Minimizing logistic costs in multistage supply chains

Shor's algorithms for the integer factorization and the discrete logarithm problems can be regarded as a negative effect of the quantum mechanism on publickey cryptography. From the computational point of view, his algorithms illustrate that quantum computation could be more powerful. It is natural to consider that the power of quantum computation could be exploited to withstand even quantum adversaries. Over the last decade, quantum cryptography has been discussed and developed even from the computational complexity-theoretic point of view. In this paper, we will survey what has been studied in quantum computational cryptography.

This paper considers bi-objective knapsack problem with fuzzy weights, says bi-objective fuzzy knapsack problem (BOFKP). Here we introduce an index which gives the possibility of choosing the item (weights and knapsack availability are fuzzy in nature) for knapsack with crisp capacity such that both the objective value are optimized. A methodology using dynamic programming technique has been introduced in this paper with an algorithm which gives the optimal solution for single objective fuzzy knapsack problem (FKP) with some possibility. Using this methodology an algorithm is given to find the Pareto frontier in case of bi-objective fuzzy knapsack problem. Compromise ratio method for decision-making under fuzzy environment has been used to find the compromise solution. The possibility index gives an idea to choose the solution according to decision-maker’s choice. An illustrative example is given to demonstrate the methodology.KeywordsBi-objective fuzzy knapsack problemTriangular fuzzy numberDynamic programmingPossibility indexCompromise ratio method

The motivation for this work is the real-time solution of a standard optimal control problem arising in robotics and aerospace applications. For example, the trajectory planning problem for air vehicles is naturally cast as an optimal control problem on the tangent bundle of the Lie Group SE(3), which is also a parallelizable Riemannian manifold. For an optimal control problem on the tangent bundle of such a manifold, we use frame co-ordinates and obtain first-order necessary conditions employing calculus of variations. The use of frame co-ordinates means that intrinsic quantities like the Levi-Civita connection and Riemannian curvature tensor appear in the equations for the co-states. The resulting equations are singularity-free and considerably simpler (from a numerical perspective) than those obtained using a local co-ordinates representation, and are thus better from a computational point of view. The first order necessary conditions result in a two point boundary value problem which we successfully solve by means of a Modified Simple Shooting Method.

In this paper we propose an algorithm for the constrained two-dimensional cutting stock problem (TDC) in which a single stock sheet has to be cut into a set of small pieces, while maximizing the value of the pieces cut. The TDC problem is NP-hard in the strong sense and finds many practical applications in the cutting and packing area. The proposed algorithm is a hybrid approach in which a depth-first search using hill-climbing strategies and dynamic programming techniques are combined. The algorithm starts with an initial (feasible) lower bound computed by solving a series of single bounded knapsack problems. In order to enhance the first-level lower bound, we introduce an incremental procedure which is used within a top-down branch-and-bound procedure. We also propose some hill-climbing strategies in order to produce a good trade-off between the computational time and the solution quality. Extensive computational testing on problem instances from the literature shows the effectiveness of the proposed approach. The obtained results are compared to the results published by Alvarez-Valdes et al. (2002).

The Service Oriented Architecture using ISO Reference Model for Open Distributed Processing is a high performance technique for providing effective services to the users. This architecture consists of five different viewpoints namely Enterprise viewpoint, Computational viewpoint, Information viewpoint, Engineering viewpoint and Technology viewpoint. This paper discusses the details about the Computational viewpoint. Computational viewpoint defines the functional decomposition of the system into a set of services that interact at interfaces. These services can be found in a security system and present as classification based on the functionality of services. Interaction between the service requester and the security system, and the communication between the security system and the service provider is carried out through external messages. The security system receives an input message then processes it and produces an output message. The semantics of the message is the issue of information view point. If the security check fails or if the processing logic can’t be correctly applied, security services can raise exceptions signalling that the errors encountered. Exception message contain annotations and other meta-information

This paper addresses a single-machine scheduling problem with nonresumable jobs and flexible maintenance activities (SJM) and its online version SJMO. It is showed that the competitive ratio of the classical list scheduling (LS) algorithm is 2 for SJMO, and it is the best possible polynomial time approximation algorithm for SJMO from both the competitive ratio point of view unless P = NP and the computational time complexity point of view. Based on the relationship between SJM and SJMO, it is showed that the LS algorithm is the best possible polynomial time approximation algorithm for SJM from both the worst-case ratio point of view unless P = NP and the computational time complexity point of view, which is a significant improvement of some of the existing results on SJM and its special case in the literature. Note that the worst-case ratio of the classical longest processing time first (LPT) algorithm is also 2 for SJM. In order to show which algorithm is better for SJM on average, numerical experiments are conducted. Computational results show that the LPT algorithm performs better than the LS algorithm in all combinations from an average-case error point of view.

Management Insights

: The solution of nonlinear optimal control problems and the computation of optimal control laws is a difficult task that requires a fast response in many real applications. During the three years of the AFOSR grant we attacked the problem via the numerical solution of Hamilton-Jacobi equations describing the value function. In the framework of viscosity solutions, the value function is uniquely characterized and it allows for the computation of optimal controls in feedback form. Unfortunately, from a computational point of view, solving real problems in high dimension via Hamilton-Jacobi equations is still a huge task and efficient algorithms are required. Our research has been devoted to the development of new Fast Marching methods and domain decomposition techniques (or a combination of them) in order to improve CPU times, avoid useless computations and reduce memory requirements.

An examination is made of the dynamic programming technique implemented on a distributed-memory multiprocessor system. The matrix chain product algorithm is investigated as an example problem. A method for parallelizing the dynamic programming technique for solving the matrix chain product problem is presented and load-balancing considerations are examined. The sequential dynamic programming technique is a fine grain algorithm and considered by many researchers to be too fine grain to execute effectively on a hypercube. The parallel algorithm yielded modest speedups for fixed-size problems. Scaled problems promise even better speedups. Results show that respectable performance for the dynamic programming algorithmic technique can be achieved on a hypercube. >

The physical Internet (PI) is a conceptual, innovative, and contemporary modular distribution logistics system that has garnered the interest of both researchers and practitioners as an alternative to conventional logistics. It aims to be more environmentally friendly, more socially responsible, and also more profitable. The key concept behind the PI logistics system is the routing of highly modular containers through transit centers (called PI hubs) to obtain a very efficient transport network that exploits consolidation opportunities. A systems model of the traditional and PI networks is developed as an aggregation of optimal point-to-point dispatch models between pairs of cities. This is then used to characterize the behavior of the traditional and PI logistics systems for several different logistics system key performance indicators. Based on the modeling assumptions in this paper, it is concluded that the benefits of the PI are in reducing the inventory cost and the total logistics system cost. However, a fewer truck trips are required in the traditional system, which also has a lower total order delivery time. While the transportation cost in the PI and traditional systems are comparable, the total cost of the PI system is significantly lower.

In this study an explicit finite difference scheme is developed to solve the Maxwell’s equations in time domain for a lossless medium. From the physical point of view, the hyperbolic system of Maxwell equations shall be discretized explicitly. From the computational point of view, this developed three-dimensional explicit scheme can be more effectively implemented in parallel in CPU/GPU with the Nvidia K-20 card. From the mathematical point of view, symplectic scheme is adopted for the approximation of temporal derivative terms so that all Hamiltonians in Maxwell’s equations can be conserved at all times. Moreover, to predict the long-time accurate solution a phase velocity preserving scheme is developed for the spatial derivative terms so that the chosen time increment and grid spacing can be excellently paired following the employed theoretical guideline. Computational performance will be assessed based on the results obtained from the computed results in one GPU card and in one I7-4820K CPU card.

Inventory management is one of the key functions in any supply chain. Since inventory cost occupies a significant portion of supply chain cost, it is essential to employ a good inventory policy in order to reduce the supply chain cost while improving the service level. This paper presents a vendor managed inventory (VMI) policy with emergency orders for a supply chain with a single vendor and two independent retailers. A mathematical model is developed using the concept of demand rate, assuming Poisson demand for the retailers. The formulated model is used to help determine the optimal system base stock level, delivery quantities to retailers and the cycle length such that the total expected inventory cost of the system is minimised. Numerical experiments and sensitivity analysis are then conducted to examine the effect of model parameters on total inventory cost and optimal solution.

Inventory management is one of the key functions in any supply chain. Since inventory cost occupies a significant portion of supply chain cost, it is essential to employ a good inventory policy in order to reduce the supply chain cost while improving the service level. This paper presents a vendor managed inventory (VMI) policy with emergency orders for a supply chain with a single vendor and two independent retailers. A mathematical model is developed using the concept of demand rate, assuming Poisson demand for the retailers. The formulated model is used to help determine the optimal system base stock level, delivery quantities to retailers and the cycle length such that the total expected inventory cost of the system is minimised. Numerical experiments and sensitivity analysis are then conducted to examine the effect of model parameters on total inventory cost and optimal solution.

Production distribution systems are increasingly crucial because of shortened product life cycles, increasing competition, and uncertainty introduced by globalization. Production distribution involves a multistage supply chain network that consists of factories, distribution centers, retailers, and various customers. Customer demands fluctuate and are unpredictable, thereby causing an imprecise customer quantity demand in each period in the production distribution model, and increasing inventory and related costs. Most studies have addressed the production distribution problem with certain demands or a single period. To fill the gap, this study aims to integrate the extended priority-based discrete particle swarm optimization and novel extended priority-based hybrid genetic algorithm for solving flexible multistage production distribution under uncertain demands in multiple periods. In particular, triangular fuzzy demands are considered for minimizing the total cost, including transportation costs, inventory costs, shortage costs, and ordering costs, in the multistage and multi-time-period supply chain. For validation, we designed numerical experiments to compare the proposed approaches with LINGO computational software (for small problems) and conventional genetic algorithms (for normal problems) in real settings. The experimental results demonstrated practical viability of the proposed approaches.

Logistics network is increasingly crucial because of shortened product life cycles, increasing competition, and uncertainty introduced by globalization. The logistics network distribution involves a multistage supply chain that consists of the flexible forward directions (i.e., factories, distribution centers, retailers, and various customers) and the flexible backward directions (i.e., re-manufacturing and reuse). Customer demands fluctuate and are unpredictable, thereby causing an imprecise customer quantity demand in each period in the production distribution model, and increasing inventory and related costs. Most studies have addressed the traditional multistage forward directions problem with certain demands or a single period. To fill the gap, this chapter proposes the hybrid genetic algorithm approaches for solving flexible, multiple periods, multiple stages, and forward/reverse logistics network. In particular, triangular fuzzy demands are considered to minimize the total cost, including transportation costs, inventory costs, shortage costs, and ordering costs, in the multistage and multi-time-period supply chain. The experimental results demonstrated practical viability for the proposed approaches.