Perishability of platelets, uncertainty of donors’ arrival and conflicting views in platelet supply chain have made platelet supply chain planning a problematic issue. In this paper, mobile blood collection system for platelet production is investigated. Two mathematical models are presented to cover the bloodmobile collection planning problem. The first model is a multi-objective fuzzy mathematical programming in which the bloodmobiles locations are considered with the aim of maximizing potential amount of blood collection and minimizing the operational cost. The second model is a vehicle routing problem with time windows which studies the shuttles routing problem. To tackle the first model, it is reformulated as a crisp multi objective linear programming model and then solved through a fuzzy multi objective programming approach. Several sensitivity analysis are conducted on important parameters to demonstrate the applicability of the proposed model. The proposed model is then solved by using a tailored Simulated Annealing (SA) algorithm. The numerical results demonstrate promising efficiency of the proposed solution method.
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.
Performance of mines can be affected by different factors such as safety and economic factors. This study aims to analyze the influence of safety and economic factors on mines’ performance. To this purpose, a framework is proposed based on a Data Envelopment Analysis (DEA), Ro-bust Data Envelopment Analysis (RDEA) and common weight Robust Data Envelopment Anal-ysis (CWRDEA) to determine the factors affecting on performance of mines. In this study, for the first time, integrated economic and safety factors are considered for evaluation of mines per-formance. To analyze safety and economic factors, this research gathers real data from a mine with 56 sites in south of Iran. Based on different DEA models, different sites become the best site among other sites, but RDEA is much closer to real situation than basic DEA and CWRDEA is the most efficient approach in real situation.
In this article, a manpower allocation and cell loading problem is studied, where demand is sto-chastic. The inter-cell and intra-cell movements are considered and attention is focused on as-signing operators with different skill levels to operations, because cell performance in addition to load cell is dependent on manpower. The purpose of this article is manpower allocation in cellu-lar manufacturing with consideration to learning and training policies. The manpower skill levels are determined in order to enhance production rate. The main contribution of this approach is the scenarios of training and learning in addition to the combination of training and learning being simulated. By using these three scenarios, the skill level of workers increase which reduces the processing time. In this regard cell layout is static where processing times and customer demand follow a normal distribution. As one of the significant costs of industrial unit is related to pro-duction cost, this study has attempted to reduce these costs by increasing the skill level of opera-tor which causes to reduce the processing time. Scenarios are evaluated by using a simulation method that finally attained results indicate this simulation provides better manpower assign-ments.
The theory of constraints is an approach for production planning and control, which emphasizes on the constraints in the system to increase throughput. The theory of constraints is often referred to as Drum-Buffer-Rope developed originally by Goldratt. Drum-Buffer-Rope uses the drum or constraint to create a schedule based on the finite capacity of the first bottleneck. Because of complexity of the job shop environment, Drum-Buffer-Rope material flow management has very little attention to job shop environment. The objective of this paper is to apply the Drum-Buffer-Rope technique in the job shop environment using a Markov chain analysis to compare traditional method with Drum-Buffer-Rope. Four measurement parameters were considered and the result showed the advantage of Drum-Buffer-Rope approach compared with traditional one.
In the existing literature, there are a huge number of studies focused on p-hub median problems and inventing heuristic or metaheuristic algorithms for solving them. But such analogous body of literature does not exist for its counterpart problem; p-hub center problem. In fact, since p-hub center has been lately introduced and has a particular objective function, minimizing the maximum cost between origin-destination nodes, there are few studies investigating the problem and the challenges for solving it. In this study, after presenting a complete definition of the uncapacitated multiple allocation p-hub center problem (UMApHCP) two well-known metaheuristic algorithms are proposed to solve the problem for small scale and large scale standard data sets. These two algorithms are one single solution-based algorithm, Simulated Annealing (SA), and one population-based metaheuristic, Genetic Algorithm (GA). Because of the particular nature of the problem, Dijkstra’s algorithm has been incorporated in the fitness function calculation part of the proposed methods. The numerical results of running the GA and SA for standard test problems show that for smaller scale test problems, single solution-based SA shows greater performance versus GA but for larger scales of data sets the GA generally yield more desirable solutions.
Assembly lines and cellular manufacturing systems (CMSs) design have been widely used in the literature. However the integration of these manufacturing concepts is neglected in an environment where parts need to be assembled after production in different shops. In this paper, a comprehensive quadratic assignment problem is developed for the assignment of machines of each part manufacturing cell, sub-assembly tasks of each sub-assembly cell as well as the assignment of different cells and final assembly tasks within the shop floor in their relevant predetermined locations. A genetic algorithm (GA) as well as a memetic algorithm (MA) consisting of the proposed GA and Tabu search (TS) algorithm are proposed and implemented on different size numerical examples. The obtained results show the efficiency of both algorithms to reach near optimal solutions compared to the optimal solution of small-sized problems.
Marketing strategies and proper inventory replenishment policies are often incorporated by enterprises to stimulate demand and maximize profit. The aim of this paper is to represent an integrated model for dynamic pricing and inventory control of deteriorating items. To reflect the dynamic characteristic of the problem, the selling price is defined as a time-dependent function of the initial selling price and the discount rate. In this regard, the price is exponentially discounted to compensate negative impact of the deterioration. The planning horizon is assumed to be infinite and the deterioration rate is time-dependent. In addition to price, the demand rate is dependent on advertisement as a powerful marketing tool. Several theoretical results and an iterative solution algorithm are developed to provide the optimal solution. Finally, to show validity of the model and illustrate the solution procedure, numerical results are presented.
Waste collection is an important municipal service that charges large expenditures to waste management (WM) system. In this study, a hierarchical structure is proposed in order to minimize total cost of waste collection routing problem. Moreover, in second stage destructive environmental effects of waste transportation are minimized concurrently through taking advantage of a road/rail transportation system. In the proposed multimodal transportation system, waste packs are transferred to final destination while travel time and risk of environmental threatening is minimized. The discussed problem is formulated mathematically in two stages. In the first stage, a household waste collection routing problem is formulated while, in second stage a multimodal transportation system is routed to transfer waste packs to final destination through roads and railroads. In order to solve the proposed NP hard models, an improved genetic algorithm is developed. Comparison of the obtained results with those of GAMS for small-size samples validates the proposed models.
This paper presents a mathematical model to choose appropriate technology for production and capacity planning. The proposed study considers manufacturing planning in textile industry as a make-to-order (MTO) system by managing employees and water consumption and the possibility of outsourcing. The resulted model has been formulated as a mixed integer programming problem, which can be solved using a commercial software package. The implementation of the proposed study has been considered for a real-world case study and the results are discussed.