International Journal of Industrial Engineering: Theory, Applications and Practice
Not a member yet
943 research outputs found
Sort by
OPTIMIZING THE INVENTORY ROUTING PROBLEM USING ADAPTIVE DIFFERENTIAL EVOLUTION ALGORITHM
No full textThe inventory routing problem (IRP) is an NP-hard optimization problem, which integrally seeks the minimal cost of inventory and transportation in supply chain management. However, the inventory management activities and transportation activities are conflicting in most cases. Therefore, how to efficiently solve the one-to-many IRP is still a challenge and practical requirement. As a powerful and popular evolutionary algorithm, differential evolution (DE) has been successfully applied to tackle complex industry optimization problems. In this paper, a novel method for the one-to-many IRP using an adaptive DE algorithm (named aDEIRP) is proposed. In the proposed aDEIRP algorithm, the DE algorithm with the deliver routing-based solution representation is used to solve the complex one-to-many IRP problem. In addition, a modified DE with adaptive parameter control is proposed to enhance the optimization performance. Comprehensive experimental results verified the effectiveness and efficiency of the proposed method
A BI-LEVEL PROGRAMMING MODEL FOR SERU ORDER ACCEPTANCE AND SCHEDULING PROBLEM CONSIDERING WORKER ASSIGNMENT
No full textAs an innovative and successful manufacturing mode in Japanese production practice, seru production has attracted extensive attention due to its high efficiency, high flexibility, and quick responsiveness. This paper focuses on the order acceptance and scheduling problem in seru production system considering worker assignment simultaneously, where the worker’s learning curve effect is also concerned because it is also an important issue for the stable performance of the entire production system. A bi-level programming model is built where the worker assignment on the upper level is to maximize worker’s skill proficiency increments, while the lower level is to maximize the total net revenue by finding out the optimal order acceptance results and scheduling plan. Then, a bi-level nesting genetic algorithm (BNGA) is developed according to the natural structure of proposed mathematical model. Finally, a numerical example is applied to illustrate the feasibility and validity of the bi-level model and BNGA
A FUZZY LOGIC-BASED ALGORITHM FOR SUPPLY CHAIN MANAGEMENT CONSIDERING DIFFERENT CASES
No full textIn this paper, a new fuzzy based method is proposed for planning closed loop supply chain systems in the presence of market uncertainty using different game theories. The aim of this method is to find best production and product distribution strategy while rivals can take different market strategies that affect the quota of market. For this purpose, a multi-period scheduling model has been proposed which is flexible enough to use in real industries. To solve the proposed problem, a hybrid Fuzzy-based Multi-layer Perceptron and Simulated Annealing Algorithm (FBMLP-SA) is developed and results are compared with Branch and Bound (BnB); a hybrid Tabu Search and Simulated Annealing (SA) algorithms and a hybrid Ant Colony Optimization (ACO) and Simulated Annealing algorithms. For evaluating the production strategies in dynamic market demands, a new measuring index is developed. Our findings indicate that the uncertain market demands affect the quota of market among suppliers. Comparing different game theories shows that the proposed FBMLP-SA method can successfully generate various and effective production strategies while rivals change their strateg
INTEGRATING PRODUCTION SCHEDULING, DELIVERY, AND 3D LOADING PROBLEMS IN A TWO STAGE SUPPLY CHAIN
No full textIn this study, a two echelon supply chain with one distribution center and several retailers is investigated. In the distribution center, handling of retailer’s orders is considered as an operation that should be completed on a single workstation. This study combines production scheduling and delivery problems with three-dimensional (3D) loading problem. In the integration of production scheduling and delivery problems, production and inventory and transportation decisions are considered simultaneously in order to minimize the sum of production setup costs, inventory costs and routing costs. The purpose of this problem is to determine the sequence and quantity of production, type of vehicles, the visiting sequence of each vehicle, and the inventory level at the distribution center and retailers, so that the total cost is minimized. The total cost includes the production setup costs, holding cost in the distribution center, transportation cost, vehicle arrangement cost and penalty costs. Then, the vehicles and batches are considered as 3D components with length, width and height and the 3D loading problem is investigated. The purpose of 3D loading problem is to provide a practical loading. A mathematical model is presented to solve the loading problem. The problem under study is NP-hard, so medium and large sized instances cannot be solved optimally in a reasonable time. Hence, a meta-heuristic algorithm based on genetic algorithm is proposed to solve the problem. The computational results show that the proposed algorithm decreases the computational times 85.29% in average and only leads to 3.22% increase in the total cost in comparison with the optimal solution
A COMPARATIVE ANALYSIS OF DISCRETE AND BATCH PICKING AND THE IDENTIFICATION OF FACTORS INFLUENCING ORDER PICKING EFFICIENCY
No full textIn a typical warehouse environment, order picking represents one of the highest prioritized activities due to its impact on warehousing productivity, operating costs, and order fulfillment. Order picking generally involves determining a sequence of visitations with inventory locations where the ordered items are stored and then retrieved with the assurance of correct product specifications and quantity according to the customer order. In the era of e-commerce, order picking has become highly labor-intensive and contributed to warehouse productivity declines due to the increased piece-by-piece picks. To mitigate the adverse impact of inefficient, unorganized order picking, we employed the alternative batch picking method and then compared its efficiency to that of a conventional discrete order picking method. This paper validated the usefulness of the proposed method by its application to order picking problems encountering the Korean processed food manufacturer. Based on a series of simulation experiments with actual data, we verified the comparative efficiency of batch picking and identified a couple of key factors affecting order picking efficiency
ANALYZING THE EFFECTS OF USING BOTH FOLDABLE AND STANDARD CONTAINERS IN OCEAN TRANSPORTATION
No full textWe analyze the effects of foldable containers using a newly developed multi-port and multi-period container planning model. The proposed model is a large-scale optimization problem, for which we develop an efficient heuristic algorithm to get near-optimal solutions within a reasonable time. Our model improves existing models by including practical assumptions on the supply of empty containers at each port in each period. Through intensive computational experiments, we analyze the effect of the imbalance in demand and the decreases in the purchasing cost and the handling cost of foldable containers on the potential economic benefits of deploying foldable containers in ocean transportatio
A MATHEMATICAL PROGRAMMING MODEL FOR USING DYNAMICALLY-POSITIONED-REWORK STATIONS FOR PERFORMING PARALLEL TASKS IN ASSEMBLY LINE BALANCING
No full textIn this study, a mathematical programming model for using dynamically-positioned-rework stations for performing parallel tasks in assembly line balancing is proposed. We first introduce a nonlinear programming model, which is quadratic in constraints resulting from the modeling of the parallel task assignment and dynamic positioning of the rework station. We also establish some novel logical conditions in the model building process while deriving the proposed formulation. In the next step, we present appropriate variable transformations for linearization to take advantage of the algorithms for solving linear programs by noting that the quadratic expressions of the model are present as either the multiplications of binaries or binaries multiplied by continuous variables. After implementing the corresponding variable transformations, the model is transformed to a linear-mixed-integer program. A numerical example is then presented using the resulted linear model for illustration. We also perform some computational experiments using sample problems from the related literature to analyze the performance of the model
INTER-CITY BUS SCHEDULING WITH CENTRAL CITY LOCATION AND TRIP SELECTION
No full textThis study considers a real-life inter-city bus scheduling problem that incorporates central city locations and trip selection sub-problems. We seek to determine the size of the bus fleet, the cities to be served, the central cities, and the routes and schedules of buses in the fleet. For this purpose, we developed a number of investment scenarios, and for each scenario, we determined possible trip alternatives between cities and resulting expected profits. We then developed an integer linear programming model and a decomposition-based heuristic, which attempt to solve the problem with the objective of profit maximization. Finally, we compare the performance of both solution approaches in terms of the solution quality and computation time for each of the scenarios. The results show that both methods are capable of achieving satisfactory performance for small and medium-size instances, whereas the heuristic outperforms the exact method significantly on large instances
AN INTEGRATED FUZZY ANP-FUZZY DEMATEL-FUZZY MULTI-OBJECTIVE LINEAR PROGRAMMING APPROACH FOR WEAPON SYSTEMS ACQUISITION
No full textWeapon systems selection is one of the most crucial steps of a successful weapon systems acquisition process. The weapon systems selection problem is a complex decision-making problem involving multiple objectives and multiple interactive factors. This study presents a novel integrated approach to the weapon systems portfolio selection problem. The proposed integrated approach combines fuzzy decision-making trial and evaluation laboratory (DEMATEL), fuzzy analytic network process (ANP), and fuzzy multi-objective linear programming (FMOLP). The integrated fuzzy DEMATEL-fuzzy ANP method is firstly applied to identify the weights of the criteria, and FMOLP is then used to determine the optimal weapon systems portfolio. Capabilities-based assessment (CBA) results and the traditional weapon system selection criteria are considered in the proposed approach, which yields a comprehensive set of criteria ranging from technological aspects to the factors identified through CBA. Furthermore, functional constraints are taken into account in the FMOLP phase of the procedure. Finally, the proposed approach is applied to a real-size problem to show its applicability and practicality
INTEGRATION OF DATA ENVELOPMENT ANALYSIS WITH DECISION MAKER PREFERENCE FOR SUPPLIER SELECTION
No full textData Envelopment Analysis (DEA) is a well-known method for selecting suppliers. Although Data Envelopment Analysis can compare suppliers in terms of their efficiencies, it cannot capture suppliers’ effectiveness and decision maker’s preference over criteria in the suppliers’ scores. We introduce a function called the Preference Function to capture the decision maker’s preference over different criteria. After applying Preference Functions on criteria’s data, we run Data Envelopment Analysis. Two different methods are proposed to derive the Preference Functions: direct and feedback-based (indirect). In the direct method, the decision-maker directly tailors Preference Functions to her preferences toward different criteria. In the feedback-based method, Preference Functions are formed via an optimization scheme. In this method, a mathematical model is solved, and then the result is used to iteratively build Preference Functions. We deploy the Particle Swarm Optimization technique to solve this problem. We illustrate both direct and indirect methods through solving a supplier selection example and compare it with plain Data Envelopment Analysis. Finally, through extensive numerical analysis, we show that Particle Swarm Optimization effectively solves the problem in the indirect method