A revised ant algorithm for solving location–allocation problem with risky demand in a multi-echelon supply chain network

This study addresses a capacitated facility location and task allocation problem of a multi-echelon supply chain against risky demands. Two and three-echelon networks are considered to maximize profit. The study represents the problem by a bi-level stochastic programming model. The revised ant algorithm proposed in the study improves the existing ant algorithm by using new design of heuristic desirability and efficient greedy heuristics to solve the problem. A set of computational experiments is reported to not only allow to fine-tune the parameters of the algorithm but also to evaluate its performance for solving the problem proposed. Experiments reveal that the proposed solution algorithm can reach 95–99% of the optimal solution against risky demands while consuming only 1000th of the computational time for large-sized problems as compared to an optimization-based tool.     

Real-time supply chain—A blockchain architecture for project deliveries

Real-time tracking and tracing are important in providing a unified view of global supply chains consisting of several parties. This paper illustrates the needs and requirements for managing supply chains in multi-company project environments by adopting various tracking and tracing technologies. This kind of tracking and tracing is especially needed within distributed architectures engaged in project-based businesses, where several vendors are involved in a single project. Such tracking and tracing data can be used extensively to generate key performance indicators, which can be used to measure and control supply chain processes. This paper also proposes a pilot system of a cloud-based portal for real-time tracking and tracing of logistics and supply chains. This portal is formed by the combination of RFID, IoT and blockchain technology into an integrated real-time view. RFID (Radio Frequency Identification) and IoT (Internet of Things) provides real-time information or data, while blockchain technology is used to provide a chain of immutable transactions. The architecture of the proposed portal system is connected to transport companies, tracking devices, consolidation points and suppliers. The pilot study also illustrates the benefits and advantages of such a portal system.     

Coordinating supplier׳s reorder point: A coordination mechanism for supply chains with long supplier lead time

In this paper, the issue of the upstream stochastic lead time in supply chain (SC) is investigated. A coordination mechanism is developed for reducing the harmful effect of upstream lead time. The supplier stochastic lead time can substantially harm the whole supply chain service level, especially when it is accumulated with downstream stochastic lead times. In this study, aggregation of both the supplier and the retailer stochastic lead time is analyzed in a two-stage supply chain (SC). To dampen harmful effects of a long aggregate lead time, a ‘per order extra payment’ model is developed for convincing the supplier to increase its reorder point. Numerical experiments show that coordinating the supplier׳s reorder point creates a significant profit for the whole supply chain. In addition, the proposed model is capable of optimizing the supplier׳s reorder point and fairly sharing the extra benefits. Some conditions are also extracted, under which the proposed model shows good performance.     

An integrated supply chain production–distribution planning with stochastic demands

In this paper a three echelon supply chain with multiple distribution centers, production sites and suppliers is modeled. For this supply chain several commodities with defined items is produced. The model is categorized as a capacitated facility location model. The hierarchical approach is used to modeling and based on decision types the model is divided into two levels. The solution approach is based on the Lagrangian Relaxation approach, improved by an efficient heuristic to solve complex sub-problems. Computational results indicated that the proposed method yields high-quality solutions within a reasonable computational time for various real size problems.     

A simulation based heuristic discrete particle swarm algorithm for generating integrated production–distribution plan

Deciding the strategy for production and distribution in a stochastic demand scenario is important for the manufacturing industries. An integrated production–distribution plan considering regular, overtime and outsourced production costs along with inventory holding, backorder, hiring/laying-off and trip-wise distribution costs is developed for a renowned bearing manufacturing industry producing three types of products at three locations. Demand is assumed to vary uniformly and a novel simulation based heuristic discrete particle swarm optimization (DPSO) algorithm is used for obtaining the best production–distribution plan that serves as a trade-off between holding inventory and backordering products. The algorithm also uses an innovative regeneration type constraint handling method which does not require a penalty operator. In addition to the bearing manufacturing industry data set, two other test data sets are also solved. The simulation based optimization approach gives good approximate solutions for the stochastic demand problems.     

Performance of supply chain collaboration – A simulation study

In the past few decades several supply chain management initiatives such as Vendor Managed Inventory, Continuous Replenishment and Collaborative Planning Forecasting and Replenishment (CPFR) have been proposed in literature to improve the performance of supply chains. But, identifying the benefits of collaboration is still a big challenge for many supply chains. Confusion around the optimum number of partners, investment in collaboration and duration of partnership are some of the barriers of healthy collaborative arrangements. To evolve competitive supply chain collaboration (SCC), all SC processes need to be assessed from time to time for evaluating the performance. In a growing field, performance measurement is highly indispensable in order to make continuous improvement; in a new field, it is equally important to check the performance to test conduciveness of SCC. In this research, collaborative performance measurement will act as a testing tool to identify conducive environment to collaborate, by the way of pinpointing areas requiring improvements before initializing collaboration. We use actual industrial data and simulation to help managerial decision-making on the number of collaborating partners, the level of investments and the involvement in supply chain processes. This approach will help the supply chains to obtain maximum benefit of collaborative relationships. The use of simulation for understanding the performance of SCC is relatively a new approach and this can be used by companies that are interested in collaboration without having to invest a huge sum of money in establishing the actual collaboration.     

Location and allocation decisions for multi-echelon supply chain network – A multi-objective evolutionary approach

This paper aims at multi-objective optimization of single-product for four-echelon supply chain architecture consisting of suppliers, production plants, distribution centers (DCs) and customer zones (CZs). The key design decisions considered are: the number and location of plants in the system, the flow of raw materials from suppliers to plants, the quantity of products to be shipped from plants to DCs, from DCs to CZs so as to minimize the combined facility location and shipment costs subject to a requirement that maximum customer demands be met. To optimize these two objectives simultaneously, four-echelon network model is mathematically represented considering the associated constraints, capacity, production and shipment costs and solved using swarm intelligence based Multi-objective Hybrid Particle Swarm Optimization (MOHPSO) algorithm. This evolutionary based algorithm incorporates non-dominated sorting algorithm into particle swarm optimization so as to allow this heuristic to optimize two objective functions simultaneously. This can be used as decision support system for location of facilities, allocation of demand points and monitoring of material flow for four-echelon supply chain network.     

A new deterministic approach in a decision support system for ship’s trajectory planning

The paper introduces a Decision Support System for ships, developed to solve a problem of collision avoidance with static and dynamic obstacles. The system maps the decision making capability of a human (navigation) expert to solve the path planning problem for a ship in a complex navigation environment. It can be further developed to provide automatic control of a ship. It utilizes a new, fast and effective, deterministic method, called the Trajectory Base Algorithm, to calculate a safe, optimal path for a ship. The system structure, a detailed explanation of a new method, followed by results of simulation tests are all presented in the paper. The results proof a successful application of the method to solve a path planning problem for ships with the consideration of both static and dynamic obstacles in the environment, marine traffic regulations and dynamic properties of a ship, what makes this approach applicable in commercial systems. The approach can also be adapted for application in mobile robots path planning. The experimental results and ability of the system to achieve a new functionality of full autonomy show significance of this contribution to the development of Expert and Intelligent Systems domain. The author believes that autonomous systems constitute the future of Expert and Intelligent Systems.     

A four-stage algorithm for updating a Burrows–Wheeler transform

We present a four-stage algorithm that updates the Burrows–Wheeler Transform of a text T$T$, when this text is modified. The Burrows–Wheeler Transform is used by many text compression applications and some self-index data structures. It operates by reordering the letters of a text T$T$ to obtain a new text bwt(T)$bwt\left(T\right)$ which can be better compressed.     

A new polynomial algorithm for a paralle identical scheduling problem

A precedence order is defined based on the release dates of jobs' direct successors. Using the defined precedence order and Heap Sort, a new polynomial algorithm is provided which aims to solve the parallel scheduling problem P|p = 1, r ,outtree|∑C . The new algorithm is shown to be more compact and easier to implement.     

A recursive algorithm for solving “a secret sharing” problem

Abstract

This paper presents a recursive algorithm for solving “a secret sharing” problem. This problem is one of the unsolved problems in the Second International Students Olympiad in Cryptography (NSUCRYPTO2015). Recently, Geut et al. solved the problem in a special case. We show that our algorithm is able to solve it in general.     

Note on “Coordinating the ordering and advertising policies for a single-period commodity in a two-level supply chain”

In a recent paper, Chen (2011) studied a two-level supply chain consisting of one manufacturer and one retailer. In this supply chain, demand is random and depends on the advertisements of both the manufacturer and the retailer. There are some serious flaws in the assumptions of the model, and consequently some of the results derived are incorrect. In this note, we point out the flaws and correct them. The corrected model is illustrated with the help of numerical examples.     

A tabu search heuristic for a sequence-dependent and time-dependent scheduling problem on a single machine

This paper introduces a tabu search heuristic for a production scheduling problem with sequence-dependent and time-dependent setup times on a single machine. The problem consists in scheduling a set of dependent jobs, where the transition between two jobs comprises an unrestricted setup that can be performed at any time, and a restricted setup that must be performed outside of a given time interval which repeats daily in the same position. The setup time between two jobs is thus a function of the completion time of the first job. The tabu search heuristic relies on shift and swap moves, and a surrogate objective function is used to speed-up the neighborhood evaluation. Computational experiments show that the proposed heuristic consistently finds better solutions in less computation time than a recent branch-and-cut algorithm. Furthermore, on instances where the branch-and-cut algorithm cannot find the optimal solution, the heuristic always identifies a better solution.     

Disassembly sequence planning using a Simplified Teaching–Learning-Based Optimization algorithm

Disassembly Sequence Planning (DSP) is a challenging NP-hard combinatorial optimization problem. As a new and promising population-based evolutional algorithm, the Teaching–Learning-Based Optimization (TLBO) algorithm has been successfully applied to various research problems. However, TLBO is not capable or effective in DSP optimization problems with discrete solution spaces and complex disassembly precedence constraints. This paper presents a Simplified Teaching–Learning-Based Optimization (STLBO) algorithm for solving DSP problems effectively. The STLBO algorithm inherits the main idea of the teaching–learning-based evolutionary mechanism from the TLBO algorithm, while the realization method for the evolutionary mechanism and the adaptation methods for the algorithm parameters are different. Three new operators are developed and incorporated in the STLBO algorithm to ensure its applicability to DSP problems with complex disassembly precedence constraints: i.e., a Feasible Solution Generator (FSG) used to generate a feasible disassembly sequence, a Teaching Phase Operator (TPO) and a Learning Phase Operator (LPO) used to learn and evolve the solutions towards better ones by applying the method of precedence preservation crossover operation. Numerical experiments with case studies on waste product disassembly planning have been carried out to demonstrate the effectiveness of the designed operators and the results exhibited that the developed algorithm performs better than other relevant algorithms under a set of public benchmarks.     

Three stage trade credit policy in a three-layer supply chain–a production-inventory model

The main purpose of this paper is to investigate the optimal replenishment lot size of supplier and optimal production rate of manufacturer under three levels of trade credit policy for supplier–manufacturer–retailer supply chain. The supplier provides a fixed credit period to settle the accounts to the manufacturer, while the manufacturer gives a fixed credit period to settle the account to the retailer and the retailer, in turn, also offers a credit period to each of its customers to settle the accounts. We assume that the supplier supplies the raw material to the manufacturer and sends back the defective raw materials to the outside supplier after completion of inspection at one lot with a sales price. The system always produces good items in the model. Also, we consider the idle times of supplier and manufacturer. Finally, numerical examples are provided to illustrate the behaviour and application of the model with graphical simulation.     

Uncertain location–allocation decisions for a bi-objective two-stage supply chain network design problem with environmental impacts

In the cases that the historical data of an uncertain event is not available, belief degree-based uncertainty theory is a useful tool to reflect such uncertainty. This study focuses on uncertain bi-objective supply chain network design problem with cost and environmental impacts under uncertainty. As such network may be designed for the first time in a geographical region, this problem is modelled by the concepts of belief degree-based uncertainty theory. This article is almost the first study on belief degree-based uncertain supply chain network design problem with environmental impacts. Two approaches such as expected value model and chance-constrained model are applied to convert the proposed uncertain problem to its crisp form. The obtained crisp forms are solved by some multi-objective optimization approaches of the literature such as TH, Niroomand, MMNV. A deep computational study with several test problems are performed to study the performance of the crisp models and the solution approaches. According to the results, the obtained crisp formulations are highly sensitive to the changes in the value of the cost parameters. On the other hand, Niroomand and MMNV solution approaches perform better than other solution approaches from the solution quality point of view.     

An AHP based heuristic DPSO algorithm for generating multi criteria production–distribution plan

Multi-criteria integrated production–distribution problems were solved by many researchers using different optimization techniques. A novel analytic hierarchy process (AHP) based heuristic discrete particle swarm optimization (DPSO) algorithm is proposed in this research for solving difficult production–distribution problems. A bearing manufacturing industry's case is considered in this paper and the mathematical model is formulated as mixed integer linear programming (MILP) problem considering multi-period, multi-product and multi-plant scenarios. The three major objectives considered are total cost reduction, minimization of change in labor levels and percentage under-utilization. The results of the AHP based heuristic DPSO algorithm are compared with the branch and bound algorithm results generated using LINGO software. The approach gives good near optimal solutions. In addition to the bearing manufacturing industry dataset, two other test datasets are also solved.     

A polynomial-time algorithm for a flow-shop batching problem with equal-length operations

A flow-shop batching problem with consistent batches is considered in which the processing times of all jobs on each machine are equal to p and all batch set-up times are equal to s. In such a problem, one has to partition the set of jobs into batches and to schedule the batches on each machine. The processing time of a batch B _i is the sum of processing times of operations in B _i and the earliest start of B _i on a machine is the finishing time of B _i on the previous machine plus the set-up time s. Cheng et al. (Naval Research Logistics 47:128–144, 2000) provided an O(n) pseudopolynomial-time algorithm for solving the special case of the problem with two machines. Mosheiov and Oron (European Journal of Operational Research 161:285–291, 2005) developed an algorithm of the same time complexity for the general case with more than two machines. Ng and Kovalyov (Journal of Scheduling 10:353–364, 2007) improved the pseudopolynomial complexity to \(O(\sqrt{n})\). In this paper, we provide a polynomial-time algorithm of time complexity O(log?³ n).     

A Kane’s based algorithm for closed-form dynamic analysis of a new design of a 3RSS-S spherical parallel manipulator

This paper proposes a systematic methodology to obtain a closed-form formulation for dynamics analysis of a new design of a fully spherical robot that is called a 3(RSS)-S parallel manipulator with real co-axial actuated shafts. The proposed robot can completely rotate about a vertical axis and can be used in celestial orientation and rehabilitation applications. After describing the robot and its inverse position, velocity and acceleration analysis is performed. Next, based on Kane’s method, a methodology for deriving the dynamical equations of motion is developed. The elaborated approach shows that the inverse dynamics of the manipulator can be reduced to solving a system of three linear equations in three unknowns. Finally, a computational algorithm to solve the inverse dynamics of the manipulator is advised and several trajectories of the moving platform are simulated.

     

A location–inventory supply chain problem: Reformulation and piecewise linearization

In this paper, we study a two-echelon inventory management problem with multiple warehouses and retailers. The problem is a natural extension to the well-known one-warehouse multi-retailer inventory problem. The problem is formulated as a mixed integer non-linear program such that its continuous relaxation is non-convex. We propose an equivalent formulation with fewer non-linear terms in the objective function so that the continuous relaxation of the new model is a convex optimization problem. We use piecewise linearization to transform the resulting MINLP to a mixed integer program and we solve it using CPLEX. Through numerical experiments, we compare the solutions obtained by solving the new formulation using CPLEX with two previously published Lagrangian relaxation based heuristics to solve the original mixed integer non-linear program. We demonstrate that the new approach is capable of providing almost the same solutions without the need of using specialized algorithms. This important contribution further implies that additional variants of the problem, such as multiple products, capacitated warehouses and routing, can be added to result in a problem that will again be solvable by commercial optimization software, while the respective Lagrangian heuristics will fail to solve such variants or extended problems.