Transshipment Problem and Its Variants: A Review

The transshipment problem is a unique Linear Programming Problem (LLP) in that it considers the assumption that all sources and sinks can both receive and distribute shipments at the same time (function in both directions). Being an extension of the classical transportation problem, the transshipment problem covers a wide range of scenarios for logistics and/or transportation inputs and products and offers optimum alternatives for same. In this work the review of literatures from the origin and current trends on the transshipment problem were carried out. This was done in view of the unique managerial needs and formulation of models/objective functions. It was revealed that the LLP offers a wide range of decision alternative for the operations manager based on the dynamic and challenging nature of logistics management. Key words: Transshipment problem, Linear Programming Problems (LPP), model, objective functions, decision alternative

A Hybrid MCDM Approach to Transshipment Port Selection

Port selection is an intrinsic supply-chain problem that has substantial impact on development of local economies. Shipping business environment developed into complex system where decision making is derived from uncertain and incomplete data. In this study we present a conceptual integrated Multi-Criteria Decision solution to transshipment port selection problem based on Best-Worst MCDM and Artificial Bee Colony Algorithm. Through literature review and expert analysis, 50 relevant criteria have been identified as relevant to the transshipment port selection problem. Decision makers within liner shipping companies evaluate transshipment port selection criteria and establish ranking that is used to determine crisp solution with lowest consistency ratio. ABC based algorithm is used to reduce computational complexity and deliver a single optimal solution by solving both objective and constraint violation functions

A Hybrid Multi-Criteria Analysis Model for Solving the Facility Location–Allocation Problem: a Case Study of Infectious Waste Disposal

Choosing locations for infectious waste disposal (IWD) is one of the most significant issues in hazardous waste management due to the risk imposed on the environment and human life. This risk can be the result of an undesirable location of IWD facilities. In this study a hybrid multi-criteria analysis (Hybrid MCA) model for solving the facility location–allocation (FLA) problem for IWD was developed by combining two objectives: total cost minimization and weight maximization. Based on an actual case of forty-seven hospitals and three candidate municipalities in the northeastern region of Thailand, first, the Fuzzy AHP and Fuzzy TOPSIS techniques were integrated to determine the closeness of the coefficient weights of each candidate municipality. After that, these weights were converted to weighting factors and then these factors were taken into the objective function of the FLA model. The results showed that the Hybrid MCA model can help decision makers to locate disposal centers, hospitals and incinerator size simultaneously. Besides that the model can be extended by incorporating additional selection criteria/objectives. Therefore, it is believed that it can also be useful for addressing other complex problems

A distribution network design for fast-moving consumer goods

A distribution network design of fast-moving consumer goods ensures distribution of products in an effective manner by giving  maximum customers’ satisfaction and minimum distribution cost. The study evaluates the distribution through direct shipment and the use of intermediate shipment for distribution of products from plant to depots. A real-life case study in Southwestern Nigeria was defined and solved as a linear programming model to minimise total cost of distribution from plant to the depots with consideration of four routing options. The results show that distribution through intermediaries gives a better solution than routing option with  direct shipment. The best routing option with intermediate points when compared with the routing option with direct shipment gives a savings of 1,819,490.00 Naira which translates to 13.46% cost savings. The study shows that the location of intermediaries is a key decision in distribution network design and that the intermediaries add value to the distribution networks in supply chain. Keywords: Distribution network; Supply chain design; Fast-moving consumer goods; Linear programmin

Supply Chain

Traditionally supply chain management has meant factories, assembly lines, warehouses, transportation vehicles, and time sheets. Modern supply chain management is a highly complex, multidimensional problem set with virtually endless number of variables for optimization. An Internet enabled supply chain may have just-in-time delivery, precise inventory visibility, and up-to-the-minute distribution-tracking capabilities. Technology advances have enabled supply chains to become strategic weapons that can help avoid disasters, lower costs, and make money. From internal enterprise processes to external business transactions with suppliers, transporters, channels and end-users marks the wide range of challenges researchers have to handle. The aim of this book is at revealing and illustrating this diversity in terms of scientific and theoretical fundamentals, prevailing concepts as well as current practical applications

A fuzzy optimization approach for procurement transport operational planning in an automobile supply chain

We consider a real-world automobile supply chain in which a first-tier supplier serves an assembler and determines its procurement transport planning for a second-tier supplier by using the automobile assembler's demand information, the available capacity of trucks and inventory levels. The proposed fuzzy multi-objective integer linear programming model (FMOILP) improves the transport planning process for material procurement at the first-tier supplier level, which is subject to product groups composed of items that must be ordered together, order lot sizes, fuzzy aspiration levels for inventory and used trucks and uncertain truck maximum available capacities and minimum percentages of demand in stock. Regarding the defuzzification process, we apply two existing methods based on the weighted average method to convert the FMOILP into a crisp MOILP to then apply two different aggregation functions, which we compare, to transform this crisp MOILP into a single objective MILP model. A sensitivity analysis is included to show the impact of the objectives weight vector on the final solutions. The model, based on the full truck load material pick method, provides the quantity of products and number of containers to be loaded per truck and period. An industrial automobile supply chain case study demonstrates the feasibility of applying the proposed model and the solution methodology to a realistic procurement transport planning problem. The results provide lower stock levels and higher occupation of the trucks used to fulfill both demand and minimum inventory requirements than those obtained by the manual spreadsheet-based method. (C) 2014 Elsevier Inc. All rights reserved.This work has been funded partly by the Spanish Ministry of Science and Technology project: Production technology based on the feedback from production, transport and unload planning and the redesign of warehouses decisions in the supply chain (Ref. DPI2010-19977) and by the Universitat Politecnica de Valencia project 'Material Requirement Planning Fourth Generation (MRPIV) (Ref. PAID-05-12)'.Díaz-Madroñero Boluda, FM.; Peidro Payá, D.; Mula, J. (2014). A fuzzy optimization approach for procurement transport operational planning in an automobile supply chain. Applied Mathematical Modelling. 38(23):5705-5725. https://doi.org/10.1016/j.apm.2014.04.053S57055725382

Best matching processes in distributed systems

The growing complexity and dynamic behavior of modern manufacturing and service industries along with competitive and globalized markets have gradually transformed traditional centralized systems into distributed networks of e- (electronic) Systems. Emerging examples include e-Factories, virtual enterprises, smart farms, automated warehouses, and intelligent transportation systems. These (and similar) distributed systems, regardless of context and application, have a property in common: They all involve certain types of interactions (collaborative, competitive, or both) among their distributed individuals—from clusters of passive sensors and machines to complex networks of computers, intelligent robots, humans, and enterprises. Having this common property, such systems may encounter common challenges in terms of suboptimal interactions and thus poor performance, caused by potential mismatch between individuals. For example, mismatched subassembly parts, vehicles—routes, suppliers—retailers, employees—departments, and products—automated guided vehicles—storage locations may lead to low-quality products, congested roads, unstable supply networks, conflicts, and low service level, respectively. This research refers to this problem as best matching, and investigates it as a major design principle of CCT, the Collaborative Control Theory. The original contribution of this research is to elaborate on the fundamentals of best matching in distributed and collaborative systems, by providing general frameworks for (1) Systematic analysis, inclusive taxonomy, analogical and structural comparison between different matching processes; (2) Specification and formulation of problems, and development of algorithms and protocols for best matching; (3) Validation of the models, algorithms, and protocols through extensive numerical experiments and case studies. The first goal is addressed by investigating matching problems in distributed production, manufacturing, supply, and service systems based on a recently developed reference model, the PRISM Taxonomy of Best Matching. Following the second goal, the identified problems are then formulated as mixed-integer programs. Due to the computational complexity of matching problems, various optimization algorithms are developed for solving different problem instances, including modified genetic algorithms, tabu search, and neighbourhood search heuristics. The dynamic and collaborative/competitive behaviors of matching processes in distributed settings are also formulated and examined through various collaboration, best matching, and task administration protocols. In line with the third goal, four case studies are conducted on various manufacturing, supply, and service systems to highlight the impact of best matching on their operational performance, including service level, utilization, stability, and cost-effectiveness, and validate the computational merits of the developed solution methodologies