Production-distribution Planning Research Articles

Model Based Demand Order Estimation by Using Optimal Architecture Artificial Neural Network with Metaheuristic Optimizations

With the increase of e-commerce volumes in recent years, it is useful to estimate daily demand order numbers in order to improve the demand forecasts, production-distribution planning and sales services. In this manner, data-driven modeling and machine learning tools have been preferred to enhance demand order predictions, timely delivery, incomes and customer satisfaction in electronic trading because real-time data collection is possible in e-commerce platforms. Artificial Neural Networks (ANNs) are widely used for data-driven modeling and prediction problems. Since affecting the approximation performance of neural network function, the modeling performance of ANNs strongly depends on the architecture of neural networks, and architectural optimization of ANN models has become a main topic in the neuroevolution field. This study presents an architecture optimization method that implements Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms to optimize ANN model architecture for the estimation of total demand order numbers from the sparse demand order data. In this approach, PSO and DE algorithm only optimizes neural model architecture according to an effective network search policy and the training of ANN models is carried out by using backpropagation algorithm. This neural architecture model optimization approach considers generalization of data, reducing neuron and training epoch numbers and it can yield an optimal architecture data-driven neural model for estimation of the daily total orders. In the experimental study, optimal architecture ANN models are obtained according to the daily order forecasting dataset.

Optimisation frameworks for integrated planning with allocation of transportation resources for industrial gas supply chains

This work addresses the integrated optimisation of production-distribution planning and allocation of transportation resources for industrial gas supply chains. The production-distribution planning decisions include the production plan, purchasing plan for both a liquefied product and raw material from external suppliers, distribution plan by railcars and trucks, and demand allocation. In contrast, the allocating decisions of transportation resources involve the number of trucks and railcars at each plant, depot, and third-party supplier. First, we propose a mixed-integer nonlinear programming (MINLP) model, and then the MINLP model is reformulated as a mixed-integer linear fractional programming (MILFP) model. Furthermore, we present a multi-objective optimisation (MOO) model as an alternative approach. As solution strategies, we adopt Dinkelbachs algorithm and the reformulation-linearisation method for the MILFP model, whereas the ε-constraint method is used for the MOO model. Finally, industry-relevant case studies illustrate the applicability and performance of the proposed models and solution methods.

Green production-distribution planning under competition in a brand-differentiated supply chain

Green production-distribution planning under competition in a brand-differentiated supply chain

Green production-distribution planning under competition in a brand-differentiated supply chain

This research proposes the development of a green IPD planning under 'non-competitive' and 'competitive' market conditions, aiming at minimising costs, maximising service level, and minimising carbon emissions. For the 'competitive market' model, a green competition and a Stackelberg competition between brand-differentiated manufacturers are considered. A real case study is investigated to evaluate the application of these models. The numerical results express that competition enhances both financial and environmental performance of supply chains and ameliorates supply of products to retailers. Moreover, this study supports informed decision-making by supply chain authorities to improve inventory control and replenishment to address customer demand sensitivity.

Production-Distribution Problem Optimization in a Green Closed-Loop Supply Chain

Integrated production-distribution planning in traditional forward supply chain has attracted a lot of attention in recent years and its economic advantages are particularly noticeable. In this paper, the issue of integrated production-distribution planning of a multi-level green closed loop, including recycling centers, manufacturing / remanufacturing factories and decentralized distribution centers, is investigated. A three-level bi-objective model with the aim of increasing profit and reducing the adverse environmental effects is presented. A revised multi-choice goal programming approach and a hierarchical iteration algorithm is used to solve it.

Integrating Mathematical and Simulation Approach for Optimizing Production and Distribution Planning With Lateral Transshipment in a Supply Chain

Supply chain planning aims to maximize the chain's profit and find an effective way to integrate production and distribution. A mathematical and simulation-based optimizations are two common disciplines in which this study integrates both of them together to consolidate their advantages. A mathematical model is formulated to find an optimal production-distribution plan. Then, the result is fed into a simulation model operating under uncertainty to verify the feasibility of the plan. Our integrated approach tries to find a feasible plan that satisfies both required customer service level and makespan limitation where safety stock is used to hedge against uncertainties, and lateral transshipment is used for emergency measures against excessive fluctuation of customer demand. A case study that optimizes the profit of an entire chain is used to demonstrate the algorithm. The outcomes of the study show that our proposed approach can yield feasible results (with near or even optimal solution) with much faster computational time as compared to the traditional simulation-based optimization.

A Bi-objective Robust Optimization Model for an Integrated Production-distribution Problem of Perishable Goods with Demand Improvement Strategies: A Case Study

This paper develops a bi-objective optimization model for the integrated production-distribution planning of perishable goods under uncertainty. The first objective seeks to maximize the profit in a specific supply chain with three levels: plants, distribution centers, and in the last level, customers. Since transportation is one of the major pollution sources in a distribution problem, the second objective is to minimize their emission. In the considered problem, the decisions of production, location, inventory, and transportation are made in an integrated structure. In developing the demand function, the effect of the product freshness and the price is formulated. Besides, to encourage customers, three strategies, including perished product return, discount, and credit policies, are proposed. Also, robust optimization is utilized to cope with the operational uncertainty of some cost parameters. To prove the applicability of this research and the feasibility of the environmental aspect, a case study is conducted. Finally, the numerical computations on the case study provide a trade-off between the environmental and economic goals and indicate a 37.5 percent increase in the profit using the developed model.

An infeasible space exploring matheuristic for the Production Routing Problem

This paper proposes a novel matheuristic algorithm for solving the Production Routing Problem (PRP). The PRP is a hard-to-solve combinatorial optimization problem with numerous practical applications in the field of freight transportation, logistics and supply chain management. A manufacturer is responsible for determining production decisions, as well as the timing and the quantity of replenishment services offered to a set of geographically dispersed customers over a multi-period time horizon. The problem calls for jointly optimizing the production, inventory, distribution and routing decisions. The paper provides a novel two-commodity flow formulation and proposes a two-phase infeasible space matheuristic algorithm for solving the examined problem. The first phase deals with a relaxation of the problem to construct production-distribution plans. In the second phase, these are completed with routing information and optimized via a local search framework which oscillates between the feasible and the infeasible solution space. The framework is equipped with mixed integer programming components for restoring infeasibility and for diversifying the conducted search. Computational experiments demonstrate that the infeasibility space exploration significantly contributes to the quality of the final solutions. The results obtained by the proposed matheuristic out-match the results of state-of-the-art approaches. More specifically, 594 and 55 new best solutions out of 1440 and 90 instances of two well-established benchmark data sets of small-medium and large instances are reported, respectively.

The beneficial effect of information sharing in the integrated production–distribution planning of textile and apparel supply chain

The present paper proposes an integrated production–distribution planning approach for a textile and apparel supply chain. Tactical and operational decisions are considered in the proposed multi-product and multi-period planning problem. Using a rolling horizon, the approach aims at defining optimal quantities to produce, to store and to deliver. The integration consists in coordinating informational flows between producer and retailer. Information sharing will allow the producer to estimate more accurately the future replenishment orders that may happen at the operational level and adjust production capacity requirements accordingly. For this purpose, a two-stage planning approach is devised; the first stage deals with the tactical level while the second stage deals with the operational level. The monthly decisions taken at the tactical planning level are accounted for in the operational planning considering a variable rolling horizon. Moreover, accurate forecasts of future replenishment orders are established based on information sharing and introduced in the operational planning to determine the weekly decisions. Linear programming models are used to build production and distribution plans at the tactical and operational levels. Using real-life data from a textile and apparel Tunisian firm, we show that producer-retailer coordination based on the sharing of current sales information, yields significant cost savings reaching up to 20% of the supply chain cost. These findings can only motivate the partnership between producer and retailer through reliable information sharing in joint tactical-operational and production–distribution planning.

Integrated Quality-Based Production-Distribution Planning in Two-Echelon Supply Chains

The integrated production-distribution (P-D) planning has turned into one of the most essential areas in supply chain (SC) management in recent years, especially in the case of perishable products in which the quality of products can change over time. Nonetheless, so far, the suggested models have focused on the P-D stages of the chain while the delivery of high-quality products to customers is of paramount significance in the perishable SC. In the present paper, a multiobjective, mixed-integer, and nonlinear programming (MOMINLP) mathematical model was developed for integrated P-D deteriorating items in a two-echelon SC that emphasizes quality degradation. Quality is monitored and calculated as a function of temperature and time throughout the SC, and the main purpose of the model is to first increase the quality of products delivered to customers and, second, minimize the SC costs. To optimize the problem, the particle swarm optimization (PSO) approach was also incorporated into the model. The obtained model was applied to a case study in Protein Gostar Sina Company in Iran, which resulted in decreased P-D costs as well as increased customer satisfaction.

INTEGRATED PRODUCTION AND DISTRIBUTION PLANNING FOR PERISHABLE PRODUCTS

Bir ürünün geçtiği aşamalar, ilk tedarikçisinden başlayarak fabrikadaki üretim hattında işlenmesi, dağıtımcılar ve perakendeciler yolunu izleyerek müşteriye ulaşması olarak sıralanabilir. Bu zincirin planlanmasında yazında ve uygulamada genel kanı olarak üretim ile dağıtım ayrı planlama aşamaları olarak kabul edilmiş, buna bağlı olarak kurulan modeller de üretimi ve dağıtımı ayrı modeller altında ele almıştır. Oysa dağıtım ve üretim birbirlerini zincirleme olarak etkileyen kararlar olduğundan birleşik bir üretimdağıtım planlaması çok daha sağlıklı ve verimli sonuçlar verecektir. Özellikle de planlaması yapılan ürün zamanla kalitesi ve değeri azalan, yani bozulabilir bir ürün ise; ürünün ne zaman, ne kadar üretildiği ile ne zaman, nasıl dağıtılacağı birbirini zincirleme olarak etkileyecektir. Bu çalışmada, bir fabrikada, birden fazla bozulabilir ürünün üretimi ve birden fazla dağıtım merkezine birden fazla araç ile dağıtımını birleşik ele alan bir karma tamsayılı doğrusal programlama modeli önerilmiştir. Modelin amacı, toplam üretim, dağıtım ve stok maliyetlerini (sabit ve değişken üretim, fabrikadaki ve dağıtım merkezlerindeki stok ile araçların sabit dağıtım maliyetleri toplamını) en az yapmaktır

Two meta-heuristic algorithms for optimizing a multi-objective supply chain scheduling problem in an identical parallel machines environment

Optimizing the trade-off between crucial decisions has been a prominent issue to help decision-makers for synchronizing the production scheduling and distribution planning in supply chain management. In this article, a bi-objective integrated scheduling problem of production and distribution is addressed in a production environment with identical parallel machines. Besides, two objective functions are considered as measures for customer satisfaction and reduction of the manufacturer’s costs. The first objective is considered aiming at minimizing the total weighted tardiness and total operation time. The second objective is considered aiming at minimizing the total cost of the company’s reputational damage due to the number of tardy orders, total earliness penalty, and total batch delivery costs. First, a mathematical programming model is developed for the problem. Then, two well-known meta-heuristic algorithms are designed to spot near-optimal solutions since the problem is strongly NP-hard. A multi-objective particle swarm optimization (MOPSO) is designed using a mutation function, followed by a non-dominated sorting genetic algorithm (NSGA-II) with a one-point crossover operator and a heuristic mutation operator. The experiments on MOPSO and NSGA-II are carried out on small, medium, and large scale problems. Moreover, the performance of the two algorithms is compared according to some comparing criteria. The computational results reveal that the NSGA-II performs highly better than the MOPSO algorithm in small scale problems. In the case of medium and large scale problems, the efficiency of the MOPSO algorithm was significantly improved. Nevertheless, the NSGA-II performs robustly in the most important criteria.

The new TOPCO hybrid algorithm to solve multi-objective optimisation problems: the integrated stochastic problem of production-distribution planning in the supply chain

This paper presents a new hybrid algorithm to solve multi-objective optimisation problems called TOPCO which has been implemented on the integrated stochastic problem of production-distribution planning in the supply chain. The proposed TOPCO hybrid algorithm is a combination of the TOPSIS algorithm and the cuckoo optimisation algorithm and is therefore named TOPCO. The cuckoo optimisation algorithm is unable to solve multi-objective problems that can be easily used for multi-objective problems using the proposed hybrid approach. The multi-objective problem under study includes an integrated model of production and distribution planning with many stochastic parameters. The speed and accuracy of the results obtained from the implementation of the proposed TOPCO algorithm on the supply chain problem show the efficiency of the algorithm in solving multi-objective problems and this algorithm can well identify the Pareto frontier of the problem. Also, due to the use of the cuckoo optimisation algorithm, the proposed TOPCO algorithm can be used in large-scale problems.

The new TOPCO hybrid algorithm to solve multi-objective optimisation problems: the integrated stochastic problem of production-distribution planning in the supply chain

This paper presents a new hybrid algorithm to solve multi-objective optimisation problems called TOPCO which has been implemented on the integrated stochastic problem of production-distribution planning in the supply chain. The proposed TOPCO hybrid algorithm is a combination of the TOPSIS algorithm and the cuckoo optimisation algorithm and is therefore named TOPCO. The cuckoo optimisation algorithm is unable to solve multi-objective problems that can be easily used for multi-objective problems using the proposed hybrid approach. The multi-objective problem under study includes an integrated model of production and distribution planning with many stochastic parameters. The speed and accuracy of the results obtained from the implementation of the proposed TOPCO algorithm on the supply chain problem show the efficiency of the algorithm in solving multi-objective problems and this algorithm can well identify the Pareto frontier of the problem. Also, due to the use of the cuckoo optimisation algorithm, the proposed TOPCO algorithm can be used in large-scale problems.

A production and distribution planning of perishable products with a fixed lifetime under vertical competition in the seller-buyer systems: A real-world application

Motivated by a real problem, in this paper, a new multi-period production-distribution planning (PDP) for perishable products with a fixed lifetime in a seller-buyer system is developed. The objective is to maximize the seller’s profit conditional on the optimality of the buyer in a three-level supply chain (SC), including the factories, distribution centers, and retailers. The factories and distribution centers act cooperatively at the leader level and, as the seller, make the location, production, inventory, and distribution decisions. While the retailers operate as the follower; therefore, a vertical competition is held in this SC. In addition to price, freshness owing to the perishability of the product is considered as an important competitive factor. Also, to encourage the retailers, three strategies in the development of a new demand function, including the return, discount policies, and credit period, are employed. Since the problem is NP-hard, a hierarchical heuristic approach based on Benders Decomposition Algorithm (BDA) and Genetic Algorithm (GA) is proposed. Finally, to demonstrate the applicability and efficiency of the developed model, it is applied to a real industrial case. The computational results of the problems in different sizes confirm the efficient performance of the proposed model and solution method.

A new prediction method of reservoir porosity based on improved Kriging interpolation

In the process of oil and gas field development, the pore characteristics of reservoir are the main factors that affect the reservoir capacity and production efficiency. The physical property parameters and pressure distribution of layers have changed greatly, and the pressure system of multi-pressure layer is formed underground of multi-layer reservoir and heterogeneous sandstone oil reservoir after water injection and chemical injection. Accurate prediction of reservoir physical properties and formation pressure distribution after changes is significance for efficient production and safe operation of reservoirs. In this paper, based on geostatistics theory and percolation mechanics theory, combined with reservoir geology, drilling, development and testing data of Bohai Oilfield, the prediction method of reservoir porosity based on improved Kriging interpolation and effective thickness has been established. This prediction method can provide certain guidance for the formulation of oilfield injection and production distribution plans at the oilfield and the shut-in and adjustment plans for injection-production wells during drilling and completion operations.

A Bilevel Programming Model for a Cohesive Decision-Making on Strategic Pricing and Production Distribution Planning for a Small-Scale Supplier

This research develops a decision support for strategic pricing and aggregate production distribution planning for a small-scale supplier intending to penetrate into a potential market engendered by a single buyer. A novel mixed integer bilevel programming model is proposed to formulate the problem in which the supplier is considered as a leader and the buyer is a follower. The proposed model subsumes the assessment of demand share against the price quotation, enabling the supplier to prepare an aggregate production distribution plan accordingly. An integer coded genetic algorithm is used to solve the model and its implementation is exhibited through a test scenario.

Investigations into control strategies of supply chain planning models: a case study

In the past, several frameworks and models have been developed in the literatures that addressed the problems and issues of logistics in a supply chain management. In the literature, only few number of variables are taken into consideration in obtaining the solution of the supply chain and most of the research is done on decentralized control strategy only. Such kind of analysis may not give the appropriate solution to the manufacturer among different alternatives. Hence it is necessary to investigate all the variables that affect the performance of the supply chain under different control strategies. The aim of the present work is to compare the centralized and decentralized supply chains to find out which type of supply chain is more advantageous in terms of cost. Two mathematical models were proposed, one for decentralized supply chain and the other for centralized supply chain for comparing the total cost of the two supply chains. In the present work to implement decentralized supply chain an agent based methodology and for centralized supply chain model a linear program model has been used. The author proposed a novel centralized supply chain which considers the same costs and capacities as were considered in the decentralized supply chain. The only difference considered here between centralized and decentralized supply chains is the amount of information sharing. The contribution of this paper is to formulate the centralized and decentralized multi-product multi-period supply chain models and to implement both the models in GAMS software and to obtain the results using NEOS optimization web site. In this paper, 1728 variables for distribution agent and 576 variables for production agent and 192 linking variables, totally 2496 variables are considered which enabled to study the complexity and intricacies of the supply chain. From the results, it was found that a saving of Rs. 4,72,44,400 is possible, if the domestic pump industry considered in the paper follows centralized production–distribution planning. The month-wise production quantity, distribution quantities from factory to distribution centre and from distribution centre to customer, month-wise inventory quantities to minimize the total cost are also obtained.

Relatively optimal production-distribution plans for stock management in supply chains over a finite time horizon

A model of the centralized dynamic production - distribution control is considered. In a supply chain a vendor supplies a product to a buyer, sending the products to the buyer’s warehouse in equal-size batches. Costs are attached to manufacturing the batch, the delivery of shipment and stockholding at both agents, and defined on production-distribution cycles (PDCs). There is only one production setup in a cycle with the restriction that in each warehouse the beginning and the ending inventory levels are the same. It is shown that the average cost of “sensible” PDCs is a continuous piece-wise convex function of their production batch sizes. For infinite time horizon the optimal production distribution plan is a constant sequence of PDCs with lowest average cost - economic production-distribution cycles (EPDCs).

Goal Programming Model for Production-Distribution Planning by Considering Carbon Emission

Companies must manage their supply chains effectively under changing conditions in marketplace in order to be successful against their competitors. As a result of some regulations in recent years, companies are forced to consider the damage they cause to the environment by their supply chain activities. In this paper, a production-distribution problem, which concerns economic and environmental effects, is considered. A multi-product, multi-stage production-distribution network with different transportation alternatives is modelled in the problem. A goal programming model is proposed to support planning decisions of this production-distribution network by considering the profit of network activities and the carbon emission value caused by material and product transportation. A randomly generated set of test data was used to evaluate the effectiveness of the proposed model. The results show that the proposed model can be used as an effective tool for environmentally friendly production-distribution planning