Flexible Multistage Forward/Reverse Logistics Network Under Uncertain Demands with Hybrid Genetic Algorithm

Production distribution systems are increasingly crucial because of shortened product life cycles, increasing competition, and uncertainty introduced by globalization. Production distribution involves a multistage supply chain network that consists of factories, distribution centers, retailers, and various customers. Customer demands fluctuate and are unpredictable, thereby causing an imprecise customer quantity demand in each period in the production distribution model, and increasing inventory and related costs. Most studies have addressed the production distribution problem with certain demands or a single period. To fill the gap, this study aims to integrate the extended priority-based discrete particle swarm optimization and novel extended priority-based hybrid genetic algorithm for solving flexible multistage production distribution under uncertain demands in multiple periods. In particular, triangular fuzzy demands are considered for minimizing the total cost, including transportation costs, inventory costs, shortage costs, and ordering costs, in the multistage and multi-time-period supply chain. For validation, we designed numerical experiments to compare the proposed approaches with LINGO computational software (for small problems) and conventional genetic algorithms (for normal problems) in real settings. The experimental results demonstrated practical viability of the proposed approaches.

One of the important aspects of supply chain management is inventory management because the cost of inventories in a supply chain accounts for about 30% of the value of the product. The main focus of this work is to study the performance of a single-product serial supply chain operating with a base-stock policy and to optimize the inventory (i.e. base stock) levels in the supply chain so as to minimize the total supply chain cost (TSCC), comprising holding and shortage costs at all the installations in the supply chain. A genetic algorithm (GA) is proposed to optimize the base-stock levels with the objective of minimizing the sum of holding and shortage costs in the entire supply chain. Simulation is used to evaluate the base-stock levels generated by the GA. The proposed GA is evaluated with the consideration of a variety of supply chain settings in order to test for its robustness of performance across different supply chain scenarios. The effectiveness of the proposed GA (in terms of generating base-stock levels with minimum TSCC) is compared with that of a random search procedure. In addition, optimal base-stock levels are obtained through complete enumeration of the solution space and compared with those yielded by the GA. It is found that the solutions generated by the proposed GA do not significantly differ from the optimal solution obtained through complete enumeration for different supply chain settings, thereby showing the effectiveness of the proposed GA.

PurposeThis work aims to measure the performance of an electric motors supply chain (SC) by using the Triple E performance measurement model to assess three performance dimensions simultaneously: efficiency, efficacy and environmental impact.Design/methodology/approachThis research presents a real case of a Brazilian company as an example for applying the Triple E performance measurement model and the discrete-event simulation to assess the performance of an SC.FindingsPerformance measurements for the three dimensions were the following: (1) for efficiency, a high inventory cost in the SC, driven by the distribution center (DC) (49.7% total cost); an order cycle time operating with an average of 21.7 days and a logistic channel transport capacity of 88%; (2) for efficacy, a service level of 98% for all channels; (3) for environmental impact, the SC emits a total of 395,733 kg of CO2 annually, with the DC and the regional distribution center (RDC2) being the largest emitters.Originality/valueThis work allowed to measure the performance of an SC interface (manufacturing process, a DC and three regional distribution centers). It was possible to measure the inventory holding cost and out-of-stock inventory costs, order cycle time, SC service level, transport capacity utilization and CO2 emissions in the transportation process of each SC stage and the whole SC. This study can be used as a decision support guide for academics and practitioners to measure and improve the SC performance.

Joint managed inventory is an advanced supply chain inventory management tool, which will effectively tackle the complicated problem between the inventory cost of supply chain and service level. The research on inventory model and its’ control under JMI environment is a hot issue at present. In this paper, the authors deeply discuss the question of the inventory time costs about the multi-product and multi-echelon control model and its’ replenishment strategy under JMI environment. With considering the foundation of JMI and time cost, the authors propose the multi-product multi-echelon inventory cost control model under time cost. Then formulate corresponding replenishment strategy. At last, through a numerical example, the authors discover that the multi-product multi-echelon joint inventory management based on time cost can effectively reduce the total inventory costs and improve the competitiveness of the entire supply chain.

This paper examines the relationship between the operations of forward and reverse logistics and the environmental performance measures like \(\mathrm{{CO}}_{2 }\) emission in the network due to transportation activities in closed-loop supply chain network design. A closed-loop structure in the green supply chain logistics and the location selection optimization was proposed in order to integrate the environmental issues into a traditional logistic system. So, we present an integrated and a generalized closed-loop network design, consisting four echelons in forward direction (i.e., suppliers, plants, and distribution centers, first customer zone) and four echelons in backward direction (i.e., collection centers, dismantlers, disposal centers, and second customer zone) for the logistics planning by formulating a cyclic logistics network problem. The model presented is bi objective and captures the trade-offs between various costs inherent in the network and of emission of greenhouse gas CO\(_{2}\). experiments were presented, and the results showed that the proposed model and algorithm were able to support the logistic decisions in a closed loop supply chain efficiently and accurately.

This study deals with the development of heuristic settings for dynamic order-up-to levels at different installations in a supply chain with non-stationary customer demand, in order to minimize the sum of inventory, shortage and transport costs. Balance equations are developed to capture the dynamics of supply chain. Mathematical expressions are derived to determine the dynamic order-up-to levels as a function of forecasted demand, forecasted replenishment leadtime and safety factor. Towards this attempt, different heuristic settings with respect to the safety factor are first proposed. Next, three levels of information sharing among the members of the supply chain are considered to study the impact of information sharing on the total cost. Finally, the use of a forecasting technique for predicting the replenishment leadtime is considered. A supply chain is simulated to evaluate the proposed dynamic order-up-to policies based on various heuristic settings. It is found that the use of forecast of replenishment leadtime and customer demand information sharing, and the proposed safety-stock heuristic settings in the determination of dynamic order-up-to levels, significantly reduce the sum of discounted inventory, shortage and transport costs of the supply chain.

In this paper, a new mathematical model is developed to optimize replenishment policies and inventory costs of a two-echelon supply chain system of kerosene product under demand uncertainty. The system consists of a fuel depot at the upper echelon and four petrol stations at the lower echelon. The petrol stations face stochastic stationary demand where inventory replenishment periods are uniformly fixed over the echelons. Adopting a Markov decision process approach, the states of a Markov chain represent possible states of demand for the inventory item. The replenishment cost, holding cost and shortage costs are combined with demand and inventory positions in order to generate the inventory cost matrix over the echelons. The matrix represents the long run measure of performance for the decision problem. The objective is to determine in each echelon of the planning horizon an optimal replenishment policy so that the long run inventory costs are minimized for a given state of demand. Using weekly equal intervals, the decisions of when to replenish additional units are made using dynamic programming over a finite period planning horizon. A numerical example demonstrates the existence of an optimal state-dependent replenishment policy and inventory costs over the echelons. DOI:10.12660/joscmv8n2p67-76URL: http://dx.doi.org/10.12660/joscmv8n2p67-76

Purpose– The purpose of this paper is to assess whether an existing sourcing strategy can effectively supply products of appropriate quality with acceptable levels of product waste if applied to an international perishable product supply chain. The authors also analyse whether the effectiveness of this sourcing strategy can be improved by including costs for expected shelf life losses while generating order policies.Design/methodology/approach– The performance of sourcing strategies is examined in a prototype international strawberry supply chain. Appropriate order policies were determined using parameters both with and without costs for expected shelf life losses. Shelf life losses during transport and storage were predicted using microbiological growth models. The performance of the resulting policies was assessed using a hybrid discrete event chain simulation model that includes continuous quality decay.Findings– The study's findings reveal that the order policies obtained with standard cost parameters result in poor product quality and large amounts of product waste. Also, including costs for expected shelf life losses in sourcing strategies significantly reduces product waste and improves product quality, although transportation costs rise.Practical implications– The study shows that in perishable product supply chain design a trade-off should be made between transportation costs, shortage costs, inventory costs, product waste, and expected shelf life losses.Originality/value– By presenting a generically applicable methodology for perishable product supply chain design, the authors contribute to research and practice efforts to reduce food waste. Furthermore, product quality information is included in supply chain network design, a research area that is still in its infancy.

A hedging policy for carriers’ selection under availability and demand uncertainty

In this paper, we consider a strategic distribution network design problem for a chain enterprise, in which the decision maker needs to decide the number and location of the distribution centers (DCs) and system inventory control decisions - such as economic order quantity and safety stock decisions. In the chain enterprise, stores face random customer demand, and each store and DC, respectively, maintains a certain amount of safety stock in order to achieve a certain service level for the customers or the stores it serves. The objective is to minimize the total cost that includes location costs, inventory costs, and distribution costs in the chain enterprise. We show that this problem can be formulated as a nonlinear integer programming model, for which we propose a Lagrangian relaxation based solution algorithm. In a numerical application, we found that the benefits of having an integrated distribution design framework that includes location, inventory, and routing decisions in the same optimization model.

The article considers the strategic guidelines of the logistics activities of poultry enterprises. It is noted that the strategies of minimizing logistics costs and investing in the logistics network, maximizing and improving logistics services, maximizing income and profits, increasing the competitive advantages of the enterprise, logistics outsourcing are the most widely used logistics strategies. It is proved that the purpose of creating a logistics system is to harmonize the interests of producers, suppliers and consumers. It is established that an important factor in improving the strategic activities of the enterprise is the introduction of the principles of distribution logistics. This concept involves the use of a systematic approach and integrated planning, as well as management of the entire flow of feed, materials, breeding and commercial poultry products in various areas from suppliers, through manufacturers of breeding and breeding products, distribution centers, to the final individual and corporate consumers of poultry products. The main stages of the business process of integration of a poultry enterprise in the supply chain are schematically shown. The necessity of introduction of new, more progressive forms of service in the process of goods movement can be achieved. The need to use regional distribution centers of poultry enterprises in the supply chain, which will significantly reduce the cost of delivery of their products through the formation of optimal transport batches of products from different manufacturers, its packaging and labeling.

A comparative study of pull control mechanisms for unreliable homogenous transfer lines

In this paper, the optimal (r, Q) replenishment policies are developed for stochastic mixture inventory models for both the without order crossover and the order crossover cases. The shortage costs are considered on a per stockout basis. Expressions of total cost are developed for both cases, and their convexities are proved. A normal approximation method is used to determine the distribution of the lead-time demand. The effect of the changing proportions of the mixture of backorders and lost sales (q) on various inventory performance measures, such as total inventory cost, service levels and turnover ratio, is studied. Further, a simulation study is performed to validate the order crossover model. The results obtained are compared with the factorial experiments, which are conducted for the expressions developed. Regression equations are developed for determining the optimal order quantity, inventory cost and safety stock factor. It is determined that there is a remarkable reduction in inventory cost by considering ordering crossover in inventory models. Finally, a sensitivity analysis is done to study the effect of the reorder point (r) on the total inventory cost. The results of a robustness study show that the order crossover model is robust and applicable to many real-world inventory problems.

Chapter 3 - Biomass-Based Supply Chains and Logistics Networks

It presents a practical modeling and solution approach for VMI cyclic inventory routing problem (IRP) under constant customer demand rates. In VMI, the supplier takes the responsibility for replenishing his customers' inventories based on their consumption data, with the aim of optimizing the overall distribution and inventory costs throughout the supply chain. The objective of this IRP problem is to determine a distribution plan that minimizes average distribution and inventory costs without causing any stockout at the customers. Deterministic constant customer demand rates are assumed and therefore, a long-term cyclical approach is adopted, integrating fleet sizing, vehicle routing, and inventory management. Further, realistic side-constraints such as limited storage capacities, driving time restrictions and constant replenishment intervals are taken into account. A heuristic solution approach is proposed, analyzed and evaluated against a comparable heuristic. The heuristic solution approach is well capable of finding the appropriate cost trade-off under varying circumstances. Furthermore, when fleet sizing is not considered, the heuristic outperforms an existing heuristic in finding the two-way trade-off between distribution and inventory costs.