Scientia Iranica
Volume:24 Issue: 6, Nove-Dec 2017

The rectangular two-dimensional Single Knapsack Problem (SKP) consists of packing a fixed rectangular space (so-called pallet) with a subset of smaller rectangular shapes (so-called pieces) of different dimensions, and without rotation. Pieces have different values. The objective is to maximize the sum of the values of the pieces packed. This paper proposes a new method for solving rectangular two-dimensional SKP based on the column generation approach. Mathematical formulation of the proposed model is simplest than the present mathematical formulations in the state-of-the art. The computational performance indicates that it is an effective method in the view of quality of solution.

In this paper, a combinatorial reverse auction mechanism is proposed to select suppliers for required items of a company. As a contribution, it is assumed that the task of supplying each required item is indivisible to multiple suppliers or the company prefers to select only one supplier for supplying each required item. So, the winner determination process is done in such a way that supplying each tendered item is assigned to only one potential supplier. The corresponding winner determination problem is formulated as a binary integer program which is an NP-complete combinatorial optimization problem. Since exact methods are failed in solving this kind of problems in a reasonable time, a meta-heuristic algorithm called scatter search is proposed for finding feasible and near-optimal solutions of the formulated winner determination problem. For evaluating the performance of the proposed algorithm, several instances of the problem with different real-world sizes are randomly generated and solved using the proposed algorithm with tuned parameters. Computational results show that the proposed scatter search method performs well in solving the problem instances.

In the process of decision making, the necessity of aggregation of input arguments into a single output becomes a key step and as a result selection of an appropriate aggregation operator is a vital aspect. The aim of this contribution is threefold. First, we study algebraic operations of trapezoidal intuitionistic fuzzy numbers (TrIFNs) and then on the basis of these operational laws, we de ne four types of harmonic mean operators with TrIFNs. Second, the required properties of the proposed operators are reviewed. After that, an approach based on the proposed operators is introduced to solve a group decision making problem. Finally, a practical example is furnished to demonstrate the applicability of the proposed operators in the decision making context. The contribution ends by introducing comparative analysis on the obtained results.

Train scheduling is one of the significant issues in the railway industry in recent years since it has an important role in efficacy of railway infrastructure. In this paper, the timetabling problem of a multiple tracked railway network is discussed. More specifically, a general model is presented here in which a set of operational and safety requirements is considered. The model handles the trains overtaking in stations and considers the stations capacity. The objective function is to minimize the total travel time. Unfortunately, the problem is NP-hard and real size problems cannot be solved in an acceptable amount of time. In order to reduce the processing time, we presented some heuristic rules, which reduce the number of binary variables. These rules are based on problem's parameters such as travel time, dwell time and safety time of stations and try to remove the impracticable areas of the solution space. Furthermore, a Lagrangian Relaxation algorithm model is presented in order to find a lower-bound. Finally, comprehensive numerical experiments on the Tehran Metro case are reported. Results show the efficiency of the heuristic rules and also the Lagrangian Relaxation method in a way that for all analyzed problems the optimum value are obtained.

Supply chain is a system involved in moving a product or service from supplier to customer. Since the supply chain in a company includes all responsibilities and operations of the company, its designing is necessarily a complete part of strategic planning procedure of the company. This paper intends to develop supply chain strategy through such tools as system dynamics and game theory. In this research, we have first identified key and principal variables of the supply chain to draw the causal diagrams with feedback loops. Then the layer and rate variables were identified, layer and flow models were created and by writing equations the simulation model is implemented for 10 years. The review of automotive industry, four main aspects is selected for programming. The issue of selecting best combination of strategy as a game with four players is considered in which each player can select three strategies. Then, the Shapely Value is used, the influence of each player in creating desirability is measured and by creation of the decision tree the best strategy combination is achieved. The results of this study showed that automotive part makers will have the greatest impact on the future of the automotive industry in Iran.

This research considers both cost and environmental protection to design a multi-objective optimization model. With multi-period customer demands, the model can solve a multi-plant resource allocation and production planning problem by focusing the decisions on supplier selection, facility selection, production batches, transportation mode selection, and distribution of the materials and commodities of a green supply network. In this paper, four transportation modes—namely, road, rail, air, and sea—have their corresponding transportation time, cost, and CO2 emissions. Based on multiple planning periods, this research calculates the minimal total cost and total CO2 emissions based on production and transportation capacity. Using numerical analyses, the results show that, when the budget is sufficient, only production capabilities with α = 1.5 and 2.0 are beneficial for improving environmental protection; carbon dioxide emissions of both production capacities are not significant differences. Furthermore, when the production batch size increases, total cost increases. Regarding transportation capacity, the results show that, when the budget is sufficient, increasing transportation quantity limits will be slightly beneficial for improving environmental protection.

A dual command (DC) cycle “dynamic sequencing” method in unit-load multiple-rack AS/RS system under time-of-use (TOU) electricity tariffs are applied in this paper. To make a type of energy efficient model, over cost of on-peak period electricity consumption, penalty cost for over power consumption, bounds on total consumed energy and accessible times of all facilities are considered in the model. Moreover, a genetic algorithm (GA) is developed to achieve a near-optimum solution of suggested energy-based mathematical model with the objective of minimizing the total cost of the AS/RS system under TOU tariffs. Considering that no benchmark is obtainable in the literature, a simulation annealing (SA) algorithm is developed in addition to certify the outcome gained. For supplementary confirmation, we comparing the total cost of our model with the single tariff model and also doing a sensitivity analysis for allowable amount of power consumption. The system throughput in terms of time and cost is calculated for the model too. In the last part, sixteen numerical examples with different number of required storage/retrieval orders are suggested to display the function of the proposed procedure. Our outcomes verified that GA was able to obtain well and closer optimal solutions and the TOU tariffs model get minimum total cost.

In this paper, the researchers presented a multi-objective model for multi-product, multi-site aggregate production planning model in a supply chain. The goals are to minimize the total cost of the supply chain, including inventory costs, manufacturing costs, work force costs, hiring, and firing costs, and to maximize the minimum of suppliers’ reliability by considering probabilistic lead times tosimultaneously improve performance of the system. Since the problem is NP-Hard, a Pareto-based multi-objective harmony search algorithm is proposed. To demonstrate the performance of the presented algorithm, a non-dominated sorting genetic algorithm (NSGA-II) and a non-dominated ranking genetic algorithm (NRGA) are applied. The results demonstrate the robustness of the proposed algorithm to probe the Pareto solutions.

The evaluation of the railway infrastructure capacity is an important task for railway companies. The goal is to find the best infrastructure development plan for scheduling new train services. The question addressed by the present study is how the existing railway infrastructure can be upgraded to decrease the total delay of existing and new trains with minimum cost. To answer this question, a mixed-integer programming formulation is extended for the integrated train scheduling and infrastructure development problem. The train timetabling model deals with the optimum schedule of trains on a railway network and determines the best stop locations for both the technical and religious services. We developed two heuristics based in variable fixing strategies to reduce the complexity of the problem. To evaluate the effect of railway infrastructure development on scheduling of the new trains, a sequential decomposition is adopted Iranian railway network. The outcomes of the empirical analysis performed in this study allow to gain beneficial insights by identifying the bottleneck corridors. The result of the proposed methodology shows that it can significantly decrease the total delay of new trains with the most emphasis on the bottleneck sections.

Control chart is widely used to monitor the quality of products of industrial or business processes. Max-CUSUM and Max-EWMA are based on memory-type control charts that monitor the process mean and standard deviation simultaneously. This article introduces four new control charts that monitor the process mean and dispersion simultaneously. The proposed control charting schemes are compared with the existing counterparts including Max-EWMA, Max-CUSUM, SS-EWMA and SS-CUSUM. A case study is presented for practical considerations using a real dataset.