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Consolidation strategies for the delivery of perishable products
Abstract
A set of agricultural suppliers with low demands can save on long-haul transportation costs by consolidating their product. We consider a system with stochastic demand and a single consolidation point near the suppliers. We propose a look-ahead heuristic that takes advantage of economies of scale by aiming to ship larger quantities. We experimentally compare the heuristic’s performance against other simple policies, a rolling horizon algorithm, and a stochastic dynamic programming model. Our numerical results demonstrate that the heuristic provides solutions that are near the lower bound provided by the dynamic programming model, and that the benefits of consolidating depend on the size of the suppliers’ demand. We also propose a proportional cost allocation rule that encourages the suppliers to cooperate with each other instead of operating independently.
... Several papers have been published on delivery truck routing (e.g., see [11,10,7,6,8,4,2,5]). However these focus on route optimization though some take into account constraints due to delivery of perishable items. ...
... In order to illustrate the benefits of the proposed approach we provide numerical results for simulated data but which is based on statistical characteristics of real data. In addition, we generate an artificial scenario with realistic assumptions, described in [6], so that we could do a sensitivity analysis of the proposed approach. We compare the proposed approach with the present mode of operation. ...
We consider the problem of optimizing the near-periodic delivery of a product to customers with the additional constraint that there is an estimated deadline for delivery of the product. Delivery after the deadline is unacceptable while early delivery increases the frequency of deliveries which is also not desirable. While satisfying these customer constraints we wish to minimize the resources required by the delivery company to provide this service. In particular, we wish to minimize the total travel distance for delivery of products to customers since this reflects the associated costs of the service.
... The subject of shipment consolidation and its potential for cost reduction has been a significant focus of logistics research. Studies in this area have focused predominantly on coordinating replenishment and delivery decisions (Nguyen et al., 2014), encompassing the management of material flows from one or multiple vendors to single or multiple retailers. Such consolidation typically involves a small number of customers with large freight per order, and they use long-distance vehicles for transportation (Capar, 2013). ...
... SteadieSeifi et al. (2017) investigate a transportation planning challenge involving multiple modes of transportation, perishable products and the administration of reusable transport items. Nguyen et al. (2014) suggested that suppliers of perishable products with low demand save on long-haul transportation costs by consolidating their products. ...
Floriculture as a branch of the horticulture industry is still at a developing stage that needs to be assessed for further orientation in the areas of management, business and economics. The commercialization of ornamental plants and cut flowers is of recent origin in developing economies which have high growth potential, both in domestic and international markets. The study highlights the evolution of the floriculture industry having a management perspective and paving the way for future research directions. The paper conducts a bibliometric analysis and rigorous systematic review of 126 papers published in peer-reviewed journals related to the floriculture industry using the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol. The Biblioshiny function of R software and VOS viewer is used for the analysis. The intellectual structure of the floriculture literature is presented through bibliographic coupling and thematic cluster analysis. The findings indicate considerable opportunities for further investigations in the field of floriculture focusing on the inclusion of sustainability practices, the evolution and role of women farmers and entrepreneurs, and supply chain management of floriculture products.
... Over the last decades, RH schemes have been applied in several industrial contexts to solve optimization models that involve uncertainty. Some good examples of these applications are the distribution of cut flowers (Nguyen et al., 2014), container planning in harbors (Zhang et al., 2003;Yang et al., 2018), wildland fire planning (Chow and Regan, 2011), health systems (Addis et al., 2014), warehouse management (Revillot-Narváez et al., 2019), sawmill (Huka and Gronalt, 2017), emissions trading (Quemin and Trotignon, 2021), and facility location (Marufuzzaman et al., 2016). ...
Over the last decade, agriculture has evolved from a human-intensive activity to a highly automated process. Multiple technological advances (e.g., harvest machines, sensors, and drones) have been incorporated to collect and transmit information, increasing harvest efficiency and more accurate and timely decisions. These advances have opened new opportunities to apply optimization models during the harvest season. In this context, to apply these models, it is necessary to consider the underlying uncertainty in agricultural operations that comes mainly from weather conditions and the biological characteristics of crops. One of the traditional strategies used to reactively manage these uncertainties in optimization models is the Rolling Horizon (RH) strategy. However, RH is typically myopic about the future, and it can be challenging to implement this approach when commitments with suppliers are signed. This work proposes a non-myopic rolling horizon method to reschedule the agricultural harvest plan. Furthermore, our RH scheme is exemplified by means of olive oil harvesting and production. Our method is based on a baseline plan generation, and after that, an adaptive rescheduling scheme is generated under new conditions. A bi-objective rescheduling problem seeking to maximize production and minimize plan variability is formulated. Computational experiments are conducted to study our methodology's impact in several rescheduling periods. A good performance in two challenging agricultural scenarios is highlighted. This proposal offers the community a framework for reactively managing complex harvest operations.
... Existing studies on horizontal cooperation generally focus on collaboration opportunities within a transport context, especially when two or more partners collaborate through joint distribution [4,14,15,[32][33][34][35]. However, in this study, we present a new approach to textile frms operating in clusters: the sharing of subcontractors with collaborating companies. ...
Home textile is the most common business line in Denizli, Turkey. However, most companies are not integrated, i.e., they do not include the entire production processes in their facilities. Different stages of manufacturing process are outsourced to other companies located in the same geographic region. As a result of this, there is a considerable increase in transportation costs. In this paper, we consider collaboration between companies in a cluster to reduce their transportation costs. A game theoretic approach is proposed such that the subcontractors can be used jointly and their fabric production capacity can be split among the companies in the coalition in order to meet their demands. A case study of four home textile companies is provided to illustrate the approach. Coalition cost is allocated using different cost allocation methods, and compared to the current situation, we obtain significant cost reductions.
The rapid growth of e-commerce has heightened the importance for express delivery companies to ensure timely deliveries. Consequently, it is essential to explore ways to deliver more packages to customers while simultaneously reducing costs through the adoption of a joint distribution mode. This study presents a two-level delivery location selection model within the joint distribution mode, considering factors such as delivery station capacity and the number of transport vehicles, with the objective of minimizing the total cost associated with selecting delivery station locations. The proposed model is addressed using a combination of the k-means algorithm and the improved discrete firefly algorithm. In addition, to facilitate equitable cost allocation among enterprises, the Shapley value method is introduced in this study. A case study based on real data from an urban distribution network in the city of Hebei Province, China, is adopted to perform the experiments. The results of this study indicate that the improved algorithm not only improves solution accuracy but also reduces solution time when compared to both the particle swarm optimization and artificial bee colony methods. Furthermore, the application of the Shapley value method demonstrates the efficacy of a rational allocation of costs.
In many large manufacturing companies, freight management is handled by a third-party logistics (3PL) provider, thus allowing manufacturers and their suppliers to focus on the production of goods rather than managing their delivery. Provided their pivotal supply chain role, in this work we propose a general framework for what we term as “the 3PL freight management problem” (3PLFMP). Our framework identifies three primary activities involved in 3PL freight management: the assignment of orders to a fleet of vehicles, efficient routing of the fleet, and packing the assigned orders in vehicles. Furthermore, we provide a specific instantiation of the 3PLFMP that considers direct vs. consolidated shipping strategies, one dimensional packing constraints, and a fixed vehicle routing schedule. We solve this instantiated problem using several Reinforcement Learning (RL) methods, including Q-learning, Double Q-learning, SARSA, Deep Q-learning, and Double Deep Q-learning, comparing against two benchmark methods, a simulated annealing heuristic and a variable neighborhood descent algorithm. We evaluate the performance of these methods on two datasets. One is fully simulated and based on past work, while another is semi-simulated using real-world automobile manufacturers and part supplier locations, and is of our own design. We find that RL methods vastly outperform the benchmark heuristic methods on both datasets, thus establishing the superiority of RL methods in solving this highly complicated and stochastic problem.
We study a shipment consolidation problem commonly faced by companies that outsource logistics operations and operate in a commit-to-delivery mode. It involves delivering a given set of orders to their destinations by their committed due times using multiple shipping methods at the minimum total shipping and inventory cost. The shipping cost is generally nonlinear in shipping quantity and can be represented by a subadditive piecewise linear function. We investigate two shipping scenarios, one involving long-haul shipping only and the other involving joint long-haul and short-haul shipping. We develop analytical results and solution algorithms for the shipment consolidation problem under each shipping scenario. The problem under the first shipping scenario is shown to be strongly [Formula: see text]-hard. We find that a simple policy, called the First-Due-First-Delivered (FDFD) policy, which assigns orders with earlier delivery due times to shipping methods with earlier destination arrival times, is very effective. This policy enables us to develop a polynomial time algorithm, which not only solves the problem under the concave shipping cost structure optimally but also achieves a performance guarantee of 2 for the problem under the general subadditive shipping cost structure. For the problem under the second shipping scenario, we extend the FDFD policy for long-haul shipping and derive another policy, called the No-Wait policy, for short-haul shipping. We use these policies to develop a polynomial time algorithm and analyze its performance guarantee. Our computational experiments show that the algorithm significantly outperforms a commercial optimization solver, and its performance is robust across different parameter settings that reflect various practical situations.
This paper was accepted by Jeannette Song, operations management.
Supplemental Material: The data and e-companion are available at https://doi.org/10.1287/mnsc.2023.4835 .
In the past two decades, California’s share of the national cut flower market has decreased from 64 percent to 20 percent. California growers’ largest competitors are South American growers; Colombia controls 75 percent of the US market. South American growers have several competitive advantages, including the favorable trucking rates they enjoy by consolidating all shipments in Miami, Florida, prior to US distribution. This paper evaluates the California cut flower industry’s current transportation practices and investigates the feasibility and cost of establishing a shipping consolidation center in Oxnard, California. Applying a simple inventory management policy, we estimate a 35 percent system-wide transportation cost decrease of $20 million per year if all California cut flower growers participate in the consolidation center. The California Cut Flower Commission incorporated our findings into an application for federal funds from the US Department of Transportation to construct a new flower transportation and logistics center in California. The state’s flower growers are also searching for alternative ways to cooperatively fund a consolidation center.
A balance maintain system of Stewart platform using AHRS(Attitude and Heading Reference System) sensor is introduced. The Stewart platform is used for controlling a standing plate to keep up horizontal level at any slopes. To know current leaned degrees, AHRS sensor is used. We made feed-back system that AHRS sensor sends current status and the Stewart platform revises top plate to be equilibrium state.
States that we have witnessed, over the last several years, a profound change in understanding the dynamics of competitive advantage. Managers now acknowledge that a firm's success is tied, in part, to the strength of its weakest supply chain partner. This paper develops the concept of supply chain management and argues that only through close collaborative linkages through the entire supply chain, can one fully achieve the benefits of cost reduction and revenue enhancing behaviors. Data are presented that look at a range of supply chain management practices and processes. By examining differences in practices and processes between buyers and sellers, along with the supply chain, attempts to understand better the challenges facing managers who espouse supply chain management. Also proposes a change in mind set for the traditional procurement manager and present insights for him/her to adapt to the requirements of the new competition.
Joint replenishment problem (JRP) is a common real problem which aims to minimize order cost and inventory holding cost. In this paper, classical, centralized and decentralized JRP models are discussed. An innovative heuristic to minimize the total cost is implemented for each model. This heuristic seeks to balance the order cost and the inventory holding costs. The results show that the innovative heuristic can best be implemented in the classical and decentralized models. For stochastic demand such as Poisson or Exponential distribution, the innovative heuristic can be implemented with the random variables which are generated by Monte Carlo simulation.
This paper considers the problem of a vendor-buyer integrated production-inventory model. The vendor manufactures the item at a rate and delivers the goods at a lot-for-lot shipment policy to the buyer. We relax the assumption of uniform demand in the hitherto existing joint economic lot sizing models and analyze the problem where the end customer demand is price-sensitive. The relation between demand and price is considered to be linear. The model proposed, based on the integrated expected total relevant proots of both buyer and vendor, out the optimal values of order quantity and mark-up percentage, using an analytical approach. Some numerical examples are also used to analyze the eeect of the price-sensitivity of demand on the improvements in joint total proot over individually derived policies.
Purpose
The purpose of this paper is to frame collaboration in supply chain as a hierarchical reflective construct.
Design/methodology/approach
This study uses data from industries in India to test the hierarchical and structural model. Partial least squares method is used to test the model.
Findings
Results show that collaboration is a third‐order, reflective construct. The paper's findings also arrange collaborative activities in terms of its importance for collaboration.
Practical implications
Collaboration is a multi‐facet activity and is a meta‐concept, and therefore this paper improves our understanding on the subject. The performance of supply chain collaboration depends on the execution of various activities, and this paper points out how the various activities are related to the collaboration, the execution of which will drive collaborative ventures towards success.
Originality/value
This paper provides empirical evidence for collaboration as a hierarchical reflective construct. The model is tested by data collected from Industries in India.
This paper focuses on the issues of coalition formation and cost allocation in a joint replenishment system involving a set of independent and freely interacting retailers purchasing an item from one supplier to meet a deterministic demand. The papers dealing with this problem are mainly focused on supperadditive games, where the cost savings associated with a coalition increase with the number of players in the coalition. The most relevant question addressed then is how to allocate the savings to the players. In this paper, we propose to go further by dealing with a non‐supperadditive game, where a set of independent retailers have the common understanding to share the cost savings according to the cost‐based proportional rule. In this setting, the global cost optimization is no longer a relevant approach to identify appealing coalitions for any retailer. Here, we provide an iterative procedure to form the so‐called efficient coalition structure and we show that this coalition structure has the nice properties of being (i) weakly stable in the sense of the coalition structure core and (ii) strongly stable under a given assumption. An exact fractional programming based solution is also given to generate such efficient coalitions.
We consider a cooperative game defined by an economic lot sizing problem with concave ordering costs over a finite time horizon, in which each player faces demand for a single product in each period and coalitions can pool orders. We show how to compute a dynamic cost allocation in the strong sequential core of this game, i.e. an allocation over time that exactly distributes costs and is stable against coalitional defections at every period of the time horizon.