliers with different characteristics is common in practice for various reasons. This paper studies a dynamic procurement planning problem in which the firm can replenish inventory from a fast and a slow supplier, both with uncertain capacities. The optimal policy is characterized by two reorder points, one for each supplier. Whenever the pre‐order inventory level is below the reorder point, a replenishment order is issued to the corresponding supplier. Interestingly, the reorder point for the slow supplier can be higher than that of the fast even if the former has a higher cost, lower reliability, and smaller capacity than the latter, suggesting the possibility of ordering exclusively from an inferior slow supplier in the short term. Moreover, the firm may allocate a larger portion of the long‐term total order quantity to the slow supplier than to the fast, even if the former does not possess any cost or reliability advantage over the latter. Such phenomena, different from the observations made in previous studies, happen when the demand is uncertain and the supply is limited or unreliable. Our observations highlight the importance of incorporating both demand uncertainty and supplier characteristics (i.e., cost, lead time, capacity and uncertainty) in a unified framework when formulating supplier selection and order allocation strategies.

Order allocation in a multiple suppliers-manufacturers environment within a dynamic cluster

This study faces the problem of how to evaluate order allocation strategies in a multiple suppliers-manufacturers environment within a dynamic industry cluster that is an environment in which suppliers adopt a policy based on their performance to select the clusters in which to operate. Therefore, the allocation strategy performed by the manufacturers is crucial to keep a long-term supply chain partnership with a specific industry cluster. We use simulation to evaluate order allocation strategies under different market conditions. We show how an allocation strategy that takes into account the whole supply chain’s perspective leads to a sustainable development of the clusters of suppliers.

Assessing business impacts of agility criterion and order allocation strategy in multi-criteria supplier selection

This paper formulates supplier evaluation and selection as a multi-criteria decision-making (MCDM) problem with subjective and fuzzy preferences of decision makers over evaluation criteria. As an outcome, this paper provides decision makers with a decision support system that presents the Pareto fronts, a set of best possible high-quality suppliers and optimized business operation levels from such suppliers. In addition, this paper quantifies the importance of the agility criterion and its sub-criteria in the process of evaluating and selecting agile suppliers by measuring the magnitude of bullwhip effect and inventory costs. The proposed system uses a fuzzy analytic hierarchy process (fuzzy AHP) and fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS) to successfully determine the priority weights of multiple criteria and selects the fittest suppliers by taking the vagueness and imprecision of human assessments into consideration. More importantly, it presents approximated Pareto fronts of the resulting supplier chains for varying priority weights of the agility criterion and its sub-criteria. Finally, we compare business costs of agile and non-agile supply chains before and after reconfigurations of original supply chains in response to unexpected disruptions under two order allocation strategies, a skewed order allocation (SOA) strategy and an even order allocation (EOA) strategy.

Order allocation for multiple supply-demand networks within a cluster

Manufacturers outside an industry cluster and suppliers within cluster form a multiple sourcing supply-demand network. Two different order allocation strategies, the production capacity-based strat...

Study on the Responsiveness of Production Load-Equilibrium Based Order Allocation Strategy

For industry cluster with multiple suppliers and multiple manufacturers, there exist a network of supply and demand relationship among suppliers and manufacturers. By using the balanced production load among suppliers as the main criteria for decision making, an order allocation model for supply chain with multi-suppliers vs. multi-manufacturers based on production load equilibrium-based strategy is proposed in this paper. In the end of paper the discrete event system simulation is used to verify that the production load equilibrium-based strategy can enhance the supplier's responsiveness effectively，which further leads to a satisfied OTD of manufacturers and the optimization of the whole supply chain.

구조적 공백 기반 주문 분배 전략의 에이전트 기반 모델링 및 시뮬레이션

Order allocation is one of the most important decision-making problems of firms having significant influences on performances of themselves and the whole supply chain. Existing researches about order allocation have mainly focused on evaluating capabilities of directly connected suppliers so that it is hard to consider effects and interactions from undirected connections over multiple lower-layers. To alleviate the limitation, this paper proposed a novel approach to order allocation using structural hole. By applying the concept of structural hole to the supply network, we could evaluate the structural supplying powers of firms with respect to both of direct and indirect connections. In the proposed approach, we derived a methodology to measure the potential supplying power of each firm by modifying the effective size as one of the measurements of structural hole and then, proposed its application, the structural hole based order allocation strategy. Furthermore, we conducted the agent based modeling of supply chain to perform the decision-making process of order allocation and simulated the proposed strategy. As a results, by coping with the variance of demand more stably, it could improve the performance of supply chain from the aspects of fill rate, inventory level and demand-supply balance.

Operational causes of bankruptcy propagation in supply chain

With the increasing interdependence among supply chain members, bankruptcy of a supply chain member may be caused by operational decisions of other members. To investigate how bankruptcy occurs and propagates in supply chain networks, we build a multi-agent simulation model for a two-stage supply chain that consists of multiple upstream manufacturers and multiple downstream retailers. Based on the developed simulation model, we study impacts of various operational parameters and decisions, such as horizontal competition among retailers, order allocation strategies of retailers, wholesale price of manufacturers, characteristics of market demand and number of retailers, on bankruptcy propagation. Since many operational decisions of a firm are made under financial constraints, we also investigate the linkage between firm's operational risks and financial decisions (e.g., the maximal risk of cash flow that a member is willing to take, and the up-front payment proportion of retailers in a two-period payment policy). Experimental results reveal that operational interactions between supply chain members and operational decisions made by supply chain members are important causes of bankruptcy propagation, but impacts of these operational parameters and decisions depend on financial decisions. These observations indicate that supply chain members can moderately hedge their operational risk through financial decisions.