An interactive decision framework for multiple objective production planning

The extraordinary fluctuations in China's hog prices, particularly since the outbreaks of the African swine fever in 2018, have brought tremendous risks to hog producers' production planning. This study proposes a decision framework for hog price prediction and production planning. Specifically, we use six forecast combination strategies to integrate multi‐step‐ahead forecasts of the hog price generated by 11 econometric and machine learning methods. Furthermore, we design a practical production plan based on the obtained hog price forecasts to determine the number of piglets purchased and fattened pigs sent to slaughter every month. Our results show that the forecast combinations consistently generate more accurate forecasts across six prediction horizons and three accuracy measures than individual forecasting methods. No model consistently performs best among the 11 individual forecasting models. The least absolute deviation strategy is particularly well‐suited for predicting hog prices. Concerning economic performance, the cumulative returns of hog producers who use the proposed production plan are consistently higher than those who use a naïve production plan. Experimental results indicate that our decision framework is a promising and practical tool for price forecasting and production planning in the hog industry.

Production planning and control is an absolute must for companies that produce goods or services. This is considered very important because it is the core of the entire production process. Every company may not carry out a production activity if it does not conduct production planning and control activities. Production plan at PT. Krakatau Wajatama on capacity planning and production of profile steel products requires a production plan based on demand that might come so that it needs an appropriate plan so that there is no overproduction. Therefore, integration between production plans and capacity plans needs to be done in order to obtain a realistic production plan. The method used is forecasting, capacity planning using RCCP can be done with the conversion process of the Production Plan or MPS based on order requests in bales in the period January 2019 to December 2019 into the time requirement of the production machine. Estimated results of 2019 demand for profile steel production are IWF-Flange, U-Chanal, L-Angels. In January 2019 it was 250836,713 minutes, February 2019 was 225731.5425 minutes, March 2019, and for IWF-Flange, U-Chanal, L-Angels products. In January 2019 it was 298080 minutes, February 2019 was 259200 minutes, March 2019 was 298080 minutes. company capacity planning needs on profile steel products namely IWF-Flange, U-Chanal, L-Angels using machine capacity that is used multiplied by hours per shift at times of shifts per day multiplied by workdays per period by converting production plans or MPS into capacity by using the RCCP concept with the Capacity Planning Using Overall Factors (CPOF) method.

Production planning is one of the most important issues of managers in industry. Production managers face up with several goals that sometimes conflict with each other. Operations research techniques with considering the constraints, optimize organizational goals. Objectives of all these techniques are raising productivity in the organization. In this paper, the researchers present a multi-objective linear fractional model of production planning in wood and metal company. One of the difficulties in solving the multiple objective fractional problems is computational problem that arises from of variability, in the example of Charnes and Cooper (1961) methods. In this research, fuzzy approach issued to solve multiple objective fractional mathematical problems of Khavar-E-Miane Wood and Metal Company. First, with some assumptions, the fuzzy linear fractional goal programming method of Pal has been used to solve the problem of production planning. Then, the fuzzy method of Dutta is utilized to solve the problem. Comparison of results showed that both methods have identical solution. Key words: Production planning, fuzzy goal programming, productivity, fuzzy fractional programming, wood and metal company.

A metaheuristic-based efficient strategy for multi-unit production planning with unique process constraints

PurposeResource capacity and product changeovers must be both considered in the preparation of a realistic production plan. The purpose of this paper is to present a heuristic for enforcing resource availability and the accompanying changeover realities into a continuous‐variable linear programming formulation that would otherwise require a mixed‐integer model. The results from both approaches are compared in terms of objective function values and computational requirements; the effectiveness of the heuristic approach is demonstrated.Design/methodology/approachA case study search was conducted to identify relevant data sets that could be used to exercise the optimization and heuristic models. The case studies found in the literature were too small and simple compared to the problem complexity desired. The authors developed a case study based on the development of a production plan for a typical flashlight. It includes two end‐products that differ in their bills of materials and process requirements. Basic processes include plastic part preparation and final assembly; various raw materials with pre‐defined lead times are purchased from external suppliers. The results of the LP‐based heuristic and the mixed‐integer programming (MIP) optimization algorithm are then compared through a statistical experiment. The experiment includes four factors: number of products, number of periods, number of machines, and percentage line capacity utilization.FindingsWhen a MIP algorithm is applied to obtain the results, most of the time the planner would have to wait days or even weeks for the algorithm to provide a solution. However, the authors' linear programming‐based procedure provides the same quality of solution in minutes and for some problems in seconds.Originality/valueThe originality of the heuristic approach resides on the avoidance of the lengthy MIP computer runs. At each iteration, the authors solve the LP production planning problem without changeover considerations, and then subtract from the original capacity the time associated with the changeovers resulting from the last LP solution. After a small number of iterations the heuristic always converges to the optimal MIP solution. The contribution of this research can be appreciated by someone who is using this tool to generate a production plan in a real world factory where setups are important and the results can immediately suggest changes to some of the assumptions or parameters used in the planning exercise.

The system developed is a scheduling system for a material manufacturer. The company focused attention on improving productivity by operating production facilities more efficiently. To this end, the company mainly aimed at increasing the lot size of production plans and decreasing equipment change losses. Production plans were done manually with a huge amount of time and were controllable only on a macro basis to prevent the occurrence of opportunity losses from operational stoppage due to the absence of inventory. Actual work instructions were issued by control departments and were loosely linked with production plans. The work instructions were formed to maximize the lot size for increasing production efficiency, and lacked in long-term outlook and total cost control. The globalization of competition called for strengthening nonprice competitiveness by shortening the manufacturing lead time as well as reducing costs. To this end, it was necessary to do away with production control aimed at improvement in the productivity of specific process units and to accomplish production plans in a short time at a low inventory level without spoiling productivity. Our scheduling model covers not only the problem of allocating lots to a process but a higher level of production planning. The objective of production planning is to incorporate the philosophy of the kanban method for manufacturing what is required in as large a quantity as required when needed. What we should especially emphasize is the simplification of the model by the hierarchization of scheduling tasks and the distribution of roles between the man and machine. The scheduling system presented is run on a daily basis and is highly rated by the users.

Evolutionary robust optimization in production planning – interactions between number of objectives, sample size and choice of robustness measure

Nowadays, sustainable reconfigurable manufacturing systems (SRMS) are attracting interest from both the academic and industrial communities. This paper addresses the multi-objective process and production planning problem in the sustainable reconfigurable manufacturing environment. A multi-objective linear mixed-integer model is presented first. Second, to solve the problem, the $\varepsilon$-constraint and the weighted sum based-approaches are developed for the case of small and medium scenarios. To demonstrate the efficiency of the two approaches, several instances of the problem are experimented and the obtained results are analyzed using two metrics, namely cardinality of the Pareto fronts (CPF) and inverted generational distance (IGD). Finally, to help the decision-maker evaluating the best process and production plans, the TOPSIS method is applied.

A vibration damping optimization algorithm for solving a new multi-objective dynamic cell formation problem with workers training

An interval nonlinear multiobjective programming model with fuzzy-interval credibility constraint for crop monthly water allocation

The aim of every manufacturing organization is to fulfil the demand of their customers at minimum total cost or at maximum profit. To fulfil the demand of geographically dispersed customers, manufacturing organizations need to produce products at multiple manufacturing sites located close to the customer. Although planning function in such a scenario becomes very complex, this multi-site environment has been found to improve efficiency and provide better services to customers. It involves interlinked decisions involving procurement, production and distribution at different plants and distribution sites. Production and distribution planning are very important aspects of planning in multi-site manufacturing. To compete in this globalized market, enterprises should focus on optimizing and integrating production and distribution functions simultaneously. The authors in the research work present an integrated production and distribution planning problem for a two-level supply chain consisting of multiple manufacturing sites serving multiple selling locations. The problem is formulated as a multi-objective mixed-integer programming model considering three important aspects of production and distribution planning, viz. set-up, backorder and transportation capacity. Total cost, delivery time and backlog level are the three conflicting objectives that need to be minimized. The proposed multi-objective mathematical problem is solved using pre-emptive goal programming method. The performance of the proposed model is illustrated through an example problem instance. Further analysis is conducted to visualize the effect of changing priority level on objective function and deviation variable values.

A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty

A Simulation Framework for the Evaluation of Production Planning and Order Management Strategies in the Sawmilling Industry

Many simple decision processes are based on a single objective such as minimizing cost, maximizing profit, minimizing runtime and so forth. However decisions must be made in an environment where more than one objective, constrains the problem, and the relative value of each of these objective is different. Such problems where multiple objectives are to be optimized are known as Multi-objective optimization (MOO) problems. The problem becomes challenging in case of mutually conflicting objectives i.e. the optimal solution widely varies with the shifting of focus from one objective to the other while all of them are quite relevant for the problem. MOO is used in various fields viz. Production Planning, Structural Design etc. There are various genetic approaches to solve multi-objective optimization problems like Non-dominated sorting GA (NSGA-II), Strength Pareto Evolutionary Approach (SPEA) etc. Each of these methods has their pros and cons and none of these is found to be perfect. In this paper we present a comparative study of the various available methods based on evolutionary genetic algorithms for Multi-Objective Optimization on different performance metrics.

Reasoning about the factors underlying habitat connectivity and the inter-habitat movement of species is essential to many areas of biological inquiry. In order to better describe and understand the ways in which the landscape may support species movement, an increasing amount of research has focused on identification of paths or corridors that may be important in providing connectivity among habitat. The least-cost path problem has proven to be an instrumental analytical tool in this sense. A complicating aspect of such path identification methods is how to best reconcile and integrate the array of criteria or objectives that species may consider in traversal of a landscape. In cases where habitat connectivity is thought to be influenced or guided by multiple objectives, numerous solutions to least-cost path problems can exist, representing tradeoffs between the objectives. In practice though, identification of these solutions can be very challenging and as such, only a small proportion of them are typically examined leading to a weak characterization of habitat connectivity. To address this computational challenge, a multiobjective optimization framework is proposed. A generalizable multiobjective least-cost path model is first detailed. A non-inferior set estimation (MONISE) algorithm for identifying supported efficient solutions to the multiobjective least-cost path model is then described. However, it is well known that unsupported efficient solutions (which are equally important) can also exist, but are typically ignored given that they are more difficult to identify. Thus, to enable the identification of the full set of efficient solutions (supported and unsupported) to the multiobjective model, a multi-criteria labeling algorithm is then proposed. The developed framework is applied to assess different conceptualizations of habitat connectivity supporting amphibian movement in a wetland system. The results highlight the range of tradeoffs in characterizations of connectivity that can exist when multiple objectives are thought to contribute to movement decisions and that the number of unsupported efficient solutions (which are typically ignored) can vastly outweigh that of the supported efficient solutions.