Supply Chain Management Problems Research Articles

Selected problems of supply chain management in monolithic works

In a logistics-production system pertaining to the complex process of concrete works, two subsystems of concrete transport can be distinguished during the realization of objects in monolithic technology. In the mathematical model, according to which the choice of an optimal machine-set for external transport of concrete mixture is made, the assumption is that the concrete production plant delivers concrete mix using its own means of transport of varied capacities to a few simultaneously realized objects. The conceptual apparatus created for this purpose allows one to present the transport system (distant and internal) as one entity and as a logical record of mutual internal relationships between its elements. It also describes its relationships with the external environment. The prepared computer program allows one to perform a complete analysis of machines and devices during the task realization. The presented algorithm of solving the task also allows one to determine the optimal order of loading the concrete truck mixers, i.e. to determine which means of transport start the work and which ones transport concrete mix to the construction site to minimize the transport costs.

A genetic algorithm approach for multi-objective optimization of supply chain networks

Supply chain network (SCN) design is to provide an optimal platform for efficient and effective supply chain management. It is an important and strategic operations management problem in supply chain management, and usually involves multiple and conflicting objectives such as cost, service level, resource utilization, etc. This paper proposes a new solution procedure based on genetic algorithms to find the set of Pareto-optimal solutions for multi-objective SCN design problem. To deal with multi-objective and enable the decision maker for evaluating a greater number of alternative solutions, two different weight approaches are implemented in the proposed solution procedure. An experimental study using actual data from a company, which is a producer of plastic products in Turkey, is carried out into two stages. While the effects of weight approaches on the performance of proposed solution procedure are investigated in the first stage, the proposed solution procedure and simulated annealing are compared according to quality of Pareto-optimal solutions in the second stage.

Logic-Based Solution Methods for Optimal Control of Hybrid Systems

Combinatorial optimization over continuous and integer variables is a useful tool for solving complex optimal control problems of dynamical systems formulated in discrete-time. Current approaches are based on mixed-integer linear (or quadratic) programming (MIP), which provides the solution after solving a sequence of relaxed linear (or quadratic) programs. MIP formulations require the translation of the discrete/logic part of the problem into mixed-integer inequalities. Although this operation can be done automatically, most of the original symbolic structure of the problem (e.g., transition functions of finite state machines, logic constraints, symbolic variables, etc.) is lost during the conversion, with a consequent loss of computational performance. In this paper, we attempt to overcome such a difficulty by combining numerical techniques for solving convex programming problems with symbolic techniques for solving constraint satisfaction problems (CSP). The resulting hybrid solver proposed here takes advantage of CSP solvers for dealing with satisfiability of logic constraints very efficiently. We propose a suitable model of the dynamics and a class of optimal control problems that embrace both symbolic and continuous variables/functions, and that are tailored to the use of the new solver. The superiority in terms of computational performance with respect to commercial MIP solvers is shown on a centralized supply chain management problem with uncertain forecast demand.

Dynamic Nonlinear Modelization of Operational Supply Chain Systems

Supply Chain Management (SCM) is an important activity in all producing facilities and in many organizations to enable vendors, manufacturers and suppliers to interact gainfully and plan optimally their flow of goods and services. To realize this, a dynamic modelling approach for characterizing supply chain activities is opportune, so as to plan efficiently the set of activities over a distributed network in a formal and scientific way. The dynamical system will result so complex that it is not generally possible to specify the functional forms and the parameters of interest, relating outputs to inputs, states and stochastic terms by experiential specification methods. Thus the algorithm that will presented is Data Driven, determining simultaneously the functional forms, the parameters and the optimal control policy from the data available for the supply chain. The aim of this paper is to present this methodology, by considering dynamical aspects of the system, the presence of nonlinear relationships and unbiased estimation procedures to quantify these relations, leading to a nonlinear and stochastic dynamical system representation of the SCM problem. Moreover, the convergence of the algorithm will be proved and the satisfaction of the required statistical conditions demonstrated. Thus SCM problems may be formulated as formal scientific procedures, with well defined algorithms and a precise calculation sequence to determine the best alternative to enact. A "Certainty equivalent principle" will be indicated to ensure that the effects of the inevitable uncertainties will not lead to indeterminate results, allowing the formulation of demonstrably asymptotically optimal management plans.

ANALYSIS OF COLLABORATIVE NETWORKS USING THE EXTREMAL GRAPH THEORY

In literature, a large number of studies on the Italian districts exhibit a portrait which points out the Italian economic specificity. A specific feature of the Italian districts is the large number of collaborative actions carried on by the associated enterprises which are not limited to the usual supply chain management problems. As not all the collaborative actions bring immediate (and measurable) economic benefits, there is the need for a reliable and unbiased method aimed at the identification and analysis of the collaborative patterns inside an industry network exploiting their presence and effectiveness. The proposed method uses a graph representation of the inter-firm relationships inside a network and induces the collaborative behaviours by the analysis of the graph topology.

Multi-Depot Vehicle Routing Problem: A One-Stage Approach

This paper introduces multi-depot vehicle routing problem with fixed distribution of vehicles (MDVRPFD) which is one important and useful variant of the traditional multi-depot vehicle routing problem (MDVRP) in the supply chain management and transportation studies. After modeling the MDVRPFD as a binary programming problem, we propose two solution methodologies: two-stage and one-stage approaches. The two-stage approach decomposes the MDVRPFD into two independent subproblems, assignment and routing, and solves them separately. In contrast, the one-stage approach integrates the assignment with the routing where there are two kinds of routing methods-draft routing and detail routing. Experimental results show that our new one-stage algorithm outperforms the published methods. Note to Practitioners-This work is based on several consultancy work that we have done for transportation companies in Hong Kong. The multi-depot vehicle routing problem (MDVRP) is one of the core optimization problems in transportation, logistics, and supply chain management, which minimizes the total travel distance (the major factor of total transportation cost) among a number of given depots. However, in real practice, the MDVRP is not reliable because of the assumption that there have unlimited number of vehicles available in each depot. In this paper, we propose a new useful variant of the MDVRP, namely multi-depot vehicle routing problem with fixed distribution of vehicles (MDVRPFD), to model the practicable cases in applications. Two-stage and one-stage solution algorithms are also proposed. The industry participators can apply our new one-stage algorithm to solve the MDVRPFD directly and efficiently. Moreover, our one-stage solution framework allows users to smoothly add new specified constraints or variants.

An analytical study of the Q( s, S) policy applied to the joint replenishment problem

We evaluate the performance of a joint replenishment inventory control policy, called the Q( s, S) policy, which was first suggested and characterized by Viswanathan [Manage. Sci. 43 (1997) 1447]. We argue why joint replenishment problems can be seen as one product, multiple location problems, and hence why they should be considered supply chain management problems. The paper uses Markov decision theory to work out an analytical solution procedure to evaluate the costs of a particular Q( s, S) policy, and thereby a method for computing the optimal Q( s, S) policy, under the assumption that demands follow a Poisson process. For a fixed order size Q, the problem can be decomposed for each item to find an optimal ( s, S) policy with a stochastic review period. These subproblems can be solved by the algorithm of Zheng and Federgruen [Oper. Res. 39 (4) (1991) 654]. Thereafter it remains to find the optimal value of Q, which is done by systematic search over all relevant values of Q. The performance of the Q( s, S) policy is compared with the performance of other known joint ordering policies as in Pantumsinchai [Decis. Sci. 23 (1992) 111] and Viswanathan [Manage. Sci. 43 (1997) 1447]. It is shown that the Q( s, S) performs best among the considered policies.

Stochastic Dynamic Programming with Localized Cost-to-go Approximators: Application to Large Scale Supply Chain Management Under Demand Uncertainty

A novel optimization algorithmic framework based on dynamic programming is proposed for solving multi-product supply chain management problems with manufacturing and distribution decisions under demand uncertainty. To generate reliable suboptimal policy for simulation and restricted state space identification, a deterministic mathematical programming (MILP) approach is utilized. The simulation data with fixed action profiles obtained from the MILPs with different demand patterns is directly utilized for real-time decision making with initial ‘profit-to-go’ values.

컨테이너 운송체계 개선을 위한 화물수거-배송계획 시스템 개발

This paper provides a method to implement IT system for supply chain management problem The fundamental scheduling issues that arise in the pickup delivery system are optimizing the system with the minimum cost, the maximum throughput and the service level. This study is concerned with the development of the customer responsive pickup-delivery system based on e-logistics and demand chain management. Through this study, the pickup-delivery problem of the vehicle using the GIS PDP and GUI- type computer programs are formulated. The computational results show that the proposed method is very effective on a set of test problems.

A study on the impact of uncertain factors on information distortion in supply chains

Information distortion is a troublesome problem in supply-chain management. The most familiar form of information distortion is the ‘bullwhip effect’, which can be predicted according to parameters such as lead-times and forecasting coefficients. However, this paper focuses on the other form of information distortion that is caused by uncertain factors. This paper first defines the concept of the bullwhip effect, and proposes a new term ‘extended-bullwhip effect’, which is employed to describe information distortion besides the bullwhip effect. Then we quantify the negative impact of uncertain factors on information distortion for a simple two-stage supply chain consisting of a single retailer and a single manufacturer. In addition, the magnifications of demand variance arisen by the extended-bullwhip effect are exemplified through a numerical simulation. It is clarified that if the members of a supply chain share information, the ‘extended-bullwhip effect’ can be completely eliminated. Finally, four forms of information distortion are identified according to the quantitative analysis.

Simulating the performance of supply chain with various alliances

Many models of supply chain management (SCM) have been popularly developed in recent years. However, this study focuses on the supply chain (SC) performance with respect to various alliances among partners. First, this study explores the game theory for formulating the SCM problem as a multi-objective programming problem. Second, various alliances, e.g., union, extreme competition, and Stackelberg competition among partners will be considered in such a problem so as to compare the SC performance under different alliances. Third, a numerical example is illustrated by two material supplier partners, three manufacturing partners, two logistics partners, and three customers, where each partner in the SC has its own objective and constraints. This SC problem is resolved across three different time periods. Finally, study results show that the maximum global benefits can be obtained only when all partners form a single union, but the inefficient partners lose significantly in such a union. The minimal global benefits exist in the extreme competition.

A multi-criterion genetic algorithm for order distribution in a demand driven supply chain

This paper develops a multi-criterion genetic optimization procedure, specifically designed for solving optimization problems in supply chain management. The proposed algorithm is discussed with an order distribution problem in a demand driven supply chain network. It combines the analytic hierarchy process (AHP) with genetic algorithms. AHP is utilized to evaluate the fitness values of chromosomes. The proposed algorithm allows decision-makers to give weighting for criteria using a pair-wise comparison approach. The numerical results obtained from the proposed algorithm are compared with the one obtained from the multi-objective mixed integer programming approach. The comparison shows that the proposed algorithm is reliable and robust. In addition, it provides more control and information for the decision-makers to gain a better insight of the supply chain network.

Multi-criteria genetic optimization for distribution network problems

This paper develops a multi-criterion genetic optimization for solving distribution network problems in supply chain management. Distribution problems deal with distribution from a number of sources to a number of destinations, in which various decision factors are closely related and influence each other. Genetic algorithms have been widely adopted as the optimization tool in solving these problems. This paper combines analytic hierarchy processes with genetic algorithms to capture the capability of multi-criterion decision-making. The proposed algorithm allows decision-makers to give weightings for criteria using a pairwise comparison approach. The numerical results obtained from the new approach are compared with the results obtained from linear programming. The result shows that the proposed algorithm is reliable and robust. In addition, it provides more control for decision-makers on the determination of the optimization solutions, and gains more information for a better insight into the distribution network.

TÆMS agents: enabling dynamic distributed supply chain management

Some dynamic supply chain problems are instances of a class of distributed optimization problems that TÆMS and other intelligent agents were made to address. In this paper we define a discrete distributed dynamic supply chain management problem and specify how TÆMS agents, equipped with new coordination mechanisms, automate and manage the supply chain. The agents increase the level of flexibility in the chain and enable members of the supply chain to be more responsive through producer/consumer negotiation and reasoning about manufacturing availability, raw material requirements, and shipping time requirements. Planning/scheduling and coordination research enables the agents to perform this level of automation on-line, responding to change as it happens in the environment, rather than relying on precomputed solutions or reasoning via abstract flow characterizations.

A high-quality-supplier selection model for supply chain management and ISO 9001 system

Supplier selection process for supply chain management (SCM) and ISO 9001 quality management system environments is considered. Determining suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of these decisions is usually complex and unstructured. This paper proposes a high-quality-supplier selection (HQSS) model to deal with supplier selection problems in supply chain management. In selecting a supplier, quality management factors are considered first, and then price, delivery, etc. Quality management factors include a quality management audit, product testing, engineering work force, capability index, training time, etc., based on a five-interval scale. Next, the HQSS model determines the final solution by considering factors such as price, production lead-time, and delivery time.

Distributed decision making in supply chain management

The paper identifies different classes of distributed decision making problems in supply chain management. Since, these problem classes have been developed in various sciences like applied mathematics, operations research, economics and artificial intelligence, the paper gives a systematic overview as to the specific contribution each single science is providing, particularly indicating possible synergies. Moreover, it points to those distributed decision-making problems that prove to be of major relevance for supply chain management.

A strategic model for supply chain design with logical constraints: formulation and solution

This paper proposes a strategic production–distribution model for supply chain design with consideration of bills of materials (BOM). Logical constraints are used to represent BOM and the associated relationships among the main entities of a supply chain such as suppliers, producers, and distribution centers. We show how these relationships are formulated as logical constraints in a mixed integer programming (MIP) model, thus capturing the role of BOM in the selection of suppliers in the strategic design of a supply chain. A test problem is presented to illustrate the effectiveness of the formulation and solution strategy. The results and their managerial implications are discussed. Scope and purpose Supply chain design is to provide an optimal platform for efficient and effective supply chain management. The problem is often an important and strategic operations management problem in supply chain management. This paper shows how the mixed integer programming modeling techniques can be applied to supply chain design problem, where some complicated relations, such as bills of materials, are involved. We discuss how to solve such a complicated model efficiently.

THE STUDY OF SUPPLY CHAIN INVENTORY STRATEGY UNDER BTO PRODUCTION ENVIRONMENT

ABSTRACT Since the rapid change in industry environment, the academic and industry proposed the concept and procedure to solve the problems of supply chain management (SCM). SCM is often affected by the production and inventory strategy and it has the information distortion. This is called bullwhip effect. The major causes of bullwhip effect include time lag, supply chain level, inventory decision, batch production and order, forecast error, and price fluctuation. The results of these causes will response on the inventory of each level of supply chain. The purpose of this paper is to study the bullwhip effect influenced by two inventory strategies, (s,S) and Vendor Management Inventory (VMI), under BTO production environment. In this paper, the bullwhip effects, such as fluctuation uppermost point, fluctuation lowest point, fluctuation stable time, upstream demand enlarge ratio, total fluctuation stable time and total enlarge ratio, are analyzed. The system dynamics models of the BTO production strategy are constructed, and the inventory strategies are expressed by the model parameters. The system dynamic models are tested by steady state test, step test, seasonal fluctuation test, and real data test. Then, the simulation results are computed based on this model in order to find optimal policy combination. This optimal policy combination can be acted as reference for SCM decision-maker.

Optimization and heuristic methods supporting distributed manufacturing

New optimization and heuristic methods are described to address supply chain management problems in distributed manufacturing settings. Specifically, integer programming formulations and heuristic methods are developed to design and evaluate optimal or near-optimal delivery plans for material movements between sites in a truckload trucking environment for the benefit of carriers, customers and professional drivers. The tools developed herein are appropriate for examining delivery needs between suppliers, manufacturers, distribution centres, and customer locations. They are equally applicable to more complex situations involving the return of packaging materials to the original shipment site, or even concurrent consideration of multiple business entities with various shipment profiles. Realistically sized case studies are provided to demonstrate the efficacy of the approaches using data supplied by J.B. Hunt Transport, Inc.

Using Soft Systems Methodology to Address Supply Chain Management Problems

This paper reports an investigation of if, and how, Soft Systems Methodology (SSM) might.be used to facilitate better management of industry supply chains. In two workshops involving supply chain managers from the Textile Clothing and Footwear (TCP) industry and industry facilitators, ways in which SSM techniques might supplement existing Supply Chain Management (SCM) workshop approaches have been explored. Specifically, the placement of SSM techniques within a workshop setting, reactions to the techniques, perceived reasons for using SSM, together with strengths and difficulties encountered, have been examined.