An integrated steering approach to improve a phosphate supply chain efficiency

The increasing level of customer awareness and application of environmental laws by governments, on the one hand, and the increasing number of competitors, on the other hand, has obliged industry owners to include green activities in the design of the supply chain network. Green activities include any type of action that reduces environmental degradation. Hence, this study seeks to develop a bi-objective, mixed integer linear programming (MILP) model for designing a green supply chain network. In the proposed model, the minimisation of costs and detrimental environmental effects are discussed. LP metric method is used to solve the bi-objective model and the proposed model is run by using simulated data in small and medium sizes in GAMS software. Finally, the model sensitivity analysis is measured in order to evaluate the validity and performance of the proposed model by using demand-driven scenarios. The results of this model indicate the effectiveness of the proposed model.

ABSTRACTToday’s scenario of manufacturing and supply chain is full of uncertainty because of numerous types of disruptions and failures such as fire, storm, machine failure, are a few names. Always supply chain disruptions present disastrous impacts, although probabilities of happening are low. In the recent robust as well as flexible supply chain has captured the focus of researchers in designing a supply chain network with consideration of disruption’s risk. In this work, an ontology-based decision support system is proposed to intensify the supply chain resilience during a disruption. The concept of semantic and ontology is adopted in developing the knowledge base for the entire supply chain network including manufacturing units. Protégé is used for defining the classes and sub-classes along with numerous types of properties and expressed in a rule-based system using semantic web rule language (SWRL). Furthermore, a mixed integer linear programming model with an objective of maximising quantified resilience to fulfil the demand. A hybrid particle swarm optimisation – differential evolution (PSO-DE) is utilised as an optimisation technique for the defined problem. In performing the study, a set of simulated data is formed and then interpreted in the ontology for an optimal selection of recovery activity.

Cellular manufacturing systems (CMS) are production systems that typically comprise a number of manufacturing cells served by a centralized material handling system. Designing such systems includes three major decisions; cell formation (CF), group layout (GL), and group scheduling (GS). Traditionally, these three decisions have been dealt with separately, which has usually lead to less than optimal system performance. In this paper, a new mixed integer linear programming (MILP) model is proposed for the integrated CF, GL and GS problem, to efficiently design and operate CMSs. The model solves the integrated problem, taking into consideration intercellular and intracellular transportation times to determine the optimal cell formation, layout of machines and schedule of parts on the machines, simultaneously. Sequence-dependent set up times are also considered in the model. The performance of the model is tested by solving problems previously introduced in the literature considering two objectives; minimizing the makespan or minimizing the mean flow time in the system. The results show that the proposed model is efficient in solving small to medium-sized problems.

This paper presents multiple readings to solve a vehicle routing problem with pickup and delivery (VRPPD) based on a real-life case study. Compared to theoretical problems, real-life ones are more difficult to address due to their richness and complexity. To handle multiple points of view in modeling our problem, we developed three different Mixed Integer Linear Programming (MILP) models, where each model covers particular constraints. The suggested models are designed for a mega poultry company in Tunisia, called CHAHIA. Our mission was to develop a prototype for CHAHIA that helps decision-makers find the best path for simultaneously delivering the company’s products and collecting the empty boxes. Based on data provided by CHAHIA, we conducted computational experiments, which have shown interesting and promising results.

Mixed Integer Linear Programming Model for Vehicle Routing Problem for Hazardous Materials Transportation

This paper presents a mixed integer linear programming (MILP) model to operate a dual source heat pump (DSHP) optimally. The DSHP draws environmental energy from an underground ice storage tank (IST) or wind infrared sensitive collectors (WISC). WISC are further used to regenerate the IST. Thermal energy storage (TES) stores the thermal output of the DSHP. A single-objective optimisation approach is applied to minimise the operational cost of the DSHP over a scheduling horizon of 24h. The developed framework is tested on various days, which are representative for each season of the year. Furthermore, it is investigated how the variable electricity price market data influence the dynamic behaviour of the DSHP.

The design of the underlying supply chain network can have a tremendous impact on the profitability, manageability, and level of risk of a global supply chain. Taxes, duties, and tariffs vary from country to country as well as trading bloc to trading bloc and can consume as much as 10% of the revenues of certain products. In the highly regulated business environment of agricultural chemicals, the country of origin of an active ingredient can determine where the final product can be marketed and the amount of taxes and duties applied to the product, making it necessary to trace all batches of product through many layers of the supply chain to their sources. This article presents a mixed integer linear programming model in use at Dow AgroSciences LLC that simultaneously optimizes the network design underlying global supply chains and the monthly production and shipping schedules for maximum profitability. This work contributes to the supply chain design literature by demonstrating a novel method of tracing products to their source for inventory valuation, taxation, and duty computation in a production environment where the products change into other products as they pass through nodes in the network. It also demonstrates an iterative scheme for determining unit fixed costs for fixed cost allocation for the same purposes. Finally, it provides a case study of a supply chain design initiative in a global enterprise.

Introducción: Las decisiones operativas de un minorista implican que a través de su surtido se pueda satisfacer la demanda logrando la mayor utilidad posible haciendo el mejor uso posible de su espacio de exhibición. Este artículo presenta un modelo de programación lineal entera mixta cuyo propósito es maximizar la rentabilidad de un minorista considerando los efectos de sustitución dinámica entre artículos, impactos de la elasticidad del espacio sobre la demanda, niveles de servicio esperados de la exhibición y las decisiones de los compradores. Objetivo: Proponer un modelo que soporte la toma de decisiones de asignación de espacio, planeación del surtido y localización en góndola de un minorista. Metodología: Primero se analizan diferentes factores que afectan las decisiones objeto de estudio usando fuentes primarias y secundarias. Luego se desarrolla el modelo matemático y se evalúa su desempeño utilizando datos reales de una categoría de productos en un punto de venta de una cadena de supermercados. Resultados: En la instancia de prueba se obtiene la solución óptima en un tiempo computacional razonable y esta solución permite establecer la forma en la cual gestionar de mejor manera el surtido en el punto de venta, superando los resultados del método actual. Conclusiones: El modelo propuesto permite aumentar la utilidad de la cadena a través del surtido adecuado, con los espacios de exhibición justos y con la localización exacta de los artículos seleccionados.

PurposeSupply chain network design is an important strategic decision that firms make considering both the short‐ and long‐term consequences of the network's performance. The typical design approach implicitly assumes that, once designed, the facilities and the links will always operate as planned. In reality, however, facilities and the links connecting them, fail from time to time due to poor weather, natural or man‐made disasters, or a combination of any other factors. This work aims to propose a design framework that addresses the facility and link failures explicitly by accounting for their impact on a network's performance measures of efficiency and robustness.Design/methodology/approachThe study incorporated a robustness metric for evaluating the resiliency of supply chains in the case of a network disruption. This robustness metric is based on expected losses incurred due to network failures. It defines efficiency and robustness in terms of operational cost and expected disruption cost (EDC), respectively. The EDC is defined in terms of loss of opportunity cost incurred due to not meeting demand on time after a disruption has occurred. The study used a scenario planning approach and formulated a mixed integer linear program model with the objective of maximizing both efficiency and robustness. It also evaluates the trade‐offs between efficiency and robustness.FindingsThe resulting supply chain is much more reliable in the long term since we have shown that a significant amount of robustness can be built into the system without compromising a lot on efficiency.Originality/valueThis work demonstrates a methodology which incorporates such disaster scenarios into the design of a supply chain network. This leads to a more reliable supply chain which would lead to higher profitability and lower disruption rates.

Optimal planning of municipal solid waste management systems in an integrated supply chain network

The unique and complex challenges of sustainable interplanetary travel necessitate a novel space logistics paradigm, which is hereafter referred to as the interplanetary supply chain. To lay the groundwork for this anticipated paradigm shift, this work aims to introduce principles and methodologies of supply chain (SC) planning to the field of space logistics. To this end, a stochastic multistage mixed integer linear programming model is proposed that explores the vision of a permanent human presence on Mars from a logistics standpoint. The model optimizes material flows, propellant production, and in-space infrastructure allocations to identify the optimal SC network design. Notably, demand uncertainty at the Mars base is considered, which makes it the first model to investigate the effects of stochasticity in space logistics planning. Several case studies are investigated, which incorporate data on Earth–Mars transfer windows and flight trajectories obtained through the National Aeronautics and Space Administration’s trajectory browser. The results reveal numerous interdependencies and, thus, highlight the need for holistic SC modeling approaches that go beyond the isolated mission planning of the extant literature. For instance, the findings show frequent interactions among spacecraft as they regularly engage in division of labor regarding cargo and propellant transportation. Moreover, complex propellant supply networks that involve strategic in-space infrastructure allocation and propellant production on celestial bodies are established. Overall, the developed model provides valuable insights into the planning of a sustainable interplanetary travel, offers a new SC perspective, and lays the foundation for future research in this area.

Motivated by some practical applications, we study a new integrated loading and transportation scheduling problem. Given a set of jobs, a single crane is available to load jobs, one by one, onto semitrailers with a given capacity. Loaded semitrailers are assigned to tractors for transportation tasks. Subject to limited resources (crane, semitrailers, and tractors), the problem is to determine (1) an assignment of jobs to semitrailers for loading tasks, (2) a sequence for the crane to load jobs onto semitrailers, (3) an assignment of loaded semitrailers to tractors for transportation tasks, and (4) a transportation schedule of assigned tractors such that the completion time of the last transportation task is minimized. We first formulate the problem as a mixed integer linear programming model (MILPM) and prove that the problem is strongly NP‐hard. Then, optimality properties are provided which are useful in establishing an improved MILPM and designing solution algorithms. We develop a constructive heuristic, two LP‐based heuristics, and a recovering beam search heuristic to solve this problem. An improved procedure for solutions by heuristics is also presented. Furthermore, two branch‐and‐bound (B&B) algorithms with two different lower bounds are developed to solve the problem to optimality. Finally, computational experiments using both real data and randomly generated data demonstrate that our heuristics are highly efficient and effective. In terms of computational time and the number of instances solved to optimality in a time limit, the B&B algorithms are better than solving the MILPM. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 416–433, 2015

Optimising the flight turnaround schedules: An improved sliding time windows approach based on MILP and CP models

The Floating-Cuts model: a general and flexible mixed-integer programming model for non-guillotine and guillotine rectangular cutting problems

Blockchain technology opens up new opportunities for Business Process Management. This is mainly due to its unprecedented capability to let transactions be automatically executed and recorded by Smart Contracts in multi-peer environments, in a decentralised fashion and without central authoritative players to govern the workflow. In this way, blockchains also provide traceability. Traceability of information plays a pivotal role particularly in those supply chains where multiple parties are involved and rigorous criteria must be fulfilled to lead to a successful outcome. In this paper, we investigate how to run a business process in the context of a supply chain on a blockchain infrastructure so as to provide full traceability of its run-time enactment. Our approach retrieves information to trace process instances execution solely from the transactions written on-chain. To do so, hash-codes are reverse-engineered based on the Solidity Smart Contract encoding of the generating process. We show the results of our investigation by means of an implemented software prototype, with a case study on the reportedly challenging context of the pharmaceutical supply chain.