신에너지 자동차배터리 산업 공급망 네트워크의 최적화

The mathematical model of a multi-product multi-period multi-echelon closed-loop supply chain network design under uncertainty is designed in this paper. The designed network consists of raw material suppliers, plants, warehouses, distribution centers, and customer zones in forward chain and collection centers, repair centers, recovery/decomposition center, and disposal center in the reverse chain. The goal of the model is to determine the quantities of products and raw material transported between the supply chain entities in each period by considering different transportation mode, the number and locations of the potential facilities, the shortage of products in each period, and the inventory of products in warehouses and plants with considering discount and uncertainty parameters. The robust possibilistic optimization approach was used to control the uncertainty parameter. At the end to solve the proposed model, five meta-heuristic algorithms include genetic algorithm, bee colony algorithm, simulated annealing, imperial competitive algorithm, and particle swarm optimization are utilized. Finally, some numerical illustrations are provided to compare the proposed algorithms. The results show the genetic algorithm is an efficient algorithm for solving the designed model in this paper.

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.

A three-level supply chain network design model with risk-pooling and lead times

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.

In general, it is difficult to directly solve an optimization problem of systems reliability formulated as a nonlinear integer programming (NIP) model. In this paper, we propose a method for solving the NIP problem for the best compromise solution while holding a nonlinear property by using the genetic algorithm. We also report that the optimization problem of systems reliability as a case study is solved using the proposed method. The numerical comparison experiments between the 0-1 LP/0-1 NP formulations are demonstrated and, from the quantitative evaluation, the efficiency of the proposed method is demonstrated. >

In this paper we study the problem of minimizing total weighted tardiness, a proxy for maximizing on-time delivery performance, on parallel nonidentical batch processing machines. We first formulate the (primal) problem as a nonlinear integer programming model. We then show that the primal problem can be solved exactly by solving a corresponding dual problem with a nonlinear relaxation. Since both the primal and the dual problems are NP-hard, we use genetic algorithms, based on random keys and multiple choice encodings, to heuristically solve them. We find that the genetic algorithms consistently outperform a standard mathematical programming package in terms of solution quality and computation time. We also compare the smaller problem instances to a breadth-first tree search algorithm that gives evidence of the quality of the solutions.

To solve the problem of long logistics delivery time in supply chain, a Mixed Integer Non-linear Program (MINLP) model is built by using Mixed Integer nonlinear programming theory. Firstly, the General algebraic modeling system (GAMS) is used to build the model to fully integrate each parameter of logistics transportation, the total distribution time of the supply chain network, the coverage radius of the logistics base, the number of users, the total capacity of the logistics base, the mode of railway and road transportation, the nonlinear programming model is built and solved by DICOPT solver in GAMS. The cost of logistics can be decreased, transportation time can be reduced, and the logistics system's operating efficiency can be increased in the long term with the help of this algorithm. The proper operation of the logistics system is critical in encouraging the supply chain circulation of various industries and has a direct impact on the society's economic development. The optimal logistics distribution plan with 5 logistics bases covered users of 18 and railway capacity of 2. With the same railway capacity and the same total budget, the larger the number of covered users, the greater the total distribution time increases, but the larger the total budget, the growth of the total distribution time slows down significantly. Experiments show that MINLP model can solve the problem of logistics-based layout optimization in nonlinear logistics management.

PurposeThis paper aims to develop a bi-objective mixed integer non-linear programing model to optimize the supply chain networks consisting of raw material providers, final product manufacturers and distribution centers. Raw material substitution caused by varying raw material supply amounts, prices and carbon emissions and final product substitution due to different product prices and carbon emissions are considered.Design/methodology/approachThe proposed model aims to achieve total profit maximization and total carbon emission minimization. The objective function of carbon emissions is converted into a maximization problem by changing minimum to maximum. The composite objective function is the weighted sum of the bias value of each objective function. The model is then solved using software Lingo12.FindingsNumerical analysis results show that an increase in the number of alternate raw materials for original raw material helps improve supply chain network performance, and variation in that number causes detectable but not significant changes in downstream final product substitution results.Originality/valueThe major differences between this work and existing research are as follows: first, although previous research considered carbon emissions in supply chain network optimization, it has not considered the substitution effects of products or raw materials. This paper considers the substitution of both raw material and productions. Second, the item substitution considered by previous research is derived from inventory shortage or price difference of original items. However, the substitution considered in the present paper is a response to differences in purchase price, production cost and carbon emissions for items.

The warehouse management problem is a critical issue in Operation Management. In modern competitive market, many firms are automating their basic warehouse activities in order to be cost effective. However, traditional mechanized warehousing systems (MWS) still represent the 75 % of the overall installations. In MWS, forklifts are adopted for the load handling. The adoption of efficient “internal logistic strategies” could help in reducing time required and costs of warehousing activities. In recent years, many firms adopted green supply chain practices (GSCP) in order to improve their environmental performances while also achieving economic goals (Wu et al. 2015). Furthermore, in planning Smart City logistics, warehousing in port or railway station storage areas is being receiving wide attention since they contribute effectively to a sustainable development of modern cities. Under this perspective, an optimal “internal logistic strategy” allowing to jointly minimizing jointly costs and environmental impacts of warehousing activities has to be adopted. The aim of this study is to develop a Nonlinear Integer Programming Model to solve a storage location assignment problem (SLAP) for optimizing the environmental performance of the internal logistic activities in a warehouse. Suitable storage strategies are identified on the basis of the type of the forklifts adopted (internal combustion or electric engine equipped) as well as the sizes and the weight of the loads to be handled.

A bi-level optimization model is proposed for locating billboards on road network links in cities. Since billboard locations are largely driven by the flow on the underlying network, factors that impact traffic considerations of users are accounted for, including link congestion and road link pricing. Safety aspects associated with billboard distraction to users of the network are also incorporated within the proposed Mixed Integer Non-Linear Programming (MINLP) model. The model is multi-objective in nature, and it is solved through adopting the augmented ε -constraint method to obtain a sample of non-dominated points on the Pareto front. Due to the non-convexity induced by the bi-level structure of the model, a model reformulation scheme based on the use of equilibrium conditions is presented. A linearization scheme is also adopted that involves the introduction of mixed integer variables.

Global optimization of multiscenario mixed integer nonlinear programming models arising in the synthesis of integrated water networks under uncertainty

Driven by ever growing concern to environments, legislative regulation and economic profitability, more and more firms pay attentions to physical design of reverse logistics networks. This paper considers the problem of determining the numbers and locations of centralized return centers (i.e., reverse consolidation points) where returned products from retailers or end-customers were collected, sorted and consolidated into a large shipment destined for manufacturers' or distributors' repair facilities. A stochastic nonlinear mixed integer programming model for the reverse logistics problem involving product returns is established. Genetic algorithm and Monte Carlo Method are used to solve the proposed model. The usefulness of the proposed model and algorithm was validated by its application to an illustrative example dealing with products returned from online sales.

A computational comparison of cargo prioritization and terminal allocation problem models

Cell formation problem mainly addresses how machines should be grouped and parts be processed in cells. In dynamic environments, product mix and demand are changed in each period of the planning horizon. Incorporating such assumption in the model increases flexibility of the system to meet customers' requirements. In this model, to ensure the reliability of the system in presence of unreliable machines, alternative routing process as well as buffer storage are considered to reduce detrimental effects of machine failure. This problem is presented by a nonlinear mixed integer programming model attempting to minimize the overall cost of the system. To solve the model in large scale for practical purposes, a genetic algorithm approach is adopted as the model belongs to NP-hard class of problems. Sensitivity analysis is used to show the validity of the proposed model. Besides, numerical examples are used both, for small-sized and large- sized instances to show the efficiency of the method in finding near optimal solution.

Green vehicle routing and scheduling problem with heterogeneous fleet including reverse logistics in the form of collecting returned goods