Distribution Planning Model Research Articles

Distribution planning model requirements for smart community integration

Food supply chains (FSCs) have long been exposed to environmental variability and shock events caused by various economic, political, and infrastructural factors. The outbreak of the COVID-19 pandemic has further exposed and identified the vulnerability of FSCs, and promoted integrated optimization approaches for building resilience. However, existing works focusing on general supply chains (SCs) and FSCs have not been fully aware of the distinct characteristics of FSCs in green logistics, i.e., the expiration of fresh products. In reality, perishable food materials can be processed into products of different processing levels (i.e., multi-level processing) for longer shelf lives, which can serve as a timely and economic strategy to increase safety stocks for mitigating disruption risks. Motivated by this fact, we study the problem of enhancing FSC with a multi-level processing strategy. An integrated location, inventory, and distribution planning model for a multi-echelon FSC under COVID-19-related disruptions is formulated to maximize the total profit over a finite planning horizon. Specifically, a two-stage stochastic programming model is presented to hedge against disruption risks, where scenarios are generated to characterize geographical impact induced by source-region disruptions. For small-scale problems, the model can be solved with commercial solvers. To exactly and efficiently solve the large-scale instances, we design an integer L-shaped method. Numerical experiments are conducted on a case study and randomly generated instances to show the efficiency of our model and solution method. Based on the case study, managerial insights are drawn.

A production planning problem related to income is addressed in a fruit supply chain of small producers, who prefer not to harvest if the market price does not allow their costs to be recovered. A mathematical model is proposed to represent the harvest decision where three elements are considered: the product perishability, the market prices behavior, and finally how much to harvest. This paper establishes that the income improvement of small agricultural producers is a strategy to support the socio-economic development of this sector. The model applied in a small citrus producer’s case study show that adequate harvest planning allows establishing a relationship between prices and sales to maximize small producer profits.

This study discusses the integration model of production and distribution planning in the shrimp agroindustry supply chain, consisting of four echelons: shrimp suppliers, shrimp agroindustry, logistics provider companies, and buyers. The shrimp agroindustry supply chain is an essential part of the supply chain of processed product food, which transforms raw shrimp into various processed shrimp frozen products. One form of collaboration between supply chain actors is the integration of production and distribution planning activities. A model is developed to determine the flow of goods from each echelon, the number of processed shrimp products in the agroindustry, and the supplies of processed shrimp products. The bi-objective mixed-integer linear programming is proposed to describe the characteristics of the problem to minimize the total supply chain costs and maximize service level. Non-dominated sorting genetic algorithm II (NSGA-II) is designed to solve the shrimp agroindustry supply chain problem. The sample problem from the shrimp agroindustry in East Java, Indonesia, is applied to exhibit an algorithm’s efficiency. The result shows the best solution for the total supply chain is 1.75 trillion, and the service level is 1,502,264.5.

Multi-objective decision-making model for distribution planning of goods and routing of vehicles in emergency multi-objective decision-making model for distribution planning of goods and routing of vehicles in emergency

Due to the TFT LCD (Thin-Film-Transistor Liquid-Crystal-Display) manifestation into many related products, this industry has been growing rapidly along with increasing demand. To satisfy the demand, most companies have increased their production capacity and capability by increasing their number of factories in different places and causing complexity in this industry. This research develops a production and distribution planning model for the multi-stage and multi-site supply chain in the TFT LCD industry. Genetic algorithm proposed in this research to solve the problem. Maximizing capacity utilization and total profit in the supply chain are become the major performance indicator in this model. Regarding the high computational effort of genetic algorithm, then parallel computation performed. Genetic algorithm conducted in multi-processor computation using OpenMP for time efficiency. To compare the computational time, the genetic algorithm conducted in five different number of processors; 1, 2, 4, 8, and 16, to know how many processors are needed to get the optimal computational time. The result is the genetic algorithm using 4 processors has the optimal expected net profit compare to the others. The result shows that a larger number of processors doesn’t mean the computation time will become automatically faster.

In this paper, a new formulation for integrating production planning and distribution planning of perishable products through lot sizing and inventory routing problem is presented. Some assumptions about the problem are as follows; there is a production facility which produces a single product, then delivers them directly using limited number of capacitated vehicles to geographically dispersed distribution centres. Also, the product is perishable, i.e., it is storable only for predetermined periods. Because of the computational complexity of the problem, using the exact methods is not feasible especially when the problem size is large. So, we have developed an efficient heuristic algorithm which is able to find good quality solutions in a reasonable time. Efficiency of the algorithm is proved through number of randomly generated test problems. The algorithm performance is compared to the LINGO commercial optimiser.

In many conventional supply chains, production planning and distribution planning are treated separately. However, it is now demonstrated that they are mutually related problems that must be tackled in an integrated way. Hence, in this paper a new integrated production and distribution planning model for perishable products is formulated. The proposed model considers a supply chain network consisting of a production facility and multiple distribution centers. The facility produces a single perishable product that is storable only for predetermined periods. A homogenous fleet of vehicles is responsible for delivering the product from facility to distribution centers. The decisions to be made are the production quantities, the distribution centers that must be visited, and the quantities to be delivered to them. The objective is to minimize the total cost, where the trip minimization is considered simultaneously. As the proposed formulation is computationally complex, a heuristic method is developed to tackle the problem. In the developed method, the problem is divided into production submodel and distribution submodel. The production submodel is solved using LINGO, and a particle swarm heuristic is developed to tackle distribution submodel. Efficiency of the algorithm is proved through a number of randomly generated test problems.

After a number of food safety crises, the design and implementation of traceability systems became an important tool for managing safety risks in the food industry. In the literature, numerous studies deal with traceability from the viewpoint of information system and technology development. However, traceability and its implications for food safety receive less attention in literature on production and distribution planning. From the viewpoint of operations management, an efficient management of food safety risks requires the consideration of the amounts of potentially recalled products, affected regions/customers, and logistics efforts connected to solving the safety problem. In this paper we are developing a production and distribution planning model for food supply chains to address these issues. We also present heuristics for solving the resulting mixed-integer linear programming model and demonstrate the effectiveness of the developed methodology in a numerical investigation.

Despite their spread, continuous process industries remain least researched vis-a-vis specific issues. This article considers a European plant, manufacturing non-discrete commodity products, formulates and tests a synchronised production and distribution-planning model, minimising energy costs. Moreover, it suggests a post-optimisation analytical approach, emphasising productivity and quality criteria in enterprise development.

Abstract Duty drawback refers to a full or partial refund of paid import duties when an imported merchandise is destroyed, exported, or consumed as a raw material to produce an export. Despite their extensive international trading operations, many manufacturers fail to save costs by claiming duty drawbacks. In the United States alone, estimates of unclaimed duty drawbacks range from US$1.5–10 billion per annum. In light of this hefty sum of unclaimed duty drawbacks, it is astounding that only one existing production–distribution model in the literature has attempted to include duty drawback. This article is the first to address this neglect of duty drawback in supply chain research by industry practitioners and academicians. To this end, it introduces the key concepts of duty drawback and explains its importance in the new economic era. Second, it presents a linear programming model incorporating three key regulatory factors—corporate taxes, import duties, and duty drawbacks—for solving a production–distribution problem in the chemical industry. Finally, through illustrative examples, it demonstrates the importance of incorporating duty drawback and other regulatory factors in production–distribution planning models. © 2005 American Institute of Chemical Engineers AIChE J, 2006

A multi-product, multi-stage, and multi-period production and distribution planning model is proposed in this paper to tackle the compromised sales prices and the total profit problem of a multi-echelon supply chain network with uncertain sales prices. This model is constructed as a mixed-integer nonlinear programming problem to achieve a maximum total profit of the whole network and to guarantee the maximum satisfactory levels of sellers’ and buyers’ preference on sales prices. For the purpose that a compensatory solution among all participants of the supply chain can be achieved, a fuzzy decision-making method is proposed and, by means of applying it to a numerical example, proved effective in providing a compromised solution in a multi-echelon supply chain network.

Today's retail business leaders use enterprise-wide decision support tools to guide market expansion strategy and optimisation of existing networks. In particular, distribution planning models are deployed to guide site selection, determine store formats and optimise store networks. These models, however, require the integration of an array of transaction, customer and demographic market data. Once combined, special methods are required to meet the unique assumptions of geospatial analysis and to handle data collected at different levels of aggregation. This paper reviews the use of analytical models within distribution planning systems. A typical distribution planning system is described. Technology, data and analytical components are detailed. Then five key applications are described for retail and banking.

The problem of a fair profit distribution for a multienterprise supply chain network is investigated in this paper. To implement this concept, we construct a multiproduct, multistage, and multiperiod production and distribution planning model to achieve multiple objectives such as maximizing the profit of each participant enterprise, the customer service level, and the safe inventory level and ensuring a fair profit distribution. A two-phase fuzzy decision-making method is proposed to attain a compromise solution among all participant companies of the supply chain. One numerical example is supplied, demonstrating that the proposed two-phase decision-making method can provide an improved compensatory solution for multiobjective optimization problems in a multienterprise supply chain network.

Linear Programming

Performance-based strategic resource allocation in supply networks

Automated design of house-floor layout with distributed planning

Implementation of a visual interactive consensus decision support system