Shipping Consolidation Across Two Warehouses with Delivery Deadline and Expedited Options for E-commerce and Omni-channel Retailers

2020 ◽  
Author(s):  
Lai Wei ◽  
Roman Kapuscinski ◽  
Stefanus Jasin
Keyword(s):  
Economies Of Scale ◽  
Optimal Solution ◽  
Problem Definition ◽  
Shipment Consolidation ◽  
On Demand ◽  
Numerical Tests ◽  
Optimal Policies ◽  
Shipping Costs ◽  
Multiple Orders ◽  
Managerial Implications

Problem definition: Shipment consolidation (i.e., shipping multiple orders together instead of shipping them separately) is commonly used to decrease total shipping costs. However, when the delivery of some orders is delayed, so they can be consolidated with future orders, a more expensive expedited shipment may be needed to meet shorter deadlines. In this paper, we study the optimal consolidation policy focusing on the trade-off between economies of scale due to combining orders and expedited shipping costs, in the setting of two warehouses. Academic/practical relevance: Our work is motivated by the application of fulfillment consolidation in e-commerce and omni-channel retail, especially with the rise of so-called on-demand logistics services. Sellers have the flexibility to take advantage of consolidation by deciding when to ship the orders and from which warehouse to fulfill the orders, as long as the orders’ deadlines are met. Methodology: We use Dynamic Programming to study the optimal policy and its structure. We also conduct extensive simulation tests to evaluate the performance of heuristics that are based on structures of the optimal policies. Results: The optimal policies and their structures are characterized. Using the insights of these structural properties, we propose two easily implementable heuristics that perform within 1%–2% of the optimal solution and outperform other benchmark consolidation methods in numerical tests. Managerial implications: Consolidation is shown to significantly reduce the outbound shipping costs. Retailers can take advantage of it to effectively improve the standard policies by simply applying the threshold-form heuristics we propose.

Joint Product Framing (Display, Ranking, Pricing) and Order Fulfillment Under the Multinomial Logit Model for E-Commerce Retailers

2021 ◽  
Author(s):  
Yanzhe (Murray) Lei ◽  
Stefanus Jasin ◽  
Joline Uichanco ◽  
Andrew Vakhutinsky
Keyword(s):  
Large Scale ◽  
Matrix Decomposition ◽  
Multinomial Logit Model ◽  
Problem Definition ◽  
Order Fulfillment ◽  
Cardinality Constraints ◽  
Joint Product ◽  
Shipping Costs ◽  
Managerial Implications ◽  
Randomization Scheme

Problem definition: We study a joint product framing and order fulfillment problem with both inventory and cardinality constraints faced by an e-commerce retailer. There is a finite selling horizon and no replenishment opportunity. In each period, the retailer needs to decide how to “frame” (i.e., display, rank, price) each product on his or her website as well as how to fulfill a new demand. Academic/practical relevance: E-commerce retail is known to suffer from thin profit margins. Using the data from a major U.S. retailer, we show that jointly planning product framing and order fulfillment can have a significant impact on online retailers’ profitability. This is a technically challenging problem as it involves both inventory and cardinality constraints. In this paper, we make progress toward resolving this challenge. Methodology: We use techniques such as randomized algorithms and graph-based algorithms to provide a tractable solution heuristic that we analyze through asymptotic analysis. Results: Our proposed randomized heuristic policy is based on the solution of a deterministic approximation to the stochastic control problem. The key challenge is in constructing a randomization scheme that is easy to implement and that guarantees the resulting policy is asymptotically optimal. We propose a novel two-step randomization scheme based on the idea of matrix decomposition and a rescaling argument. Managerial implications: Our numerical tests show that the proposed policy is very close to optimal, can be applied to large-scale problems in practice, and highlights the value of jointly optimizing product framing and order fulfillment decisions. When inventory across the network is imbalanced, the widespread practice of planning product framing without considering its impact on fulfillment can result in high shipping costs, regardless of the fulfillment policy used. Our proposed policy significantly reduces shipping costs by using product framing to manage demand so that it occurs close to the location of the inventory.

Prepositioning and Local Purchasing for Emergency Operations Under Budget, Demand, and Supply Uncertainty

2021 ◽  
Author(s):  
Mahyar Eftekhar ◽  
Jing-Sheng Jeannette Song ◽  
Scott Webster
Keyword(s):  
Optimal Solution ◽  
Rapid Onset ◽  
Problem Definition ◽  
Supply Uncertainty ◽  
Approximate Methods ◽  
Humanitarian Organizations ◽  
Solution Methods ◽  
Local Supply ◽  
Managerial Implications ◽  
The Impact

Problem definition: Considering a mix of prepositioning and local purchasing, common to cover humanitarian demands in the aftermath of a rapid-onset disaster, we propose policies to determine preposition stock. These formulations are developed in the presence of demand, budget, and local supply uncertainties and for single-items delivery. Academic/practical relevance: The immediate period aftermath of a disaster is the most crucial period during which humanitarian organizations must supply relief items to beneficiaries. Yet, because of many unknowns such as time, place, and magnitude of a disaster, supply management is a significant challenge, and these decisions are made intuitively. The features and complexities we examine have not been studied in the literature. Methodology: We derive properties of the optimal solution, identify exact solution methods, and determine approximate methods that are easy to implement. Results: We (i) characterize the interplay of supply, demand, and budget uncertainties, as well as the impact of product characteristics on optimal prepo stock levels; (ii) show in what conditions the prepo stock is a simple newsvendor solution; and (iii) discuss the value of emergency funds. Managerial implications: We show that budget level is a key determinant of the optimal policy. When it is above a threshold, inventory increases in disaster frequency and severity, but the reverse is true otherwise. When budget is limited, the rate of savings from improved forecasts is amplified (attenuated) for critical (noncritical) items, reflecting opposing directional effects of mismatch cost and cost of insufficient funding. Our model can also be used to estimate the value of initiatives to mitigate constraints on local spend (e.g., a line of credit underwritten by large donors that is available during the immediate relief period).

scholarly journals Exploring the application of machine learning to the assembly line feeding problem

2021 ◽  
Author(s):  
Moretti Emilio ◽  
Tappia Elena ◽  
Limère Veronique ◽  
Melacini Marco
Keyword(s):  
Machine Learning ◽  
Assembly Line ◽  
Optimal Solution ◽  
Product Variety ◽  
Supervised Machine Learning ◽  
Assembly System ◽  
Optimization Approach ◽  
Feeding Problem ◽  
Managerial Implications ◽  
Multiple Feeding

AbstractAs a large number of companies are resorting to increased product variety and customization, a growing attention is being put on the design and management of part feeding systems. Recent works have proved the effectiveness of hybrid feeding policies, which consist in using multiple feeding policies in the same assembly system. In this context, the assembly line feeding problem (ALFP) refers to the selection of a suitable feeding policy for each part. In literature, the ALFP is addressed either by developing optimization models or by categorizing the parts and assigning these categories to policies based on some characteristics of both the parts and the assembly system. This paper presents a new approach for selecting a suitable feeding policy for each part, based on supervised machine learning. The developed approach is applied to an industrial case and its performance is compared with the one resulting from an optimization approach. The application to the industrial case allows deepening the existing trade-off between efficiency (i.e., amount of data to be collected and dedicated resources) and quality of the ALFP solution (i.e., closeness to the optimal solution), discussing the managerial implications of different ALFP solution approaches and showing the potential value stemming from machine learning application.

Partial Completion as a Nonprofit Strategy

2021 ◽  
Author(s):  
Can Zhang ◽  
Atalay Atasu ◽  
Karthik Ramachandran
Keyword(s):  
Nonprofit Organizations ◽  
Service Provision ◽  
Problem Definition ◽  
Product Service ◽  
Service Portfolio ◽  
Wide Range ◽  
Partial Completion ◽  
Innovative Solution ◽  
Managerial Implications ◽  
Level 3

Problem definition: Faced with the challenge of serving beneficiaries with heterogeneous needs and under budget constraints, some nonprofit organizations (NPOs) have adopted an innovative solution: providing partially complete products or services to beneficiaries. We seek to understand what drives an NPO’s choice of partial completion as a design strategy and how it interacts with the level of variety offered in the NPO’s product or service portfolio. Academic/practical relevance: Although partial product or service provision has been observed in the nonprofit operations, there is limited understanding of when it is an appropriate strategy—a void that we seek to fill in this paper. Methodology: We synthesize the practices of two NPOs operating in different contexts to develop a stylized analytical model to study an NPO’s product/service completion and variety choices. Results: We identify when and to what extent partial completion is optimal for an NPO. We also characterize a budget allocation structure for an NPO between product/service variety and completion. Our analysis sheds light on how beneficiary characteristics (e.g., heterogeneity of their needs, capability to self-complete) and NPO objectives (e.g., total-benefit maximization versus fairness) affect the optimal levels of variety and completion. Managerial implications: We provide three key observations. (1) Partial completion is not a compromise solution to budget limitations but can be an optimal strategy for NPOs under a wide range of circumstances, even in the presence of ample resources. (2) Partial provision is particularly valuable when beneficiary needs are highly heterogeneous, or beneficiaries have high self-completion capabilities. A higher self-completion capability generally implies a lower optimal completion level; however, it may lead to either a higher or a lower optimal variety level. (3) Although providing incomplete products may appear to burden beneficiaries, a lower completion level can be optimal when fairness is factored into an NPO’s objective or when beneficiary capabilities are more heterogeneous.

The Effects of Patent Extension and Take-Back Regulation on Green Pharmacy

2021 ◽  
Author(s):  
Tianqin Shi ◽  
Nicholas C. Petruzzi ◽  
Dilip Chhajed
Keyword(s):  
Pharmaceutical Industry ◽  
Social Welfare ◽  
Technical Information ◽  
Problem Definition ◽  
Theoretic Model ◽  
Innovative Company ◽  
Take Back ◽  
Starting Point ◽  
European Environmental Agency ◽  
Managerial Implications

Problem definition: The eco-toxicity arising from unused pharmaceuticals has regulators advocating the benign design concept of “green pharmacy,” but high research and development expenses can be prohibitive. We therefore examine the impacts of two regulatory mechanisms, patent extension and take-back regulation, on inducing drug manufacturers to go green. Academic/practical relevance: One incentive suggested by the European Environmental Agency is a patent extension for a company that redesigns its already patented pharmaceutical to be more environmentally friendly. This incentive can encourage both the development of degradable drugs and the disclosure of technical information. Yet, it is unclear how effective the extension would be in inducing green pharmacy and in maximizing social welfare. Methodology: We develop a game-theoretic model in which an innovative company collects monopoly profits for a patented pharmaceutical but faces competition from a generic rival after the patent expires. A social-welfare-maximizing regulator is the Stackelberg leader. The regulator leads by offering a patent extension to the innovative company while also imposing take-back regulation on the pharmaceutical industry. Then the two-profit maximizing companies respond by setting drug prices and choosing whether to invest in green pharmacy. Results: The regulator’s optimal patent extension offer can induce green pharmacy but only if the offer exceeds a threshold length that depends on the degree of product differentiation present in the pharmaceutical industry. The regulator’s correspondingly optimal take-back regulation generally prescribes a required collection rate that decreases as its optimal patent extension offer increases, and vice versa. Managerial implications: By isolating green pharmacy as a potential target to address pharmaceutical eco-toxicity at its source, the regulatory policy that we consider, which combines the incentive inherent in earning a patent extension on the one hand with the penalty inherent in complying with take-back regulation on the other hand, serves as a useful starting point for policymakers to optimally balance economic welfare considerations with environmental stewardship considerations.

Truthful Mechanisms for Medical Surplus Product Allocation

2020 ◽  
Vol 22 (4) ◽  
pp. 735-753 ◽  
Author(s):  
Can Zhang ◽  
Atalay Atasu ◽  
Turgay Ayer ◽  
L. Beril Toktay
Keyword(s):  
Private Information ◽  
Value Added ◽  
Information Provision ◽  
Problem Definition ◽  
Preference Ranking ◽  
Additional Increase ◽  
Healthcare Facilities ◽  
Selection Decisions ◽  
Managerial Implications ◽  
Recipient Selection

Problem definition: We analyze a resource allocation problem faced by medical surplus recovery organizations (MSROs) that recover medical surplus products to fulfill the needs of underserved healthcare facilities in developing countries. The objective of this study is to identify implementable strategies to support recipient selection decisions to improve MSROs’ value provision capability. Academic/practical relevance: MSRO supply chains face several challenges that differ from those in traditional for-profit settings, and there is a lack of both academic and practical understanding of how to better match supply with demand in this setting where recipient needs are typically private information. Methodology: We propose a mechanism design approach to determine which recipient to serve at each shipping opportunity based on recipients’ reported preference rankings of different products. Results: We find that when MSRO inventory information is shared with recipients, the only truthful mechanism is random selection among recipients, which defeats the purpose of eliciting information. Subsequently, we show that (1) eliminating inventory information provision enlarges the set of truthful mechanisms, thereby increasing the total value provision; and (2) further withholding information regarding other recipients leads to an additional increase in total value provision. Finally, we show that under a class of implementable mechanisms, eliciting recipient valuations has no value added beyond eliciting preference rankings. Managerial implications: (1) MSROs with large recipient bases and low inventory levels can significantly improve their value provision by appropriately determining the recipients to serve through a simple scoring mechanism; (2) to truthfully elicit recipient needs information to support the recipient selection decisions, MSROs should withhold inventory and recipient-base information; and (3) under a set of easy-to-implement scoring mechanisms, it is sufficient for MSROs to elicit recipients’ preference ranking information. Our findings have already led to a change in the practice of an award-winning MSRO.

scholarly journals Optimizing preservation strategies for deteriorating items with time-varying holding cost and stock-dependent demand

2020 ◽  
Vol 30 (2) ◽  
pp. 237-250
Author(s):  
Aditi Khanna ◽  
P Priyamvada ◽  
Chandra Jaggi
Keyword(s):  
Optimal Solution ◽  
Optimal Investment ◽  
Deteriorating Items ◽  
Time Varying ◽  
Preservation Technology ◽  
Special Cases ◽  
Stock Dependent Demand ◽  
Managerial Implications ◽  
Holding Cost ◽  
Dependent Demand

Organizations are keen on rethinking and optimizing their existing inventory strategies so as to attain profitability. The phenomenon of deterioration is a common phenomenon while managing any inventory system. However, it could become a major challenge for the business if not dealt carefully. An investment in preservation technology is by far the most inuential move towards dealing with deterioration proficiently. Additionally, it is noticed that the demand pattern of many products is reliant on its availability and usability. Thus, considering demand of the product to be ?stock-dependent" is a more practical approach. Further, in case of deteriorating items, it is observed that the longer an item stays in the system the higher is its holding cost. Therefore, the model assumes the holding cost to be time varying. Hence, the proposed framework aims to develop an inventory model for deteriorating items with stock-dependent demand and time-varying holding cost under an investment in preservation technology. The objective is to determine the optimal investment in preservation technology and the optimal cycle length so as to minimize the total cost. Numerical example with various special cases have been discussed which signifies the effect of preservation technology investment in controlling the loss due to deterioration. Finally, the effect of key model features on the optimal solution is studied through sensitivity analysis which provides some important managerial implications.

Managing Congestion in Matching Markets

2021 ◽  
Author(s):  
Nick Arnosti ◽  
Ramesh Johari ◽  
Yash Kanoria
Keyword(s):  
Problem Definition ◽  
Theoretic Model ◽  
Matching Markets ◽  
Contact Potential ◽  
Managerial Implications ◽  
The Commons ◽  
Game Theoretic ◽  
Face Search ◽  
Uniqueness Of Equilibrium ◽  
Never Screened

Problem definition: Participants in matching markets face search and screening costs when seeking a match. We study how platform design can reduce the effort required to find a suitable partner. Practical/academic relevance: The success of matching platforms requires designs that minimize search effort and facilitate efficient market clearing. Methodology: We study a game-theoretic model in which “applicants” and “employers” pay costs to search and screen. An important feature of our model is that both sides may waste effort: Some applications are never screened, and employers screen applicants who may have already matched. We prove existence and uniqueness of equilibrium and characterize welfare for participants on both sides of the market. Results: We identify that the market operates in one of two regimes: It is either screening-limited or application-limited. In screening-limited markets, employer welfare is low, and some employers choose not to participate. This occurs when application costs are low and there are enough employers that most applicants match, implying that many screened applicants are unavailable. In application-limited markets, applicants face a “tragedy of the commons” and send many applications that are never read. The resulting inefficiency is worst when there is a shortage of employers. We show that simple interventions—such as limiting the number of applications that an individual can send, making it more costly to apply, or setting an appropriate market-wide wage—can significantly improve the welfare of agents on one or both sides of the market. Managerial implications: Our results suggest that platforms cannot focus exclusively on attracting participants and making it easy to contact potential match partners. A good user experience requires that participants not waste effort considering possibilities that are unlikely to be available. The operational interventions we study alleviate congestion by ensuring that potential match partners are likely to be available.

Incentives for Shared Services: Multiserver Queueing Systems with Priorities

2021 ◽  
Author(s):  
Hanlin Liu ◽  
Yimin Yu
Keyword(s):  
Cooperative Game ◽  
Cost Allocation ◽  
Service Providers ◽  
Queueing Systems ◽  
Service Level ◽  
Problem Definition ◽  
Shared Services ◽  
Scheduling Policy ◽  
Shared Service ◽  
Managerial Implications

Problem definition: We study shared service whereby multiple independent service providers collaborate by pooling their resources into a shared service center (SSC). The SSC deploys an optimal priority scheduling policy for their customers collectively by accounting for their individual waiting costs and service-level requirements. We model the SSC as a multiclass [Formula: see text] queueing system subject to service-level constraints. Academic/practical relevance: Shared services are increasingly popular among firms for saving operational costs and improving service quality. One key issue in fostering collaboration is the allocation of costs among different firms. Methodology: To incentivize collaboration, we investigate cost allocation rules for the SSC by applying concepts from cooperative game theory. Results: To empower our analysis, we show that a cooperative game with polymatroid optimization can be analyzed via simple auxiliary games. By exploiting the polymatroidal structures of the multiclass queueing systems, we show when the games possess a core allocation. We explore the extent to which our results remain valid for some general cases. Managerial implications: We provide operational insights and guidelines on how to allocate costs for the SSC under the multiserver queueing context with priorities.

Dynamic Type Matching

2021 ◽  
Author(s):  
Ming Hu ◽  
Yun Zhou
Keyword(s):  
Sufficient Conditions ◽  
Sharing Economy ◽  
Problem Definition ◽  
Periodic Review ◽  
Optimal Matching ◽  
Demand And Supply ◽  
Holding Costs ◽  
Current Period ◽  
Dynamic Type ◽  
Managerial Implications

Problem definition: We consider an intermediary’s problem of dynamically matching demand and supply of heterogeneous types in a periodic-review fashion. Specifically, there are two disjoint sets of demand and supply types, and a reward for each possible matching of a demand type and a supply type. In each period, demand and supply of various types arrive in random quantities. The platform decides on the optimal matching policy to maximize the expected total discounted rewards, given that unmatched demand and supply may incur waiting or holding costs, and will be fully or partially carried over to the next period. Academic/practical relevance: The problem is crucial to many intermediaries who manage matchings centrally in a sharing economy. Methodology: We formulate the problem as a dynamic program. We explore the structural properties of the optimal policy and propose heuristic policies. Results: We provide sufficient conditions on matching rewards such that the optimal matching policy follows a priority hierarchy among possible matching pairs. We show that those conditions are satisfied by vertically and unidirectionally horizontally differentiated types, for which quality and distance determine priority, respectively. Managerial implications: The priority property simplifies the matching decision within a period, and the trade-off reduces to a choice between matching in the current period and that in the future. Then the optimal matching policy has a match-down-to structure when considering a specific pair of demand and supply types in the priority hierarchy.

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