When a supplier announces a price increase at a certain time in the future, for each retailer it is important to choose whether to purchase supplementary stock to take benefit of the current lower price or procure at a new price. This article focuses on the possible effects of price increase on a retailer’s replenishment strategy for constant deterioration of items. Here, quadratic demand is debated; which is appropriate for the products for which demand increases initially and subsequently it starts to decrease with the new version of the substitute. We discuss two scenarios in this study: (I) when the special order time coincides with the retailer’s replenishment time and (II) when the special order time falls during the retailer’s sales period. We determine an optimal ordering policy for each case by maximizing total cost savings between special and regular orders during the depletion time of the special order quantity. Scenarios are established and illustrated with numerical examples. Through, sensitivity analysis important inventory parameters are classified. Graphical results, in two and three dimensions, are exhibited with supervisory decision.
In this paper we introduce the concept of equilibrium for a non-cooperative multiobjective bimatrix game with payoff matrices and goals of Z-Numbers. In the recent studies of the authors, the problem of finding equilibrium for a non-cooperative bimatrix of Z-Numbers are investigated. Multiple payoffs are often dealt with in games because a decision making problem under conflict usually involves multiple objectives or attributes such as cost, time and productivity. We let each of the objectives of the problem correspond to each of the payoffs of the game. To aggregate multiple goals, we employ two basic methods, one by weighting coefficients and the other by a minimum component. In order to find the equilibrium solution in such circumstances, we developed a mathematical programming problem to maximize the aggregated goal subject to constraint of satisfying an aspiration level of confidence in the equilibrium solution. Finally a method is presented to determine the equilibrium solution with respect to the level of achievement to the aggregated goal.
In the competitive business world, applying a reliable and powerful mechanism to support decision makers in manufacturing companies and helping them save time by considering varieties of effective factors is an inevitable issue. Advanced Available-to-Promise is a perfect tool to design and perform such a mechanism. In this study, this mechanism which is compatible with the Make-to-Forecast production systems is presented. The ability to distinguish between batch mode and real-time mode advanced available-to-promise is one of the unique superiorities of the proposed model. We also try to strengthen this mechanism by integrating the inventory allocation and job shop scheduling by considering due dates and weighted earliness/tardiness cost that leads to more precise decisions. A mixed integer programming (MIP) model and a heuristic algorithm according to its disability to solve large size problems are presented. The designed experiments and the obtained results have proved the efficiency of the proposed heuristic method.
Nowadays, most manufacturing units try to locate their requirements and the depot vehicle routing in order to transport the goods at optimum cost. Needless to mention that the locations of the required warehouses influence on the performance of vehicle routing. In this paper, a mathematical programming model to optimize the storage location and vehicle routing are presented. The first objective function of the model minimizes the total cost associated with the transportation and storage, and the second objective function minimizes the difference distance traveled by vehicles. The study uses Imperialist Competitive Algorithm (ICA) to solve the resulted problems in different sizes. The preliminary results have indicated that the proposed study has performed better than NSGA-II and PAES methods in terms of Quality metric and Spacing metric.
The present study investigates an inventory model for non-instantaneous deteriorating items under inflationary conditions with partially backlogged shortages. In today’s market structure consumers are looking for goods for which there is a delay in deterioration. At the same time, the consumers’ willingness to wait has been decreased over time, which leads to lost sales. Moreover in financial decision-making, the effects of inflation and time value of money cannot be oblivious to an inventory system. In this scenario, managing inventory of goods remains a challenging task for the decision makers, who may also have to rent warehouse under different prevailing factors such as, bulk discount, limited space in the retail outlet, or increasing inflation rates. With a focus on reduction of costs and increasing customer service, warehouse decision models are crucial for an organization’s profitability. Hence a mathematical model has been developed in the view of above scenario, in order to determine the optimal policy for the decision maker, by minimizing the present worth of total cost. The optimization procedure has been illustrated by a numerical example and detailed sensitivity analysis of the optimal solution has been performed to showcase the effect of various parameters. Managerial implications has also been presented to aid the decision making process.
In this paper we investigate the use of lot streaming in non-permutation flowshop scheduling problems. The objective is to minimize the makespan subject to the standard flowshop constraints, but where it is now permitted to reorder jobs between machines. In addition, the jobs can be divided into manageable sublots, a strategy known as lot streaming. Computational experiments show that lot streaming reduces the makespan up to 43% for a wide range of instances when compared to the case in which no job splitting is applied. The benefits grow as the number of stages in the production process increases but reach a limit. Beyond a certain point, the division of jobs into additional sublots does not improve the solution.
Flexible manufacturing system (FMS) promises a wide range of manufacturing benefits in terms of flexibility and productivity. These benefits are targeted by efficient production planning. Part type selection, machine grouping, deciding production ratio, resource allocation and machine loading are five identified production planning problems. Machine loading is the most identified complex problem solved with aid of computers. System up gradation and newer technology adoption are the primary needs of efficient FMS generating new scopes of research in the field. The literature review is carried and the critical analysis is being executed in the present work. This paper presents the outcomes of the mathematical modelling techniques for loading of machines in FMS’s. It was also analysed that the mathematical modelling is necessary for accurate and reliable analysis for practical applications. However, excessive computations need to be avoided and heuristics have to be used for real-world problems. This paper presents the heuristics-mathematical modelling of loading problem with machine processing time as primary input. The aim of the present work is to solve a real-world machine loading problem with an objective of balancing the workload of the FMS with decreased computational time. A Matlab code is developed for the solution and the results are found most accurate and reliable as presented in the paper.
Leasing currently plays an important role for the global economy. The equipment leasing earning acquired through leasing rather than cash or credit, has reached a dominant level. With this regards, this paper represents a basic mixed-integer non-linear programming model. The study deliberates a firm that leases new products and remanufactured leased merchandises. The proposed study considers the end of lease contract, which contains several options: Return the leased product, return the used product and purchase other remanufactured product and buying the leased product. The primary objective is to maximize the discrepancy between the revenue and the costs of a firm, which leases new products as well as selling remanufactured ones. The product deteriorates with time and the difference between a new and used good is obvious. The product must undergo a remanufacturing procedure before being sold as a remanufactured product.
One subcategory of project scheduling is the resource constrained project scheduling problem (RCPSP). The present study proposes a differential evolution algorithm for solving the RCPSP making a small change in the method to comply with the model. The RCPSP is intended to program a group of activities of minimal duration while considering precedence and resource constraints. The present study introduces a differential evolution algorithm and local search was added to improve the performance of the algorithm. The problems were then solved to evaluate the performance of the algorithm and the results are compared with genetic algorithm. Computational results confirm that the differential evolution algorithm performs better than genetic algorithm.